---
_id: '12716'
abstract:
- lang: eng
text: "The process of detecting and evaluating sensory information to guide behaviour
is termed perceptual decision-making (PDM), and is critical for the ability of
an organism to interact with its external world. Individuals with autism, a neurodevelopmental
condition primarily characterised by social and communication difficulties, frequently
exhibit altered sensory processing and PDM difficulties are widely reported. Recent
technological advancements have pushed forward our understanding of the genetic
changes accompanying this condition, however our understanding of how these mutations
affect the function of specific neuronal circuits and bring about the corresponding
behavioural changes remains limited. Here, we use an innate PDM task, the looming
avoidance response (LAR) paradigm, to identify a convergent behavioural abnormality
across three molecularly distinct genetic mouse models of autism (Cul3, Setd5
and Ptchd1). Although mutant mice can rapidly detect threatening visual stimuli,
their responses are consistently delayed, requiring longer to initiate an appropriate
response than their wild-type siblings. Mutant animals show abnormal adaptation
in both their stimulus- evoked escape responses and exploratory dynamics following
repeated stimulus presentations. Similarly delayed behavioural responses are observed
in wild-type animals when faced with more ambiguous threats, suggesting the mutant
phenotype could arise from a dysfunction in the flexible control of this PDM process.\r\nOur
knowledge of the core neuronal circuitry mediating the LAR facilitated a detailed
dissection of the neuronal mechanisms underlying the behavioural impairment. In
vivo extracellular recording revealed that visual responses were unaffected within
a key brain region for the rapid processing of visual threats, the superior colliculus
(SC), indicating that the behavioural delay was unlikely to originate from sensory
impairments. Delayed behavioural responses were recapitulated in the Setd5 model
following optogenetic stimulation of the excitatory output neurons of the SC,
which are known to mediate escape initiation through the activation of cells in
the underlying dorsal periaqueductal grey (dPAG). In vitro patch-clamp recordings
of dPAG cells uncovered a stark hypoexcitability phenotype in two out of the three
genetic models investigated (Setd5 and Ptchd1), that in Setd5, is mediated by
the misregulation of voltage-gated potassium channels. Overall, our results show
that the ability to use visual information to drive efficient escape responses
is impaired in three diverse genetic mouse models of autism and that, in one of
the models studied, this behavioural delay likely originates from differences
in the intrinsic excitability of a key subcortical node, the dPAG. Furthermore,
this work showcases the use of an innate behavioural paradigm to mechanistically
dissect PDM processes in autism."
acknowledged_ssus:
- _id: PreCl
- _id: Bio
- _id: LifeSc
- _id: M-Shop
- _id: CampIT
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Laura
full_name: Burnett, Laura
id: 3B717F68-F248-11E8-B48F-1D18A9856A87
last_name: Burnett
orcid: 0000-0002-8937-410X
citation:
ama: Burnett L. To flee, or not to flee? Using innate defensive behaviours to investigate
rapid perceptual decision-making through subcortical circuits in mouse models
of autism. 2023. doi:10.15479/at:ista:12716
apa: Burnett, L. (2023). To flee, or not to flee? Using innate defensive behaviours
to investigate rapid perceptual decision-making through subcortical circuits in
mouse models of autism. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:12716
chicago: Burnett, Laura. “To Flee, or Not to Flee? Using Innate Defensive Behaviours
to Investigate Rapid Perceptual Decision-Making through Subcortical Circuits in
Mouse Models of Autism.” Institute of Science and Technology Austria, 2023. https://doi.org/10.15479/at:ista:12716.
ieee: L. Burnett, “To flee, or not to flee? Using innate defensive behaviours to
investigate rapid perceptual decision-making through subcortical circuits in mouse
models of autism,” Institute of Science and Technology Austria, 2023.
ista: Burnett L. 2023. To flee, or not to flee? Using innate defensive behaviours
to investigate rapid perceptual decision-making through subcortical circuits in
mouse models of autism. Institute of Science and Technology Austria.
mla: Burnett, Laura. To Flee, or Not to Flee? Using Innate Defensive Behaviours
to Investigate Rapid Perceptual Decision-Making through Subcortical Circuits in
Mouse Models of Autism. Institute of Science and Technology Austria, 2023,
doi:10.15479/at:ista:12716.
short: L. Burnett, To Flee, or Not to Flee? Using Innate Defensive Behaviours to
Investigate Rapid Perceptual Decision-Making through Subcortical Circuits in Mouse
Models of Autism, Institute of Science and Technology Austria, 2023.
date_created: 2023-03-08T15:19:45Z
date_published: 2023-03-10T00:00:00Z
date_updated: 2023-04-05T10:59:04Z
day: '10'
ddc:
- '599'
- '573'
degree_awarded: PhD
department:
- _id: GradSch
- _id: MaJö
doi: 10.15479/at:ista:12716
ec_funded: 1
file:
- access_level: closed
checksum: 6c6d9cc2c4cdacb74e6b1047a34d7332
content_type: application/vnd.openxmlformats-officedocument.wordprocessingml.document
creator: lburnett
date_created: 2023-03-08T15:08:46Z
date_updated: 2023-03-08T15:08:46Z
file_id: '12717'
file_name: Burnett_Thesis_2023.docx
file_size: 23029260
relation: source_file
- access_level: open_access
checksum: cebc77705288bf4382db9b3541483cd0
content_type: application/pdf
creator: lburnett
date_created: 2023-03-08T15:08:46Z
date_updated: 2023-03-08T15:08:46Z
file_id: '12718'
file_name: Burnett_Thesis_2023_pdfA.pdf
file_size: 11959869
relation: main_file
success: 1
file_date_updated: 2023-03-08T15:08:46Z
has_accepted_license: '1'
language:
- iso: eng
month: '03'
oa: 1
oa_version: Published Version
page: '178'
project:
- _id: 2634E9D2-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '756502'
name: Circuits of Visual Attention
publication_identifier:
issn:
- 2663-337X
publication_status: published
publisher: Institute of Science and Technology Austria
status: public
supervisor:
- first_name: Maximilian A
full_name: Jösch, Maximilian A
id: 2BD278E6-F248-11E8-B48F-1D18A9856A87
last_name: Jösch
orcid: 0000-0002-3937-1330
title: To flee, or not to flee? Using innate defensive behaviours to investigate rapid
perceptual decision-making through subcortical circuits in mouse models of autism
type: dissertation
user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9
year: '2023'
...
---
_id: '14641'
acknowledged_ssus:
- _id: LifeSc
- _id: Bio
- _id: CampIT
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Mike
full_name: Hennessey-Wesen, Mike
id: 3F338C72-F248-11E8-B48F-1D18A9856A87
last_name: Hennessey-Wesen
citation:
ama: Hennessey-Wesen M. Adaptive mutation in E. coli modulated by luxS. 2023. doi:10.15479/at:ista:14641
apa: Hennessey-Wesen, M. (2023). Adaptive mutation in E. coli modulated by luxS.
Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:14641
chicago: Hennessey-Wesen, Mike. “Adaptive Mutation in E. Coli Modulated by LuxS.”
Institute of Science and Technology Austria, 2023. https://doi.org/10.15479/at:ista:14641.
ieee: M. Hennessey-Wesen, “Adaptive mutation in E. coli modulated by luxS,” Institute
of Science and Technology Austria, 2023.
ista: Hennessey-Wesen M. 2023. Adaptive mutation in E. coli modulated by luxS. Institute
of Science and Technology Austria.
mla: Hennessey-Wesen, Mike. Adaptive Mutation in E. Coli Modulated by LuxS.
Institute of Science and Technology Austria, 2023, doi:10.15479/at:ista:14641.
short: M. Hennessey-Wesen, Adaptive Mutation in E. Coli Modulated by LuxS, Institute
of Science and Technology Austria, 2023.
date_created: 2023-12-04T13:17:37Z
date_published: 2023-11-30T00:00:00Z
date_updated: 2023-12-07T14:12:25Z
day: '30'
ddc:
- '570'
degree_awarded: PhD
department:
- _id: GradSch
- _id: BjHo
doi: 10.15479/at:ista:14641
ec_funded: 1
file:
- access_level: closed
checksum: 4127c285b34f4bf7fb31ef24f9d14c25
content_type: application/vnd.oasis.opendocument.text
creator: mhenness
date_created: 2023-12-06T13:13:26Z
date_updated: 2023-12-06T13:13:26Z
file_id: '14648'
file_name: mike_thesis_v06-12-2023.odt
file_size: 46405919
relation: source_file
- access_level: closed
checksum: f5203a61eddaf35235bbc51904d73982
content_type: application/pdf
creator: mhenness
date_created: 2023-12-06T13:14:15Z
date_updated: 2023-12-06T13:14:15Z
embargo: 2024-11-30
embargo_to: open_access
file_id: '14649'
file_name: mike_thesis_v06-12-2023.pdf
file_size: 21282155
relation: main_file
file_date_updated: 2023-12-06T13:14:15Z
has_accepted_license: '1'
keyword:
- microfluidics
- miceobiology
- mutations
- quorum sensing
language:
- iso: eng
month: '11'
oa_version: Published Version
page: '104'
project:
- _id: 2564DBCA-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '665385'
name: International IST Doctoral Program
publication_identifier:
issn:
- 2663 - 337X
publication_status: published
publisher: Institute of Science and Technology Austria
status: public
supervisor:
- first_name: Björn
full_name: Hof, Björn
id: 3A374330-F248-11E8-B48F-1D18A9856A87
last_name: Hof
orcid: 0000-0003-2057-2754
title: Adaptive mutation in E. coli modulated by luxS
type: dissertation
user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9
year: '2023'
...
---
_id: '9418'
abstract:
- lang: eng
text: "Deep learning is best known for its empirical success across a wide range
of applications\r\nspanning computer vision, natural language processing and speech.
Of equal significance,\r\nthough perhaps less known, are its ramifications for
learning theory: deep networks have\r\nbeen observed to perform surprisingly well
in the high-capacity regime, aka the overfitting\r\nor underspecified regime.
Classically, this regime on the far right of the bias-variance curve\r\nis associated
with poor generalisation; however, recent experiments with deep networks\r\nchallenge
this view.\r\n\r\nThis thesis is devoted to investigating various aspects of underspecification
in deep learning.\r\nFirst, we argue that deep learning models are underspecified
on two levels: a) any given\r\ntraining dataset can be fit by many different functions,
and b) any given function can be\r\nexpressed by many different parameter configurations.
We refer to the second kind of\r\nunderspecification as parameterisation redundancy
and we precisely characterise its extent.\r\nSecond, we characterise the implicit
criteria (the inductive bias) that guide learning in the\r\nunderspecified regime.
Specifically, we consider a nonlinear but tractable classification\r\nsetting,
and show that given the choice, neural networks learn classifiers with a large
margin.\r\nThird, we consider learning scenarios where the inductive bias is not
by itself sufficient to\r\ndeal with underspecification. We then study different
ways of ‘tightening the specification’: i)\r\nIn the setting of representation
learning with variational autoencoders, we propose a hand-\r\ncrafted regulariser
based on mutual information. ii) In the setting of binary classification, we\r\nconsider
soft-label (real-valued) supervision. We derive a generalisation bound for linear\r\nnetworks
supervised in this way and verify that soft labels facilitate fast learning. Finally,
we\r\nexplore an application of soft-label supervision to the training of multi-exit
models."
acknowledged_ssus:
- _id: ScienComp
- _id: CampIT
- _id: E-Lib
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Phuong
full_name: Bui Thi Mai, Phuong
id: 3EC6EE64-F248-11E8-B48F-1D18A9856A87
last_name: Bui Thi Mai
citation:
ama: Phuong M. Underspecification in deep learning. 2021. doi:10.15479/AT:ISTA:9418
apa: Phuong, M. (2021). Underspecification in deep learning. Institute of
Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:9418
chicago: Phuong, Mary. “Underspecification in Deep Learning.” Institute of Science
and Technology Austria, 2021. https://doi.org/10.15479/AT:ISTA:9418.
ieee: M. Phuong, “Underspecification in deep learning,” Institute of Science and
Technology Austria, 2021.
ista: Phuong M. 2021. Underspecification in deep learning. Institute of Science
and Technology Austria.
mla: Phuong, Mary. Underspecification in Deep Learning. Institute of Science
and Technology Austria, 2021, doi:10.15479/AT:ISTA:9418.
short: M. Phuong, Underspecification in Deep Learning, Institute of Science and
Technology Austria, 2021.
date_created: 2021-05-24T13:06:23Z
date_published: 2021-05-30T00:00:00Z
date_updated: 2023-09-08T11:11:12Z
day: '30'
ddc:
- '000'
degree_awarded: PhD
department:
- _id: GradSch
- _id: ChLa
doi: 10.15479/AT:ISTA:9418
file:
- access_level: open_access
checksum: 4f0abe64114cfed264f9d36e8d1197e3
content_type: application/pdf
creator: bphuong
date_created: 2021-05-24T11:22:29Z
date_updated: 2021-05-24T11:22:29Z
file_id: '9419'
file_name: mph-thesis-v519-pdfimages.pdf
file_size: 2673905
relation: main_file
success: 1
- access_level: closed
checksum: f5699e876bc770a9b0df8345a77720a2
content_type: application/zip
creator: bphuong
date_created: 2021-05-24T11:56:02Z
date_updated: 2021-05-24T11:56:02Z
file_id: '9420'
file_name: thesis.zip
file_size: 92995100
relation: source_file
file_date_updated: 2021-05-24T11:56:02Z
has_accepted_license: '1'
language:
- iso: eng
month: '05'
oa: 1
oa_version: Published Version
page: '125'
publication_identifier:
issn:
- 2663-337X
publication_status: published
publisher: Institute of Science and Technology Austria
related_material:
record:
- id: '7435'
relation: part_of_dissertation
status: deleted
- id: '7481'
relation: part_of_dissertation
status: public
- id: '9416'
relation: part_of_dissertation
status: public
- id: '7479'
relation: part_of_dissertation
status: public
status: public
supervisor:
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
title: Underspecification in deep learning
type: dissertation
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
year: '2021'
...
---
_id: '8390'
abstract:
- lang: eng
text: "Deep neural networks have established a new standard for data-dependent feature
extraction pipelines in the Computer Vision literature. Despite their remarkable
performance in the standard supervised learning scenario, i.e. when models are
trained with labeled data and tested on samples that follow a similar distribution,
neural networks have been shown to struggle with more advanced generalization
abilities, such as transferring knowledge across visually different domains, or
generalizing to new unseen combinations of known concepts. In this thesis we argue
that, in contrast to the usual black-box behavior of neural networks, leveraging
more structured internal representations is a promising direction\r\nfor tackling
such problems. In particular, we focus on two forms of structure. First, we tackle
modularity: We show that (i) compositional architectures are a natural tool for
modeling reasoning tasks, in that they efficiently capture their combinatorial
nature, which is key for generalizing beyond the compositions seen during training.
We investigate how to to learn such models, both formally and experimentally,
for the task of abstract visual reasoning. Then, we show that (ii) in some settings,
modularity allows us to efficiently break down complex tasks into smaller, easier,
modules, thereby improving computational efficiency; We study this behavior in
the context of generative models for colorization, as well as for small objects
detection. Secondly, we investigate the inherently layered structure of representations
learned by neural networks, and analyze its role in the context of transfer learning
and domain adaptation across visually\r\ndissimilar domains. "
acknowledged_ssus:
- _id: CampIT
- _id: ScienComp
acknowledgement: Last but not least, I would like to acknowledge the support of the
IST IT and scientific computing team for helping provide a great work environment.
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Amélie
full_name: Royer, Amélie
id: 3811D890-F248-11E8-B48F-1D18A9856A87
last_name: Royer
orcid: 0000-0002-8407-0705
citation:
ama: Royer A. Leveraging structure in Computer Vision tasks for flexible Deep Learning
models. 2020. doi:10.15479/AT:ISTA:8390
apa: Royer, A. (2020). Leveraging structure in Computer Vision tasks for flexible
Deep Learning models. Institute of Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:8390
chicago: Royer, Amélie. “Leveraging Structure in Computer Vision Tasks for Flexible
Deep Learning Models.” Institute of Science and Technology Austria, 2020. https://doi.org/10.15479/AT:ISTA:8390.
ieee: A. Royer, “Leveraging structure in Computer Vision tasks for flexible Deep
Learning models,” Institute of Science and Technology Austria, 2020.
ista: Royer A. 2020. Leveraging structure in Computer Vision tasks for flexible
Deep Learning models. Institute of Science and Technology Austria.
mla: Royer, Amélie. Leveraging Structure in Computer Vision Tasks for Flexible
Deep Learning Models. Institute of Science and Technology Austria, 2020, doi:10.15479/AT:ISTA:8390.
short: A. Royer, Leveraging Structure in Computer Vision Tasks for Flexible Deep
Learning Models, Institute of Science and Technology Austria, 2020.
date_created: 2020-09-14T13:42:09Z
date_published: 2020-09-14T00:00:00Z
date_updated: 2023-10-16T10:04:02Z
day: '14'
ddc:
- '000'
degree_awarded: PhD
department:
- _id: ChLa
doi: 10.15479/AT:ISTA:8390
file:
- access_level: open_access
checksum: c914d2f88846032f3d8507734861b6ee
content_type: application/pdf
creator: dernst
date_created: 2020-09-14T13:39:14Z
date_updated: 2020-09-14T13:39:14Z
file_id: '8391'
file_name: 2020_Thesis_Royer.pdf
file_size: 30224591
relation: main_file
success: 1
- access_level: closed
checksum: ae98fb35d912cff84a89035ae5794d3c
content_type: application/x-zip-compressed
creator: dernst
date_created: 2020-09-14T13:39:17Z
date_updated: 2020-09-14T13:39:17Z
file_id: '8392'
file_name: thesis_sources.zip
file_size: 74227627
relation: main_file
file_date_updated: 2020-09-14T13:39:17Z
has_accepted_license: '1'
language:
- iso: eng
license: https://creativecommons.org/licenses/by-nc-sa/4.0/
month: '09'
oa: 1
oa_version: Published Version
page: '197'
publication_identifier:
isbn:
- 978-3-99078-007-7
issn:
- 2663-337X
publication_status: published
publisher: Institute of Science and Technology Austria
related_material:
record:
- id: '7936'
relation: part_of_dissertation
status: public
- id: '7937'
relation: part_of_dissertation
status: public
- id: '8193'
relation: part_of_dissertation
status: public
- id: '8092'
relation: part_of_dissertation
status: public
- id: '911'
relation: part_of_dissertation
status: public
status: public
supervisor:
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
title: Leveraging structure in Computer Vision tasks for flexible Deep Learning models
tmp:
image: /images/cc_by_nc_sa.png
legal_code_url: https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode
name: Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC
BY-NC-SA 4.0)
short: CC BY-NC-SA (4.0)
type: dissertation
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
year: '2020'
...
---
_id: '8983'
abstract:
- lang: eng
text: Metabolic adaptation is a critical feature of migrating cells. It tunes the
metabolic programs of migrating cells to allow them to efficiently exert their
crucial roles in development, inflammatory responses and tumor metastasis. Cell
migration through physically challenging contexts requires energy. However, how
the metabolic reprogramming that underlies in vivo cell invasion is controlled
is still unanswered. In my PhD project, I identify a novel conserved metabolic
shift in Drosophila melanogaster immune cells that by modulating their bioenergetic
potential controls developmentally programmed tissue invasion. We show that this
regulation requires a novel conserved nuclear protein, named Atossa. Atossa enhances
the transcription of a set of proteins, including an RNA helicase Porthos and
two metabolic enzymes, each of which increases the tissue invasion of leading
Drosophila macrophages and can rescue the atossa mutant phenotype. Porthos selectively
regulates the translational efficiency of a subset of mRNAs containing a 5’-UTR
cis-regulatory TOP-like sequence. These 5’TOPL mRNA targets encode mitochondrial-related
proteins, including subunits of mitochondrial oxidative phosphorylation (OXPHOS)
components III and V and other metabolic-related proteins. Porthos powers up mitochondrial
OXPHOS to engender a sufficient ATP supply, which is required for tissue invasion
of leading macrophages. Atossa’s two vertebrate orthologs rescue the invasion
defect. In my PhD project, I elucidate that Atossa displays a conserved developmental
metabolic control to modulate metabolic capacities and the cellular energy state,
through altered transcription and translation, to aid the tissue infiltration
of leading cells into energy demanding barriers.
acknowledged_ssus:
- _id: Bio
- _id: LifeSc
- _id: E-Lib
- _id: CampIT
acknowledgement: Also, I would like to express my appreciation and thanks to the Bioimaging
facility, LSF, GSO, library, and IT people at IST Austria.
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Shamsi
full_name: Emtenani, Shamsi
id: 49D32318-F248-11E8-B48F-1D18A9856A87
last_name: Emtenani
orcid: 0000-0001-6981-6938
citation:
ama: Emtenani S. Metabolic regulation of Drosophila macrophage tissue invasion.
2020. doi:10.15479/AT:ISTA:8983
apa: Emtenani, S. (2020). Metabolic regulation of Drosophila macrophage tissue
invasion. Institute of Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:8983
chicago: Emtenani, Shamsi. “Metabolic Regulation of Drosophila Macrophage Tissue
Invasion.” Institute of Science and Technology Austria, 2020. https://doi.org/10.15479/AT:ISTA:8983.
ieee: S. Emtenani, “Metabolic regulation of Drosophila macrophage tissue invasion,”
Institute of Science and Technology Austria, 2020.
ista: Emtenani S. 2020. Metabolic regulation of Drosophila macrophage tissue invasion.
Institute of Science and Technology Austria.
mla: Emtenani, Shamsi. Metabolic Regulation of Drosophila Macrophage Tissue Invasion.
Institute of Science and Technology Austria, 2020, doi:10.15479/AT:ISTA:8983.
short: S. Emtenani, Metabolic Regulation of Drosophila Macrophage Tissue Invasion,
Institute of Science and Technology Austria, 2020.
date_created: 2020-12-30T15:41:26Z
date_published: 2020-12-30T00:00:00Z
date_updated: 2023-09-07T13:24:17Z
day: '30'
ddc:
- '570'
degree_awarded: PhD
department:
- _id: DaSi
doi: 10.15479/AT:ISTA:8983
file:
- access_level: open_access
checksum: ec2797ab7a6f253b35df0572b36d1b43
content_type: application/pdf
creator: semtenan
date_created: 2020-12-30T15:34:01Z
date_updated: 2021-12-31T23:30:04Z
embargo: 2021-12-30
file_id: '8984'
file_name: Thesis_Shamsi_Emtenani_pdfA.pdf
file_size: 10848175
relation: main_file
- access_level: closed
checksum: cc30e6608a9815414024cf548dff3b3a
content_type: application/pdf
creator: semtenan
date_created: 2020-12-30T15:37:36Z
date_updated: 2021-12-31T23:30:04Z
embargo_to: open_access
file_id: '8985'
file_name: Thesis_Shamsi_Emtenani_source file.pdf
file_size: 10073648
relation: source_file
file_date_updated: 2021-12-31T23:30:04Z
has_accepted_license: '1'
language:
- iso: eng
month: '12'
oa: 1
oa_version: Published Version
page: '141'
publication_identifier:
issn:
- 2663-337X
publication_status: published
publisher: Institute of Science and Technology Austria
related_material:
record:
- id: '8557'
relation: part_of_dissertation
status: public
- id: '6187'
relation: part_of_dissertation
status: public
status: public
supervisor:
- first_name: Daria E
full_name: Siekhaus, Daria E
id: 3D224B9E-F248-11E8-B48F-1D18A9856A87
last_name: Siekhaus
orcid: 0000-0001-8323-8353
title: Metabolic regulation of Drosophila macrophage tissue invasion
type: dissertation
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
year: '2020'
...
---
_id: '8032'
abstract:
- lang: eng
text: "Algorithms in computational 3-manifold topology typically take a triangulation
as an input and return topological information about the underlying 3-manifold.
However, extracting the desired information from a triangulation (e.g., evaluating
an invariant) is often computationally very expensive. In recent years this complexity
barrier has been successfully tackled in some cases by importing ideas from the
theory of parameterized algorithms into the realm of 3-manifolds. Various computationally
hard problems were shown to be efficiently solvable for input triangulations that
are sufficiently “tree-like.”\r\nIn this thesis we focus on the key combinatorial
parameter in the above context: we consider the treewidth of a compact, orientable
3-manifold, i.e., the smallest treewidth of the dual graph of any triangulation
thereof. By building on the work of Scharlemann–Thompson and Scharlemann–Schultens–Saito
on generalized Heegaard splittings, and on the work of Jaco–Rubinstein on layered
triangulations, we establish quantitative relations between the treewidth and
classical topological invariants of a 3-manifold. In particular, among other results,
we show that the treewidth of a closed, orientable, irreducible, non-Haken 3-manifold
is always within a constant factor of its Heegaard genus."
acknowledged_ssus:
- _id: E-Lib
- _id: CampIT
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Kristóf
full_name: Huszár, Kristóf
id: 33C26278-F248-11E8-B48F-1D18A9856A87
last_name: Huszár
orcid: 0000-0002-5445-5057
citation:
ama: Huszár K. Combinatorial width parameters for 3-dimensional manifolds. 2020.
doi:10.15479/AT:ISTA:8032
apa: Huszár, K. (2020). Combinatorial width parameters for 3-dimensional manifolds.
Institute of Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:8032
chicago: Huszár, Kristóf. “Combinatorial Width Parameters for 3-Dimensional Manifolds.”
Institute of Science and Technology Austria, 2020. https://doi.org/10.15479/AT:ISTA:8032.
ieee: K. Huszár, “Combinatorial width parameters for 3-dimensional manifolds,” Institute
of Science and Technology Austria, 2020.
ista: Huszár K. 2020. Combinatorial width parameters for 3-dimensional manifolds.
Institute of Science and Technology Austria.
mla: Huszár, Kristóf. Combinatorial Width Parameters for 3-Dimensional Manifolds.
Institute of Science and Technology Austria, 2020, doi:10.15479/AT:ISTA:8032.
short: K. Huszár, Combinatorial Width Parameters for 3-Dimensional Manifolds, Institute
of Science and Technology Austria, 2020.
date_created: 2020-06-26T10:00:36Z
date_published: 2020-06-26T00:00:00Z
date_updated: 2023-09-07T13:18:27Z
day: '26'
ddc:
- '514'
degree_awarded: PhD
department:
- _id: UlWa
doi: 10.15479/AT:ISTA:8032
file:
- access_level: open_access
checksum: bd8be6e4f1addc863dfcc0fad29ee9c3
content_type: application/pdf
creator: khuszar
date_created: 2020-06-26T10:03:58Z
date_updated: 2020-07-14T12:48:08Z
file_id: '8034'
file_name: Kristof_Huszar-Thesis.pdf
file_size: 2637562
relation: main_file
- access_level: closed
checksum: d5f8456202b32f4a77552ef47a2837d1
content_type: application/x-zip-compressed
creator: khuszar
date_created: 2020-06-26T10:10:06Z
date_updated: 2020-07-14T12:48:08Z
file_id: '8035'
file_name: Kristof_Huszar-Thesis-source.zip
file_size: 7163491
relation: source_file
file_date_updated: 2020-07-14T12:48:08Z
has_accepted_license: '1'
language:
- iso: eng
license: https://creativecommons.org/licenses/by/4.0/
month: '06'
oa: 1
oa_version: Published Version
page: xviii+120
publication_identifier:
isbn:
- 978-3-99078-006-0
issn:
- 2663-337X
publication_status: published
publisher: Institute of Science and Technology Austria
related_material:
record:
- id: '6556'
relation: dissertation_contains
status: public
- id: '7093'
relation: dissertation_contains
status: public
status: public
supervisor:
- first_name: Uli
full_name: Wagner, Uli
id: 36690CA2-F248-11E8-B48F-1D18A9856A87
last_name: Wagner
orcid: 0000-0002-1494-0568
- first_name: Jonathan
full_name: Spreer, Jonathan
last_name: Spreer
title: Combinatorial width parameters for 3-dimensional manifolds
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: dissertation
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
year: '2020'
...
---
_id: '6755'
abstract:
- lang: eng
text: 'Differentiated sex chromosomes are accompanied by a difference in gene dose
between X/Z-specific and autosomal genes. At the transcriptomic level, these sex-linked
genes can lead to expression imbalance, or gene dosage can be compensated by epigenetic
mechanisms and results into expression level equalization. Schistosoma mansoni
has been previously described as a ZW species (i.e., female heterogamety, in opposition
to XY male heterogametic species) with a partial dosage compensation, but underlying
mechanisms are still unexplored. Here, we combine transcriptomic (RNA-Seq) and
epigenetic data (ChIP-Seq against H3K4me3, H3K27me3,andH4K20me1histonemarks) in
free larval cercariae and intravertebrate parasitic stages. For the first time,
we describe differences in dosage compensation status in ZW females, depending
on the parasitic status: free cercariae display global dosage compensation, whereas
intravertebrate stages show a partial dosage compensation. We also highlight regional
differences of gene expression along the Z chromosome in cercariae, but not in
the intravertebrate stages. Finally, we feature a consistent permissive chromatin
landscape of the Z chromosome in both sexes and stages. We argue that dosage compensation
in schistosomes is characterized by chromatin remodeling mechanisms in the Z-specific
region.'
acknowledged_ssus:
- _id: CampIT
article_processing_charge: No
article_type: original
author:
- first_name: Marion A L
full_name: Picard, Marion A L
id: 2C921A7A-F248-11E8-B48F-1D18A9856A87
last_name: Picard
orcid: 0000-0002-8101-2518
- first_name: Beatriz
full_name: Vicoso, Beatriz
id: 49E1C5C6-F248-11E8-B48F-1D18A9856A87
last_name: Vicoso
orcid: 0000-0002-4579-8306
- first_name: David
full_name: Roquis, David
last_name: Roquis
- first_name: Ingo
full_name: Bulla, Ingo
last_name: Bulla
- first_name: Ronaldo C.
full_name: Augusto, Ronaldo C.
last_name: Augusto
- first_name: Nathalie
full_name: Arancibia, Nathalie
last_name: Arancibia
- first_name: Christoph
full_name: Grunau, Christoph
last_name: Grunau
- first_name: Jérôme
full_name: Boissier, Jérôme
last_name: Boissier
- first_name: Céline
full_name: Cosseau, Céline
last_name: Cosseau
citation:
ama: 'Picard MAL, Vicoso B, Roquis D, et al. Dosage compensation throughout the
Schistosoma mansoni lifecycle: Specific chromatin landscape of the Z chromosome.
Genome biology and evolution. 2019;11(7):1909-1922. doi:10.1093/gbe/evz133'
apa: 'Picard, M. A. L., Vicoso, B., Roquis, D., Bulla, I., Augusto, R. C., Arancibia,
N., … Cosseau, C. (2019). Dosage compensation throughout the Schistosoma mansoni
lifecycle: Specific chromatin landscape of the Z chromosome. Genome Biology
and Evolution. Oxford Academic Press. https://doi.org/10.1093/gbe/evz133'
chicago: 'Picard, Marion A L, Beatriz Vicoso, David Roquis, Ingo Bulla, Ronaldo
C. Augusto, Nathalie Arancibia, Christoph Grunau, Jérôme Boissier, and Céline
Cosseau. “Dosage Compensation throughout the Schistosoma Mansoni Lifecycle: Specific
Chromatin Landscape of the Z Chromosome.” Genome Biology and Evolution.
Oxford Academic Press, 2019. https://doi.org/10.1093/gbe/evz133.'
ieee: 'M. A. L. Picard et al., “Dosage compensation throughout the Schistosoma
mansoni lifecycle: Specific chromatin landscape of the Z chromosome,” Genome
biology and evolution, vol. 11, no. 7. Oxford Academic Press, pp. 1909–1922,
2019.'
ista: 'Picard MAL, Vicoso B, Roquis D, Bulla I, Augusto RC, Arancibia N, Grunau
C, Boissier J, Cosseau C. 2019. Dosage compensation throughout the Schistosoma
mansoni lifecycle: Specific chromatin landscape of the Z chromosome. Genome biology
and evolution. 11(7), 1909–1922.'
mla: 'Picard, Marion A. L., et al. “Dosage Compensation throughout the Schistosoma
Mansoni Lifecycle: Specific Chromatin Landscape of the Z Chromosome.” Genome
Biology and Evolution, vol. 11, no. 7, Oxford Academic Press, 2019, pp. 1909–22,
doi:10.1093/gbe/evz133.'
short: M.A.L. Picard, B. Vicoso, D. Roquis, I. Bulla, R.C. Augusto, N. Arancibia,
C. Grunau, J. Boissier, C. Cosseau, Genome Biology and Evolution 11 (2019) 1909–1922.
date_created: 2019-08-04T21:59:18Z
date_published: 2019-07-01T00:00:00Z
date_updated: 2023-08-29T06:53:58Z
day: '01'
ddc:
- '570'
department:
- _id: BeVi
doi: 10.1093/gbe/evz133
external_id:
isi:
- '000484039500018'
pmid:
- '31273378'
file:
- access_level: open_access
checksum: f9e8f6863a406dcc5a36b2be001c138c
content_type: application/pdf
creator: dernst
date_created: 2019-08-05T07:55:02Z
date_updated: 2020-07-14T12:47:39Z
file_id: '6765'
file_name: 2019_GenomeBiology_Picard.pdf
file_size: 580205
relation: main_file
file_date_updated: 2020-07-14T12:47:39Z
has_accepted_license: '1'
intvolume: ' 11'
isi: 1
issue: '7'
language:
- iso: eng
month: '07'
oa: 1
oa_version: Published Version
page: 1909-1922
pmid: 1
publication: Genome biology and evolution
publication_identifier:
eissn:
- 1759-6653
publication_status: published
publisher: Oxford Academic Press
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'Dosage compensation throughout the Schistosoma mansoni lifecycle: Specific
chromatin landscape of the Z chromosome'
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: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 11
year: '2019'
...