---
_id: '9831'
abstract:
- lang: eng
  text: 'Implementation of the inference method in Matlab, including three applications
    of the method: The first one for the model of ant motion, the second one for bacterial
    chemotaxis, and the third one for the motion of fish.'
article_processing_charge: No
author:
- first_name: Katarína
  full_name: Bod’Ová, Katarína
  last_name: Bod’Ová
- first_name: Gabriel
  full_name: Mitchell, Gabriel
  id: 315BCD80-F248-11E8-B48F-1D18A9856A87
  last_name: Mitchell
- first_name: Roy
  full_name: Harpaz, Roy
  last_name: Harpaz
- first_name: Elad
  full_name: Schneidman, Elad
  last_name: Schneidman
- first_name: Gašper
  full_name: Tkačik, Gašper
  id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
  last_name: Tkačik
  orcid: 0000-0002-6699-1455
citation:
  ama: Bod’Ová K, Mitchell G, Harpaz R, Schneidman E, Tkačik G. Implementation of
    the inference method in Matlab. 2018. doi:<a href="https://doi.org/10.1371/journal.pone.0193049.s001">10.1371/journal.pone.0193049.s001</a>
  apa: Bod’Ová, K., Mitchell, G., Harpaz, R., Schneidman, E., &#38; Tkačik, G. (2018).
    Implementation of the inference method in Matlab. Public Library of Science. <a
    href="https://doi.org/10.1371/journal.pone.0193049.s001">https://doi.org/10.1371/journal.pone.0193049.s001</a>
  chicago: Bod’Ová, Katarína, Gabriel Mitchell, Roy Harpaz, Elad Schneidman, and Gašper
    Tkačik. “Implementation of the Inference Method in Matlab.” Public Library of
    Science, 2018. <a href="https://doi.org/10.1371/journal.pone.0193049.s001">https://doi.org/10.1371/journal.pone.0193049.s001</a>.
  ieee: K. Bod’Ová, G. Mitchell, R. Harpaz, E. Schneidman, and G. Tkačik, “Implementation
    of the inference method in Matlab.” Public Library of Science, 2018.
  ista: Bod’Ová K, Mitchell G, Harpaz R, Schneidman E, Tkačik G. 2018. Implementation
    of the inference method in Matlab, Public Library of Science, <a href="https://doi.org/10.1371/journal.pone.0193049.s001">10.1371/journal.pone.0193049.s001</a>.
  mla: Bod’Ová, Katarína, et al. <i>Implementation of the Inference Method in Matlab</i>.
    Public Library of Science, 2018, doi:<a href="https://doi.org/10.1371/journal.pone.0193049.s001">10.1371/journal.pone.0193049.s001</a>.
  short: K. Bod’Ová, G. Mitchell, R. Harpaz, E. Schneidman, G. Tkačik, (2018).
date_created: 2021-08-09T07:01:24Z
date_published: 2018-03-07T00:00:00Z
date_updated: 2023-09-15T12:06:18Z
day: '07'
department:
- _id: GaTk
doi: 10.1371/journal.pone.0193049.s001
month: '03'
oa_version: Published Version
publisher: Public Library of Science
related_material:
  record:
  - id: '406'
    relation: used_in_publication
    status: public
status: public
title: Implementation of the inference method in Matlab
type: research_data_reference
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
year: '2018'
...
---
_id: '406'
abstract:
- lang: eng
  text: 'Recent developments in automated tracking allow uninterrupted, high-resolution
    recording of animal trajectories, sometimes coupled with the identification of
    stereotyped changes of body pose or other behaviors of interest. Analysis and
    interpretation of such data represents a challenge: the timing of animal behaviors
    may be stochastic and modulated by kinematic variables, by the interaction with
    the environment or with the conspecifics within the animal group, and dependent
    on internal cognitive or behavioral state of the individual. Existing models for
    collective motion typically fail to incorporate the discrete, stochastic, and
    internal-state-dependent aspects of behavior, while models focusing on individual
    animal behavior typically ignore the spatial aspects of the problem. Here we propose
    a probabilistic modeling framework to address this gap. Each animal can switch
    stochastically between different behavioral states, with each state resulting
    in a possibly different law of motion through space. Switching rates for behavioral
    transitions can depend in a very general way, which we seek to identify from data,
    on the effects of the environment as well as the interaction between the animals.
    We represent the switching dynamics as a Generalized Linear Model and show that:
    (i) forward simulation of multiple interacting animals is possible using a variant
    of the Gillespie’s Stochastic Simulation Algorithm; (ii) formulated properly,
    the maximum likelihood inference of switching rate functions is tractably solvable
    by gradient descent; (iii) model selection can be used to identify factors that
    modulate behavioral state switching and to appropriately adjust model complexity
    to data. To illustrate our framework, we apply it to two synthetic models of animal
    motion and to real zebrafish tracking data. '
acknowledgement: This work was supported by the Human Frontier Science Program RGP0065/2012
  (GT, ES).
article_processing_charge: Yes
author:
- first_name: Katarína
  full_name: Bod’Ová, Katarína
  last_name: Bod’Ová
- first_name: Gabriel
  full_name: Mitchell, Gabriel
  id: 315BCD80-F248-11E8-B48F-1D18A9856A87
  last_name: Mitchell
- first_name: Roy
  full_name: Harpaz, Roy
  last_name: Harpaz
- first_name: Elad
  full_name: Schneidman, Elad
  last_name: Schneidman
- first_name: Gasper
  full_name: Tkacik, Gasper
  id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
  last_name: Tkacik
  orcid: 0000-0002-6699-1455
citation:
  ama: Bod’Ová K, Mitchell G, Harpaz R, Schneidman E, Tkačik G. Probabilistic models
    of individual and collective animal behavior. <i>PLoS One</i>. 2018;13(3). doi:<a
    href="https://doi.org/10.1371/journal.pone.0193049">10.1371/journal.pone.0193049</a>
  apa: Bod’Ová, K., Mitchell, G., Harpaz, R., Schneidman, E., &#38; Tkačik, G. (2018).
    Probabilistic models of individual and collective animal behavior. <i>PLoS One</i>.
    Public Library of Science. <a href="https://doi.org/10.1371/journal.pone.0193049">https://doi.org/10.1371/journal.pone.0193049</a>
  chicago: Bod’Ová, Katarína, Gabriel Mitchell, Roy Harpaz, Elad Schneidman, and Gašper
    Tkačik. “Probabilistic Models of Individual and Collective Animal Behavior.” <i>PLoS
    One</i>. Public Library of Science, 2018. <a href="https://doi.org/10.1371/journal.pone.0193049">https://doi.org/10.1371/journal.pone.0193049</a>.
  ieee: K. Bod’Ová, G. Mitchell, R. Harpaz, E. Schneidman, and G. Tkačik, “Probabilistic
    models of individual and collective animal behavior,” <i>PLoS One</i>, vol. 13,
    no. 3. Public Library of Science, 2018.
  ista: Bod’Ová K, Mitchell G, Harpaz R, Schneidman E, Tkačik G. 2018. Probabilistic
    models of individual and collective animal behavior. PLoS One. 13(3).
  mla: Bod’Ová, Katarína, et al. “Probabilistic Models of Individual and Collective
    Animal Behavior.” <i>PLoS One</i>, vol. 13, no. 3, Public Library of Science,
    2018, doi:<a href="https://doi.org/10.1371/journal.pone.0193049">10.1371/journal.pone.0193049</a>.
  short: K. Bod’Ová, G. Mitchell, R. Harpaz, E. Schneidman, G. Tkačik, PLoS One 13
    (2018).
date_created: 2018-12-11T11:46:18Z
date_published: 2018-03-07T00:00:00Z
date_updated: 2023-09-15T12:06:19Z
day: '07'
ddc:
- '530'
- '571'
department:
- _id: GaTk
doi: 10.1371/journal.pone.0193049
external_id:
  isi:
  - '000426896800032'
file:
- access_level: open_access
  checksum: 684229493db75b43e98a46cd922da497
  content_type: application/pdf
  creator: system
  date_created: 2018-12-12T10:15:43Z
  date_updated: 2020-07-14T12:46:22Z
  file_id: '5165'
  file_name: IST-2018-995-v1+1_2018_Bodova_Probabilistic.pdf
  file_size: 6887358
  relation: main_file
file_date_updated: 2020-07-14T12:46:22Z
has_accepted_license: '1'
intvolume: '        13'
isi: 1
issue: '3'
language:
- iso: eng
license: https://creativecommons.org/licenses/by/4.0/
month: '03'
oa: 1
oa_version: Submitted Version
project:
- _id: 255008E4-B435-11E9-9278-68D0E5697425
  grant_number: RGP0065/2012
  name: Information processing and computation in fish groups
publication: PLoS One
publication_status: published
publisher: Public Library of Science
publist_id: '7423'
pubrep_id: '995'
quality_controlled: '1'
related_material:
  record:
  - id: '9831'
    relation: research_data
    status: public
scopus_import: '1'
status: public
title: Probabilistic models of individual and collective animal behavior
tmp:
  image: /images/cc_by.png
  legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
  name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
  short: CC BY (4.0)
type: journal_article
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
volume: 13
year: '2018'
...
---
_id: '1203'
abstract:
- lang: eng
  text: Haemophilus haemolyticus has been recently discovered to have the potential
    to cause invasive disease. It is closely related to nontypeable Haemophilus influenzae
    (NT H. influenzae). NT H. influenzae and H. haemolyticus are often misidentified
    because none of the existing tests targeting the known phenotypes of H. haemolyticus
    are able to specifically identify H. haemolyticus. Through comparative genomic
    analysis of H. haemolyticus and NT H. influenzae, we identified genes unique to
    H. haemolyticus that can be used as targets for the identification of H. haemolyticus.
    A real-time PCR targeting purT (encoding phosphoribosylglycinamide formyltransferase
    2 in the purine synthesis pathway) was developed and evaluated. The lower limit
    of detection was 40 genomes/PCR; the sensitivity and specificity in detecting
    H. haemolyticus were 98.9% and 97%, respectively. To improve the discrimination
    of H. haemolyticus and NT H. influenzae, a testing scheme combining two targets
    (H. haemolyticus purT and H. influenzae hpd, encoding protein D lipoprotein) was
    also evaluated and showed 96.7% sensitivity and 98.2% specificity for the identification
    of H. haemolyticus and 92.8% sensitivity and 100% specificity for the identification
    of H. influenzae, respectively. The dual-target testing scheme can be used for
    the diagnosis and surveillance of infection and disease caused by H. haemolyticus
    and NT H. influenzae.
acknowledgement: We are grateful to ABCs for providing strains and the Bacterial Meningitis
  Laboratory for technical support.
author:
- first_name: Fang
  full_name: Hu, Fang
  last_name: Hu
- first_name: Lavanya
  full_name: Rishishwar, Lavanya
  last_name: Rishishwar
- first_name: Ambily
  full_name: Sivadas, Ambily
  last_name: Sivadas
- first_name: Gabriel
  full_name: Mitchell, Gabriel
  id: 315BCD80-F248-11E8-B48F-1D18A9856A87
  last_name: Mitchell
- first_name: Jordan
  full_name: King, Jordan
  last_name: King
- first_name: Timothy
  full_name: Murphy, Timothy
  last_name: Murphy
- first_name: Janet
  full_name: Gilsdorf, Janet
  last_name: Gilsdorf
- first_name: Leonard
  full_name: Mayer, Leonard
  last_name: Mayer
- first_name: Xin
  full_name: Wang, Xin
  last_name: Wang
citation:
  ama: Hu F, Rishishwar L, Sivadas A, et al. Comparative genomic analysis of Haemophilus
    haemolyticus and nontypeable Haemophilus influenzae and a new testing scheme for
    their discrimination. <i>Journal of Clinical Microbiology</i>. 2016;54(12):3010-3017.
    doi:<a href="https://doi.org/10.1128/JCM.01511-16">10.1128/JCM.01511-16</a>
  apa: Hu, F., Rishishwar, L., Sivadas, A., Mitchell, G., King, J., Murphy, T., …
    Wang, X. (2016). Comparative genomic analysis of Haemophilus haemolyticus and
    nontypeable Haemophilus influenzae and a new testing scheme for their discrimination.
    <i>Journal of Clinical Microbiology</i>. American Society for Microbiology. <a
    href="https://doi.org/10.1128/JCM.01511-16">https://doi.org/10.1128/JCM.01511-16</a>
  chicago: Hu, Fang, Lavanya Rishishwar, Ambily Sivadas, Gabriel Mitchell, Jordan
    King, Timothy Murphy, Janet Gilsdorf, Leonard Mayer, and Xin Wang. “Comparative
    Genomic Analysis of Haemophilus Haemolyticus and Nontypeable Haemophilus Influenzae
    and a New Testing Scheme for Their Discrimination.” <i>Journal of Clinical Microbiology</i>.
    American Society for Microbiology, 2016. <a href="https://doi.org/10.1128/JCM.01511-16">https://doi.org/10.1128/JCM.01511-16</a>.
  ieee: F. Hu <i>et al.</i>, “Comparative genomic analysis of Haemophilus haemolyticus
    and nontypeable Haemophilus influenzae and a new testing scheme for their discrimination,”
    <i>Journal of Clinical Microbiology</i>, vol. 54, no. 12. American Society for
    Microbiology, pp. 3010–3017, 2016.
  ista: Hu F, Rishishwar L, Sivadas A, Mitchell G, King J, Murphy T, Gilsdorf J, Mayer
    L, Wang X. 2016. Comparative genomic analysis of Haemophilus haemolyticus and
    nontypeable Haemophilus influenzae and a new testing scheme for their discrimination.
    Journal of Clinical Microbiology. 54(12), 3010–3017.
  mla: Hu, Fang, et al. “Comparative Genomic Analysis of Haemophilus Haemolyticus
    and Nontypeable Haemophilus Influenzae and a New Testing Scheme for Their Discrimination.”
    <i>Journal of Clinical Microbiology</i>, vol. 54, no. 12, American Society for
    Microbiology, 2016, pp. 3010–17, doi:<a href="https://doi.org/10.1128/JCM.01511-16">10.1128/JCM.01511-16</a>.
  short: F. Hu, L. Rishishwar, A. Sivadas, G. Mitchell, J. King, T. Murphy, J. Gilsdorf,
    L. Mayer, X. Wang, Journal of Clinical Microbiology 54 (2016) 3010–3017.
date_created: 2018-12-11T11:50:41Z
date_published: 2016-12-01T00:00:00Z
date_updated: 2021-01-12T06:49:04Z
day: '01'
department:
- _id: GaTk
doi: 10.1128/JCM.01511-16
intvolume: '        54'
issue: '12'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5121393/
month: '12'
oa: 1
oa_version: Submitted Version
page: 3010 - 3017
publication: Journal of Clinical Microbiology
publication_status: published
publisher: American Society for Microbiology
publist_id: '6146'
quality_controlled: '1'
scopus_import: 1
status: public
title: Comparative genomic analysis of Haemophilus haemolyticus and nontypeable Haemophilus
  influenzae and a new testing scheme for their discrimination
type: journal_article
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 54
year: '2016'
...
