Contrasting genomic shifts underlie parallel phenotypic evolution in response to fishing

Science ◽  
2019 ◽  
Vol 365 (6452) ◽  
pp. 487-490 ◽  
Author(s):  
Nina O. Therkildsen ◽  
Aryn P. Wilder ◽  
David O. Conover ◽  
Stephan B. Munch ◽  
Hannes Baumann ◽  
...  
Keyword(s):  
Phenotypic Evolution ◽  
Rapid Adaptation ◽  
Protein Coding ◽  
Selection Gradients ◽  
Genomic Changes ◽  
Experimental Fish ◽  
Selective Harvest ◽  
The Face ◽  
In The Wild ◽  
Genomic Basis

Humans cause widespread evolutionary change in nature, but we still know little about the genomic basis of rapid adaptation in the Anthropocene. We tracked genomic changes across all protein-coding genes in experimental fish populations that evolved pronounced shifts in growth rates due to size-selective harvest over only four generations. Comparisons of replicate lines show parallel allele frequency shifts that recapitulate responses to size-selection gradients in the wild across hundreds of unlinked variants concentrated in growth-related genes. However, a supercluster of genes also rose rapidly in frequency and dominated the evolutionary dynamic in one replicate line but not in others. Parallel phenotypic changes thus masked highly divergent genomic responses to selection, illustrating how contingent rapid adaptation can be in the face of strong human-induced selection.

scholarly journals Hybrid Attention Cascade Network for Facial Expression Recognition

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2003 ◽  
Author(s):  
Xiaoliang Zhu ◽  
Shihao Ye ◽  
Liang Zhao ◽  
Zhicheng Dai
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Expression Recognition ◽  
Spatial Features ◽  
Face Images ◽  
Temporal Features ◽  
The Face ◽  
In The Wild ◽  
Fusion Features

As a sub-challenge of EmotiW (the Emotion Recognition in the Wild challenge), how to improve performance on the AFEW (Acted Facial Expressions in the wild) dataset is a popular benchmark for emotion recognition tasks with various constraints, including uneven illumination, head deflection, and facial posture. In this paper, we propose a convenient facial expression recognition cascade network comprising spatial feature extraction, hybrid attention, and temporal feature extraction. First, in a video sequence, faces in each frame are detected, and the corresponding face ROI (range of interest) is extracted to obtain the face images. Then, the face images in each frame are aligned based on the position information of the facial feature points in the images. Second, the aligned face images are input to the residual neural network to extract the spatial features of facial expressions corresponding to the face images. The spatial features are input to the hybrid attention module to obtain the fusion features of facial expressions. Finally, the fusion features are input in the gate control loop unit to extract the temporal features of facial expressions. The temporal features are input to the fully connected layer to classify and recognize facial expressions. Experiments using the CK+ (the extended Cohn Kanade), Oulu-CASIA (Institute of Automation, Chinese Academy of Sciences) and AFEW datasets obtained recognition accuracy rates of 98.46%, 87.31%, and 53.44%, respectively. This demonstrated that the proposed method achieves not only competitive performance comparable to state-of-the-art methods but also greater than 2% performance improvement on the AFEW dataset, proving the significant outperformance of facial expression recognition in the natural environment.

scholarly journals Cytochrome P450 monooxygenase genes in the wild silkworm, Bombyx mandarina

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e10818
Author(s):  
Linrong Wan ◽  
Anlian Zhou ◽  
Wenfu Xiao ◽  
Bangxing Zou ◽  
Yaming Jiang ◽  
...  
Keyword(s):  
Cytochrome P450 ◽  
Enrichment Analysis ◽  
Silk Gland ◽  
P450 Monooxygenase ◽  
Protein Coding ◽  
Bombyx Mandarina ◽  
Physical Maps ◽  
Cytochrome P450 Genes ◽  
In The Wild ◽  
Genome Assemblies

Wild (Bombyx mandarina) and domestic silkworms (B. mori) are good models for investigating insect domestication, as 5000 years of artificial breeding and selection have resulted in significant differences between B. mandarina and B. mori. In this study, we improved the genome assemblies to the chromosome level and updated the protein-coding gene annotations for B. mandarina. Based on this updated genome, we identified 68 cytochrome P450 genes in B. mandarina. The cytochrome P450 repository in B. mandarina is smaller than in B. mori. Certain currently unknown key genes, rather than gene number, are critical for insecticide resistance in B. mandarina, which shows greater resistance to insecticides than B. mori. Based on the physical maps of B. mandarina, we located 66 cytochrome P450s on 18 different chromosomes, and 27 of the cytochrome P450 genes were concentrated into seven clusters. KEGG enrichment analysis of the P450 genes revealed the involvement of cytochrome P450 genes in hormone biosynthesis. Analyses of the silk gland transcriptome identified candidate cytochrome P450 genes (CYP306A) involved in ecdysteroidogenesis and insecticide metabolism in B. mandarina.

scholarly journals A nonlinear relationship between prediction errors and learning rates in human reinforcement learning

2019 ◽  
Author(s):  
Erdem Pulcu
Keyword(s):  
Reinforcement Learning ◽  
Nonlinear Relationship ◽  
Prediction Errors ◽  
Learning Rates ◽  
The Face ◽  
In The Wild ◽  
Actual Outcome ◽  
Update Rules ◽  
Different Sources

AbstractWe are living in a dynamic world in which stochastic relationships between cues and outcome events create different sources of uncertainty1 (e.g. the fact that not all grey clouds bring rain). Living in an uncertain world continuously probes learning systems in the brain, guiding agents to make better decisions. This is a type of value-based decision-making which is very important for survival in the wild and long-term evolutionary fitness. Consequently, reinforcement learning (RL) models describing cognitive/computational processes underlying learning-based adaptations have been pivotal in behavioural2,3 and neural sciences4–6, as well as machine learning7,8. This paper demonstrates the suitability of novel update rules for RL, based on a nonlinear relationship between prediction errors (i.e. difference between the agent’s expectation and the actual outcome) and learning rates (i.e. a coefficient with which agents update their beliefs about the environment), that can account for learning-based adaptations in the face of environmental uncertainty. These models illustrate how learners can flexibly adapt to dynamically changing environments.

scholarly journals Deep learning for pose-invariant face detection in unconstrained environment

2019 ◽  
Vol 9 (1) ◽  
pp. 577
Author(s):  
Shivkaran Ravidas ◽  
M. A. Ansari
Keyword(s):  
Neural Network ◽  
Recent Past ◽  
Probabilistic Measure ◽  
Low Level ◽  
Face Images ◽  
Analysis Experiment ◽  
Out Of Plane ◽  
The Face ◽  
In The Wild ◽  
Visual Matching

<span lang="EN-US">In the recent past, convolutional neural networks (CNNs) have seen resurgence and have performed extremely well on vision tasks.  Visually the model resembles a series of layers each of which is processed by a function to form a next layer. It is argued that CNN first models the low level features such as edges and joints and then expresses higher level features as a composition of these low level features. The aim of this paper is to detect multi-view faces using deep convolutional neural network (DCNN). Implementation, detection and retrieval of faces will be obtained with the help of direct visual matching technology. Further, the probabilistic measure of the similarity of the face images will be done using Bayesian analysis. Experiment detects faces with ±90 degree out of plane rotations. Fine tuned AlexNet is used to detect pose invariant faces. For this work, we extracted examples of training from AFLW (Annotated Facial Landmarks in the Wild) dataset that involve 21K images with 24K annotations of the face.</span>

scholarly journals The genomic basis of Red Queen dynamics during rapid reciprocal host–pathogen coevolution

2018 ◽  
Vol 116 (3) ◽  
pp. 923-928 ◽  
Author(s):  
Andrei Papkou ◽  
Thiago Guzella ◽  
Wentao Yang ◽  
Svenja Koepper ◽  
Barbara Pees ◽  
...  
Keyword(s):  
Genomic Analysis ◽  
Copy Number Variations ◽  
Complex Model ◽  
Time Shift ◽  
Red Queen ◽  
Genomic Changes ◽  
Selection Dynamics ◽  
Host Pathogen ◽  
Genomic Basis ◽  
Evolution With Time

Red Queen dynamics, involving coevolutionary interactions between species, are ubiquitous, shaping the evolution of diverse biological systems. To date, information on the underlying selection dynamics and the involved genome regions is mainly available for bacteria–phage systems or only one of the antagonists of a eukaryotic host–pathogen interaction. We add to our understanding of these important coevolutionary interactions using an experimental host–pathogen model, which includes the nematode Caenorhabditis elegans and its pathogen Bacillus thuringiensis. We combined experimental evolution with time-shift experiments, in which a focal host or pathogen is tested against a coevolved antagonist from the past, present, or future, followed by genomic analysis. We show that (i) coevolution occurs rapidly within few generations, (ii) temporal coadaptation at the phenotypic level is found in parallel across replicate populations, consistent with antagonistic frequency-dependent selection, (iii) genomic changes in the pathogen match the phenotypic pattern and include copy number variations of a toxin-encoding plasmid, and (iv) host genomic changes do not match the phenotypic pattern and likely involve selective responses at more than one locus. By exploring the dynamics of coevolution at the phenotypic and genomic level for both host and pathogen simultaneously, our findings demonstrate a more complex model of the Red Queen, consisting of distinct selective processes acting on the two antagonists during rapid and reciprocal coadaptation.

scholarly journals Face Verification with Multi-Task and Multi-Scale Features Fusion

2017 ◽  
Author(s):  
Xiaojun Lu ◽  
Yue Yang ◽  
Weilin Zhang ◽  
Qi Wang ◽  
Yang Wang
Keyword(s):  
Training Dataset ◽  
Face Verification ◽  
Deep Convolutional Neural Networks ◽  
Computation Cost ◽  
Face Representation ◽  
Features Fusion ◽  
Multi Scale ◽  
The Face ◽  
In The Wild ◽  
Deep Cnn

Face verification for unrestricted faces in the wild is a challenging task. This paper proposes a method based on two deep convolutional neural networks(CNN) for face verification. In this work, we explore to use identification signal to supervise one CNN and the combination of semi-verification and identification to train the other one. In order to estimate semi-verification loss at a low computation cost, a circle, which is composed of all faces, is used for selecting face pairs from pairwise samples. In the process of face normalization, we propose to use different landmarks of faces to solve the problems caused by poses. And the final face representation is formed by the concatenating feature of each deep CNN after PCA reduction. What's more, each feature is a combination of multi-scale representations through making use of auxiliary classifiers. For the final verification, we only adopt the face representation of one region and one resolution of a face jointing Joint Bayesian classifier. Experiments show that our method can extract effective face representation with a small training dataset and our algorithm achieves 99.71% verification accuracy on LFW dataset.

scholarly journals Adaptación del sistema táctico de inteligencia para la toma de decisiones militares ante una pandemia

2021 ◽  
Vol 12 (26) ◽  
pp. 14-22
Author(s):  
Eider Yovanny Vargas
Keyword(s):  
Mathematical Model ◽  
Decision Maker ◽  
Administrative Support ◽  
Decision Makers ◽  
Rapid Adaptation ◽  
Process Data ◽  
The Face ◽  
Toma De Decisiones ◽  
The Military ◽  
Available Resources

The purpose of this work is to identify a tool that allows a military decision maker at the tactical level to manage the military resources available in the event of a pandemic. The research focused on finding and adapting an epidemiological mathematical model to process data collected in a military jurisdiction and with it the development of prospective scenarios in a military jurisdiction in the event of a pandemic. The results indicate that in the face of a pandemic, military decision makers must have a model of prospective scenarios and the adaptation of the intelligence process, especially the means of searching for information and the recording and analysis instruments to diligently manage the available resources. It is concluded that, given the appearance of a pandemic in a place with geographical conditions that hinder rapid accessibility and administrative support, military decision makers require a procedure that allows rapid adaptation to the new tactical scenario.

scholarly journals The genome of the endangered Macadamia jansenii displays little diversity but represents an important genetic resource for plant breeding

2021 ◽  
Author(s):  
Priyanka Sharma ◽  
Valentine Murigneux ◽  
Jasmine Haimovitz ◽  
Catherine J. Nock ◽  
Wei Tian ◽  
...  
Keyword(s):  
Genome Assembly ◽  
Morphological Characteristics ◽  
Single Copy ◽  
Protein Coding ◽  
Core Eudicots ◽  
Wide Range ◽  
Genes Encoding ◽  
Long Read ◽  
In The Wild

SummaryMacadamia, a recently domesticated expanding nut crop in the tropical and subtropical regions of the world, is one of the most economically important genera in the diverse and widely adapted Proteaceae family. All four species of Macadamia are rare in the wild with the most recently discovered, M. jansenii, being endangered. The M. jansenii genome has been used as a model for testing sequencing methods using a wide range of long read sequencing techniques. Here we report a chromosome level genome assembly, generated using a combination of Pacific Biosciences sequencing and Hi-C, comprising 14 pseudo-molecules, with a N50 of 58 Mb and a total 758 Mb genome assembly size of which 56% is repetitive. Completeness assessment revealed that the assembly covered 96.9% of the conserved single copy genes. Annotation predicted 31,591 protein coding genes and allowed the characterization of genes encoding biosynthesis of cyanogenic glycosides, fatty acid metabolism and anti-microbial proteins. Re-sequencing of seven other genotypes confirmed low diversity and low heterozygosity within this endangered species. Important morphological characteristics of this species such as small tree size and high kernel recovery suggest that M. jansenii is an important source of these commercial traits for breeding. As a member of a small group of families that are sister to the core eudicots, this high-quality genome also provides a key resource for evolutionary and comparative genomics studies.

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