9 research outputs found
A Critical Realist Exploration of the Relationship Between Personal and Professional Value Systems in Social Workers and the Impact on Motivations for Participation in a Social Work Community of Practice
This study addresses questions about the nature of relationships between personal and professional value systems and between personal and professional identities, about motivations for engaging in a social work community of practice, and about alternative statistical methods for evaluating the psychometric properties of an original measure of motivation for participation in a social work community of practice. By merging communities of practice theory, derived from social learning theory, and critical social realist theory, this study bridges an ideological gap between the origins and evolution of personal and social identities. The study utilizes a mixed-method approach to (1) develop a measure of motivations for participating in a community of practice and compare confirmatory factor analysis and multidimensional item response theory in the evaluation of the measure, (2) assess a theoretically derived structural equation model relating attitudes toward diversity, endorsement of professional social work values, and motivations for entering a MSW program, and (3) develop a grounded theory of how students experience and make sense of the interaction, negotiation, and resolution of personal values about diversity, attitudes towards professional social work values, and motivations for pursuing a MSW degree. Implications are identified and discussed for (1) the field of psychometrics, (2) social work education, and (3) social work practice
Probabilistic Traversability Model for Risk-Aware Motion Planning in Off-Road Environments
A key challenge in off-road navigation is that even visually similar terrains
or ones from the same semantic class may have substantially different traction
properties. Existing work typically assumes no wheel slip or uses the expected
traction for motion planning, where the predicted trajectories provide a poor
indication of the actual performance if the terrain traction has high
uncertainty. In contrast, this work proposes to analyze terrain traversability
with the empirical distribution of traction parameters in unicycle dynamics,
which can be learned by a neural network in a self-supervised fashion. The
probabilistic traction model leads to two risk-aware cost formulations that
account for the worst-case expected cost and traction. To help the learned
model generalize to unseen environment, terrains with features that lead to
unreliable predictions are detected via a density estimator fit to the trained
network's latent space and avoided via auxiliary penalties during planning.
Simulation results demonstrate that the proposed approach outperforms existing
work that assumes no slip or uses the expected traction in both navigation
success rate and completion time. Furthermore, avoiding terrains with low
density-based confidence score achieves up to 30% improvement in success rate
when the learned traction model is used in a novel environment.Comment: To appear in IROS23. Video and code:
https://github.com/mit-acl/mppi_numb
RAMP: A Risk-Aware Mapping and Planning Pipeline for Fast Off-Road Ground Robot Navigation
A key challenge in fast ground robot navigation in 3D terrain is balancing
robot speed and safety. Recent work has shown that 2.5D maps (2D
representations with additional 3D information) are ideal for real-time safe
and fast planning. However, the prevalent approach of generating 2D occupancy
grids through raytracing makes the generated map unsafe to plan in, due to
inaccurate representation of unknown space. Additionally, existing planners
such as MPPI do not consider speeds in known free and unknown space separately,
leading to slower overall plans. The RAMP pipeline proposed here solves these
issues using new mapping and planning methods. This work first presents ground
point inflation with persistent spatial memory as a way to generate accurate
occupancy grid maps from classified pointclouds. Then we present an MPPI-based
planner with embedded variability in horizon, to maximize speed in known free
space while retaining cautionary penetration into unknown space. Finally, we
integrate this mapping and planning pipeline with risk constraints arising from
3D terrain, and verify that it enables fast and safe navigation using
simulations and hardware demonstrations.Comment: 7 pages submitted to ICRA 202
EVORA: Deep Evidential Traversability Learning for Risk-Aware Off-Road Autonomy
Traversing terrain with good traction is crucial for achieving fast off-road
navigation. Instead of manually designing costs based on terrain features,
existing methods learn terrain properties directly from data via
self-supervision, but challenges remain to properly quantify and mitigate risks
due to uncertainties in learned models. This work efficiently quantifies both
aleatoric and epistemic uncertainties by learning discrete traction
distributions and probability densities of the traction predictor's latent
features. Leveraging evidential deep learning, we parameterize Dirichlet
distributions with the network outputs and propose a novel uncertainty-aware
squared Earth Mover's distance loss with a closed-form expression that improves
learning accuracy and navigation performance. The proposed risk-aware planner
simulates state trajectories with the worst-case expected traction to handle
aleatoric uncertainty, and penalizes trajectories moving through terrain with
high epistemic uncertainty. Our approach is extensively validated in simulation
and on wheeled and quadruped robots, showing improved navigation performance
compared to methods that assume no slip, assume the expected traction, or
optimize for the worst-case expected cost.Comment: Under review. Journal extension for arXiv:2210.00153. Project
website: https://xiaoyi-cai.github.io/evora
Comparative cellular analysis of motor cortex in human, marmoset and mouse
The primary motor cortex (M1) is essential for voluntary fine-motor control and is functionally conserved across mammals1. Here, using high-throughput transcriptomic and epigenomic profiling of more than 450,000 single nuclei in humans, marmoset monkeys and mice, we demonstrate a broadly conserved cellular makeup of this region, with similarities that mirror evolutionary distance and are consistent between the transcriptome and epigenome. The core conserved molecular identities of neuronal and non-neuronal cell types allow us to generate a cross-species consensus classification of cell types, and to infer conserved properties of cell types across species. Despite the overall conservation, however, many species-dependent specializations are apparent, including differences in cell-type proportions, gene expression, DNA methylation and chromatin state. Few cell-type marker genes are conserved across species, revealing a short list of candidate genes and regulatory mechanisms that are responsible for conserved features of homologous cell types, such as the GABAergic chandelier cells. This consensus transcriptomic classification allows us to use patch-seq (a combination of whole-cell patch-clamp recordings, RNA sequencing and morphological characterization) to identify corticospinal Betz cells from layer 5 in non-human primates and humans, and to characterize their highly specialized physiology and anatomy. These findings highlight the robust molecular underpinnings of cell-type diversity in M1 across mammals, and point to the genes and regulatory pathways responsible for the functional identity of cell types and their species-specific adaptations
A multimodal cell census and atlas of the mammalian primary motor cortex
ABSTRACT We report the generation of a multimodal cell census and atlas of the mammalian primary motor cortex (MOp or M1) as the initial product of the BRAIN Initiative Cell Census Network (BICCN). This was achieved by coordinated large-scale analyses of single-cell transcriptomes, chromatin accessibility, DNA methylomes, spatially resolved single-cell transcriptomes, morphological and electrophysiological properties, and cellular resolution input-output mapping, integrated through cross-modal computational analysis. Together, our results advance the collective knowledge and understanding of brain cell type organization: First, our study reveals a unified molecular genetic landscape of cortical cell types that congruently integrates their transcriptome, open chromatin and DNA methylation maps. Second, cross-species analysis achieves a unified taxonomy of transcriptomic types and their hierarchical organization that are conserved from mouse to marmoset and human. Third, cross-modal analysis provides compelling evidence for the epigenomic, transcriptomic, and gene regulatory basis of neuronal phenotypes such as their physiological and anatomical properties, demonstrating the biological validity and genomic underpinning of neuron types and subtypes. Fourth, in situ single-cell transcriptomics provides a spatially-resolved cell type atlas of the motor cortex. Fifth, integrated transcriptomic, epigenomic and anatomical analyses reveal the correspondence between neural circuits and transcriptomic cell types. We further present an extensive genetic toolset for targeting and fate mapping glutamatergic projection neuron types toward linking their developmental trajectory to their circuit function. Together, our results establish a unified and mechanistic framework of neuronal cell type organization that integrates multi-layered molecular genetic and spatial information with multi-faceted phenotypic properties
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Comparative cellular analysis of motor cortex in human, marmoset and mouse.
The primary motor cortex (M1) is essential for voluntary fine-motor control and is functionally conserved across mammals1. Here, using high-throughput transcriptomic and epigenomic profiling of more than 450,000 single nuclei in humans, marmoset monkeys and mice, we demonstrate a broadly conserved cellular makeup of this region, with similarities that mirror evolutionary distance and are consistent between the transcriptome and epigenome. The core conserved molecular identities of neuronal and non-neuronal cell types allow us to generate a cross-species consensus classification of cell types, and to infer conserved properties of cell types across species. Despite the overall conservation, however, many species-dependent specializations are apparent, including differences in cell-type proportions, gene expression, DNA methylation and chromatin state. Few cell-type marker genes are conserved across species, revealing a short list of candidate genes and regulatory mechanisms that are responsible for conserved features of homologous cell types, such as the GABAergic chandelier cells. This consensus transcriptomic classification allows us to use patch-seq (a combination of whole-cell patch-clamp recordings, RNA sequencing and morphological characterization) to identify corticospinal Betz cells from layer 5 in non-human primates and humans, and to characterize their highly specialized physiology and anatomy. These findings highlight the robust molecular underpinnings of cell-type diversity in M1 across mammals, and point to the genes and regulatory pathways responsible for the functional identity of cell types and their species-specific adaptations
A multimodal cell census and atlas of the mammalian primary motor cortex
none258Here we report the generation of a multimodal cell census and atlas of the mammalian primary motor cortex as the initial product of the BRAIN Initiative Cell Census Network (BICCN). This was achieved by coordinated large-scale analyses of single-cell transcriptomes, chromatin accessibility, DNA methylomes, spatially resolved single-cell transcriptomes, morphological and electrophysiological properties and cellular resolution input-output mapping, integrated through cross-modal computational analysis. Our results advance the collective knowledge and understanding of brain cell-type organization1-5. First, our study reveals a unified molecular genetic landscape of cortical cell types that integrates their transcriptome, open chromatin and DNA methylation maps. Second, cross-species analysis achieves a consensus taxonomy of transcriptomic types and their hierarchical organization that is conserved from mouse to marmoset and human. Third, in situ single-cell transcriptomics provides a spatially resolved cell-type atlas of the motor cortex. Fourth, cross-modal analysis provides compelling evidence for the transcriptomic, epigenomic and gene regulatory basis of neuronal phenotypes such as their physiological and anatomical properties, demonstrating the biological validity and genomic underpinning of neuron types. We further present an extensive genetic toolset for targeting glutamatergic neuron types towards linking their molecular and developmental identity to their circuit function. Together, our results establish a unifying and mechanistic framework of neuronal cell-type organization that integrates multi-layered molecular genetic and spatial information with multi-faceted phenotypic properties.openCallaway, Edward M.; Dong, Hong-Wei; Ecker, Joseph R.; Hawrylycz, Michael J.; Huang, Z. Josh; Lein, Ed S.; Ngai, John; Osten, Pavel; Ren, Bing; Tolias, Andreas Savas; White, Owen; Zeng, Hongkui; Zhuang, Xiaowei; Ascoli, Giorgio A.; Behrens, M. Margarita; Chun, Jerold; Feng, Guoping; Gee, James C.; Ghosh, Satrajit S.; Halchenko, Yaroslav O.; Hertzano, Ronna; Lim, Byung Kook; Martone, Maryann E.; Ng, Lydia; Pachter, Lior; Ropelewski, Alexander J.; Tickle, Timothy L.; Yang, X. William; Zhang, Kun; Bakken, Trygve E.; Berens, Philipp; Daigle, Tanya L.; Harris, Julie A.; Jorstad, Nikolas L.; Kalmbach, Brian E.; Kobak, Dmitry; Li, Yang Eric; Liu, Hanqing; Matho, Katherine S.; Mukamel, Eran A.; Naeemi, Maitham; Scala, Federico; Tan, Pengcheng; Ting, Jonathan T.; Xie, Fangming; Zhang, Meng; Zhang, Zhuzhu; Zhou, Jingtian; Zingg, Brian; Armand, Ethan; Yao, Zizhen; Bertagnolli, Darren; Casper, Tamara; Crichton, Kirsten; Dee, Nick; Diep, Dinh; Ding, Song-Lin; Dong, Weixiu; Dougherty, Elizabeth L.; Fong, Olivia; Goldman, Melissa; Goldy, Jeff; Hodge, Rebecca D.; Hu, Lijuan; Keene, C. Dirk; Krienen, Fenna M.; Kroll, Matthew; Lake, Blue B.; Lathia, Kanan; Linnarsson, Sten; Liu, Christine S.; Macosko, Evan Z.; McCarroll, Steven A.; McMillen, Delissa; Nadaf, Naeem M.; Nguyen, Thuc Nghi; Palmer, Carter R.; Pham, Thanh; Plongthongkum, Nongluk; Reed, Nora M.; Regev, Aviv; Rimorin, Christine; Romanow, William J.; Savoia, Steven; Siletti, Kimberly; Smith, Kimberly; Sulc, Josef; Tasic, Bosiljka; Tieu, Michael; Torkelson, Amy; Tung, Herman; van Velthoven, Cindy T. J.; Vanderburg, Charles R.; Yanny, Anna Marie; Fang, Rongxin; Hou, Xiaomeng; Lucero, Jacinta D.; Osteen, Julia K.; Pinto-Duarte, Antonio; Poirion, Olivier; Preissl, Sebastian; Wang, Xinxin; Aldridge, Andrew I.; Bartlett, Anna; Boggeman, Lara; OâConnor, Carolyn; Castanon, Rosa G.; Chen, Huaming; Fitzpatrick, Conor; Luo, Chongyuan; Nery, Joseph R.; Nunn, Michael; Rivkin, Angeline C.; Tian, Wei; Dominguez, Bertha; Ito-Cole, Tony; Jacobs, Matthew; Jin, Xin; Lee, Cheng-Ta; Lee, Kuo-Fen; Miyazaki, Paula Assakura; Pang, Yan; Rashid, Mohammad; Smith, Jared B.; Vu, Minh; Williams, Elora; Biancalani, Tommaso; Booeshaghi, A. Sina; Crow, Megan; Dudoit, Sandrine; Fischer, Stephan; Gillis, Jesse; Hu, Qiwen; Kharchenko, Peter V.; Niu, Sheng-Yong; Ntranos, Vasilis; Purdom, Elizabeth; Risso, Davide; de BĂ©zieux, Hector Roux; Somasundaram, Saroja; Street, Kelly; Svensson, Valentine; Vaishnav, Eeshit Dhaval; Van den Berge, Koen; Welch, Joshua D.; An, Xu; Bateup, Helen S.; Bowman, Ian; Chance, Rebecca K.; Foster, Nicholas N.; Galbavy, William; Gong, Hui; Gou, Lin; Hatfield, Joshua T.; Hintiryan, Houri; Hirokawa, Karla E.; Kim, Gukhan; Kramer, Daniel J.; Li, Anan; Li, Xiangning; Luo, Qingming; Muñoz-Castañeda, Rodrigo; Stafford, David A.; Feng, Zhao; Jia, Xueyan; Jiang, Shengdian; Jiang, Tao; Kuang, Xiuli; Larsen, Rachael; Lesnar, Phil; Li, Yaoyao; Li, Yuanyuan; Liu, Lijuan; Peng, Hanchuan; Qu, Lei; Ren, Miao; Ruan, Zongcai; Shen, Elise; Song, Yuanyuan; Wakeman, Wayne; Wang, Peng; Wang, Yimin; Wang, Yun; Yin, Lulu; Yuan, Jing; Zhao, Sujun; Zhao, Xuan; Narasimhan, Arun; Palaniswamy, Ramesh; Banerjee, Samik; Ding, Liya; Huilgol, Dhananjay; Huo, Bingxing; Kuo, Hsien-Chi; Laturnus, Sophie; Li, Xu; Mitra, Partha P.; Mizrachi, Judith; Wang, Quanxin; Xie, Peng; Xiong, Feng; Yu, Yang; Eichhorn, Stephen W.; Berg, Jim; Bernabucci, Matteo; Bernaerts, Yves; Cadwell, Cathryn RenĂ©; Castro, Jesus Ramon; Dalley, Rachel; Hartmanis, Leonard; Horwitz, Gregory D.; Jiang, Xiaolong; Ko, Andrew L.; Miranda, Elanine; Mulherkar, Shalaka; Nicovich, Philip R.; Owen, Scott F.; Sandberg, Rickard; Sorensen, Staci A.; Tan, Zheng Huan; Allen, Shona; Hockemeyer, Dirk; Lee, Angus Y.; Veldman, Matthew B.; Adkins, Ricky S.; Ament, Seth A.; Bravo, HĂ©ctor Corrada; Carter, Robert; Chatterjee, Apaala; Colantuoni, Carlo; Crabtree, Jonathan; Creasy, Heather; Felix, Victor; Giglio, Michelle; Herb, Brian R.; Kancherla, Jayaram; Mahurkar, Anup; McCracken, Carrie; Nickel, Lance; Olley, Dustin; Orvis, Joshua; Schor, Michael; Hood, Greg; Dichter, Benjamin; Grauer, Michael; Helba, Brian; Bandrowski, Anita; Barkas, Nikolaos; Carlin, Benjamin; DâOrazi, Florence D.; Degatano, Kylee; Gillespie, Thomas H.; Khajouei, Farzaneh; Konwar, Kishori; Thompson, Carol; Kelly, Kathleen; Mok, Stephanie; Sunkin, SusanCallaway, Edward M.; Dong, Hong-Wei; Ecker, Joseph R.; Hawrylycz, Michael J.; Huang, Z. Josh; Lein, Ed S.; Ngai, John; Osten, Pavel; Ren, Bing; Tolias, Andreas Savas; White, Owen; Zeng, Hongkui; Zhuang, Xiaowei; Ascoli, Giorgio A.; Behrens, M. Margarita; Chun, Jerold; Feng, Guoping; Gee, James C.; Ghosh, Satrajit S.; Halchenko, Yaroslav O.; Hertzano, Ronna; Lim, Byung Kook; Martone, Maryann E.; Ng, Lydia; Pachter, Lior; Ropelewski, Alexander J.; Tickle, Timothy L.; Yang, X. William; Zhang, Kun; Bakken, Trygve E.; Berens, Philipp; Daigle, Tanya L.; Harris, Julie A.; Jorstad, Nikolas L.; Kalmbach, Brian E.; Kobak, Dmitry; Li, Yang Eric; Liu, Hanqing; Matho, Katherine S.; Mukamel, Eran A.; Naeemi, Maitham; Scala, Federico; Tan, Pengcheng; Ting, Jonathan T.; Xie, Fangming; Zhang, Meng; Zhang, Zhuzhu; Zhou, Jingtian; Zingg, Brian; Armand, Ethan; Yao, Zizhen; Bertagnolli, Darren; Casper, Tamara; Crichton, Kirsten; Dee, Nick; Diep, Dinh; Ding, Song-Lin; Dong, Weixiu; Dougherty, Elizabeth L.; Fong, Olivia; Goldman, Melissa; Goldy, Jeff; Hodge, Rebecca D.; Hu, Lijuan; Keene, C. Dirk; Krienen, Fenna M.; Kroll, Matthew; Lake, Blue B.; Lathia, Kanan; Linnarsson, Sten; Liu, Christine S.; Macosko, Evan Z.; Mccarroll, Steven A.; Mcmillen, Delissa; Nadaf, Naeem M.; Nguyen, Thuc Nghi; Palmer, Carter R.; Pham, Thanh; Plongthongkum, Nongluk; Reed, Nora M.; Regev, Aviv; Rimorin, Christine; Romanow, William J.; Savoia, Steven; Siletti, Kimberly; Smith, Kimberly; Sulc, Josef; Tasic, Bosiljka; Tieu, Michael; Torkelson, Amy; Tung, Herman; van Velthoven, Cindy T. J.; Vanderburg, Charles R.; Yanny, Anna Marie; Fang, Rongxin; Hou, Xiaomeng; Lucero, Jacinta D.; Osteen, Julia K.; Pinto-Duarte, Antonio; Poirion, Olivier; Preissl, Sebastian; Wang, Xinxin; Aldridge, Andrew I.; Bartlett, Anna; Boggeman, Lara; OâConnor, Carolyn; Castanon, Rosa G.; Chen, Huaming; Fitzpatrick, Conor; Luo, Chongyuan; Nery, Joseph R.; Nunn, Michael; Rivkin, Angeline C.; Tian, Wei; Dominguez, Bertha; Ito-Cole, Tony; Jacobs, Matthew; Jin, Xin; Lee, Cheng-Ta; Lee, Kuo-Fen; Miyazaki, Paula Assakura; Pang, Yan; Rashid, Mohammad; Smith, Jared B.; Vu, Minh; Williams, Elora; Biancalani, Tommaso; Booeshaghi, A. Sina; Crow, Megan; Dudoit, Sandrine; Fischer, Stephan; Gillis, Jesse; Hu, Qiwen; Kharchenko, Peter V.; Niu, Sheng-Yong; Ntranos, Vasilis; Purdom, Elizabeth; Risso, Davide; de BĂ©zieux, Hector Roux; Somasundaram, Saroja; Street, Kelly; Svensson, Valentine; Vaishnav, Eeshit Dhaval; Van den Berge, Koen; Welch, Joshua D.; An, Xu; Bateup, Helen S.; Bowman, Ian; Chance, Rebecca K.; Foster, Nicholas N.; Galbavy, William; Gong, Hui; Gou, Lin; Hatfield, Joshua T.; Hintiryan, Houri; Hirokawa, Karla E.; Kim, Gukhan; Kramer, Daniel J.; Li, Anan; Li, Xiangning; Luo, Qingming; Muñoz-Castañeda, Rodrigo; Stafford, David A.; Feng, Zhao; Jia, Xueyan; Jiang, Shengdian; Jiang, Tao; Kuang, Xiuli; Larsen, Rachael; Lesnar, Phil; Li, Yaoyao; Li, Yuanyuan; Liu, Lijuan; Peng, Hanchuan; Qu, Lei; Ren, Miao; Ruan, Zongcai; Shen, Elise; Song, Yuanyuan; Wakeman, Wayne; Wang, Peng; Wang, Yimin; Wang, Yun; Yin, Lulu; Yuan, Jing; Zhao, Sujun; Zhao, Xuan; Narasimhan, Arun; Palaniswamy, Ramesh; Banerjee, Samik; Ding, Liya; Huilgol, Dhananjay; Huo, Bingxing; Kuo, Hsien-Chi; Laturnus, Sophie; Li, Xu; Mitra, Partha P.; Mizrachi, Judith; Wang, Quanxin; Xie, Peng; Xiong, Feng; Yu, Yang; Eichhorn, Stephen W.; Berg, Jim; Bernabucci, Matteo; Bernaerts, Yves; Cadwell, Cathryn RenĂ©; Castro, Jesus Ramon; Dalley, Rachel; Hartmanis, Leonard; Horwitz, Gregory D.; Jiang, Xiaolong; Ko, Andrew L.; Miranda, Elanine; Mulherkar, Shalaka; Nicovich, Philip R.; Owen, Scott F.; Sandberg, Rickard; Sorensen, Staci A.; Tan, Zheng Huan; Allen, Shona; Hockemeyer, Dirk; Lee, Angus Y.; Veldman, Matthew B.; Adkins, Ricky S.; Ament, Seth A.; Bravo, HĂ©ctor Corrada; Carter, Robert; Chatterjee, Apaala; Colantuoni, Carlo; Crabtree, Jonathan; Creasy, Heather; Felix, Victor; Giglio, Michelle; Herb, Brian R.; Kancherla, Jayaram; Mahurkar, Anup; Mccracken, Carrie; Nickel, Lance; Olley, Dustin; Orvis, Joshua; Schor, Michael; Hood, Greg; Dichter, Benjamin; Grauer, Michael; Helba, Brian; Bandrowski, Anita; Barkas, Nikolaos; Carlin, Benjamin; DâOrazi, Florence D.; Degatano, Kylee; Gillespie, Thomas H.; Khajouei, Farzaneh; Konwar, Kishori; Thompson, Carol; Kelly, Kathleen; Mok, Stephanie; Sunkin, Susa