scholarly journals Toward a Computational Neuroethology of Vocal Communication: From Bioacoustics to Neurophysiology, Emerging Tools and Future Directions

2021 ◽  
Vol 15 ◽  
Author(s):  
Tim Sainburg ◽  
Timothy Q. Gentner
Keyword(s):  
Signal Processing ◽  
Computational Methods ◽  
Large Scale ◽  
Vocal Communication ◽  
Communication Behavior ◽  
Model Species ◽  
Future Directions ◽  
Communication Signals ◽  
Brain Behavior ◽  
Computational Neuroethology

Recently developed methods in computational neuroethology have enabled increasingly detailed and comprehensive quantification of animal movements and behavioral kinematics. Vocal communication behavior is well poised for application of similar large-scale quantification methods in the service of physiological and ethological studies. This review describes emerging techniques that can be applied to acoustic and vocal communication signals with the goal of enabling study beyond a small number of model species. We review a range of modern computational methods for bioacoustics, signal processing, and brain-behavior mapping. Along with a discussion of recent advances and techniques, we include challenges and broader goals in establishing a framework for the computational neuroethology of vocal communication.

Comparison and Analysis of Computational Methods for Identifying N6 - methyladenosine Sites in Saccharomyces Cerevisiae

2020 ◽  
Vol 26 ◽  
Author(s):  
Pengmian Feng ◽  
Lijing Feng ◽  
Chaohui Tang
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Computational Methods ◽  
Computational Analysis ◽  
Computational Prediction ◽  
Experimental Methods ◽  
Biological Processes ◽  
Biological Functions ◽  
Precise Location ◽  
Future Directions ◽  
The Past

Background and Purpose: N 6 -methyladenosine (m6A) plays critical roles in a broad set of biological processes. Knowledge about the precise location of m6A site in the transcriptome is vital for deciphering its biological functions. Although experimental techniques have made substantial contributions to identify m6A, they are still labor intensive and time consuming. As good complements to experimental methods, in the past few years, a series of computational approaches have been proposed to identify m6A sites. Methods: In order to facilitate researchers to select appropriate methods for identifying m6A sites, it is necessary to give a comprehensive review and comparison on existing methods. Results: Since researches on m6A in Saccharomyces cerevisiae are relatively clear, in this review, we summarized recent progresses on computational prediction of m6A sites in S. cerevisiae and assessed the performance of existing computational methods. Finally, future directions of computationally identifying m6A sites were presented. Conclusion: Taken together, we anticipate that this review will provide important guides for computational analysis of m 6A modifications.

scholarly journals A Survey on Contrastive Self-Supervised Learning

Technologies ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 2
Author(s):  
Ashish Jaiswal ◽  
Ashwin Ramesh Babu ◽  
Mohammad Zaki Zadeh ◽  
Debapriya Banerjee ◽  
Fillia Makedon
Keyword(s):  
Computer Vision ◽  
Supervised Learning ◽  
Language Processing ◽  
Large Scale ◽  
Performance Comparison ◽  
Extensive Review ◽  
Future Directions ◽  
Dominant Component ◽  
Supervised Methods ◽  
The Cost

Self-supervised learning has gained popularity because of its ability to avoid the cost of annotating large-scale datasets. It is capable of adopting self-defined pseudolabels as supervision and use the learned representations for several downstream tasks. Specifically, contrastive learning has recently become a dominant component in self-supervised learning for computer vision, natural language processing (NLP), and other domains. It aims at embedding augmented versions of the same sample close to each other while trying to push away embeddings from different samples. This paper provides an extensive review of self-supervised methods that follow the contrastive approach. The work explains commonly used pretext tasks in a contrastive learning setup, followed by different architectures that have been proposed so far. Next, we present a performance comparison of different methods for multiple downstream tasks such as image classification, object detection, and action recognition. Finally, we conclude with the limitations of the current methods and the need for further techniques and future directions to make meaningful progress.

scholarly journals Genetic and Transcriptomic Biomarkers in Neurodegenerative Diseases: Current Situation and the Road Ahead

Cells ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 1030
Author(s):  
Julie Lake ◽  
Catherine S. Storm ◽  
Mary B. Makarious ◽  
Sara Bandres-Ciga
Keyword(s):  
Neurodegenerative Diseases ◽  
Large Scale ◽  
Disease Diagnosis ◽  
Open Science ◽  
Future Directions ◽  
The Road ◽  
Current State ◽  
Molecular Complexity ◽  
Science Framework ◽  
Lateral Sclerosis

Neurodegenerative diseases are etiologically and clinically heterogeneous conditions, often reflecting a spectrum of disease rather than well-defined disorders. The underlying molecular complexity of these diseases has made the discovery and validation of useful biomarkers challenging. The search of characteristic genetic and transcriptomic indicators for preclinical disease diagnosis, prognosis, or subtyping is an area of ongoing effort and interest. The next generation of biomarker studies holds promise by implementing meaningful longitudinal and multi-modal approaches in large scale biobank and healthcare system scale datasets. This work will only be possible in an open science framework. This review summarizes the current state of genetic and transcriptomic biomarkers in Parkinson’s disease, Alzheimer’s disease, and amyotrophic lateral sclerosis, providing a comprehensive landscape of recent literature and future directions.

scholarly journals A study of computational methods for wake structure and base pressure prediction of a generic SUV model with fixed and rotating wheels

2017 ◽  
Vol 231 (9) ◽  
pp. 1222-1238 ◽  
Author(s):  
David Forbes ◽  
Gary Page ◽  
Martin Passmore ◽  
Adrian Gaylard
Keyword(s):  
Experimental Data ◽  
Computational Methods ◽  
Large Scale ◽  
Ground Plane ◽  
Computational Cost ◽  
Rear Wheel ◽  
Base Pressure ◽  
Detached Eddy Simulation ◽  
Wake Structure ◽  
Wake Interactions

This study is an evaluation of the computational methods in reproducing experimental data for a generic sports utility vehicle (SUV) geometry and an assessment on the influence of fixed and rotating wheels for this geometry. Initially, comparisons are made in the wake structure and base pressures between several CFD codes and experimental data. It was shown that steady-state RANS methods are unsuitable for this geometry due to a large scale unsteadiness in the wake caused by separation at the sharp trailing edge and rear wheel wake interactions. unsteady RANS (URANS) offered no improvements in wake prediction despite a significant increase in computational cost. The detached-eddy simulation (DES) and Lattice–Boltzmann methods showed the best agreement with the experimental results in both the wake structure and base pressure, with LBM running in approximately a fifth of the time for DES. The study then continues by analysing the influence of rotating wheels and a moving ground plane over a fixed wheel and ground plane arrangement. The introduction of wheel rotation and a moving ground was shown to increase the base pressure and reduce the drag acting on the vehicle when compared to the fixed case. However, when compared to the experimental standoff case, variations in drag and lift coefficients were minimal but misleading, as significant variations to the surface pressures were present.

scholarly journals Adherence and competence assessment in studies of CBT for psychosis: current status and future directions

2011 ◽  
Vol 20 (2) ◽  
pp. 121-126 ◽  
Author(s):  
D. Fowler ◽  
R. Rollinson ◽  
P. French
Keyword(s):  
Cognitive Therapy ◽  
Large Scale ◽  
Rating Scale ◽  
Current Status ◽  
Behaviour Therapy ◽  
Future Directions ◽  
Making Sense ◽  
Cognitive Behaviour ◽  
The Uk ◽  
Careful Assessment

All good quality trials of psychological interventions need to check formally that therapists have used the techniques prescribed in the published therapy manuals, and that the therapy has been carried out competently. This paper reviews methods of assessing adherence and competence used in recent large-scale trials of Cognitive Behaviour Therapy (CBT) for psychosis in the UK carried out by our research groups. A combination of the Cognitive Therapy Rating Scale and specific versions of the Cognitive Therapy for Psychosis Adherence Scales provides an optimal assessment of adherence and competence. Careful assessment of the competence and adherence can help identify the procedures actually carried out with individuals within trials. The basic use of such assessments is to provide an external check on treatment fidelity on a sample of sessions. Such assessment can also provide the first step towards moving research towards making sense of CBT for psychosis as a complex intervention and identifying which techniques work for which problems of people with psychosis, at which stages of disorder?

scholarly journals Functional Pituitary Networks in Vertebrates

2021 ◽  
Vol 11 ◽  
Author(s):  
Yorgui Santiago-Andres ◽  
Matan Golan ◽  
Tatiana Fiordelisio
Keyword(s):  
Large Scale ◽  
3D Imaging ◽  
Vertebrate Evolution ◽  
Endocrine Gland ◽  
Pituitary Cells ◽  
Future Directions ◽  
Physiological Demands ◽  
Large Scale Networks ◽  
Hormone Pulses ◽  
Cell Networks

The pituitary is a master endocrine gland that developed early in vertebrate evolution and therefore exists in all modern vertebrate classes. The last decade has transformed our view of this key organ. Traditionally, the pituitary has been viewed as a randomly organized collection of cells that respond to hypothalamic stimuli by secreting their content. However, recent studies have established that pituitary cells are organized in tightly wired large-scale networks that communicate with each other in both homo and heterotypic manners, allowing the gland to quickly adapt to changing physiological demands. These networks functionally decode and integrate the hypothalamic and systemic stimuli and serve to optimize the pituitary output into the generation of physiologically meaningful hormone pulses. The development of 3D imaging methods and transgenic models have allowed us to expand the research of functional pituitary networks into several vertebrate classes. Here we review the establishment of pituitary cell networks throughout vertebrate evolution and highlight the main perspectives and future directions needed to decipher the way by which pituitary networks serve to generate hormone pulses in vertebrates.

scholarly journals Attending to Entities for Better Text Understanding

2020 ◽  
Vol 34 (05) ◽  
pp. 7554-7561
Author(s):  
Pengxiang Cheng ◽  
Katrin Erk
Keyword(s):  
Large Scale ◽  
Human Performance ◽  
State Of The Art ◽  
Syntactic Structure ◽  
Semantic Knowledge ◽  
Training Data ◽  
Language Models ◽  
Long Distance ◽  
Future Directions ◽  
Text Understanding

Recent progress in NLP witnessed the development of large-scale pre-trained language models (GPT, BERT, XLNet, etc.) based on Transformer (Vaswani et al. 2017), and in a range of end tasks, such models have achieved state-of-the-art results, approaching human performance. This clearly demonstrates the power of the stacked self-attention architecture when paired with a sufficient number of layers and a large amount of pre-training data. However, on tasks that require complex and long-distance reasoning where surface-level cues are not enough, there is still a large gap between the pre-trained models and human performance. Strubell et al. (2018) recently showed that it is possible to inject knowledge of syntactic structure into a model through supervised self-attention. We conjecture that a similar injection of semantic knowledge, in particular, coreference information, into an existing model would improve performance on such complex problems. On the LAMBADA (Paperno et al. 2016) task, we show that a model trained from scratch with coreference as auxiliary supervision for self-attention outperforms the largest GPT-2 model, setting the new state-of-the-art, while only containing a tiny fraction of parameters compared to GPT-2. We also conduct a thorough analysis of different variants of model architectures and supervision configurations, suggesting future directions on applying similar techniques to other problems.

scholarly journals How public perceptions of social distancing evolved over a critical time period: communication lessons learnt from the American state of Wisconsin

2020 ◽  
Vol 19 (05) ◽  
pp. A11
Author(s):  
Kaiping Chen ◽  
Luye Bao ◽  
Anqi Shao ◽  
Pauline Ho ◽  
Shiyu Yang ◽  
...  
Keyword(s):  
Public Health ◽  
Computational Methods ◽  
Large Scale ◽  
Critical Time ◽  
Effective Communication ◽  
Public Perceptions ◽  
Social Distancing ◽  
Time Period ◽  
Large Scale Survey ◽  
Main Barrier

Understanding how individuals perceive the barriers and benefits of precautionary actions is key for effective communication about public health crises, such as the COVID-19 outbreak. This study used innovative computational methods to analyze 30,000 open-ended responses from a large-scale survey to track how Wisconsin (U.S.A.) residents' perceptions of the benefits of and barriers to performing social distancing evolved over a critical time period (March 19th to April 1st, 2020). Initially, the main barrier was practical related, however, individuals later perceived more multifaceted barriers to social distancing. Communication about COVID-19 should be dynamic and evolve to address people's experiences and needs overtime.

scholarly journals The role of airborne volcanic ash for the surface ocean biogeochemical iron-cycle: a review

2009 ◽  
Vol 6 (4) ◽  
pp. 6441-6489 ◽  
Author(s):  
S. Duggen ◽  
N. Olgun ◽  
P. Croot ◽  
L. Hoffmann ◽  
H. Dietze ◽  
...  
Keyword(s):  
Large Scale ◽  
Volcanic Ash ◽  
Volcanic Eruptions ◽  
Research Field ◽  
Ash Deposition ◽  
Future Directions ◽  
Iron Cycle ◽  
Surface Ocean ◽  
Wide Range

Abstract. Iron is a key micronutrient for phytoplankton growth in the surface ocean. Yet the significance of volcanism for the marine biogeochemical iron-cycle is poorly constrained. Recent studies, however, suggest that offshore deposition of airborne ash from volcanic eruptions is a way to inject significant amounts of bio-available iron into the surface ocean. Volcanic ash may be transported up to several tens of kilometres high into the atmosphere during large-scale eruptions and fine ash may encircle the globe for years, thereby reaching even the remotest and most iron-starved oceanic areas. Scientific ocean drilling demonstrates that volcanic ash layers and dispersed ash particles are frequently found in marine sediments and that therefore volcanic ash deposition and iron-injection into the oceans took place throughout much of the Earth's history. The data from geochemical and biological experiments, natural evidence and satellite techniques now available suggest that volcanic ash is a so far underestimated source for iron in the surface ocean, possibly of similar importance as aeolian dust. Here we summarise the development of and the knowledge in this fairly young research field. The paper covers a wide range of chemical and biological issues and we make recommendations for future directions in these areas. The review paper may thus be helpful to improve our understanding of the role of volcanic ash for the marine biogeochemical iron-cycle, marine primary productivity and the ocean-atmosphere exchange of CO2 and other gases relevant for climate throughout the Earth's history.

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