diff --git a/config/config.cns.yaml b/config/config.cns.yaml
index 43b30d53..a65858e7 100644
--- a/config/config.cns.yaml
+++ b/config/config.cns.yaml
@@ -1,13 +1,14 @@
 # neuView Configuration
 neuprint:
-  server: "neuprint.janelia.org"
-  dataset: "male-cns:v0.9"
+  server: "neuprint-cns.janelia.org"
+  dataset: "male-cns:v1.0"
 
 output:
   directory: "output"
 
 neuroglancer:
   base_url: "https://neuroglancer-demo.appspot.com/"
+  template: "neuroglancer-cns.js.jinja"
 
 # Auto-discovery settings for neuron types
 discovery:
diff --git a/config/config.example.yaml b/config/config.example.yaml
index ce530050..fbc9fd19 100644
--- a/config/config.example.yaml
+++ b/config/config.example.yaml
@@ -9,6 +9,7 @@ output:
 
 neuroglancer:
   base_url: "https://clio-ng.janelia.org/"
+  template: "neuroglancer-cns.js.jinja"
 
 html:
   title_prefix: "Neuron Type Report"
diff --git a/config/config.fafb.yaml b/config/config.fafb.yaml
index 222c42a2..b3c77ff4 100644
--- a/config/config.fafb.yaml
+++ b/config/config.fafb.yaml
@@ -8,6 +8,7 @@ output:
 
 neuroglancer:
   base_url: "https://clio-ng.janelia.org/"
+  template: "neuroglancer-fafb.js.jinja"
 
 # Auto-discovery settings for neuron types
 discovery:
diff --git a/config/config.optic-lobe.yaml b/config/config.optic-lobe.yaml
index 56029392..ebebfea1 100644
--- a/config/config.optic-lobe.yaml
+++ b/config/config.optic-lobe.yaml
@@ -9,6 +9,7 @@ output:
 
 neuroglancer:
   base_url: "https://clio-ng.janelia.org/"
+  template: "neuroglancer-cns.js.jinja"
 
 # Auto-discovery settings for neuron types
 discovery:
diff --git a/input/README.md b/input/README.md
index 115837f8..741655be 100644
--- a/input/README.md
+++ b/input/README.md
@@ -50,9 +50,12 @@ Provides citation information for scientific references linked to neuron synonym
 
 ### Format
 ```csv
-Citation Key, DOI or URL, "Paper Title"
+Citation Key, DOI or URL, "Authors (YEAR):Paper Title"
 ```
 
+The authors are listed with their full names, if possible. A good source for Authors list and the paper title seems to be https://openalex.org/.
+
+
 ### Examples
 ```csv
 Lee 2002, 10.1080/01677060216292,"Gyunghee Lee, Jeffrey C. Hall, and Jae H. Park. (2002): Doublesex Gene Expression in the Central Nervous System of Drosophila melanogaster"
@@ -216,4 +219,4 @@ Enable verbose logging to see detailed file loading information:
 neuview generate --verbose
 ```
 
-Check logs for specific error messages about file loading and data parsing issues.
\ No newline at end of file
+Check logs for specific error messages about file loading and data parsing issues.
diff --git a/input/citations.csv b/input/citations.csv
index a272c10e..f14a1216 100644
--- a/input/citations.csv
+++ b/input/citations.csv
@@ -13,7 +13,7 @@ von Philipsborn 2014, 10.1016/j.cub.2013.12.015,"Anne C. von Philipsborn, Sabrin
 Zhou 2014, 10.1117/1.JBO.19.9.090506,"Qingping Tian, Jing Yuan, Yuxin Li, Tao Jiang, Hui Gong, Wei Zhou (2014): Fast and automatic imaging of immunoenzyme-stained neuronal circuits in the whole brain of Drosophila"
 Vaughan 2014,10.1016/j.cub.2014.03.048,"Alexander G. Vaughan, Chuan Zhou, Devanand S. Manoli, and Bruce S. Baker (2014): Neural Pathways for the Detection and Discrimination of Conspecific Song in D. melanogaster"
 Pool 2014,10.1016/j.neuron.2014.05.006,"Allan-Hermann Pool, Pal Kvello, Kevin Mann, Samantha K. Cheung, Michael D. Gordon, Liming Wang, and Kristin Scott (2014): Four GABAergic Interneurons Impose Feeding Restraint in Drosophila"
-Tanaka 2012,10.1002/cne.23142,"Nobuaki K. Tanaka, Keita Endo, and Kei Ito (2012):Organization of antennal lobe‐associated neurons in adult Drosophila melanogaster brain"
+Tanaka 2012,10.1002/cne.23142,"Nobuaki K. Tanaka, Keita Endo, and Kei Ito (2012): Organization of antennal lobe‐associated neurons in adult Drosophila melanogaster brain"
 Hampel 2015,10.7554/eLife.08758,"Stefanie Hampel, Romain Franconville, Julie H.Simpson, and Andrew M. Seeds (2015): A neural command circuit for grooming movement control"
 Kallman 2015,10.7554/eLife.11188,"Benjamin R. Kallman, Heesoo Kim, and Kristin Scott (2015): Excitation and inhibition onto central courtship neurons biases Drosophila mate choice"
 Kimura 2015,10.1371/journal.pone.0126445,"Ken-ichi Kimura, Chiaki Sato, Masayuki Koganezawa, and Daisuke Yamamoto (2015): Drosophila Ovipositor Extension in Mating Behavior and Egg Deposition Involves Distinct Sets of Brain Interneurons"
@@ -23,7 +23,7 @@ Suver 2016,10.1523/JNEUROSCI.2277-16.2016,"Marie P. Suver, Ainul Huda, Nicole Iw
 Kim 2017,10.7554/eLife.23386,"Heesoo Kim, Colleen Kirkhart, and Kristin Scott (2017): Long-range projection neurons in the taste circuit of Drosophila"
 Kennedy and Broadie 2018,10.1523/ENEURO.0346-18.2018,"Tyler Kennedy and Kendal Broadie (2018): Newly Identified Electrically Coupled Neurons Support Development of the Drosophilal Giant Fiber Model Circuit"
 Scheunemann 2018,10.1016/j.neuron.2018.03.032,"Lisa Scheunemann, Pierre-Yves Plaçais, Yann Dromard, Martin Schwärzel, and Thomas Preat (2018): Dunce Phosphodiesterase Acts as a Checkpoint for Drosophila Long-Term Memory in a Pair of Serotonergic Neurons"
-Carreira-Rosario 2018,10.7554/eLife.38554,"Arnaldo Carreira-Rosario, Aref Arzan Zarin, Matthew Q Clark, Laurina Manning, Richard D Fetter, Albert Cardona, and Chris Q Doe (2018):MDN brain descending neurons coordinately activate backward and inhibit forward locomotion"
+Carreira-Rosario 2018,10.7554/eLife.38554,"Arnaldo Carreira-Rosario, Aref Arzan Zarin, Matthew Q Clark, Laurina Manning, Richard D Fetter, Albert Cardona, and Chris Q Doe (2018): MDN brain descending neurons coordinately activate backward and inhibit forward locomotion"
 McKellar 2019, 10.1016/j.cub.2018.12.019,"Claire E. McKellar, Joshua L. Lillvis, Daniel E. Bath, James E. Fitzgerald, John G. Cannon, Julie H. Simpson, and Barry J. Dickson (2019): Threshold-Based Ordering of Sequential Actions during Drosophila Courtship"
 Sen 2019,10.1016/j.cub.2019.11.004,"Rajyashree Sen, Kaiyu Wang, and Barry J. Dickson (2019): TwoLumps Ascending Neurons Mediate Touch-Evoked Reversal of Walking Direction in Drosophila"
 Scheffer 2020,10.7554/eLife.57443,"Louis K. Scheffer, C. Shan Xu, Michal Januszewski, Zhiyuan Lu, Shin-ya Takemura, Kenneth J. Hayworth, Gary B. Huang, Kazunori Shinomiya, Jeremy Maitlin-Shepard, Stuart Berg, Jody Clements, Philip M. Hubbard, William T. Katz, Lowell Umayam, Ting Zhao, David Ackerman, Tim Blakely, John Bogovic, Tom Dolafi, Dagmar Kainmueller, Takashi Kawase, Khaled A. Khairy, Laramie Leavitt, Peter H. Li, Larry Lindsey, Nicole Neubarth, Donald J. Olbris, Hideo Otsuna, Eric T. Trautman, Masayoshi Ito, Alexander S. Bates, Jens Goldammer, Tanya Wolff, Robert Svirskas, Philipp Schlegel, Erika Neace, Christopher J. Knecht, Chelsea X. Alvarado, Dennis A. Bailey, Samantha Ballinger, Jolanta A. Borycz, Brandon S. Canino, Natasha Cheatham, Michael Cook, Marisa Dreher, Octave Duclos, Bryon Eubanks, Kelli Fairbanks, Samantha Finley, Nora Forknall, Audrey Francis, Gary Patrick Hopkins, Emily M. Joyce, SungJin Kim, Nicole A. Kirk, Julie Kovalyak, Shirley A. Lauchie, Alanna Lohff, Charli Maldonado, Emily A. Manley, Sari McLin, Caroline Mooney, Miatta Ndama, Omotara Ogundeyi, Nneoma Okeoma, Christopher Ordish, Nicholas Padilla, Christopher M. Patrick, Tyler Paterson, Elliott E. Phillips, Emily M. Phillips, Neha Rampally, Caitlin Ribeiro, Madelaine K. Robertson, Jon Thomson Rymer, Sean M. Ryan, Megan Sammons, Anne K. Scott, Ashley L. Scott, Aya Shinomiya, Claire Smith, Kelsey Smith, Natalie L. Smith, Margaret A. Sobeski, Alia Suleiman, Jackie Swift, Satoko Takemura, Iris Talebi, Dorota Tarnogorska, Emily Tenshaw, Temour Tokhi, John J. Walsh, Tansy Yang, Jane Anne Horne, Feng Li, Ruchi Parekh, Patricia K. Rivlin, Vivek Jayaraman, Marta Costa, Gregory SXE Jefferis, Kei Ito, Stephan Saalfeld, Reed George, Ian A. Meinertzhagen, Gerald M. Rubin, Harald F. Hess, Viren Jain, Stephen M. Plaza (2020): A connectome and analysis of the adult Drosophila central brain"
@@ -37,11 +37,16 @@ Fujiwara 2022,10.1016/j.neuron.2022.04.008,"Terufumi Fujiwara, Margarida Brotas,
 Shiu 2022, 10.7554/eLife.79887,"Philip K. Shiu, Gabriella R. Sterne, Stefanie Engert, Barry J. Dickson, and Kristin Scott (2022): Taste quality and hunger interactions in a feeding sensorimotor circuit"
 Yao & Scott 2022, 10.1016/j.neuron.2021.12.028,"Zepeng Yao and Kristin Scott (2022): Serotonergic neurons translate taste detection into internal nutrient regulation"
 Chen 2023,10.1073/pnas.2307632120,"Tianli Chen, Mengyu Zhang, Zhaowen Ding, Jiao Hu, Jie Yang, Lei He, Jia Jia, Jingjing Yang, Junfei Yang, Xiaoxu Song, Peng Chen, Zongzhao Zhai, Jing Huang, Yirong Wang, and Hongtao Qin (2023): The Drosophila NPY-like system protects against chronic stress–induced learning deficit by preventing the disruption of autophagic flux"
-Taisz 2023,10.1016/j.cell.2023.04.038,"István Taisz, Erika Donà, Daniel Münch, Shanice N. Bailey, Billy J. Morris, Kimberly I. Meechan, Katie M. Stevens, Irene Varela-Martínez, Marina Gkantia, Philipp Schlegel, Carlos Ribeiro, Gregory S.X.E. Jefferis, and Dana S. Galili (2023):Generating parallel representations of position and identity in the olfactory system"
+Taisz 2023,10.1016/j.cell.2023.04.038,"István Taisz, Erika Donà, Daniel Münch, Shanice N. Bailey, Billy J. Morris, Kimberly I. Meechan, Katie M. Stevens, Irene Varela-Martínez, Marina Gkantia, Philipp Schlegel, Carlos Ribeiro, Gregory S.X.E. Jefferis, and Dana S. Galili (2023): Generating parallel representations of position and identity in the olfactory system"
 Babski 2024,10.1016/j.heliyon.2024.e29952,"Helene Babski, Marcello Codianni, Vikas Bhandawat (2024): Octopaminergic descending neurons in Drosophila: Connectivity, tonic activity and relation to locomotion"
 Cheong 2024,10.1016/j.cub.2024.01.071,"Han S.J. Cheong, Kaitlyn N. Boone, Marryn M. Bennett, Farzaan Salman, Jacob D. Ralston, Kaleb Hatch, Raven F. Allen, Alec M. Phelps, Andrew P. Cook, Jasper S. Phelps, Mert Erginkaya, Wei-Chung A. Lee, Gwyneth M. Card, Kevin C. Daly, and Andrew M. Dacks (2024): Organization of an ascending circuit that conveys flight motor state in Drosophila"
 Lillvis 2024, 10.1016/j.cub.2024.01.015,"Joshua L. Lillvis, Kaiyu Wang, Hiroshi M. Shiozaki, Min Xu, David L. Stern, and Barry J. Dickson (2024): Nested neural circuits generate distinct acoustic signals during Drosophila courtship"
 Dorkenwald 2024,10.1038/s41586-024-07558-y,"Sven Dorkenwald, Arie Matsliah, Amy R. Sterling, Philipp Schlegel, Szi-chieh Yu, Claire E. McKellar, Albert Lin, Marta Costa, Katharina Eichler, Yijie Yin, Will Silversmith, Casey Schneider-Mizell, Chris S. Jordan, Derrick Brittain, Akhilesh Halageri, Kai Kuehner, Oluwaseun Ogedengbe, Ryan Morey, Jay Gager, Krzysztof Kruk, Eric Perlman, Runzhe Yang, David Deutsch, Doug Bland, Marissa Sorek, Ran Lu, Thomas Macrina, Kisuk Lee, J. Alexander Bae, Shang Mu, Barak Nehoran, Eric Mitchell, Sergiy Popovych, Jingpeng Wu, Zhen Jia, Manuel A. Castro, Nico Kemnitz, Dodam Ih, Alexander Shakeel Bates, Nils Eckstein, Jan Funke, Forrest Collman, Davi D. Bock, Gregory S. X. E. Jefferis, H. Sebastian Seung, Mala Murthy, The FlyWire Consortium, Zairene Lenizo, Austin T. Burke, Kyle Patrick Willie, Nikitas Serafetinidis, Nashra Hadjerol, Ryan Willie, Ben Silverman, John Anthony Ocho, Joshua Bañez, Rey Adrian Candilada, Anne Kristiansen, Nelsie Panes, Arti Yadav, Remer Tancontian, Shirleyjoy Serona, Jet Ivan Dolorosa, Kendrick Joules Vinson, Dustin Garner, Regine Salem, Ariel Dagohoy, Jaime Skelton, Mendell Lopez, Laia Serratosa Capdevila, Griffin Badalamente, Thomas Stocks, Anjali Pandey, Darrel Jay Akiatan, James Hebditch, Celia David, Dharini Sapkal, Shaina Mae Monungolh, Varun Sane, Mark Lloyd Pielago, Miguel Albero, Jacquilyn Laude, Márcia dos Santos, Zeba Vohra, Kaiyu Wang, Allien Mae Gogo, Emil Kind, Alvin Josh Mandahay, Chereb Martinez, John David Asis, Chitra Nair, Dhwani Patel, Marchan Manaytay, Imaan F. M. Tamimi, Clyde Angelo Lim, Philip Lenard Ampo, Michelle Darapan Pantujan, Alexandre Javier, Daril Bautista, Rashmita Rana, Jansen Seguido, Bhargavi Parmar, John Clyde Saguimpa, Merlin Moore, Markus William Pleijzier, Mark Larson, Joseph Hsu, Itisha Joshi, Dhara Kakadiya, Amalia Braun, Cathy Pilapil, Marina Gkantia, Kaushik Parmar, Quinn Vanderbeck, Irene Salgarella, Christopher Dunne, Eva Munnelly, Chan Hyuk Kang, Lena Lörsch, Jinmook Lee, Lucia Kmecova, Gizem Sancer, Christa Baker, Jenna Joroff, Steven Calle, Yashvi Patel, Olivia Sato, Siqi Fang, Janice Salocot, Farzaan Salman, Sebastian Molina-Obando, Paul Brooks, Mai Bui, Matthew Lichtenberger, Edward Tamboboy, Katie Molloy, Alexis E. Santana-Cruz, Anthony Hernandez, Seongbong Yu, Arzoo Diwan, Monika Patel, Travis R. Aiken, Sarah Morejohn, Sanna Koskela, Tansy Yang, Daniel Lehmann, Jonas Chojetzki, Sangeeta Sisodiya, Selden Koolman, Philip K. Shiu, Sky Cho, Annika Bast, Brian Reicher, Marlon Blanquart, Lucy Houghton, Hyungjun Choi, Maria Ioannidou, Matt Collie, Joanna Eckhardt, Benjamin Gorko, Li Guo, Zhihao Zheng, Alisa Poh, Marina Lin, István Taisz, Wes Murfin, Álvaro Sanz Díez, Nils Reinhard, Peter Gibb, Nidhi Patel, Sandeep Kumar, Minsik Yun, Megan Wang, Devon Jones, Lucas Encarnacion-Rivera, Annalena Oswald, Akanksha Jadia, Mert Erginkaya, Nik Drummond, Leonie Walter, Ibrahim Tastekin, Xin Zhong, Yuta Mabuchi, Fernando J. Figueroa Santiago, Urja Verma, Nick Byrne, Edda Kunze, Thomas Crahan, Ryan Margossian, Haein Kim, Iliyan Georgiev, Fabianna Szorenyi, Atsuko Adachi, Benjamin Bargeron, Tomke Stürner, Damian Demarest, Burak Gür, Andrea N. Becker, Robert Turnbull, Ashley Morren, Andrea Sandoval, Anthony Moreno-Sanchez, Diego A. Pacheco, Eleni Samara, Haley Croke, Alexander Thomson, Connor Laughland, Suchetana B. Dutta, Paula Guiomar Alarcón de Antón, Binglin Huang, Patricia Pujols, Isabel Haber, Amanda González-Segarra, Daniel T. Choe, Veronika Lukyanova, Nino Mancini, Zequan Liu, Tatsuo Okubo, Miriam A. Flynn, Gianna Vitelli, Meghan Laturney, Feng Li, Shuo Cao, Carolina Manyari-Diaz, Hyunsoo Yim, Anh Duc Le, Kate Maier, Seungyun Yu, Yeonju Nam, Daniel Bąba, Amanda Abusaif, Audrey Francis, Jesse Gayk, Sommer S. Huntress, Raquel Barajas, Mindy Kim, Xinyue Cui, Gabriella R. Sterne, Anna Li, Keehyun Park, Georgia Dempsey, Alan Mathew, Jinseong Kim, Taewan Kim, Guan-ting Wu, Serene Dhawan, Margarida Brotas, Cheng-hao Zhang, Shanice Bailey, Alexander Del Toro, Runzhe Yang, Stephan Gerhard, Andrew Champion, David J. Anderson, Rudy Behnia, Salil S. Bidaye, Alexander Borst, Eugenia Chiappe, Kenneth J. Colodner, Andrew Dacks, Barry Dickson, Denise Garcia, Stefanie Hampel, Volker Hartenstein, Bassem Hassan, Charlotte Helfrich-Forster, Wolf Huetteroth, Jinseop Kim, Sung Soo Kim, Young-Joon Kim, Jae Young Kwon, Wei-Chung Lee, Gerit A. Linneweber, Gaby Maimon, Richard Mann, Stéphane Noselli, Michael Pankratz, Lucia Prieto-Godino, Jenny Read, Michael Reiser, Katie von Reyn, Carlos Ribeiro, Kristin Scott, Andrew M. Seeds, Mareike Selcho, Marion Silies, Julie Simpson, Scott Waddell, Mathias F. Wernet, Rachel I. Wilson, Fred W. Wolf, Zepeng Yao, Nilay Yapici, and Meet Zandawala (2024): Neuronal wiring diagram of an adult brain"
 Sapkal 2024,10.1038/s41586-024-07854-7,"Neha Sapkal, Nino Mancini, Divya Sthanu Kumar, Nico Spiller, Kazuma Murakami, Gianna Vitelli, Benjamin Bargeron, Kate Maier, Katharina Eichler, Gregory S. X. E. Jefferis, Philip K. Shiu, Gabriella R. Sterne, and Salil S. Bidaye (2024): Neural circuit mechanisms underlying context-specific halting in Drosophila"
-Wu 2024,10.1016/j.cell.2024.07.047,"Guangyan Wu, Tianji Ma, Clare E. Hancock, Santiago Gonzalez, Binod Aryal, Sharon Vaz, Gabrielle Chan, Madison Palarca-Wong, Nick Allen, Chan-I. Chung, Xiaokun Shu, and Qili Liu (2024):Opposing GPCR signaling programs protein intake setpoint in Drosophila"
+Wu 2024,10.1016/j.cell.2024.07.047,"Guangyan Wu, Tianji Ma, Clare E. Hancock, Santiago Gonzalez, Binod Aryal, Sharon Vaz, Gabrielle Chan, Madison Palarca-Wong, Nick Allen, Chan-I. Chung, Xiaokun Shu, and Qili Liu (2024): Opposing GPCR signaling programs protein intake setpoint in Drosophila"
 Allen 2025, 10.1101/2025.06.04.657833,"Aaron M. Allen, Megan C. Neville, Tetsuya Nojima, Faredin Alejevski, and Stephen F. Goodwin (2025): A Role for Exaptation in Sculpting Sexually Dimorphic Brains from Shared Neural Lineages"
+von Philipsborn 2011, 10.1016/j.neuron.2011.01.011,"Anne C. von Philipsborn, Tianxiao Liu, Jai Y. Yu, Christopher Masser, Salil S. Bidaye, and Barry J. Dickson (2011): Neuronal Control of Drosophila Courtship Song"
+Zandawala 2021, 10.1371/journal.pgen.1009425,"Meet Zandawala, Thomas Nguyen, Marta Balanyà Segura, Helena A. D. Johard, Mirjam Amcoff, Christian Wegener, Jean-Paul Paluzzi, and Dick R. Nässel (2021): A neuroendocrine pathway modulating osmotic stress in Drosophila"
+Gera 2025, 10.7554/eLife.97043.3,"Jayati Gera, Marishia Agard, Hannah Nave, Austin B Baldridge, Farwa Sajadi, Leena Thorat, Theresa H. McKim, Shu Kondo, Dick R. Nässel, Mitchell H. Omar, Jean‐Paul Paluzzi, and Meet Zandawala (2025): Anti-diuretic hormone ITP signals via a guanylate cyclase receptor to modulate systemic homeostasis in Drosophila"
+Palacios et al. 2026, 10.64898/2026.02.06.704492,"Lesly M. Palacios Castillo, Valentina Fajner, Ezgi Yalbir, Shafana Shahul, Bruce Ruff, Victor Junmyung Lee, Aundrea Koger, Juliet Heller, Kenichi Ishii, Veronica Morad, Max Trask, Carter Warren, Giovanni Frighetto, Mark A. Frye, and Kenta Asahina (2026): Cellular and functional dissection of the octopaminergic system in the Drosophila brain"
+Zandawala 2017, 10.1007/s00018-017-2682-y,"Meet Zandawala, Richard Marley, Shireen A. Davies, and Dick R. Nässel (2017): Characterization of a set of abdominal neuroendocrine cells that regulate stress physiology using colocalized diuretic peptides in Drosophila"
diff --git a/pixi.lock b/pixi.lock
index 5b794bdf..4cd581fd 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -1,30 +1,85 @@
-version: 6
+version: 7
+platforms:
+- name: linux-64
+- name: osx-64
+- name: osx-arm64
+- name: win-64
 environments:
   default:
     channels:
     - url: https://conda.anaconda.org/conda-forge/
     indexes:
     - https://pypi.org/simple
-    options:
-      pypi-prerelease-mode: if-necessary-or-explicit
     packages:
       linux-64:
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_gnu.tar.bz2
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/astroid-3.3.11-py314hdafbbf9_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/autopep8-2.0.4-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/black-25.1.0-pyh866005b_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hda65f42_8.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2025.11.12-hbd8a1cb_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/cairo-1.18.4-h3394656_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/cairocffi-1.7.1-pyhd8ed1ab_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/cairosvg-2.8.2-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-20_gnu.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/astroid-4.0.4-py314hdafbbf9_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hda65f42_9.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/cairo-1.18.4-he90730b_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/cffi-2.0.0-py314h4a8dc5f_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/click-8.3.1-pyh707e725_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/fontconfig-2.17.1-h27c8c51_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/icu-78.3-h33c6efd_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/lcms2-2.19.1-h0c24ade_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.45.1-default_hbd61a6d_102.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/lerc-4.1.0-hdb68285_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.25-h17f619e_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.8.1-hecca717_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libffi-3.5.2-h3435931_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libfreetype-2.14.3-ha770c72_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libfreetype6-2.14.3-h73754d4_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.2.0-he0feb66_19.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-15.2.0-h69a702a_19.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libglib-2.88.1-h0d30a3d_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgomp-15.2.0-he0feb66_19.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libiconv-1.18-h3b78370_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libjpeg-turbo-3.1.4.1-hb03c661_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/liblzma-5.8.3-hb03c661_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libmpdec-4.0.0-hb03c661_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libpng-1.6.58-h421ea60_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.53.1-h0c1763c_0.conda
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diff --git a/pyproject.toml b/pyproject.toml
index 553249d9..e55c1163 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [project]
 name = "neuview"
-version = "2.7.20"
+version = "2.7.21"
 requires-python = ">= 3.11"
 authors = [
     { name = "Frank Loesche", email = "loeschef@janelia.hhmi.org" },
diff --git a/src/neuview/config.py b/src/neuview/config.py
index 4261ccaa..08671314 100644
--- a/src/neuview/config.py
+++ b/src/neuview/config.py
@@ -43,6 +43,7 @@ class NeuroglancerConfig:
     """Neuroglancer configuration."""
 
     base_url: str = "https://clio-ng.janelia.org/"
+    template: str = "neuroglancer.js.jinja"
 
 
 @dataclass
diff --git a/src/neuview/services/neuroglancer_js_service.py b/src/neuview/services/neuroglancer_js_service.py
index 88fb2e66..21269879 100644
--- a/src/neuview/services/neuroglancer_js_service.py
+++ b/src/neuview/services/neuroglancer_js_service.py
@@ -2,8 +2,8 @@
 Neuroglancer JavaScript Generation Service for neuView.
 
 This service generates the neuroglancer-url-generator.js file dynamically,
-selecting the appropriate neuroglancer template based on the dataset configuration.
-This replaces the static JavaScript file with a template-based approach.
+using the neuroglancer template named in the configuration
+(``neuroglancer.template``).
 """
 
 import json
@@ -40,12 +40,7 @@ def generate_neuroglancer_js(self, output_dir: Path) -> bool:
             True if generation successful, False otherwise
         """
         try:
-            # Determine which neuroglancer template to use
-            if "fafb" in self.config.neuprint.dataset.lower():
-                template_name = "neuroglancer-fafb.js.jinja"
-            else:
-                template_name = "neuroglancer.js.jinja"
-
+            template_name = self.config.neuroglancer.template
             logger.debug(
                 f"Using Neuroglancer template: {template_name} for dataset: {self.config.neuprint.dataset}"
             )
@@ -129,10 +124,7 @@ def get_neuroglancer_template_name(self) -> str:
         Returns:
             Template name string
         """
-        if "fafb" in self.config.neuprint.dataset.lower():
-            return "neuroglancer-fafb.js.jinja"
-        else:
-            return "neuroglancer.js.jinja"
+        return self.config.neuroglancer.template
 
     def validate_templates(self) -> Dict[str, bool]:
         """
@@ -143,14 +135,14 @@ def validate_templates(self) -> Dict[str, bool]:
         """
         results = {}
 
-        # Check neuroglancer templates
-        for template_name in ["neuroglancer.js.jinja", "neuroglancer-fafb.js.jinja"]:
-            try:
-                self.env.get_template(template_name)
-                results[template_name] = True
-            except Exception as e:
-                logger.warning(f"Template {template_name} not available: {e}")
-                results[template_name] = False
+        # Check the configured neuroglancer template
+        template_name = self.config.neuroglancer.template
+        try:
+            self.env.get_template(template_name)
+            results[template_name] = True
+        except Exception as e:
+            logger.warning(f"Template {template_name} not available: {e}")
+            results[template_name] = False
 
         # Check JavaScript template
         try:
diff --git a/src/neuview/services/url_generation_service.py b/src/neuview/services/url_generation_service.py
index a12d6232..14202166 100644
--- a/src/neuview/services/url_generation_service.py
+++ b/src/neuview/services/url_generation_service.py
@@ -61,12 +61,7 @@ def generate_neuroglancer_url(
         conn_bids = {"upstream": {}, "downstream": {}}
 
         try:
-            # Load appropriate neuroglancer template based on dataset
-            if "fafb" in self.config.neuprint.dataset.lower():
-                template_name = "neuroglancer-fafb.js.jinja"
-            else:
-                template_name = "neuroglancer.js.jinja"
-
+            template_name = self.config.neuroglancer.template
             logger.debug(
                 f"Using Neuroglancer template: {template_name} for dataset: {self.config.neuprint.dataset}"
             )
diff --git a/templates/neuroglancer-cns.js.jinja b/templates/neuroglancer-cns.js.jinja
new file mode 100644
index 00000000..49ea9eb3
--- /dev/null
+++ b/templates/neuroglancer-cns.js.jinja
@@ -0,0 +1,1305 @@
+{
+  "title": "{{ website_title}}",
+  "dimensions": {
+    "x": [
+      8e-9,
+      "m"
+    ],
+    "y": [
+      8e-9,
+      "m"
+    ],
+    "z": [
+      8e-9,
+      "m"
+    ]
+  },
+  "position": [
+    48686.5,
+    27515.5,
+    24721.5
+  ],
+  "crossSectionScale": 88.13122564876332,
+  "projectionScale": 134522.22897188037,
+  "layers": [
+    {
+      "type": "image",
+      "source": {
+        "url": "precomputed://gs://flyem-male-cns/em/em-clahe-jpeg",
+        "subsources": {
+          "default": true
+        },
+        "enableDefaultSubsources": false
+      },
+      "tab": "rendering",
+      "name": "em-clahe"
+    },
+    {
+      "type": "segmentation",
+      "source": [
+        {
+          "url": "precomputed://gs://flyem-male-cns/v1.0/segmentation",
+          "subsources": {
+            "default": true,
+            "mesh": true
+          },
+          "enableDefaultSubsources": false
+        },
+        {
+          "url": "precomputed://gs://flyem-male-cns/v1.0/segmentation/type_property",
+          "subsources": {
+            "default": true
+          },
+          "enableDefaultSubsources": false
+        },
+        {
+          "url": "precomputed://gs://flyem-male-cns/v1.0/segmentation/type_and_group_property",
+          "enableDefaultSubsources": false
+        },
+        {
+          "url": "precomputed://gs://flyem-male-cns/v1.0/segmentation/tags_property",
+          "subsources": {
+            "default": true
+          },
+          "enableDefaultSubsources": false
+        },
+        {
+          "url": "precomputed://gs://flyem-male-cns/v1.0/segmentation/numeric_properties",
+          "subsources": {
+            "default": true
+          },
+          "enableDefaultSubsources": false
+        },
+        {
+          "url": "precomputed://gs://flyem-male-cns/v1.0/segmentation/instance_property",
+          "enableDefaultSubsources": false
+        },
+        {
+          "url": "precomputed://gs://flyem-male-cns/v1.0/segmentation/flywireType_property",
+          "enableDefaultSubsources": false
+        },
+        {
+          "url": "precomputed://gs://flyem-male-cns/v1.0/segmentation/meshes-malecns/single-res-meshes",
+          "enableDefaultSubsources": false
+        }
+      ],
+      "toolBindings": {
+        "Q": "selectSegments"
+      },
+      "tab": "segments",
+      "segments": {{ visible_neurons | tojson}},
+      "segmentQuery": "{{neuron_query|safe}}",
+      "name": "cns-seg"
+    },
+    {
+      "type": "segmentation",
+      "source": {
+        "url": "precomputed://gs://flyem-male-cns/rois/fullbrain-major-shells",
+        "subsources": {
+          "default": true,
+          "properties": true,
+          "mesh": true
+        },
+        "enableDefaultSubsources": false
+      },
+      "pick": false,
+      "tab": "rendering",
+      "selectedAlpha": 0,
+      "saturation": 0,
+      "meshSilhouetteRendering": 7,
+      "segments": [
+        "1",
+        "2",
+        "3"
+      ],
+      "segmentDefaultColor": "#ffffff",
+      "name": "brain-neuropil-shell"
+    },
+    {
+      "type": "segmentation",
+      "source": {
+        "url": "precomputed://gs://flyem-male-cns/rois/brain-shell-v2.2",
+        "subsources": {
+          "default": true,
+          "properties": true,
+          "mesh": true
+        },
+        "enableDefaultSubsources": false
+      },
+      "pick": false,
+      "tab": "rendering",
+      "selectedAlpha": 0,
+      "saturation": 0,
+      "meshSilhouetteRendering": 7,
+      "segments": [
+        "1"
+      ],
+      "segmentDefaultColor": "#ffffff",
+      "name": "brain-shell",
+      "archived": true
+    },
+    {
+      "type": "segmentation",
+      "source": {
+        "url": "precomputed://gs://flyem-male-cns/rois/brain-shell-with-lamina-v2.1",
+        "subsources": {
+          "default": true,
+          "properties": true,
+          "mesh": true
+        },
+        "enableDefaultSubsources": false
+      },
+      "pick": false,
+      "tab": "rendering",
+      "selectedAlpha": 0,
+      "saturation": 0,
+      "meshSilhouetteRendering": 7,
+      "segments": [
+        "1"
+      ],
+      "segmentDefaultColor": "#ffffff",
+      "name": "brain-shell-with-lamina",
+      "archived": true
+    },
+    {
+      "type": "segmentation",
+      "source": {
+        "url": "precomputed://gs://flyem-male-cns/rois/vnc-neuropil-shell-v2",
+        "subsources": {
+          "default": true,
+          "properties": true,
+          "mesh": true
+        },
+        "enableDefaultSubsources": false
+      },
+      "pick": false,
+      "tab": "rendering",
+      "selectedAlpha": 0,
+      "saturation": 0,
+      "meshSilhouetteRendering": 7,
+      "segments": [
+        "1"
+      ],
+      "segmentDefaultColor": "#ffffff",
+      "name": "vnc-neuropil-shell"
+    },
+    {
+      "type": "segmentation",
+      "source": {
+        "url": "precomputed://gs://flyem-male-cns/rois/vnc-shell-v2",
+        "subsources": {
+          "default": true,
+          "properties": true,
+          "mesh": true
+        },
+        "enableDefaultSubsources": false
+      },
+      "pick": false,
+      "tab": "rendering",
+      "selectedAlpha": 0,
+      "saturation": 0,
+      "meshSilhouetteRendering": 7,
+      "segments": [
+        "1"
+      ],
+      "segmentDefaultColor": "#ffffff",
+      "name": "vnc-shell",
+      "archived": true
+    },
+    {
+      "type": "segmentation",
+      "source": {
+        "url": "precomputed://gs://flyem-male-cns/rois/fullbrain-roi-v5",
+        "subsources": {
+          "default": true,
+          "properties": true,
+          "mesh": true
+        },
+        "enableDefaultSubsources": false
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+      "pick": false,
+      "tab": "segments",
+      "selectedAlpha": 0,
+      "meshSilhouetteRendering": 4,
+      "segments": [
+        "1",
+        "10",
+        "11",
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+        "199"
+      ],
+      "name": "brain-neuropil-subcompartments",
+      "archived": true
+    },
+    {
+      "type": "segmentation",
+      "source": {
+        "url": "precomputed://gs://flyem-male-cns/rois/malecns-vnc-neuropil-roi-v0",
+        "subsources": {
+          "default": true,
+          "properties": true,
+          "mesh": true
+        },
+        "enableDefaultSubsources": false
+      },
+      "pick": false,
+      "tab": "segments",
+      "selectedAlpha": 0,
+      "meshSilhouetteRendering": 4,
+      "segments": [
+        "10",
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+      "name": "vnc-neuropils",
+      "archived": true
+    },
+    {
+      "type": "segmentation",
+      "source": {
+        "url": "precomputed://gs://flyem-male-cns/rois/malecns-vnc-nerve-roi-v2",
+        "subsources": {
+          "default": true,
+          "properties": true,
+          "mesh": true
+        },
+        "enableDefaultSubsources": false
+      },
+      "pick": false,
+      "tab": "segments",
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+        "9"
+      ],
+      "name": "vnc-nerves",
+      "archived": true
+    },
+    {
+      "type": "segmentation",
+      "source": {
+        "url": "precomputed://gs://flyem-male-cns/rois/malecns-major-compartments-v2",
+        "subsources": {
+          "default": true,
+          "properties": true,
+          "mesh": true
+        },
+        "enableDefaultSubsources": false
+      },
+      "tab": "segments",
+      "meshSilhouetteRendering": 4,
+      "segments": [
+        "1",
+        "2",
+        "3",
+        "4",
+        "5"
+      ],
+      "name": "major-compartments",
+      "archived": true
+    },
+    {
+      "type": "segmentation",
+      "source": "precomputed://gs://flyem-male-cns/rois/pointcloud-shells",
+      "pick": false,
+      "tab": "segments",
+      "selectedAlpha": 0,
+      "saturation": 0,
+      "segments": [
+        "1",
+        "2"
+      ],
+      "segmentDefaultColor": "#ffffff",
+      "name": "pointcloud-shells",
+      "archived": true
+    },
+    {
+      "type": "annotation",
+      "source": "precomputed://gs://flyem-male-cns/v1.0/malecns-v1.0-soma-points",
+      "tab": "rendering",
+      "annotationColor": "#ffffff",
+      "shader": "#uicontrol float radius slider(min=1, max=30, step=1, default=5)\n#uicontrol bool color_by_birthtime checkbox(default=false)\n#uicontrol bool color_by_itoleeHl checkbox(default=false)\n#uicontrol bool color_by_trumanHl checkbox(default=false)\n\n#uicontrol bool color_by_top_input_compartment checkbox(default=false)\n#uicontrol bool color_by_top_output_compartment checkbox(default=false)\n\n#uicontrol bool color_by_top_input_neuropil checkbox(default=false)\n#uicontrol bool color_by_top_output_neuropil checkbox(default=false)\n\n#uicontrol bool color_by_optic_column checkbox(default=false)\n#uicontrol bool color_by_top_input_optic_layer checkbox(default=false)\n#uicontrol bool color_by_top_output_optic_layer checkbox(default=false)\n\n#uicontrol int color_seed slider(min=0, max=4294967295, default=0);\n#uicontrol bool hide_uncolored checkbox(default=true)\n#uicontrol bool hide_vnc checkbox(default=false)\n#uicontrol bool hide_brain checkbox(default=false)\n#uicontrol bool hide_tosoma checkbox(default=false)\n\n\nuint hash_function(uint state, uint value) {\n    uint k1 = 0xcc9e2d51u;\n    uint k2 = 0x1b873593u;\n    \n    value = value * k1;\n    value = (value << 15) | (value >> 17);\n    value = value * k2;\n    state = state ^ value;\n    state = (state << 13) | (state >> 19);\n    state = state * 5u + 0xe6546b64u;\n    \n    return state;\n}\n\nvec3 hsv_to_rgb(float h, float s, float v) {\n    h *= 6.0;\n    float hue_index = floor(h);\n    float remainder = h - hue_index;\n    float val1 = v * (1.0 - s);\n    float val2 = v * (1.0 - (s * remainder));\n    float val3 = v * (1.0 - (s * (1.0 - remainder)));\n    \n    int hue_remainder = int(hue_index) % 6;\n    \n    if (hue_remainder == 0) {\n        return vec3(v, val3, val1);\n    } else if (hue_remainder == 1) {\n        return vec3(val2, v, val1);\n    } else if (hue_remainder == 2) {\n        return vec3(val1, v, val3);\n    } else if (hue_remainder == 3) {\n        return vec3(val1, val2, v);\n    } else if (hue_remainder == 4) {\n        return vec3(val3, val1, v);\n    } else { // hue_remainder == 5\n        return vec3(v, val1, val2);\n    }\n}\n\nvec3 color_from_segment_id(uint color_seed_, uint segment_id) {\n    uint result = hash_function(color_seed_, segment_id);\n    \n    // We only support 32-bit segment IDs, so newvalue is always 0,\n    // and (id >> 32) would be undefined behavior!\n    // uint newvalue = id >> 32;\n    \n    uint newvalue = 0u;\n    uint result2 = hash_function(result, newvalue);\n    \n    float c0 = float(result2 & 0xFFu) / 255.0;\n    float c1 = float((result2 >> 8) & 0xFFu) / 255.0;\n    \n    float h = c0;\n    float s = 0.5 + 0.5 * c1;\n    float v = 1.0;\n    \n    return hsv_to_rgb(h, s, v);\n}\n\nvoid set_color_for_id(uint id) {\n    if (id == 0u) {\n        if (hide_uncolored) {\n            discard;\n        }\n        //setPointMarkerBorderWidth(0.2);\n        //setPointMarkerColor(vec4(vec3(0.0, 0.0, 0.0), 0.0));\n        //setPointMarkerBorderColor(vec3(1.0, 1.0, 1.0));\n    }\n    else {\n        setColor(color_from_segment_id(uint(color_seed), id));\n    }\n}\n\nvoid main() {\n    setPointMarkerSize(radius);\n    setPointMarkerBorderWidth(1.0);\n    setPointMarkerBorderColor(vec3(0.0, 0.0, 0.0));\n    \n    if (hide_vnc && prop_is_vnc() == 1u) {\n        discard;\n    }\n    \n    if (hide_brain && prop_is_vnc() == 0u) {\n        discard;\n    }\n    \n    if (prop_kind() == 1u && hide_tosoma)\n    discard;\n    \n    // default color: soma vs. tosoma\n    if (prop_kind() == 1u) {\n        setColor(vec3(0.0, 1.0, 1.0));\n    }\n    else {\n        setColor(vec3(1.0, 1.0, 1.0));\n    }\n    \n    if (\n        int(color_by_top_input_compartment) +\n        int(color_by_top_output_compartment) +\n        int(color_by_top_input_neuropil) +\n        int(color_by_top_output_neuropil) +\n        int(color_by_top_input_optic_layer) +\n        int(color_by_top_output_optic_layer) +\n        int(color_by_optic_column) +\n        int(color_by_birthtime) +\n        int(color_by_itoleeHl || color_by_trumanHl)\n        > 1) {\n        discard;\n    }\n        \n    if (color_by_top_input_compartment) {\n        set_color_for_id(uint(prop_top_input_compartment()));\n        return;\n    }\n    if (color_by_top_output_compartment) {\n        set_color_for_id(uint(prop_top_output_compartment()));\n        return;\n    }\n    if (color_by_top_input_neuropil) {\n        set_color_for_id(uint(prop_top_input_neuropil()));\n        return;\n    }\n    if (color_by_top_output_neuropil) {\n        set_color_for_id(uint(prop_top_output_neuropil()));\n        return;\n    }\n    if (color_by_top_input_optic_layer) {\n        set_color_for_id(uint(prop_top_input_optic_layer()));\n        return;\n    }\n    if (color_by_top_output_optic_layer) {\n        set_color_for_id(uint(prop_top_output_optic_layer()));\n        return;\n    }\n    if (color_by_optic_column) {\n        set_color_for_id(uint(prop_assigned_optic_column()));\n        return;\n    }\n    if (color_by_birthtime) {\n        set_color_for_id(uint(prop_birthtime()));\n        return;\n    }\n    \n    // We permit showing both itoleeHl and trumanHl simultaneously\n    if (color_by_itoleeHl && color_by_trumanHl) {\n        uint id = bool(prop_itoleeHl()) ? uint(prop_itoleeHl()) : uint(prop_trumanHl());\n        set_color_for_id(id);\n        return;\n    }\n    \n    if (color_by_itoleeHl) {\n        set_color_for_id(uint(prop_itoleeHl()));\n        return;\n    }\n    if (color_by_trumanHl) {\n        set_color_for_id(uint(prop_trumanHl()));\n        return;\n    }\n}",
+      "shaderControls": {
+        "hide_tosoma": true
+      },
+      "linkedSegmentationLayer": {
+        "body": "cns-seg"
+      },
+      "filterBySegmentation": [
+        "body"
+      ],
+      "name": "soma-points",
+      "archived": true
+    },
+    {
+      "type": "segmentation",
+      "source": {
+        "url": "precomputed://gs://flyem-male-cns/v1.0/malecns-v1.0-nuclei-seg-16nm",
+        "subsources": {
+          "default": true,
+          "mesh": true
+        },
+        "enableDefaultSubsources": false
+      },
+      "tab": "rendering",
+      "segments": [],
+      "name": "nuclei",
+      "archived": true
+    },
+    {
+      "type": "segmentation",
+      "source": [
+        {
+          "url": "precomputed://gs://flyem-male-cns/v1.0/segmentation/meshes-malecns-mirrored/single-res-meshes",
+          "subsources": {
+            "default": true
+          },
+          "enableDefaultSubsources": false
+        },
+        {
+          "url": "precomputed://gs://flyem-male-cns/v1.0/segmentation/skeletons-malecns-mirrored/skeletons-precomputed",
+          "enableDefaultSubsources": false
+        }
+      ],
+      "tab": "segments",
+      "linkedSegmentationColorGroup": "cns-seg",
+      "segments": [],
+      "name": "cns-mirror",
+      "archived": true
+    },
+    {
+      "type": "segmentation",
+      "source": {
+        "url": "precomputed://gs://flyem-male-cns/rois/fullbrain-defects",
+        "subsources": {
+          "default": true,
+          "properties": true,
+          "mesh": true
+        },
+        "enableDefaultSubsources": false
+      },
+      "tab": "segments",
+      "meshSilhouetteRendering": 2,
+      "segments": [
+        "1",
+        "2",
+        "3",
+        "4",
+        "5"
+      ],
+      "name": "brain-defects",
+      "archived": true
+    },
+    {
+      "type": "segmentation",
+      "source": {
+        "url": "precomputed://gs://flyem-male-cns/rois/vnc-defects",
+        "subsources": {
+          "default": true,
+          "properties": true,
+          "mesh": true
+        },
+        "enableDefaultSubsources": false
+      },
+      "pick": false,
+      "tab": "segments",
+      "meshSilhouetteRendering": 2,
+      "segments": [
+        "1",
+        "2"
+      ],
+      "name": "vnc-defects",
+      "archived": true
+    },
+    {
+      "type": "segmentation",
+      "source": {
+        "url": "precomputed://gs://flyem-male-cns/rois/ME(R)-columns-v8",
+        "subsources": {
+          "default": true,
+          "properties": true,
+          "mesh": true
+        },
+        "enableDefaultSubsources": false
+      },
+      "pick": false,
+      "tab": "segments",
+      "segments": [],
+      "name": "ME(R)-columns",
+      "archived": true
+    },
+    {
+      "type": "segmentation",
+      "source": {
+        "url": "precomputed://gs://flyem-male-cns/rois/ME(R)-layers-v8",
+        "subsources": {
+          "default": true,
+          "properties": true,
+          "mesh": true
+        },
+        "enableDefaultSubsources": false
+      },
+      "pick": false,
+      "tab": "segments",
+      "segments": [],
+      "name": "ME(R)-layers",
+      "archived": true
+    },
+    {
+      "type": "segmentation",
+      "source": {
+        "url": "precomputed://gs://flyem-male-cns/rois/LO(R)-columns-v8",
+        "subsources": {
+          "default": true,
+          "properties": true,
+          "mesh": true
+        },
+        "enableDefaultSubsources": false
+      },
+      "pick": false,
+      "tab": "segments",
+      "segments": [],
+      "name": "LO(R)-columns",
+      "archived": true
+    },
+    {
+      "type": "segmentation",
+      "source": {
+        "url": "precomputed://gs://flyem-male-cns/rois/LO(R)-layers-v8",
+        "subsources": {
+          "default": true,
+          "properties": true,
+          "mesh": true
+        },
+        "enableDefaultSubsources": false
+      },
+      "pick": false,
+      "tab": "segments",
+      "segments": [],
+      "name": "LO(R)-layers",
+      "archived": true
+    },
+    {
+      "type": "segmentation",
+      "source": {
+        "url": "precomputed://gs://flyem-male-cns/rois/LOP(R)-columns-v8",
+        "subsources": {
+          "default": true,
+          "properties": true,
+          "mesh": true
+        },
+        "enableDefaultSubsources": false
+      },
+      "pick": false,
+      "tab": "segments",
+      "segments": [],
+      "name": "LOP(R)-columns",
+      "archived": true
+    },
+    {
+      "type": "segmentation",
+      "source": {
+        "url": "precomputed://gs://flyem-male-cns/rois/LOP(R)-layers-v8",
+        "subsources": {
+          "default": true,
+          "properties": true,
+          "mesh": true
+        },
+        "enableDefaultSubsources": false
+      },
+      "pick": false,
+      "tab": "segments",
+      "segments": [],
+      "name": "LOP(R)-layers",
+      "archived": true
+    },
+    {
+      "type": "segmentation",
+      "source": {
+        "url": "precomputed://gs://flyem-male-cns/rois/ME(L)-columns-v3",
+        "subsources": {
+          "default": true,
+          "properties": true,
+          "mesh": true
+        },
+        "enableDefaultSubsources": false
+      },
+      "pick": false,
+      "tab": "segments",
+      "segments": [],
+      "name": "ME(L)-columns",
+      "archived": true
+    },
+    {
+      "type": "segmentation",
+      "source": {
+        "url": "precomputed://gs://flyem-male-cns/rois/ME(L)-layers-v3",
+        "subsources": {
+          "default": true,
+          "properties": true,
+          "mesh": true
+        },
+        "enableDefaultSubsources": false
+      },
+      "pick": false,
+      "tab": "segments",
+      "segments": [],
+      "name": "ME(L)-layers",
+      "archived": true
+    },
+    {
+      "type": "segmentation",
+      "source": {
+        "url": "precomputed://gs://flyem-male-cns/rois/LO(L)-columns-v3",
+        "subsources": {
+          "default": true,
+          "properties": true,
+          "mesh": true
+        },
+        "enableDefaultSubsources": false
+      },
+      "pick": false,
+      "tab": "segments",
+      "segments": [],
+      "name": "LO(L)-columns",
+      "archived": true
+    },
+    {
+      "type": "segmentation",
+      "source": {
+        "url": "precomputed://gs://flyem-male-cns/rois/LO(L)-layers-v3",
+        "subsources": {
+          "default": true,
+          "properties": true,
+          "mesh": true
+        },
+        "enableDefaultSubsources": false
+      },
+      "pick": false,
+      "tab": "segments",
+      "segments": [],
+      "name": "LO(L)-layers",
+      "archived": true
+    },
+    {
+      "type": "segmentation",
+      "source": {
+        "url": "precomputed://gs://flyem-male-cns/rois/LOP(L)-columns-v3",
+        "subsources": {
+          "default": true,
+          "properties": true,
+          "mesh": true
+        },
+        "enableDefaultSubsources": false
+      },
+      "pick": false,
+      "tab": "segments",
+      "segments": [],
+      "name": "LOP(L)-columns",
+      "archived": true
+    },
+    {
+      "type": "segmentation",
+      "source": {
+        "url": "precomputed://gs://flyem-male-cns/rois/LOP(L)-layers-v3",
+        "subsources": {
+          "default": true,
+          "properties": true,
+          "mesh": true
+        },
+        "enableDefaultSubsources": false
+      },
+      "pick": false,
+      "tab": "segments",
+      "segments": [],
+      "name": "LOP(L)-layers",
+      "archived": true
+    },
+    {
+      "type": "annotation",
+      "source": "precomputed://gs://flyem-male-cns/v1.0/malecns-v1.0-optic-lobe-column-pins/",
+      "tab": "rendering",
+      "codeVisible": false,
+      "shader": "// These should match the segment IDs\n// in the brain-neuropils layer.\n#define LO_L 43\n#define LO_R 44\n#define LOP_L 45\n#define LOP_R 46\n#define ME_L 47\n#define ME_R 48\n\n// These are hard-coded from the data\n#define LO_MAX_DEPTH 75\n#define LOP_MAX_DEPTH 50\n#define ME_MAX_DEPTH 120\n  \nvec3 viridis(float t) {\n\n    const vec3 c0 = vec3(0.2777273272234177, 0.005407344544966578, 0.3340998053353061);\n    const vec3 c1 = vec3(0.1050930431085774, 1.404613529898575, 1.384590162594685);\n    const vec3 c2 = vec3(-0.3308618287255563, 0.214847559468213, 0.09509516302823659);\n    const vec3 c3 = vec3(-4.634230498983486, -5.799100973351585, -19.33244095627987);\n    const vec3 c4 = vec3(6.228269936347081, 14.17993336680509, 56.69055260068105);\n    const vec3 c5 = vec3(4.776384997670288, -13.74514537774601, -65.35303263337234);\n    const vec3 c6 = vec3(-5.435455855934631, 4.645852612178535, 26.3124352495832);\n\n    return c0+t*(c1+t*(c2+t*(c3+t*(c4+t*(c5+t*c6)))));\n}\n\nvoid main() {\n  float normalized_depth;\n  switch (int(prop_roi())) {\n    case LO_R:\n    case LO_L:\n      normalized_depth = float(prop_depth()) / float(LO_MAX_DEPTH);\n      break;\n    case LOP_R:\n    case LOP_L:\n      normalized_depth = float(prop_depth()) / float(LOP_MAX_DEPTH);\n      break;\n    case ME_R:\n    case ME_L:\n      normalized_depth = float(prop_depth()) / float(ME_MAX_DEPTH);\n      break;\n  }\n  vec3 depth_color = viridis(normalized_depth);\n  setColor(depth_color);\n  setEndpointMarkerSize(0.0, 0.0);\n}\n",
+      "linkedSegmentationLayer": {
+        "neuropil": "brain-neuropils",
+        "ME(L)_column_segment": "ME(L)-columns",
+        "LO(L)_column_segment": "LO(L)-columns",
+        "LOP(L)_column_segment": "LOP(L)-columns",
+        "ME(R)_column_segment": "ME(R)-columns",
+        "LO(R)_column_segment": "LO(R)-columns",
+        "LOP(R)_column_segment": "LOP(R)-columns"
+      },
+      "filterBySegmentation": [
+        "LO(L)_column_segment",
+        "LO(R)_column_segment",
+        "LOP(L)_column_segment",
+        "LOP(R)_column_segment",
+        "ME(L)_column_segment",
+        "ME(R)_column_segment",
+        "neuropil"
+      ],
+      "name": "optic-column-pins",
+      "archived": true
+    },
+    {
+      "type": "segmentation",
+      "source": {
+        "url": "precomputed://gs://flyem-male-cns/rois/OL(R)-v0",
+        "subsources": {
+          "default": true,
+          "properties": true,
+          "mesh": true
+        },
+        "enableDefaultSubsources": false
+      },
+      "pick": false,
+      "tab": "rendering",
+      "meshSilhouetteRendering": 4,
+      "segments": [
+        "1"
+      ],
+      "segmentDefaultColor": "#ffffff",
+      "name": "OL(R)",
+      "archived": true
+    },
+    {
+      "type": "segmentation",
+      "source": {
+        "url": "precomputed://gs://flyem-male-cns/rois/OL(R)-release",
+        "subsources": {
+          "default": true,
+          "properties": true,
+          "mesh": true
+        },
+        "enableDefaultSubsources": false
+      },
+      "pick": false,
+      "tab": "rendering",
+      "meshSilhouetteRendering": 4,
+      "segments": [
+        "1"
+      ],
+      "segmentDefaultColor": "#00ffff",
+      "name": "OL(R)-release-mask",
+      "archived": true
+    },
+    {
+      "type": "segmentation",
+      "source": [
+        "precomputed://gs://flyem-male-cns/rois/hemibrain_mask_in_malecns_coords/mesh",
+        "precomputed://gs://flyem-male-cns/rois/hemibrain_mask_in_malecns_coords/segment_properties"
+      ],
+      "tab": "segments",
+      "objectAlpha": 0.41,
+      "segments": [
+        "1"
+      ],
+      "segmentDefaultColor": "#ffffff",
+      "name": "hemibrain-region",
+      "archived": true
+    },
+    {
+      "type": "segmentation",
+      "source": "precomputed://gs://flyem-male-cns/hemibrain2mcns_meshes/v1.2/meshes/",
+      "tab": "segments",
+      "segments": [],
+      "segmentDefaultColor": "#e90002",
+      "name": "hemibrain-meshes",
+      "archived": true
+    },
+    {
+      "type": "segmentation",
+      "source": "precomputed://gs://flyem-male-cns/flywire2mcns_meshes/783/",
+      "tab": "segments",
+      "segments": [],
+      "segmentDefaultColor": "#d400ff",
+      "name": "flywire-meshes",
+      "archived": true
+    },
+    {
+      "type": "segmentation",
+      "source": {
+        "url": "precomputed://gs://flyem-male-cns/manc2mcns_meshes/v1.2/meshes/",
+        "subsources": {
+          "default": true,
+          "properties": true
+        },
+        "enableDefaultSubsources": false
+      },
+      "tab": "segments",
+      "segments": [],
+      "segmentDefaultColor": "#00ff00",
+      "name": "manc-meshes",
+      "archived": true
+    },
+    {
+      "type": "segmentation",
+      "source": "precomputed://gs://flyem-male-cns/banc2mcns_meshes/626",
+      "tab": "segments",
+      "segments": [],
+      "segmentDefaultColor": "#ff7e00",
+      "name": "banc-meshes",
+      "archived": true
+    },
+    {
+      "type": "segmentation",
+      "source": {
+        "url": "precomputed://gs://flyem-male-cns/malecns-semantic-masks",
+        "subsources": {
+          "default": true,
+          "properties": true
+        },
+        "enableDefaultSubsources": false
+      },
+      "tab": "segments",
+      "segments": [],
+      "name": "semantic-masks",
+      "archived": true
+    },
+    {
+      "type": "image",
+      "source": [
+        {
+          "url": "gs://flyem-male-cns/micro-ct/Z0720-07_luz_g-_CropRotate8bit.n5/|n5:",
+          "transform": {
+            "matrix": [
+              [
+                0.9659,
+                -0.2588,
+                0,
+                -6000
+              ],
+              [
+                -0.25,
+                -0.933,
+                -0.2588,
+                150000
+              ],
+              [
+                0.067,
+                0.25,
+                -0.9659,
+                139000
+              ]
+            ],
+            "outputDimensions": {
+              "x": [
+                8e-9,
+                "m"
+              ],
+              "y": [
+                8e-9,
+                "m"
+              ],
+              "z": [
+                8e-9,
+                "m"
+              ]
+            },
+            "inputDimensions": {
+              "x": [
+                0.0000011993,
+                "m"
+              ],
+              "y": [
+                0.0000011993,
+                "m"
+              ],
+              "z": [
+                0.0000011993,
+                "m"
+              ]
+            }
+          },
+          "subsources": {
+            "default": true
+          },
+          "enableDefaultSubsources": false
+        },
+        "# NOTE: The micro-ct layer is unaligned to the EM. Also, the Z-resolution may be wrong."
+      ],
+      "tab": "rendering",
+      "shaderControls": {
+        "normalized": {
+          "range": [
+            36,
+            255
+          ]
+        }
+      },
+      "volumeRendering": "on",
+      "volumeRenderingDepthSamples": 10447.551088355996,
+      "name": "micro-ct",
+      "archived": true
+    },
+    {
+      "type": "image",
+      "source": {
+        "url": "precomputed://gs://flyem-male-cns/malecns-tbar-point-cloud-512nm-smoothed/",
+        "subsources": {
+          "default": true
+        },
+        "enableDefaultSubsources": false
+      },
+      "tab": "rendering",
+      "shaderControls": {
+        "normalized": {
+          "range": [
+            -0.000922614113694098,
+            0.5755773291420491
+          ],
+          "window": [
+            -0.39765375785958196,
+            0.972308472887937
+          ]
+        }
+      },
+      "volumeRendering": "on",
+      "volumeRenderingDepthSamples": 7131.550214521843,
+      "name": "tbar-cloud",
+      "archived": true
+    },
+    {
+      "type": "annotation",
+      "source": "precomputed://gs://flyem-male-cns/v1.0/synapse-ground-truth/malecns-v1.0-synapse-ground-truth-lines/",
+      "tab": "rendering",
+      "codeVisible": false,
+      "shader": "#uicontrol bool show_pre checkbox(default=true)\n#uicontrol bool show_post checkbox(default=true)\n\n#uicontrol float pre_size slider(min=1.0, max=20.0, default=6.0)\n#uicontrol float post_size slider(min=1.0, max=20.0, default=4.0)\n\n#uicontrol vec3 pre_default color(default=\"red\")\n#uicontrol vec3 post_default color(default=\"cyan\")\n\n#define PROP_KIND_PRESYN 0\n#define PROP_KIND_PRESYN_TO_POSTSYN 1\n\nvoid main() {\n    vec3 pre_color = pre_default;\n    vec3 post_color = post_default;\n    bool _show_post = show_post && int(prop_kind()) == PROP_KIND_PRESYN_TO_POSTSYN;\n\n    setLineWidth(0.0);\n    setLineColor(vec3(1.0, 1.0, 1.0));\n    setEndpointMarkerColor(\n        vec4(pre_color, 1.0), vec4(post_color, 1.0)\n    );\n\n    if (show_pre && _show_post) {\n        setLineWidth(1.0);\n        setEndpointMarkerSize(pre_size, post_size);\n    }\n    else if (_show_post) {\n        setEndpointMarkerSize(0.0, post_size);\n    }\n    else if (show_pre) {\n        setEndpointMarkerSize(pre_size, 0.0);\n    }\n    else {\n        setEndpointMarkerSize(0.0, 0.0);\n    }\n}",
+      "name": "synapse-groundtruth",
+      "archived": true
+    },
+    {
+      "type": "annotation",
+      "source": "precomputed://gs://flyem-male-cns/v1.0/synapse-ground-truth/malecns-v1.0-synapse-ground-truth-boxes/",
+      "tab": "rendering",
+      "codeVisible": false,
+      "shader": "#uicontrol bool show_pre_boxes checkbox(default=true)\n#uicontrol bool show_post_boxes checkbox(default=true)\n\n#uicontrol vec3 presyn_boxes color(default=\"red\")\n#uicontrol vec3 postsyn_boxes color(default=\"cyan\")\n\nvoid main() {\n  if (int(prop_kind()) == 0) {\n    if (!show_pre_boxes) {\n      discard;\n    }\n    setColor(vec4(presyn_boxes, 1.0));\n  } else {\n    if (!show_post_boxes) {\n      discard;\n    }\n    setColor(vec4(postsyn_boxes, 1.0));\n  }\n}\n",
+      "shaderControls": {
+        "presyn_boxes": "#ff00ff"
+      },
+      "name": "synapse-groundtruth-boxes",
+      "archived": true
+    },
+    {
+      "type": "annotation",
+      "source": "precomputed://gs://flyem-male-cns/v1.0/male-cns-v1.0-synapses-precomputed/",
+      "tab": "rendering",
+      "annotationColor": "#000000",
+      "codeVisible": false,
+      "shader": "// To use the \"only_show_shader_hardcoded_connections\"\n// checkbox, edit the #define below and\n// paste in neuron segment IDs of your choosing.\n// (Don't list more than a few hundred.)\n// Only connections in which BOTH pre and post\n// belong to these segments will be shown.\n\n#define HARDCODED_SEGMENT_LIST 111, 222, 333\n\n#uicontrol bool show_pre checkbox(default=true)\n#uicontrol bool show_post_partners checkbox(default=false)\nbool show_post = show_post_partners;\n\n#uicontrol float pre_size slider(min=1.0, max=20.0, default=6.0)\n#uicontrol float post_size slider(min=1.0, max=20.0, default=4.0)\n\n#uicontrol vec3 pre_default color(default=\"red\")\n#uicontrol vec3 post_default color(default=\"blue\")\n\n#define acetylcholine_code 0\n#define dopamine_code 1\n#define gaba_code 2\n#define glutamate_code 3\n#define histamine_code 4\n#define octopamine_code 5\n#define serotonin_code 6\n#define unknown_nt_code 7\n\n#uicontrol bool color_by_neurotransmitter checkbox(default=true)\n\n#uicontrol vec3 acetylcholine color(default=\"#1f77b4\")\n#uicontrol vec3 dopamine color(default=\"#ff7f0e\")\n#uicontrol vec3 gaba color(default=\"#2ca02c\")\n#uicontrol vec3 glutamate color(default=\"#d62728\")\n#uicontrol vec3 histamine color(default=\"#9467bd\")\n#uicontrol vec3 octopamine color(default=\"#8c564b\")\n#uicontrol vec3 serotonin color(default=\"#e377c2\")\n#uicontrol vec3 unknown_nt color(default=\"#7f7f7f\")\n\n#uicontrol bool color_by_nt_probability checkbox(default=false)\n#uicontrol bool color_by_nt_confidence checkbox(default=false)\n\n#uicontrol bool color_by_tbar_fanout checkbox(default=false)\n#uicontrol float max_fanout_display slider(min=1.0, max=20.0, step=1.0, default=10.0)\n\n#define unknown_compartment_code 0\n#define axon_code 1\n#define dendrite_code 2\n#define linker_code 3\n#define cell_body_fiber_code 4\n\n#uicontrol bool color_by_compartment checkbox(default=true)\n\n#uicontrol vec3 axon color(default=\"#1f77b4\")\n#uicontrol vec3 dendrite color(default=\"#ff7f0e\")\n#uicontrol vec3 linker color(default=\"#2ca02c\")\n#uicontrol vec3 cell_body_fiber color(default=\"#d62728\")\n#uicontrol vec3 unknown_compartment color(default=\"#7f7f7f\")\n\n#uicontrol int color_seed slider(min=0, max=4294967295, default=0);\n#uicontrol bool color_by_neuropil checkbox(default=true)\n#uicontrol bool color_by_optic_column checkbox(default=false)\n#uicontrol bool color_by_optic_layer checkbox(default=false)\n#uicontrol bool color_by_neuron_id checkbox(default=false)\n#uicontrol bool hide_uncolored checkbox(default=true)\n\n#ifndef HARDCODED_SEGMENT_LIST\n  #define HARDCODED_SEGMENT_LIST 0\n#endif\n\nconst int segment_list[] = int[](HARDCODED_SEGMENT_LIST);\n#uicontrol bool only_show_shader_hardcoded_connections checkbox(default=false)\n\n\nvec3 viridis(float t) {\n\n    const vec3 c0 = vec3(0.2777273272234177, 0.005407344544966578, 0.3340998053353061);\n    const vec3 c1 = vec3(0.1050930431085774, 1.404613529898575, 1.384590162594685);\n    const vec3 c2 = vec3(-0.3308618287255563, 0.214847559468213, 0.09509516302823659);\n    const vec3 c3 = vec3(-4.634230498983486, -5.799100973351585, -19.33244095627987);\n    const vec3 c4 = vec3(6.228269936347081, 14.17993336680509, 56.69055260068105);\n    const vec3 c5 = vec3(4.776384997670288, -13.74514537774601, -65.35303263337234);\n    const vec3 c6 = vec3(-5.435455855934631, 4.645852612178535, 26.3124352495832);\n\n    return c0+t*(c1+t*(c2+t*(c3+t*(c4+t*(c5+t*c6)))));\n}\n  \nuint hash_function(uint state, uint value) {\n    uint k1 = 0xcc9e2d51u;\n    uint k2 = 0x1b873593u;\n    \n    value = value * k1;\n    value = (value << 15) | (value >> 17);\n    value = value * k2;\n    state = state ^ value;\n    state = (state << 13) | (state >> 19);\n    state = state * 5u + 0xe6546b64u;\n    \n    return state;\n}\n\nvec3 hsv_to_rgb(float h, float s, float v) {\n    h *= 6.0;\n    float hue_index = floor(h);\n    float remainder = h - hue_index;\n    float val1 = v * (1.0 - s);\n    float val2 = v * (1.0 - (s * remainder));\n    float val3 = v * (1.0 - (s * (1.0 - remainder)));\n    \n    int hue_remainder = int(hue_index) % 6;\n    \n    if (hue_remainder == 0) {\n        return vec3(v, val3, val1);\n    } else if (hue_remainder == 1) {\n        return vec3(val2, v, val1);\n    } else if (hue_remainder == 2) {\n        return vec3(val1, v, val3);\n    } else if (hue_remainder == 3) {\n        return vec3(val1, val2, v);\n    } else if (hue_remainder == 4) {\n        return vec3(val3, val1, v);\n    } else { // hue_remainder == 5\n        return vec3(v, val1, val2);\n    }\n}\n\nvec3 color_from_segment_id(uint color_seed_, uint segment_id) {\n    uint result = hash_function(color_seed_, segment_id);\n    \n    // We only support 32-bit segment IDs, so newvalue is always 0,\n    // and (id >> 32) would be undefined behavior!\n    // uint newvalue = id >> 32;\n    \n    uint newvalue = 0u;\n    uint result2 = hash_function(result, newvalue);\n    \n    float c0 = float(result2 & 0xFFu) / 255.0;\n    float c1 = float((result2 >> 8) & 0xFFu) / 255.0;\n    \n    float h = c0;\n    float s = 0.5 + 0.5 * c1;\n    float v = 1.0;\n    \n    return hsv_to_rgb(h, s, v);\n}\n\nvec3 get_color_for_id(uint id) {\n    if (id == 0u) {\n        if (hide_uncolored) {\n            discard;\n        }\n        //setPointMarkerBorderWidth(0.2);\n        vec3 white = vec3(1.0, 1.0, 1.0);\n        return white;\n    }\n    else {\n        return color_from_segment_id(uint(color_seed), id);\n    }\n}\n\nvec3 get_nt_color() {\n    vec3 pre_color;\n    \n    switch (int(prop_predicted_nt())) {\n        case acetylcholine_code:\n            pre_color = acetylcholine;\n            break;\n        case dopamine_code:\n            pre_color = dopamine;\n            break;\n        case gaba_code:\n            pre_color = gaba;\n            break;\n        case glutamate_code:\n            pre_color = glutamate;\n            break;\n        case histamine_code:\n            pre_color = histamine;\n            break;\n        case octopamine_code:\n            pre_color = octopamine;\n            break;\n        case serotonin_code:\n            pre_color = serotonin;\n            break;\n        case unknown_nt_code:\n            pre_color = unknown_nt;\n            break;\n        default:\n            pre_color = vec3(1.0, 1.0, 1.0);\n            break;\n    }\n    return pre_color;\n}\n\nvec3 get_compartment_color(int compartment_code) {\n    vec3 color;\n\n    switch (compartment_code) {\n        case axon_code:\n            color = axon;\n            break;\n        case dendrite_code:\n            color = dendrite;\n            break;\n        case linker_code:\n            color = linker;\n            break;\n        case cell_body_fiber_code:\n            color = cell_body_fiber;\n            break;\n        case unknown_compartment_code:\n            color = unknown_compartment;\n            break;\n        default:\n            color = vec3(1.0, 1.0, 1.0);\n            break;\n    }\n    return color;\n}\n\nbool is_in_segment_list(uint value) {\n    for (int i = 0; i < segment_list.length(); i++) {\n        if (uint(segment_list[i]) == value) return true;\n    }\n    return false;\n}\n\nvoid main() {    \n    vec3 pre_color = pre_default;\n    vec3 post_color = post_default;\n\n    if (only_show_shader_hardcoded_connections) {\n        if (!is_in_segment_list(prop_body_pre_u32()) || !is_in_segment_list(prop_body_post_u32())) {\n            discard;\n        }\n    }\n\n    setLineColor(vec3(1.0, 1.0, 1.0));\n    setEndpointMarkerColor(\n        vec4(pre_color, 1.0), vec4(post_color, 1.0)\n    );\n\n    if (int(color_by_neurotransmitter) + \n        int(color_by_nt_probability) + \n        int(color_by_nt_confidence) + \n        int(color_by_tbar_fanout) + \n        int(color_by_compartment) + \n        int(color_by_neuropil) + \n        int(color_by_optic_column) + \n        int(color_by_optic_layer) + \n        int(color_by_neuron_id)\n    > 1) {\n        // Invalid selection.\n        discard;\n    }\n    else if (color_by_neurotransmitter) {\n        pre_color = get_nt_color();\n    }\n    else if (color_by_nt_probability) {\n       pre_color = viridis(prop_predicted_nt_prob());\n    }\n    else if (color_by_nt_confidence) {\n       pre_color = viridis(prop_nt_tbar_confidence_score());\n    }\n    else if (color_by_tbar_fanout) {\n       float display_fanout = min(float(max_fanout_display), float(prop_tbar_fanout())) / float(max_fanout_display);\n       pre_color = viridis(display_fanout);\n    }\n    else if (color_by_compartment) {\n        pre_color = get_compartment_color(int(prop_compartment_pre()));\n        post_color = get_compartment_color(int(prop_compartment_post()));\n    }\n    else if (color_by_neuropil) {\n        pre_color = post_color = get_color_for_id(uint(prop_primary_roi()));\n    }\n    else if (color_by_optic_column) {\n        pre_color = post_color = get_color_for_id(uint(prop_optic_column()));\n    }\n    else if (color_by_optic_layer) {\n        pre_color = post_color = get_color_for_id(uint(prop_optic_layer()));\n    }\n    else if (color_by_neuron_id) {\n        pre_color = get_color_for_id(uint(prop_body_pre_u32()));\n        post_color = get_color_for_id(uint(prop_body_post_u32()));\n    }\n\n    setEndpointMarkerColor(\n        vec4(pre_color, 1.0), vec4(post_color, 1.0)\n    );\n\n    if (show_pre && show_post) {\n        setLineWidth(1.0);\n        setEndpointMarkerSize(pre_size, post_size);\n    }\n    else if (show_post) {\n        setLineWidth(0.0);\n        setEndpointMarkerSize(0.0, post_size);\n    }\n    else if (show_pre) {\n        setLineWidth(0.0);\n        setEndpointMarkerSize(pre_size, 0.0);\n    }\n    else {\n        setLineWidth(0.0);\n        setEndpointMarkerSize(0.0, 0.0);\n    }\n}\n",
+      "shaderControls": {
+        "color_by_neurotransmitter": false,
+        "color_by_compartment": false,
+        "color_by_neuropil": false
+      },
+      "linkedSegmentationLayer": {
+        "body_pre": "cns-seg",
+        "body_post": "cns-seg"
+      },
+      "filterBySegmentation": [
+        "body_pre"
+      ],
+      "name": "presyn",
+      "visible": false
+    },
+    {
+      "type": "annotation",
+      "source": "precomputed://gs://flyem-male-cns/v1.0/male-cns-v1.0-synapses-precomputed/",
+      "tab": "rendering",
+      "codeVisible": false,
+      "shader": "// To use the \"only_show_shader_hardcoded_connections\"\n// checkbox, edit the #define below and\n// paste in neuron segment IDs of your choosing.\n// (Don't list more than a few hundred.)\n// Only connections in which BOTH pre and post\n// belong to these segments will be shown.\n\n#define HARDCODED_SEGMENT_LIST 111, 222, 333\n\n#uicontrol bool show_pre_partners checkbox(default=true)\n#uicontrol bool show_post checkbox(default=false)\nbool show_pre = show_pre_partners;\n\n#uicontrol float pre_size slider(min=1.0, max=20.0, default=6.0)\n#uicontrol float post_size slider(min=1.0, max=20.0, default=4.0)\n\n#uicontrol vec3 pre_default color(default=\"red\")\n#uicontrol vec3 post_default color(default=\"blue\")\n\n#define acetylcholine_code 0\n#define dopamine_code 1\n#define gaba_code 2\n#define glutamate_code 3\n#define histamine_code 4\n#define octopamine_code 5\n#define serotonin_code 6\n#define unknown_nt_code 7\n\n#uicontrol bool color_by_neurotransmitter checkbox(default=true)\n\n#uicontrol vec3 acetylcholine color(default=\"#1f77b4\")\n#uicontrol vec3 dopamine color(default=\"#ff7f0e\")\n#uicontrol vec3 gaba color(default=\"#2ca02c\")\n#uicontrol vec3 glutamate color(default=\"#d62728\")\n#uicontrol vec3 histamine color(default=\"#9467bd\")\n#uicontrol vec3 octopamine color(default=\"#8c564b\")\n#uicontrol vec3 serotonin color(default=\"#e377c2\")\n#uicontrol vec3 unknown_nt color(default=\"#7f7f7f\")\n\n#uicontrol bool color_by_nt_probability checkbox(default=false)\n#uicontrol bool color_by_nt_confidence checkbox(default=false)\n\n#uicontrol bool color_by_tbar_fanout checkbox(default=false)\n#uicontrol float max_fanout_display slider(min=1.0, max=20.0, step=1.0, default=10.0)\n\n#define unknown_compartment_code 0\n#define axon_code 1\n#define dendrite_code 2\n#define linker_code 3\n#define cell_body_fiber_code 4\n\n#uicontrol bool color_by_compartment checkbox(default=true)\n\n#uicontrol vec3 axon color(default=\"#1f77b4\")\n#uicontrol vec3 dendrite color(default=\"#ff7f0e\")\n#uicontrol vec3 linker color(default=\"#2ca02c\")\n#uicontrol vec3 cell_body_fiber color(default=\"#d62728\")\n#uicontrol vec3 unknown_compartment color(default=\"#7f7f7f\")\n\n#uicontrol int color_seed slider(min=0, max=4294967295, default=0);\n#uicontrol bool color_by_neuropil checkbox(default=true)\n#uicontrol bool color_by_optic_column checkbox(default=false)\n#uicontrol bool color_by_optic_layer checkbox(default=false)\n#uicontrol bool color_by_neuron_id checkbox(default=false)\n#uicontrol bool hide_uncolored checkbox(default=true)\n\n#ifndef HARDCODED_SEGMENT_LIST\n  #define HARDCODED_SEGMENT_LIST 0\n#endif\n\nconst int segment_list[] = int[](HARDCODED_SEGMENT_LIST);\n#uicontrol bool only_show_shader_hardcoded_connections checkbox(default=false)\n\n\nvec3 viridis(float t) {\n\n    const vec3 c0 = vec3(0.2777273272234177, 0.005407344544966578, 0.3340998053353061);\n    const vec3 c1 = vec3(0.1050930431085774, 1.404613529898575, 1.384590162594685);\n    const vec3 c2 = vec3(-0.3308618287255563, 0.214847559468213, 0.09509516302823659);\n    const vec3 c3 = vec3(-4.634230498983486, -5.799100973351585, -19.33244095627987);\n    const vec3 c4 = vec3(6.228269936347081, 14.17993336680509, 56.69055260068105);\n    const vec3 c5 = vec3(4.776384997670288, -13.74514537774601, -65.35303263337234);\n    const vec3 c6 = vec3(-5.435455855934631, 4.645852612178535, 26.3124352495832);\n\n    return c0+t*(c1+t*(c2+t*(c3+t*(c4+t*(c5+t*c6)))));\n}\n  \nuint hash_function(uint state, uint value) {\n    uint k1 = 0xcc9e2d51u;\n    uint k2 = 0x1b873593u;\n    \n    value = value * k1;\n    value = (value << 15) | (value >> 17);\n    value = value * k2;\n    state = state ^ value;\n    state = (state << 13) | (state >> 19);\n    state = state * 5u + 0xe6546b64u;\n    \n    return state;\n}\n\nvec3 hsv_to_rgb(float h, float s, float v) {\n    h *= 6.0;\n    float hue_index = floor(h);\n    float remainder = h - hue_index;\n    float val1 = v * (1.0 - s);\n    float val2 = v * (1.0 - (s * remainder));\n    float val3 = v * (1.0 - (s * (1.0 - remainder)));\n    \n    int hue_remainder = int(hue_index) % 6;\n    \n    if (hue_remainder == 0) {\n        return vec3(v, val3, val1);\n    } else if (hue_remainder == 1) {\n        return vec3(val2, v, val1);\n    } else if (hue_remainder == 2) {\n        return vec3(val1, v, val3);\n    } else if (hue_remainder == 3) {\n        return vec3(val1, val2, v);\n    } else if (hue_remainder == 4) {\n        return vec3(val3, val1, v);\n    } else { // hue_remainder == 5\n        return vec3(v, val1, val2);\n    }\n}\n\nvec3 color_from_segment_id(uint color_seed_, uint segment_id) {\n    uint result = hash_function(color_seed_, segment_id);\n    \n    // We only support 32-bit segment IDs, so newvalue is always 0,\n    // and (id >> 32) would be undefined behavior!\n    // uint newvalue = id >> 32;\n    \n    uint newvalue = 0u;\n    uint result2 = hash_function(result, newvalue);\n    \n    float c0 = float(result2 & 0xFFu) / 255.0;\n    float c1 = float((result2 >> 8) & 0xFFu) / 255.0;\n    \n    float h = c0;\n    float s = 0.5 + 0.5 * c1;\n    float v = 1.0;\n    \n    return hsv_to_rgb(h, s, v);\n}\n\nvec3 get_color_for_id(uint id) {\n    if (id == 0u) {\n        if (hide_uncolored) {\n            discard;\n        }\n        //setPointMarkerBorderWidth(0.2);\n        vec3 white = vec3(1.0, 1.0, 1.0);\n        return white;\n    }\n    else {\n        return color_from_segment_id(uint(color_seed), id);\n    }\n}\n\nvec3 get_nt_color() {\n    vec3 pre_color;\n    \n    switch (int(prop_predicted_nt())) {\n        case acetylcholine_code:\n            pre_color = acetylcholine;\n            break;\n        case dopamine_code:\n            pre_color = dopamine;\n            break;\n        case gaba_code:\n            pre_color = gaba;\n            break;\n        case glutamate_code:\n            pre_color = glutamate;\n            break;\n        case histamine_code:\n            pre_color = histamine;\n            break;\n        case octopamine_code:\n            pre_color = octopamine;\n            break;\n        case serotonin_code:\n            pre_color = serotonin;\n            break;\n        case unknown_nt_code:\n            pre_color = unknown_nt;\n            break;\n        default:\n            pre_color = vec3(1.0, 1.0, 1.0);\n            break;\n    }\n    return pre_color;\n}\n\nvec3 get_compartment_color(int compartment_code) {\n    vec3 color;\n\n    switch (compartment_code) {\n        case axon_code:\n            color = axon;\n            break;\n        case dendrite_code:\n            color = dendrite;\n            break;\n        case linker_code:\n            color = linker;\n            break;\n        case cell_body_fiber_code:\n            color = cell_body_fiber;\n            break;\n        case unknown_compartment_code:\n            color = unknown_compartment;\n            break;\n        default:\n            color = vec3(1.0, 1.0, 1.0);\n            break;\n    }\n    return color;\n}\n\nbool is_in_segment_list(uint value) {\n    for (int i = 0; i < segment_list.length(); i++) {\n        if (uint(segment_list[i]) == value) return true;\n    }\n    return false;\n}\n\nvoid main() {    \n    vec3 pre_color = pre_default;\n    vec3 post_color = post_default;\n\n    if (only_show_shader_hardcoded_connections) {\n        if (!is_in_segment_list(prop_body_pre_u32()) || !is_in_segment_list(prop_body_post_u32())) {\n            discard;\n        }\n    }\n\n    setLineColor(vec3(1.0, 1.0, 1.0));\n    setEndpointMarkerColor(\n        vec4(pre_color, 1.0), vec4(post_color, 1.0)\n    );\n\n    if (int(color_by_neurotransmitter) + \n        int(color_by_nt_probability) + \n        int(color_by_nt_confidence) + \n        int(color_by_tbar_fanout) + \n        int(color_by_compartment) + \n        int(color_by_neuropil) + \n        int(color_by_optic_column) + \n        int(color_by_optic_layer) + \n        int(color_by_neuron_id)\n    > 1) {\n        // Invalid selection.\n        discard;\n    }\n    else if (color_by_neurotransmitter) {\n        pre_color = get_nt_color();\n    }\n    else if (color_by_nt_probability) {\n       pre_color = viridis(prop_predicted_nt_prob());\n    }\n    else if (color_by_nt_confidence) {\n       pre_color = viridis(prop_nt_tbar_confidence_score());\n    }\n    else if (color_by_tbar_fanout) {\n       float display_fanout = min(float(max_fanout_display), float(prop_tbar_fanout())) / float(max_fanout_display);\n       pre_color = viridis(display_fanout);\n    }\n    else if (color_by_compartment) {\n        pre_color = get_compartment_color(int(prop_compartment_pre()));\n        post_color = get_compartment_color(int(prop_compartment_post()));\n    }\n    else if (color_by_neuropil) {\n        pre_color = post_color = get_color_for_id(uint(prop_primary_roi()));\n    }\n    else if (color_by_optic_column) {\n        pre_color = post_color = get_color_for_id(uint(prop_optic_column()));\n    }\n    else if (color_by_optic_layer) {\n        pre_color = post_color = get_color_for_id(uint(prop_optic_layer()));\n    }\n    else if (color_by_neuron_id) {\n        pre_color = get_color_for_id(uint(prop_body_pre_u32()));\n        post_color = get_color_for_id(uint(prop_body_post_u32()));\n    }\n\n    setEndpointMarkerColor(\n        vec4(pre_color, 1.0), vec4(post_color, 1.0)\n    );\n\n    if (show_pre && show_post) {\n        setLineWidth(1.0);\n        setEndpointMarkerSize(pre_size, post_size);\n    }\n    else if (show_post) {\n        setLineWidth(0.0);\n        setEndpointMarkerSize(0.0, post_size);\n    }\n    else if (show_pre) {\n        setLineWidth(0.0);\n        setEndpointMarkerSize(pre_size, 0.0);\n    }\n    else {\n        setLineWidth(0.0);\n        setEndpointMarkerSize(0.0, 0.0);\n    }\n}\n",
+      "shaderControls": {
+        "show_pre_partners": false,
+        "show_post": true,
+        "color_by_neurotransmitter": false,
+        "color_by_compartment": false,
+        "color_by_neuropil": false
+      },
+      "linkedSegmentationLayer": {
+        "body_post": "cns-seg",
+        "body_pre": "cns-seg"
+      },
+      "filterBySegmentation": [
+        "body_post"
+      ],
+      "name": "postsyn",
+      "visible": false
+    }
+  ],
+  "showSlices": false,
+  "gpuMemoryLimit": 2000000000,
+  "systemMemoryLimit": 4000000000,
+  "selectedLayer": {
+    "flex": 1.57,
+    "size": 415,
+    "visible": true,
+    "layer": "cns-seg"
+  },
+  "layout": "3d",
+  "selection": {
+    "side": "left",
+    "row": 3,
+    "flex": 0.5,
+    "visible": false
+  },
+  "layerListPanel": {
+    "flex": 0.93
+  }
+}
diff --git a/templates/neuroglancer.js.jinja b/templates/neuroglancer.js.jinja
deleted file mode 100644
index ea52ac82..00000000
--- a/templates/neuroglancer.js.jinja
+++ /dev/null
@@ -1,913 +0,0 @@
-{
-  "title": "{{ website_title}}",
-  "dimensions": {
-    "x": [
-      8e-9,
-      "m"
-    ],
-    "y": [
-      8e-9,
-      "m"
-    ],
-    "z": [
-      8e-9,
-      "m"
-    ]
-  },
-  "position": [
-    48311.90234375,
-    25295.765625,
-    28509.505859375
-  ],
-  "crossSectionScale": 88.13122564876332,
-  "projectionOrientation": [
-    0,
-    -0.002181659685447812,
-    0,
-    0.999997615814209
-  ],
-  "projectionScale": 99656.51830908704,
-  "layers": [
-    {
-      "type": "image",
-      "source": {
-        "url": "precomputed://gs://flyem-male-cns/em/em-clahe-jpeg",
-        "subsources": {
-          "default": true
-        },
-        "enableDefaultSubsources": false
-      },
-      "tab": "rendering",
-      "name": "em-clahe"
-    },
-    {
-      "type": "segmentation",
-      "source": [
-        {
-          "url": "precomputed://gs://flyem-male-cns/v0.9/segmentation",
-          "subsources": {
-            "default": true,
-            "mesh": true
-          },
-          "enableDefaultSubsources": false
-        },
-        {
-          "url": "precomputed://gs://flyem-male-cns/v0.9/segmentation/type_property",
-          "subsources": {
-            "default": true
-          },
-          "enableDefaultSubsources": false
-        },
-        {
-          "url": "precomputed://gs://flyem-male-cns/v0.9/segmentation/tags_property",
-          "subsources": {
-            "default": true
-          },
-          "enableDefaultSubsources": false
-        },
-        {
-          "url": "precomputed://gs://flyem-male-cns/v0.9/segmentation/numeric_properties",
-          "subsources": {
-            "default": true
-          },
-          "enableDefaultSubsources": false
-        },
-        {
-          "url": "precomputed://gs://flyem-male-cns/v0.9/segmentation/instance_property",
-          "enableDefaultSubsources": false
-        },
-        {
-          "url": "precomputed://gs://flyem-male-cns/v0.9/segmentation/flywireType_property",
-          "enableDefaultSubsources": false
-        },
-        {
-          "url": "precomputed://gs://flyem-male-cns/v0.9/segmentation/meshes-malecns/single-res-meshes",
-          "enableDefaultSubsources": false
-        }
-      ],
-      "toolBindings": {
-        "Q": "selectSegments"
-      },
-      "tab": "segments",
-      "segments": {{ visible_neurons | tojson}},
-      "segmentQuery": "{{neuron_query|safe}}",
-      "name": "cns-seg"
-    },
-    {
-      "type": "segmentation",
-      "source": {
-        "url": "precomputed://gs://flyem-male-cns/rois/fullbrain-major-shells",
-        "subsources": {
-          "default": true,
-          "properties": true,
-          "mesh": true
-        },
-        "enableDefaultSubsources": false
-      },
-      "pick": false,
-      "tab": "rendering",
-      "selectedAlpha": 0,
-      "saturation": 0,
-      "meshSilhouetteRendering": 7,
-      "segments": [
-        "1",
-        "2",
-        "3"
-      ],
-      "segmentDefaultColor": "#ffffff",
-      "name": "brain-neuropil-shell"
-    },
-    {
-      "type": "segmentation",
-      "source": {
-        "url": "precomputed://gs://flyem-male-cns/rois/brain-shell-v2.2",
-        "subsources": {
-          "default": true,
-          "properties": true,
-          "mesh": true
-        },
-        "enableDefaultSubsources": false
-      },
-      "pick": false,
-      "tab": "rendering",
-      "selectedAlpha": 0,
-      "saturation": 0,
-      "meshSilhouetteRendering": 7,
-      "segments": [
-        "1"
-      ],
-      "segmentDefaultColor": "#ffffff",
-      "name": "brain-shell",
-      "archived": true
-    },
-    {
-      "type": "segmentation",
-      "source": {
-        "url": "precomputed://gs://flyem-male-cns/rois/brain-shell-with-lamina-v2.1",
-        "subsources": {
-          "default": true,
-          "properties": true,
-          "mesh": true
-        },
-        "enableDefaultSubsources": false
-      },
-      "pick": false,
-      "tab": "rendering",
-      "selectedAlpha": 0,
-      "saturation": 0,
-      "meshSilhouetteRendering": 7,
-      "segments": [
-        "1"
-      ],
-      "segmentDefaultColor": "#ffffff",
-      "name": "brain-shell-with-lamina",
-      "archived": true
-    },
-    {
-      "type": "segmentation",
-      "source": {
-        "url": "precomputed://gs://flyem-male-cns/rois/vnc-neuropil-shell",
-        "subsources": {
-          "default": true,
-          "properties": true,
-          "mesh": true
-        },
-        "enableDefaultSubsources": false
-      },
-      "pick": false,
-      "tab": "rendering",
-      "selectedAlpha": 0,
-      "saturation": 0,
-      "meshSilhouetteRendering": 7,
-      "segments": [
-        "1"
-      ],
-      "segmentDefaultColor": "#ffffff",
-      "name": "vnc-neuropil-shell"
-    },
-    {
-      "type": "segmentation",
-      "source": {
-        "url": "precomputed://gs://flyem-male-cns/rois/vnc-shell",
-        "subsources": {
-          "default": true,
-          "properties": true,
-          "mesh": true
-        },
-        "enableDefaultSubsources": false
-      },
-      "pick": false,
-      "tab": "rendering",
-      "selectedAlpha": 0,
-      "saturation": 0,
-      "meshSilhouetteRendering": 7,
-      "segments": [
-        "1"
-      ],
-      "segmentDefaultColor": "#ffffff",
-      "name": "vnc-shell",
-      "archived": true
-    },
-    {
-      "type": "segmentation",
-      "source": {
-        "url": "precomputed://gs://flyem-male-cns/rois/fullbrain-roi-v4",
-        "subsources": {
-          "default": true,
-          "properties": true,
-          "mesh": true
-        },
-        "enableDefaultSubsources": false
-      },
-      "pick": false,
-      "tab": "segments",
-      "selectedAlpha": 0,
-      "meshSilhouetteRendering": 4,
-      "segments": [
-        "1",
-        "10",
-        "11",
-        "12",
-        "13",
-        "14",
-        "15",
-        "16",
-        "17",
-        "18",
-        "19",
-        "2",
-        "20",
-        "21",
-        "22",
-        "23",
-        "24",
-        "25",
-        "26",
-        "27",
-        "28",
-        "29",
-        "3",
-        "30",
-        "31",
-        "32",
-        "33",
-        "34",
-        "35",
-        "36",
-        "37",
-        "38",
-        "39",
-        "4",
-        "40",
-        "41",
-        "42",
-        "43",
-        "44",
-        "45",
-        "46",
-        "47",
-        "48",
-        "49",
-        "5",
-        "50",
-        "51",
-        "52",
-        "53",
-        "54",
-        "55",
-        "56",
-        "57",
-        "58",
-        "59",
-        "6",
-        "60",
-        "61",
-        "62",
-        "63",
-        "64",
-        "65",
-        "66",
-        "67",
-        "68",
-        "69",
-        "7",
-        "70",
-        "71",
-        "72",
-        "73",
-        "74",
-        "75",
-        "76",
-        "77",
-        "78",
-        "79",
-        "8",
-        "80",
-        "81",
-        "82",
-        "83",
-        "84",
-        "85",
-        "86",
-        "9",
-        "93",
-        "94",
-        "96"
-      ],
-      "name": "brain-neuropils"
-    },
-    {
-      "type": "segmentation",
-      "source": {
-        "url": "precomputed://gs://flyem-male-cns/rois/malecns-vnc-neuropil-roi-v0",
-        "subsources": {
-          "default": true,
-          "properties": true,
-          "mesh": true
-        },
-        "enableDefaultSubsources": false
-      },
-      "pick": false,
-      "tab": "segments",
-      "selectedAlpha": 0,
-      "meshSilhouetteRendering": 4,
-      "segments": [
-        "10",
-        "11",
-        "12",
-        "13",
-        "14",
-        "15",
-        "16",
-        "17",
-        "18",
-        "19",
-        "20",
-        "21",
-        "22",
-        "23",
-        "24",
-        "25",
-        "26",
-        "27",
-        "5",
-        "6",
-        "7",
-        "8",
-        "9"
-      ],
-      "name": "vnc-neuropils"
-    },
-    {
-      "type": "segmentation",
-      "source": {
-        "url": "precomputed://gs://flyem-male-cns/rois/malecns-vnc-nerve-roi-v2",
-        "subsources": {
-          "default": true,
-          "properties": true,
-          "mesh": true
-        },
-        "enableDefaultSubsources": false
-      },
-      "pick": false,
-      "tab": "segments",
-      "meshSilhouetteRendering": 3,
-      "segments": [
-        "10",
-        "11",
-        "12",
-        "13",
-        "14",
-        "15",
-        "16",
-        "17",
-        "18",
-        "19",
-        "2",
-        "20",
-        "21",
-        "22",
-        "23",
-        "24",
-        "25",
-        "26",
-        "27",
-        "28",
-        "29",
-        "3",
-        "30",
-        "31",
-        "32",
-        "33",
-        "34",
-        "35",
-        "36",
-        "37",
-        "4",
-        "5",
-        "6",
-        "7",
-        "8",
-        "9"
-      ],
-      "name": "vnc-nerves",
-      "archived": true
-    },
-    {
-      "type": "segmentation",
-      "source": {
-        "url": "precomputed://gs://flyem-male-cns/rois/malecns-major-compartments-v2",
-        "subsources": {
-          "default": true,
-          "properties": true,
-          "mesh": true
-        },
-        "enableDefaultSubsources": false
-      },
-      "tab": "segments",
-      "meshSilhouetteRendering": 4,
-      "segments": [
-        "1",
-        "2",
-        "3",
-        "4",
-        "5"
-      ],
-      "name": "major-compartments",
-      "archived": true
-    },
-    {
-      "type": "segmentation",
-      "source": "precomputed://gs://flyem-male-cns/rois/pointcloud-shells",
-      "pick": false,
-      "tab": "segments",
-      "selectedAlpha": 0,
-      "saturation": 0,
-      "segments": [
-        "1",
-        "2"
-      ],
-      "segmentDefaultColor": "#ffffff",
-      "name": "pointcloud-shells",
-      "archived": true
-    },
-    {
-      "type": "annotation",
-      "source": "precomputed://gs://flyem-male-cns/v0.9/malecns-v0.9-soma-points",
-      "tab": "rendering",
-      "annotationColor": "#ffffff",
-      "shader": "#uicontrol float radius slider(min=1, max=30, step=1, default=5)\n#uicontrol bool color_by_birthtime checkbox(default=false)\n#uicontrol bool color_by_itoleeHl checkbox(default=false)\n#uicontrol bool color_by_trumanHl checkbox(default=false)\n\n#uicontrol bool color_by_top_input_compartment checkbox(default=false)\n#uicontrol bool color_by_top_output_compartment checkbox(default=false)\n\n#uicontrol bool color_by_top_input_neuropil checkbox(default=false)\n#uicontrol bool color_by_top_output_neuropil checkbox(default=false)\n\n#uicontrol bool color_by_optic_column checkbox(default=false)\n#uicontrol bool color_by_top_input_optic_layer checkbox(default=false)\n#uicontrol bool color_by_top_output_optic_layer checkbox(default=false)\n\n#uicontrol int color_seed slider(min=0, max=4294967295, default=0);\n#uicontrol bool hide_uncolored checkbox(default=true)\n#uicontrol bool hide_vnc checkbox(default=false)\n#uicontrol bool hide_brain checkbox(default=false)\n#uicontrol bool hide_tosoma checkbox(default=false)\n\n\nuint hash_function(uint state, uint value) {\n    uint k1 = 0xcc9e2d51u;\n    uint k2 = 0x1b873593u;\n    \n    value = value * k1;\n    value = (value << 15) | (value >> 17);\n    value = value * k2;\n    state = state ^ value;\n    state = (state << 13) | (state >> 19);\n    state = state * 5u + 0xe6546b64u;\n    \n    return state;\n}\n\nvec3 hsv_to_rgb(float h, float s, float v) {\n    h *= 6.0;\n    float hue_index = floor(h);\n    float remainder = h - hue_index;\n    float val1 = v * (1.0 - s);\n    float val2 = v * (1.0 - (s * remainder));\n    float val3 = v * (1.0 - (s * (1.0 - remainder)));\n    \n    int hue_remainder = int(hue_index) % 6;\n    \n    if (hue_remainder == 0) {\n        return vec3(v, val3, val1);\n    } else if (hue_remainder == 1) {\n        return vec3(val2, v, val1);\n    } else if (hue_remainder == 2) {\n        return vec3(val1, v, val3);\n    } else if (hue_remainder == 3) {\n        return vec3(val1, val2, v);\n    } else if (hue_remainder == 4) {\n        return vec3(val3, val1, v);\n    } else { // hue_remainder == 5\n        return vec3(v, val1, val2);\n    }\n}\n\nvec3 color_from_segment_id(uint color_seed_, uint segment_id) {\n    uint result = hash_function(color_seed_, segment_id);\n    \n    // We only support 32-bit segment IDs, so newvalue is always 0,\n    // and (id >> 32) would be undefined behavior!\n    // uint newvalue = id >> 32;\n    \n    uint newvalue = 0u;\n    uint result2 = hash_function(result, newvalue);\n    \n    float c0 = float(result2 & 0xFFu) / 255.0;\n    float c1 = float((result2 >> 8) & 0xFFu) / 255.0;\n    \n    float h = c0;\n    float s = 0.5 + 0.5 * c1;\n    float v = 1.0;\n    \n    return hsv_to_rgb(h, s, v);\n}\n\nvoid set_color_for_id(uint id) {\n    if (id == 0u) {\n        if (hide_uncolored) {\n            discard;\n        }\n        //setPointMarkerBorderWidth(0.2);\n        //setPointMarkerColor(vec4(vec3(0.0, 0.0, 0.0), 0.0));\n        //setPointMarkerBorderColor(vec3(1.0, 1.0, 1.0));\n    }\n    else {\n        setColor(color_from_segment_id(uint(color_seed), id));\n    }\n}\n\nvoid main() {\n    setPointMarkerSize(radius);\n    setPointMarkerBorderWidth(1.0);\n    setPointMarkerBorderColor(vec3(0.0, 0.0, 0.0));\n    \n    if (hide_vnc && prop_is_vnc() == 1u) {\n        discard;\n    }\n    \n    if (hide_brain && prop_is_vnc() == 0u) {\n        discard;\n    }\n    \n    if (prop_kind() == 1u && hide_tosoma)\n    discard;\n    \n    // default color: soma vs. tosoma\n    if (prop_kind() == 1u) {\n        setColor(vec3(0.0, 1.0, 1.0));\n    }\n    else {\n        setColor(vec3(1.0, 1.0, 1.0));\n    }\n    \n    if (\n        int(color_by_top_input_compartment) +\n        int(color_by_top_output_compartment) +\n        int(color_by_top_input_neuropil) +\n        int(color_by_top_output_neuropil) +\n        int(color_by_top_input_optic_layer) +\n        int(color_by_top_output_optic_layer) +\n        int(color_by_optic_column) +\n        int(color_by_birthtime) +\n        int(color_by_itoleeHl || color_by_trumanHl)\n        > 1) {\n        discard;\n    }\n        \n    if (color_by_top_input_compartment) {\n        set_color_for_id(uint(prop_top_input_compartment()));\n        return;\n    }\n    if (color_by_top_output_compartment) {\n        set_color_for_id(uint(prop_top_output_compartment()));\n        return;\n    }\n    if (color_by_top_input_neuropil) {\n        set_color_for_id(uint(prop_top_input_neuropil()));\n        return;\n    }\n    if (color_by_top_output_neuropil) {\n        set_color_for_id(uint(prop_top_output_neuropil()));\n        return;\n    }\n    if (color_by_top_input_optic_layer) {\n        set_color_for_id(uint(prop_top_input_optic_layer()));\n        return;\n    }\n    if (color_by_top_output_optic_layer) {\n        set_color_for_id(uint(prop_top_output_optic_layer()));\n        return;\n    }\n    if (color_by_optic_column) {\n        set_color_for_id(uint(prop_assigned_optic_column()));\n        return;\n    }\n    if (color_by_birthtime) {\n        set_color_for_id(uint(prop_birthtime()));\n        return;\n    }\n    \n    // We permit showing both itoleeHl and trumanHl simultaneously\n    if (color_by_itoleeHl && color_by_trumanHl) {\n        uint id = bool(prop_itoleeHl()) ? uint(prop_itoleeHl()) : uint(prop_trumanHl());\n        set_color_for_id(id);\n        return;\n    }\n    \n    if (color_by_itoleeHl) {\n        set_color_for_id(uint(prop_itoleeHl()));\n        return;\n    }\n    if (color_by_trumanHl) {\n        set_color_for_id(uint(prop_trumanHl()));\n        return;\n    }\n}",
-      "shaderControls": {
-        "hide_tosoma": true
-      },
-      "linkedSegmentationLayer": {
-        "body": "cns-seg"
-      },
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-        "precomputed://gs://flyem-male-cns/rois/hemibrain_mask_in_malecns_coords/mesh",
-        "precomputed://gs://flyem-male-cns/rois/hemibrain_mask_in_malecns_coords/segment_properties"
-      ],
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-      "source": "gs://flyem-male-cns/hemibrain2mcns_meshes/|neuroglancer-precomputed:",
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-      "segmentDefaultColor": "#e90002",
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-      "type": "segmentation",
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-      "tab": "segments",
-      "segments": [],
-      "segmentDefaultColor": "#d400ff",
-      "name": "flywire-meshes",
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-    {
-      "type": "segmentation",
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-        "url": "precomputed://gs://flyem-male-cns/malecns-semantic-masks",
-        "subsources": {
-          "default": true,
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-      "tab": "segments",
-      "segments": [],
-      "name": "semantic-masks",
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-    {
-      "type": "annotation",
-      "source": "precomputed://gs://flyem-male-cns/v0.9/male-cns-v0.9-synapses-precomputed/",
-      "tab": "rendering",
-      "annotationColor": "#000000",
-      "codeVisible": false,
-      "shader": "#uicontrol bool show_pre checkbox(default=true)\n#uicontrol bool show_post_partners checkbox(default=false)\n\n#uicontrol float pre_size slider(min=1.0, max=20.0, default=6.0)\n#uicontrol float post_size slider(min=1.0, max=20.0, default=4.0)\n\n#uicontrol vec3 pre_default color(default=\"red\")\n#uicontrol vec3 post_default color(default=\"blue\")\n\n#define acetylcholine_code 0\n#define dopamine_code 1\n#define gaba_code 2\n#define glutamate_code 3\n#define histamine_code 4\n#define octopamine_code 5\n#define serotonin_code 6\n#define unknown_nt_code 7\n\n#uicontrol bool color_by_neurotransmitter checkbox(default=true)\n\n#uicontrol vec3 acetylcholine color(default=\"#1f77b4\")\n#uicontrol vec3 dopamine color(default=\"#ff7f0e\")\n#uicontrol vec3 gaba color(default=\"#2ca02c\")\n#uicontrol vec3 glutamate color(default=\"#d62728\")\n#uicontrol vec3 histamine color(default=\"#9467bd\")\n#uicontrol vec3 octopamine color(default=\"#8c564b\")\n#uicontrol vec3 serotonin color(default=\"#e377c2\")\n#uicontrol vec3 unknown_nt color(default=\"#7f7f7f\")\n\n#uicontrol bool color_by_nt_probability checkbox(default=false)\n#uicontrol bool color_by_nt_confidence checkbox(default=false)\n\n#uicontrol bool color_by_tbar_fanout checkbox(default=false)\n#uicontrol float max_fanout_display slider(min=1.0, max=20.0, step=1.0, default=10.0)\n\n#define unknown_compartment_code 0\n#define axon_code 1\n#define dendrite_code 2\n#define linker_code 3\n#define cell_body_fiber_code 4\n\n#uicontrol bool color_by_compartment checkbox(default=true)\n\n#uicontrol vec3 axon color(default=\"#1f77b4\")\n#uicontrol vec3 dendrite color(default=\"#ff7f0e\")\n#uicontrol vec3 linker color(default=\"#2ca02c\")\n#uicontrol vec3 cell_body_fiber color(default=\"#d62728\")\n#uicontrol vec3 unknown_compartment color(default=\"#7f7f7f\")\n\n#uicontrol int color_seed slider(min=0, max=4294967295, default=0);\n#uicontrol bool color_by_neuropil checkbox(default=true)\n#uicontrol bool color_by_optic_column checkbox(default=false)\n#uicontrol bool color_by_optic_layer checkbox(default=false)\n#uicontrol bool color_by_neuron_id checkbox(default=false)\n#uicontrol bool hide_uncolored checkbox(default=true)\n\nvec3 viridis(float t) {\n\n    const vec3 c0 = vec3(0.2777273272234177, 0.005407344544966578, 0.3340998053353061);\n    const vec3 c1 = vec3(0.1050930431085774, 1.404613529898575, 1.384590162594685);\n    const vec3 c2 = vec3(-0.3308618287255563, 0.214847559468213, 0.09509516302823659);\n    const vec3 c3 = vec3(-4.634230498983486, -5.799100973351585, -19.33244095627987);\n    const vec3 c4 = vec3(6.228269936347081, 14.17993336680509, 56.69055260068105);\n    const vec3 c5 = vec3(4.776384997670288, -13.74514537774601, -65.35303263337234);\n    const vec3 c6 = vec3(-5.435455855934631, 4.645852612178535, 26.3124352495832);\n\n    return c0+t*(c1+t*(c2+t*(c3+t*(c4+t*(c5+t*c6)))));\n}\n  \nuint hash_function(uint state, uint value) {\n    uint k1 = 0xcc9e2d51u;\n    uint k2 = 0x1b873593u;\n    \n    value = value * k1;\n    value = (value << 15) | (value >> 17);\n    value = value * k2;\n    state = state ^ value;\n    state = (state << 13) | (state >> 19);\n    state = state * 5u + 0xe6546b64u;\n    \n    return state;\n}\n\nvec3 hsv_to_rgb(float h, float s, float v) {\n    h *= 6.0;\n    float hue_index = floor(h);\n    float remainder = h - hue_index;\n    float val1 = v * (1.0 - s);\n    float val2 = v * (1.0 - (s * remainder));\n    float val3 = v * (1.0 - (s * (1.0 - remainder)));\n    \n    int hue_remainder = int(hue_index) % 6;\n    \n    if (hue_remainder == 0) {\n        return vec3(v, val3, val1);\n    } else if (hue_remainder == 1) {\n        return vec3(val2, v, val1);\n    } else if (hue_remainder == 2) {\n        return vec3(val1, v, val3);\n    } else if (hue_remainder == 3) {\n        return vec3(val1, val2, v);\n    } else if (hue_remainder == 4) {\n        return vec3(val3, val1, v);\n    } else { // hue_remainder == 5\n        return vec3(v, val1, val2);\n    }\n}\n\nvec3 color_from_segment_id(uint color_seed_, uint segment_id) {\n    uint result = hash_function(color_seed_, segment_id);\n    \n    // We only support 32-bit segment IDs, so newvalue is always 0,\n    // and (id >> 32) would be undefined behavior!\n    // uint newvalue = id >> 32;\n    \n    uint newvalue = 0u;\n    uint result2 = hash_function(result, newvalue);\n    \n    float c0 = float(result2 & 0xFFu) / 255.0;\n    float c1 = float((result2 >> 8) & 0xFFu) / 255.0;\n    \n    float h = c0;\n    float s = 0.5 + 0.5 * c1;\n    float v = 1.0;\n    \n    return hsv_to_rgb(h, s, v);\n}\n\nvec3 get_color_for_id(uint id) {\n    if (id == 0u) {\n        if (hide_uncolored) {\n            discard;\n        }\n        //setPointMarkerBorderWidth(0.2);\n        vec3 white = vec3(1.0, 1.0, 1.0);\n        return white;\n    }\n    else {\n        return color_from_segment_id(uint(color_seed), id);\n    }\n}\n\nvec3 get_nt_color() {\n    vec3 pre_color;\n    \n    switch (int(prop_predicted_nt())) {\n        case acetylcholine_code:\n            pre_color = acetylcholine;\n            break;\n        case dopamine_code:\n            pre_color = dopamine;\n            break;\n        case gaba_code:\n            pre_color = gaba;\n            break;\n        case glutamate_code:\n            pre_color = glutamate;\n            break;\n        case histamine_code:\n            pre_color = histamine;\n            break;\n        case octopamine_code:\n            pre_color = octopamine;\n            break;\n        case serotonin_code:\n            pre_color = serotonin;\n            break;\n        case unknown_nt_code:\n            pre_color = unknown_nt;\n            break;\n        default:\n            pre_color = vec3(1.0, 1.0, 1.0);\n            break;\n    }\n    return pre_color;\n}\n\nvec3 get_compartment_color(int compartment_code) {\n    vec3 color;\n\n    switch (compartment_code) {\n        case axon_code:\n            color = axon;\n            break;\n        case dendrite_code:\n            color = dendrite;\n            break;\n        case linker_code:\n            color = linker;\n            break;\n        case cell_body_fiber_code:\n            color = cell_body_fiber;\n            break;\n        case unknown_compartment_code:\n            color = unknown_compartment;\n            break;\n        default:\n            color = vec3(1.0, 1.0, 1.0);\n            break;\n    }\n    return color;\n}\n\nvoid main() {    \n    vec3 pre_color = pre_default;\n    vec3 post_color = post_default;\n    bool show_post = show_post_partners;\n\n    setLineColor(vec3(1.0, 1.0, 1.0));\n    setEndpointMarkerColor(\n        vec4(pre_color, 1.0), vec4(post_color, 1.0)\n    );\n\n    if (int(color_by_neurotransmitter) + \n        int(color_by_nt_probability) + \n        int(color_by_nt_confidence) + \n        int(color_by_tbar_fanout) + \n        int(color_by_compartment) + \n        int(color_by_neuropil) + \n        int(color_by_optic_column) + \n        int(color_by_optic_layer) + \n        int(color_by_neuron_id)\n    > 1) {\n        // No special color if the user has invalid selections.\n    }\n    else if (color_by_neurotransmitter) {\n        pre_color = get_nt_color();\n    }\n    else if (color_by_nt_probability) {\n       pre_color = viridis(prop_predicted_nt_prob());\n    }\n    else if (color_by_nt_confidence) {\n       pre_color = viridis(prop_nt_tbar_confidence_score());\n    }\n    else if (color_by_tbar_fanout) {\n       float display_fanout = min(float(max_fanout_display), float(prop_tbar_fanout())) / float(max_fanout_display);\n       pre_color = viridis(display_fanout);\n    }\n    else if (color_by_compartment) {\n        pre_color = get_compartment_color(int(prop_compartment_pre()));\n        post_color = get_compartment_color(int(prop_compartment_post()));\n    }\n    else if (color_by_neuropil) {\n        pre_color = post_color = get_color_for_id(uint(prop_primary_roi()));\n    }\n    else if (color_by_optic_column) {\n        pre_color = post_color = get_color_for_id(uint(prop_optic_column()));\n    }\n    else if (color_by_optic_layer) {\n        pre_color = post_color = get_color_for_id(uint(prop_optic_layer()));\n    }\n    else if (color_by_neuron_id) {\n        pre_color = get_color_for_id(uint(prop_body_pre_u32()));\n        post_color = get_color_for_id(uint(prop_body_post_u32()));\n    }\n\n    setEndpointMarkerColor(\n        vec4(pre_color, 1.0), vec4(post_color, 1.0)\n    );\n\n    if (show_pre && show_post) {\n        setLineWidth(1.0);\n        setEndpointMarkerSize(pre_size, post_size);\n    }\n    else if (show_post) {\n        setLineWidth(0.0);\n        setEndpointMarkerSize(0.0, post_size);\n    }\n    else if (show_pre) {\n        setLineWidth(0.0);\n        setEndpointMarkerSize(pre_size, 0.0);\n    }\n    else {\n        setLineWidth(0.0);\n        setEndpointMarkerSize(0.0, 0.0);\n    }\n}\n",
-      "shaderControls": {
-        "color_by_neurotransmitter": false,
-        "color_by_compartment": false,
-        "color_by_neuropil": false
-      },
-      "linkedSegmentationLayer": {
-        "body_pre": "cns-seg",
-        "body_post": "cns-seg"
-      },
-      "filterBySegmentation": [
-        "body_pre"
-      ],
-      "name": "presyn",
-      "visible": false
-    },
-    {
-      "type": "annotation",
-      "source": "precomputed://gs://flyem-male-cns/v0.9/male-cns-v0.9-synapses-precomputed/",
-      "tab": "rendering",
-      "codeVisible": false,
-      "shader": "#uicontrol bool show_pre_partners checkbox(default=true)\n#uicontrol bool show_post checkbox(default=false)\n\n#uicontrol float pre_size slider(min=1.0, max=20.0, default=6.0)\n#uicontrol float post_size slider(min=1.0, max=20.0, default=4.0)\n\n#uicontrol vec3 pre_default color(default=\"red\")\n#uicontrol vec3 post_default color(default=\"blue\")\n\n#define acetylcholine_code 0\n#define dopamine_code 1\n#define gaba_code 2\n#define glutamate_code 3\n#define histamine_code 4\n#define octopamine_code 5\n#define serotonin_code 6\n#define unknown_nt_code 7\n\n#uicontrol bool color_by_neurotransmitter checkbox(default=true)\n\n#uicontrol vec3 acetylcholine color(default=\"#1f77b4\")\n#uicontrol vec3 dopamine color(default=\"#ff7f0e\")\n#uicontrol vec3 gaba color(default=\"#2ca02c\")\n#uicontrol vec3 glutamate color(default=\"#d62728\")\n#uicontrol vec3 histamine color(default=\"#9467bd\")\n#uicontrol vec3 octopamine color(default=\"#8c564b\")\n#uicontrol vec3 serotonin color(default=\"#e377c2\")\n#uicontrol vec3 unknown_nt color(default=\"#7f7f7f\")\n\n#uicontrol bool color_by_nt_probability checkbox(default=false)\n#uicontrol bool color_by_nt_confidence checkbox(default=false)\n\n#uicontrol bool color_by_tbar_fanout checkbox(default=false)\n#uicontrol float max_fanout_display slider(min=1.0, max=20.0, step=1.0, default=10.0)\n\n#define unknown_compartment_code 0\n#define axon_code 1\n#define dendrite_code 2\n#define linker_code 3\n#define cell_body_fiber_code 4\n\n#uicontrol bool color_by_compartment checkbox(default=true)\n\n#uicontrol vec3 axon color(default=\"#1f77b4\")\n#uicontrol vec3 dendrite color(default=\"#ff7f0e\")\n#uicontrol vec3 linker color(default=\"#2ca02c\")\n#uicontrol vec3 cell_body_fiber color(default=\"#d62728\")\n#uicontrol vec3 unknown_compartment color(default=\"#7f7f7f\")\n\n#uicontrol int color_seed slider(min=0, max=4294967295, default=0);\n#uicontrol bool color_by_neuropil checkbox(default=true)\n#uicontrol bool color_by_optic_column checkbox(default=false)\n#uicontrol bool color_by_optic_layer checkbox(default=false)\n#uicontrol bool color_by_neuron_id checkbox(default=false)\n#uicontrol bool hide_uncolored checkbox(default=true)\n\nvec3 viridis(float t) {\n\n    const vec3 c0 = vec3(0.2777273272234177, 0.005407344544966578, 0.3340998053353061);\n    const vec3 c1 = vec3(0.1050930431085774, 1.404613529898575, 1.384590162594685);\n    const vec3 c2 = vec3(-0.3308618287255563, 0.214847559468213, 0.09509516302823659);\n    const vec3 c3 = vec3(-4.634230498983486, -5.799100973351585, -19.33244095627987);\n    const vec3 c4 = vec3(6.228269936347081, 14.17993336680509, 56.69055260068105);\n    const vec3 c5 = vec3(4.776384997670288, -13.74514537774601, -65.35303263337234);\n    const vec3 c6 = vec3(-5.435455855934631, 4.645852612178535, 26.3124352495832);\n\n    return c0+t*(c1+t*(c2+t*(c3+t*(c4+t*(c5+t*c6)))));\n}\n  \nuint hash_function(uint state, uint value) {\n    uint k1 = 0xcc9e2d51u;\n    uint k2 = 0x1b873593u;\n    \n    value = value * k1;\n    value = (value << 15) | (value >> 17);\n    value = value * k2;\n    state = state ^ value;\n    state = (state << 13) | (state >> 19);\n    state = state * 5u + 0xe6546b64u;\n    \n    return state;\n}\n\nvec3 hsv_to_rgb(float h, float s, float v) {\n    h *= 6.0;\n    float hue_index = floor(h);\n    float remainder = h - hue_index;\n    float val1 = v * (1.0 - s);\n    float val2 = v * (1.0 - (s * remainder));\n    float val3 = v * (1.0 - (s * (1.0 - remainder)));\n    \n    int hue_remainder = int(hue_index) % 6;\n    \n    if (hue_remainder == 0) {\n        return vec3(v, val3, val1);\n    } else if (hue_remainder == 1) {\n        return vec3(val2, v, val1);\n    } else if (hue_remainder == 2) {\n        return vec3(val1, v, val3);\n    } else if (hue_remainder == 3) {\n        return vec3(val1, val2, v);\n    } else if (hue_remainder == 4) {\n        return vec3(val3, val1, v);\n    } else { // hue_remainder == 5\n        return vec3(v, val1, val2);\n    }\n}\n\nvec3 color_from_segment_id(uint color_seed_, uint segment_id) {\n    uint result = hash_function(color_seed_, segment_id);\n    \n    // We only support 32-bit segment IDs, so newvalue is always 0,\n    // and (id >> 32) would be undefined behavior!\n    // uint newvalue = id >> 32;\n    \n    uint newvalue = 0u;\n    uint result2 = hash_function(result, newvalue);\n    \n    float c0 = float(result2 & 0xFFu) / 255.0;\n    float c1 = float((result2 >> 8) & 0xFFu) / 255.0;\n    \n    float h = c0;\n    float s = 0.5 + 0.5 * c1;\n    float v = 1.0;\n    \n    return hsv_to_rgb(h, s, v);\n}\n\nvec3 get_color_for_id(uint id) {\n    if (id == 0u) {\n        if (hide_uncolored) {\n            discard;\n        }\n        //setPointMarkerBorderWidth(0.2);\n        vec3 white = vec3(1.0, 1.0, 1.0);\n        return white;\n    }\n    else {\n        return color_from_segment_id(uint(color_seed), id);\n    }\n}\n\nvec3 get_nt_color() {\n    vec3 pre_color;\n    \n    switch (int(prop_predicted_nt())) {\n        case acetylcholine_code:\n            pre_color = acetylcholine;\n            break;\n        case dopamine_code:\n            pre_color = dopamine;\n            break;\n        case gaba_code:\n            pre_color = gaba;\n            break;\n        case glutamate_code:\n            pre_color = glutamate;\n            break;\n        case histamine_code:\n            pre_color = histamine;\n            break;\n        case octopamine_code:\n            pre_color = octopamine;\n            break;\n        case serotonin_code:\n            pre_color = serotonin;\n            break;\n        case unknown_nt_code:\n            pre_color = unknown_nt;\n            break;\n        default:\n            pre_color = vec3(1.0, 1.0, 1.0);\n            break;\n    }\n    return pre_color;\n}\n\nvec3 get_compartment_color(int compartment_code) {\n    vec3 color;\n\n    switch (compartment_code) {\n        case axon_code:\n            color = axon;\n            break;\n        case dendrite_code:\n            color = dendrite;\n            break;\n        case linker_code:\n            color = linker;\n            break;\n        case cell_body_fiber_code:\n            color = cell_body_fiber;\n            break;\n        case unknown_compartment_code:\n            color = unknown_compartment;\n            break;\n        default:\n            color = vec3(1.0, 1.0, 1.0);\n            break;\n    }\n    return color;\n}\n\nvoid main() {    \n    vec3 pre_color = pre_default;\n    vec3 post_color = post_default;\n    bool show_pre = show_pre_partners;\n\n    setLineColor(vec3(1.0, 1.0, 1.0));\n    setEndpointMarkerColor(\n        vec4(pre_color, 1.0), vec4(post_color, 1.0)\n    );\n\n    if (int(color_by_neurotransmitter) + \n        int(color_by_nt_probability) + \n        int(color_by_nt_confidence) + \n        int(color_by_tbar_fanout) + \n        int(color_by_compartment) + \n        int(color_by_neuropil) + \n        int(color_by_optic_column) + \n        int(color_by_optic_layer) + \n        int(color_by_neuron_id)\n    > 1) {\n        // No special color if the user has invalid selections.\n    }\n    else if (color_by_neurotransmitter) {\n        pre_color = get_nt_color();\n    }\n    else if (color_by_nt_probability) {\n       pre_color = viridis(prop_predicted_nt_prob());\n    }\n    else if (color_by_nt_confidence) {\n       pre_color = viridis(prop_nt_tbar_confidence_score());\n    }\n    else if (color_by_tbar_fanout) {\n       float display_fanout = min(float(max_fanout_display), float(prop_tbar_fanout())) / float(max_fanout_display);\n       pre_color = viridis(display_fanout);\n    }\n    else if (color_by_compartment) {\n        pre_color = get_compartment_color(int(prop_compartment_pre()));\n        post_color = get_compartment_color(int(prop_compartment_post()));\n    }\n    else if (color_by_neuropil) {\n        pre_color = post_color = get_color_for_id(uint(prop_primary_roi()));\n    }\n    else if (color_by_optic_column) {\n        pre_color = post_color = get_color_for_id(uint(prop_optic_column()));\n    }\n    else if (color_by_optic_layer) {\n        pre_color = post_color = get_color_for_id(uint(prop_optic_layer()));\n    }\n    else if (color_by_neuron_id) {\n        pre_color = get_color_for_id(uint(prop_body_pre_u32()));\n        post_color = get_color_for_id(uint(prop_body_post_u32()));\n    }\n\n    setEndpointMarkerColor(\n        vec4(pre_color, 1.0), vec4(post_color, 1.0)\n    );\n\n    if (show_pre && show_post) {\n        setLineWidth(1.0);\n        setEndpointMarkerSize(pre_size, post_size);\n    }\n    else if (show_post) {\n        setLineWidth(0.0);\n        setEndpointMarkerSize(0.0, post_size);\n    }\n    else if (show_pre) {\n        setLineWidth(0.0);\n        setEndpointMarkerSize(pre_size, 0.0);\n    }\n    else {\n        setLineWidth(0.0);\n        setEndpointMarkerSize(0.0, 0.0);\n    }\n}\n",
-      "shaderControls": {
-        "color_by_neurotransmitter": false,
-        "color_by_compartment": false,
-        "color_by_neuropil": false
-      },
-      "linkedSegmentationLayer": {
-        "body_post": "cns-seg",
-        "body_pre": "cns-seg"
-      },
-      "filterBySegmentation": [
-        "body_post"
-      ],
-      "name": "postsyn",
-      "visible": false
-    }
-  ],
-  "showSlices": false,
-  "gpuMemoryLimit": 2000000000,
-  "systemMemoryLimit": 4000000000,
-  "selectedLayer": {
-    "flex": 1.57,
-    "size": 415,
-    "visible": true,
-    "layer": "cns-seg"
-  },
-  "layout": "3d",
-  "selection": {
-    "side": "left",
-    "row": 3,
-    "flex": 0.5,
-    "visible": false
-  },
-  "layerListPanel": {
-    "flex": 0.93
-  }
-}
diff --git a/test/services/test_neuroglancer_js_service.py b/test/services/test_neuroglancer_js_service.py
index cea7e952..b0def25d 100644
--- a/test/services/test_neuroglancer_js_service.py
+++ b/test/services/test_neuroglancer_js_service.py
@@ -21,6 +21,7 @@ def __init__(
         self,
         dataset="hemibrain:v1.2.1",
         neuroglancer_base_url="https://clio-ng.janelia.org/",
+        template="neuroglancer.js.jinja",
     ):
         self.neuprint = Mock()
         self.neuprint.dataset = dataset
@@ -28,6 +29,7 @@ def __init__(
 
         self.neuroglancer = Mock()
         self.neuroglancer.base_url = neuroglancer_base_url
+        self.neuroglancer.template = template
 
 
 class TestNeuroglancerJSService:
@@ -69,9 +71,10 @@ def create_service(
         self,
         dataset="hemibrain:v1.2.1",
         neuroglancer_base_url="https://clio-ng.janelia.org/",
+        template="neuroglancer.js.jinja",
     ):
         """Create NeuroglancerJSService with specified configuration."""
-        config = MockConfig(dataset, neuroglancer_base_url)
+        config = MockConfig(dataset, neuroglancer_base_url, template)
         return NeuroglancerJSService(config=config, jinja_env=self.jinja_env)
 
     def test_generates_js_with_correct_base_url(self):
@@ -165,9 +168,9 @@ def test_strips_trailing_slash_from_base_url(self):
                 "Trailing slash should be stripped from base URL"
             )
 
-    def test_uses_standard_template_for_non_fafb_dataset(self):
-        """Test that standard neuroglancer template is used for non-FAFB datasets."""
-        service = self.create_service("hemibrain:v1.2.1")
+    def test_uses_configured_standard_template(self):
+        """Configured `neuroglancer.js.jinja` template is rendered."""
+        service = self.create_service(template="neuroglancer.js.jinja")
 
         with tempfile.TemporaryDirectory() as temp_dir:
             output_dir = Path(temp_dir)
@@ -184,9 +187,12 @@ def test_uses_standard_template_for_non_fafb_dataset(self):
                 "Standard template should be used"
             )
 
-    def test_uses_fafb_template_for_fafb_dataset(self):
-        """Test that FAFB-specific template is used for FAFB datasets."""
-        service = self.create_service("flywire-fafb:v783b")
+    def test_uses_configured_fafb_template(self):
+        """Configured `neuroglancer-fafb.js.jinja` template is rendered."""
+        service = self.create_service(
+            dataset="flywire-fafb:v783b",
+            template="neuroglancer-fafb.js.jinja",
+        )
 
         with tempfile.TemporaryDirectory() as temp_dir:
             output_dir = Path(temp_dir)
@@ -244,7 +250,10 @@ def test_creates_output_directory_structure(self):
     @patch("neuview.services.neuroglancer_js_service.logger")
     def test_logs_template_selection(self, mock_logger):
         """Test that template selection is logged."""
-        service = self.create_service("flywire-fafb:v783b")
+        service = self.create_service(
+            dataset="flywire-fafb:v783b",
+            template="neuroglancer-fafb.js.jinja",
+        )
 
         with tempfile.TemporaryDirectory() as temp_dir:
             output_dir = Path(temp_dir)
@@ -256,34 +265,40 @@ def test_logs_template_selection(self, mock_logger):
             )
 
     def test_get_neuroglancer_template_name(self):
-        """Test the get_neuroglancer_template_name method."""
-        # Test standard template selection
-        service = self.create_service("hemibrain:v1.2.1")
+        """get_neuroglancer_template_name returns the configured template."""
+        service = self.create_service(template="neuroglancer.js.jinja")
         assert service.get_neuroglancer_template_name() == "neuroglancer.js.jinja"
 
-        # Test FAFB template selection
-        service_fafb = self.create_service("flywire-fafb:v783b")
+        service_fafb = self.create_service(template="neuroglancer-fafb.js.jinja")
         assert (
             service_fafb.get_neuroglancer_template_name()
             == "neuroglancer-fafb.js.jinja"
         )
 
     def test_validate_templates(self):
-        """Test template validation functionality."""
-        service = self.create_service()
+        """validate_templates reports the configured neuroglancer template + JS template."""
+        service = self.create_service(template="neuroglancer.js.jinja")
         results = service.validate_templates()
 
-        # Verify all expected templates are validated
+        # The configured template and the JS generator template must both be present and available.
         expected_templates = [
             "neuroglancer.js.jinja",
-            "neuroglancer-fafb.js.jinja",
             "neuroglancer-url-generator.js.jinja",
         ]
-
         for template in expected_templates:
             assert template in results, f"Template {template} should be validated"
             assert results[template] is True, f"Template {template} should be available"
 
+        # A different configured template is the one that gets reported.
+        service_fafb = self.create_service(template="neuroglancer-fafb.js.jinja")
+        results_fafb = service_fafb.validate_templates()
+        assert "neuroglancer-fafb.js.jinja" in results_fafb
+        assert results_fafb["neuroglancer-fafb.js.jinja"] is True
+        assert "neuroglancer.js.jinja" not in results_fafb, (
+            "Only the configured neuroglancer template should appear, "
+            "not every known template"
+        )
+
     def test_get_template_info(self):
         """Test the get_template_info method."""
         dataset = "test-dataset:v1.0"
diff --git a/test/services/test_url_generation_service.py b/test/services/test_url_generation_service.py
index 3ee3fedf..2928e14b 100644
--- a/test/services/test_url_generation_service.py
+++ b/test/services/test_url_generation_service.py
@@ -14,13 +14,14 @@
 class MockConfig:
     """Mock configuration object for testing."""
 
-    def __init__(self, dataset="hemibrain:v1.2.1"):
+    def __init__(self, dataset="hemibrain:v1.2.1", template="neuroglancer.js.jinja"):
         self.neuprint = Mock()
         self.neuprint.dataset = dataset
         self.neuprint.server = "neuprint.janelia.org"
 
         self.neuroglancer = Mock()
         self.neuroglancer.base_url = "https://clio-ng.janelia.org/"
+        self.neuroglancer.template = template
 
 
 class TestURLGenerationService:
@@ -41,9 +42,11 @@ def setup_method(self):
         self.neuron_selection_service = Mock()
         self.database_query_service = Mock()
 
-    def create_service(self, dataset="hemibrain:v1.2.1"):
+    def create_service(
+        self, dataset="hemibrain:v1.2.1", template="neuroglancer.js.jinja"
+    ):
         """Create URLGenerationService with specified dataset."""
-        config = MockConfig(dataset)
+        config = MockConfig(dataset, template)
         return URLGenerationService(
             config=config,
             jinja_env=self.jinja_env,
@@ -51,9 +54,9 @@ def create_service(self, dataset="hemibrain:v1.2.1"):
             database_query_service=self.database_query_service,
         )
 
-    def test_uses_standard_template_for_non_fafb_dataset(self):
-        """Test that standard template is used for non-FAFB datasets."""
-        service = self.create_service("hemibrain:v1.2.1")
+    def test_uses_configured_standard_template(self):
+        """Standard template is used when configured."""
+        service = self.create_service(template="neuroglancer.js.jinja")
 
         # Mock neuron data
         neuron_data = {"neurons": None}
@@ -76,9 +79,11 @@ def test_uses_standard_template_for_non_fafb_dataset(self):
         # Verify template variables indicate standard template was used
         assert template_vars["website_title"] == "TestType"
 
-    def test_uses_fafb_template_for_fafb_dataset(self):
-        """Test that FAFB-specific template is used for FAFB datasets."""
-        service = self.create_service("flywire-fafb:v783b")
+    def test_uses_configured_fafb_template(self):
+        """FAFB template is used when configured."""
+        service = self.create_service(
+            dataset="flywire-fafb:v783b", template="neuroglancer-fafb.js.jinja"
+        )
 
         # Mock neuron data
         neuron_data = {"neurons": None}
@@ -101,66 +106,12 @@ def test_uses_fafb_template_for_fafb_dataset(self):
         # Verify template variables
         assert template_vars["website_title"] == "TestType"
 
-    def test_fafb_detection_case_insensitive(self):
-        """Test that FAFB detection is case insensitive."""
-        # Test various case combinations
-        fafb_datasets = [
-            "flywire-fafb:v783b",
-            "FLYWIRE-FAFB:V783B",
-            "FlywIre-FaFb:v783b",
-            "some-FAFB-dataset:v1",
-            "fafb:v1",
-        ]
-
-        for dataset in fafb_datasets:
-            service = self.create_service(dataset)
-
-            # Mock dependencies
-            neuron_data = {"neurons": None}
-            self.neuron_selection_service.select_bodyids_by_soma_side.return_value = []
-            self.database_query_service.get_connected_bodyids.return_value = {
-                "upstream": {},
-                "downstream": {},
-            }
-
-            # Should use FAFB template (test that it doesn't fail)
-            url, template_vars = service.generate_neuroglancer_url(
-                "TestType", neuron_data
-            )
-            expected_base = service.config.neuroglancer.base_url.rstrip("/")
-            assert url.startswith(f"{expected_base}/")
-
-    def test_non_fafb_datasets_use_standard_template(self):
-        """Test that non-FAFB datasets use the standard template."""
-        non_fafb_datasets = [
-            "hemibrain:v1.2.1",
-            "cns:v1.0",
-            "optic-lobe:v1.0",
-            "some-other-dataset:v1",
-        ]
-
-        for dataset in non_fafb_datasets:
-            service = self.create_service(dataset)
-
-            # Mock dependencies
-            neuron_data = {"neurons": None}
-            self.neuron_selection_service.select_bodyids_by_soma_side.return_value = []
-            self.database_query_service.get_connected_bodyids.return_value = {
-                "upstream": {},
-                "downstream": {},
-            }
-
-            # Should use standard template
-            url, template_vars = service.generate_neuroglancer_url(
-                "TestType", neuron_data
-            )
-            expected_base = service.config.neuroglancer.base_url.rstrip("/")
-            assert url.startswith(f"{expected_base}/")
-
     @patch("neuview.services.url_generation_service.logger")
     def test_logs_template_selection(self, mock_logger):
         """Test that template selection is logged."""
-        service = self.create_service("flywire-fafb:v783b")
+        service = self.create_service(
+            dataset="flywire-fafb:v783b", template="neuroglancer-fafb.js.jinja"
+        )
 
         # Mock dependencies
         neuron_data = {"neurons": None}
@@ -195,7 +146,9 @@ def test_fallback_on_template_error(self):
 
     def test_template_variables_passed_correctly(self):
         """Test that all required template variables are passed to the template."""
-        service = self.create_service("flywire-fafb:v783b")
+        service = self.create_service(
+            dataset="flywire-fafb:v783b", template="neuroglancer-fafb.js.jinja"
+        )
 
         # Create mock neuron DataFrame
         import pandas as pd