scholarly journals Hierarchical Compression Reveals Sub-Second to Day-Long Structure in Larval Zebrafish Behaviour

2019 ◽  
Author(s):  
Marcus Ghosh ◽  
Jason Rihel
Keyword(s):  
Unsupervised Learning ◽  
Large Scale ◽  
Motif Analysis ◽  
Computational Tools ◽  
Multiple Timescales ◽  
Larval Zebrafish ◽  
Behavioural Patterns ◽  
Hierarchical Compression ◽  
Mutant Phenotypes ◽  
Long Timescales

AbstractAnimal behaviour is dynamic, evolving over multiple timescales from milliseconds to days and even across a lifetime. To understand the mechanisms governing these dynamics, it is necessary to capture multi-timescale structure from behavioural data. Here, we develop computational tools and study the behaviour of hundreds of larval zebrafish tracked continuously across multiple 24-hour day/night cycles. We extracted millions of movements and pauses, termed bouts, and used unsupervised learning to reduce each larva’s behaviour to an alternating sequence of active and inactive bout types, termed modules. Through hierarchical compression, we identified recurrent behavioural patterns, termed motifs. Module and motif usage varied across the day/night cycle, revealing structure at sub-second to day-long timescales. We further demonstrate that module and motif analysis can uncover novel pharmacological and genetic mutant phenotypes. Overall, our work reveals the organisation of larval zebrafish behaviour at multiple timescales and provides tools to identify structure from large-scale behavioural datasets.

scholarly journals Advances in decomposing complex metabolite mixtures using substructure- and network-based computational metabolomics approaches

2021 ◽  
Author(s):  
Mehdi A. Beniddir ◽  
Kyo Bin Kang ◽  
Grégory Genta-Jouve ◽  
Florian Huber ◽  
Simon Rogers ◽  
...  
Keyword(s):  
Natural Product ◽  
Large Scale ◽  
Scale Analysis ◽  
Computational Tools ◽  
Natural Product Discovery ◽  
Large Scale Analysis ◽  
Metabolite Annotation

This review highlights the key computational tools and emerging strategies for metabolite annotation, and discusses how these advances will enable integrated large-scale analysis to accelerate natural product discovery.

scholarly journals The daunting polygenicity of mental illness: making a new map

2018 ◽  
Vol 373 (1742) ◽  
pp. 20170031 ◽  
Author(s):  
Steven E. Hyman
Keyword(s):  
Mental Health ◽  
Large Scale ◽  
Computational Models ◽  
Model Organisms ◽  
Human Biology ◽  
Genetic Studies ◽  
Computational Tools ◽  
Experimental Systems ◽  
Terra Incognita ◽  
Phenotypic Complexity

An epochal opportunity to elucidate the pathogenic mechanisms of psychiatric disorders has emerged from advances in genomic technology, new computational tools and the growth of international consortia committed to data sharing. The resulting large-scale, unbiased genetic studies have begun to yield new biological insights and with them the hope that a half century of stasis in psychiatric therapeutics will come to an end. Yet a sobering picture is coming into view; it reveals daunting genetic and phenotypic complexity portending enormous challenges for neurobiology. Successful exploitation of results from genetics will require eschewal of long-successful reductionist approaches to investigation of gene function, a commitment to supplanting much research now conducted in model organisms with human biology, and development of new experimental systems and computational models to analyse polygenic causal influences. In short, psychiatric neuroscience must develop a new scientific map to guide investigation through a polygenic terra incognita . This article is part of a discussion meeting issue ‘Of mice and mental health: facilitating dialogue between basic and clinical neuroscientists’.

scholarly journals TADOSS: computational estimation of tandem domain swap stability

2018 ◽  
Vol 35 (14) ◽  
pp. 2507-2508 ◽  
Author(s):  
Aleix Lafita ◽  
Pengfei Tian ◽  
Robert B Best ◽  
Alex Bateman
Keyword(s):  
Large Scale ◽  
Domain Swapping ◽  
Coarse Grained ◽  
Supplementary Information ◽  
Simulation Studies ◽  
Computational Tools ◽  
Domain Swap ◽  
Computational Estimation ◽  
High Propensity ◽  
The Stability

Abstract Summary Proteins with highly similar tandem domains have shown an increased propensity for misfolding and aggregation. Several molecular explanations have been put forward, such as swapping of adjacent domains, but there is a lack of computational tools to systematically analyze them. We present the TAndem DOmain Swap Stability predictor (TADOSS), a method to computationally estimate the stability of tandem domain-swapped conformations from the structures of single domains, based on previous coarse-grained simulation studies. The tool is able to discriminate domains susceptible to domain swapping and to identify structural regions with high propensity to form hinge loops. TADOSS is a scalable method and suitable for large scale analyses. Availability and implementation Source code and documentation are freely available under an MIT license on GitHub at https://github.com/lafita/tadoss. Supplementary information Supplementary data are available at Bioinformatics online.

Adaptive-Curvature Structures with Auxetic Materials

2018 ◽  
Vol 1149 ◽  
pp. 53-63
Author(s):  
Roberto Naboni ◽  
Stefano Sartori ◽  
Lorenzo Mirante
Keyword(s):  
Large Scale ◽  
Morphological Features ◽  
Geometrical Parameters ◽  
Computational Tools ◽  
Auxetic Materials ◽  
Initial Geometry ◽  
Tools And Methods

Advancements in computational tools are offering designers the possibility to change their relationship with materials and establishing new synergies between matter, form and behaviour. This work explores this paradigm by introducing the use of auxetic metamaterials, specifically engineered to obtain properties beyond those found in nature, to generate structures with adaptive curvature obtained from planar construction elements. It is discussed how through programming an initial geometry with the strategic negotiation of several geometrical parameters it is possible to control finely the structural and morphological features of a structure. The paper presents approach, tools and methods for designing auxetics for large scale applications, and use them to create heterogeneous active-bending structures.

scholarly journals REVA as a Well-curated Database for Human Expression-modulating Variants

2021 ◽  
Author(s):  
Yu Wang ◽  
Fang-Yuan Shi ◽  
Yu Liang ◽  
Ge Gao
Keyword(s):  
Large Scale ◽  
Regulatory Mechanism ◽  
State Of The Art ◽  
Scale Analysis ◽  
Computational Tools ◽  
Functional Annotations ◽  
Link Type ◽  
Large Scale Analysis ◽  
Multiple State ◽  
Limited Sensitivity

AbstractMore than 80% of disease- and trait-associated human variants are noncoding. By systematically screening multiple large-scale studies, we compiled REVA, a manually curated database for over 11.8 million experimentally tested noncoding variants with expression-modulating potentials. We provided 2424 functional annotations that could be used to pinpoint plausible regulatory mechanism of these variants. We further benchmarked multiple state-of-the-art computational tools and found their limited sensitivity remains a serious challenge for effective large-scale analysis. REVA provides high-qualify experimentally tested expression-modulating variants with extensive functional annotations, which will be useful for users in the noncoding variants community. REVA is available at http://reva.gao-lab.org.

Experiments on Gas Mixing and Stratification Driven by Jets and Plumes in Large-Scale, Multi-Compartment Geometries

2006 ◽  
Author(s):  
Robert Zboray ◽  
Domenico Paladino ◽  
Olivier Auban
Keyword(s):  
High Resolution ◽  
Time Evolution ◽  
Large Scale ◽  
Nuclear Reactor ◽  
Safety Analysis ◽  
Mixing Process ◽  
Gas Mixing ◽  
Computational Tools ◽  
Different Gases

The present paper discusses experiments carried out to examine mixing of different gases (steam, air) and the evolution their distributions in large-scale, multi compartment geometry imitating nuclear reactor containment compartments. The flow and the mixing process in the experiments are driven by plumes and jets representing source structures with different momentum-to-buoyancy strength. The time evolution of the relevant parameters like gas concentrations, velocities and temperatures are followed using dedicated instrumentation. The data obtained is meant to be used for the validation and development of high-resolution, mainly CFD based, 3D computational tools for nuclear reactor containment safety analysis.

Changing Healthcare Institutions with Large Information Technology Projects

2011 ◽  
pp. 123-136
Author(s):  
Matthew W. Guah
Keyword(s):  
Theoretical Basis ◽  
Large Scale ◽  
Healthcare Industry ◽  
Information Technology Projects ◽  
It Investments ◽  
Evaluation Procedures ◽  
Information Processes ◽  
Proposed Model ◽  
Behavioural Patterns ◽  
Working Practices

This article reviews the development of institutional theory in direct relations to historical changes within the UK’s National Health Service (NHS) with an eye to contributing to the theoretical specification of healthcare information processes. This is done partly by extending certain paradigms (see Meyer & Rowan, 1991; Powell & DiMaggio, 1991; Tolbert & Zucker, 1994) through a proposed model of causes and consequences of variations in levels of institutionalisation in the healthcare industry. It reports findings from a 5-year study on the NHS implementation of the largest civil ISs worldwide at an estimated cost of $10 billion over a 10-year period. The theoretical basis for analysis is developed, using concepts drawn from neo-institutionalism, realisation of business value, and organisational logic, as well as mixed empirical results about the lack of IT investments value in the NHS. The findings suggest that large scale, IT change imposed upon a highly institutionalised healthcare industry is fraught with difficulty mainly because culturally embedded norms, values, and behavioural patterns serve to impede centrally imposed initiatives to automate clinical working practices. It concludes with a discussion about the nature of evaluation procedures in relation to the process of institutionalising IS in healthcare.

scholarly journals Zebrafish-Based Discovery of Antiseizure Compounds from the North Sea: Isoquinoline Alkaloids TMC-120A and TMC-120B

Marine Drugs ◽  
2019 ◽  
Vol 17 (11) ◽  
pp. 607 ◽  
Author(s):  
Daniëlle Copmans ◽  
Sara Kildgaard ◽  
Silas A. Rasmussen ◽  
Monika Ślęzak ◽  
Nina Dirkx ◽  
...  
Keyword(s):  
Large Scale ◽  
Brain Activity ◽  
Isoquinoline Alkaloids ◽  
Seizure Duration ◽  
Larval Zebrafish ◽  
The North ◽  
Structural Analogues ◽  
The North Sea ◽  
Large Scale Screening ◽  
The Eu

There is a high need for the development of new and improved antiseizure drugs (ASDs) to treat epilepsy. Despite the potential of marine natural products (MNPs), the EU marine biodiscovery consortium PharmaSea has made the only effort to date to perform ASD discovery based on large-scale screening of MNPs. To this end, the embryonic zebrafish photomotor response assay and the larval zebrafish pentylenetetrazole (PTZ) model were used to screen MNP extracts for neuroactivity and antiseizure activity, respectively. Here we report the identification of the two known isoquinoline alkaloids TMC-120A and TMC-120B as novel antiseizure compounds, which were isolated by bioactivity-guided purification from the marine-derived fungus Aspergillus insuetus. TMC-120A and TMC-120B were observed to significantly lower PTZ-induced seizures and epileptiform brain activity in the larval zebrafish PTZ seizure model. In addition, their structural analogues TMC-120C, penicisochroman G, and ustusorane B were isolated and also significantly lowered PTZ-induced seizures. Finally, TMC-120A and TMC-120B were investigated in a mouse model of drug-resistant focal seizures. Compound treatment significantly shortened the seizure duration, thereby confirming their antiseizure activity. These data underscore the possibility to translate findings in zebrafish to mice in the field of epilepsy and the potential of the marine environment for ASD discovery.

scholarly journals Almost Unsupervised Learning for Dense Crowd Counting

2019 ◽  
Vol 33 ◽  
pp. 8868-8875 ◽  
Author(s):  
Deepak Babu Sam ◽  
Neeraj N Sajjan ◽  
Himanshu Maurya ◽  
R. Venkatesh Babu
Keyword(s):  
Unsupervised Learning ◽  
Large Scale ◽  
Learning Method ◽  
Large Variability ◽  
Proposed Model ◽  
Dense Crowd ◽  
Winner Take All ◽  
Learned Features ◽  
Take All

We present an unsupervised learning method for dense crowd count estimation. Marred by large variability in appearance of people and extreme overlap in crowds, enumerating people proves to be a difficult task even for humans. This implies creating large-scale annotated crowd data is expensive and directly takes a toll on the performance of existing CNN based counting models on account of small datasets. Motivated by these challenges, we develop Grid Winner-Take-All (GWTA) autoencoder to learn several layers of useful filters from unlabeled crowd images. Our GWTA approach divides a convolution layer spatially into a grid of cells. Within each cell, only the maximally activated neuron is allowed to update the filter. Almost 99.9% of the parameters of the proposed model are trained without any labeled data while the rest 0.1% are tuned with supervision. The model achieves superior results compared to other unsupervised methods and stays reasonably close to the accuracy of supervised baseline. Furthermore, we present comparisons and analyses regarding the quality of learned features across various models.

scholarly journals Construction of a Large Signature-Tagged Mini-Tn5 Transposon Library and Its Application to Mutagenesis of Sinorhizobium meliloti

2006 ◽  
Vol 72 (6) ◽  
pp. 4329-4337 ◽  
Author(s):  
Nataliya Pobigaylo ◽  
Danijel Wetter ◽  
Silke Szymczak ◽  
Ulf Schiller ◽  
Stefan Kurtz ◽  
...  
Keyword(s):  
Large Scale ◽  
Sinorhizobium Meliloti ◽  
High Specificity ◽  
Growth Patterns ◽  
Single Experiment ◽  
Tn5 Transposon ◽  
Melting Temperatures ◽  
Validation Experiment ◽  
Insertion Sites ◽  
Mutant Phenotypes

ABSTRACT Sinorhizobium meliloti genome sequence determination has provided the basis for different approaches of functional genomics for this symbiotic nitrogen-fixing alpha-proteobacterium. One of these approaches is gene disruption with subsequent analysis of mutant phenotypes. This method is efficient for single genes; however, it is laborious and time-consuming if it is used on a large scale. Here, we used a signature-tagged transposon mutagenesis method that allowed analysis of the survival and competitiveness of many mutants in a single experiment. A novel set of signature tags characterized by similar melting temperatures and G+C contents of the tag sequences was developed. The efficiencies of amplification of all tags were expected to be similar. Thus, no preselection of the tags was necessary to create a library of 412 signature-tagged transposons. To achieve high specificity of tag detection, each transposon was bar coded by two signature tags. In order to generate defined, nonredundant sets of signature-tagged S. meliloti mutants for subsequent experiments, 12,000 mutants were constructed, and insertion sites for more than 5,000 mutants were determined. One set consisting of 378 mutants was used in a validation experiment to identify mutants showing altered growth patterns.

Sign in / Sign up

Export Citation Format

Share Document