Heritable variation in gene expression is common within and between species. This variation arises from mutations that alter the form or function of molecular gene regulatory networks that are then ...filtered by natural selection. High-throughput methods for introducing mutations and characterizing their cis- and trans-regulatory effects on gene expression (particularly, transcription) are revealing how different molecular mechanisms generate regulatory variation, and studies comparing these mutational effects with variation seen in the wild are teasing apart the role of neutral and non-neutral evolutionary processes. This integration of molecular and evolutionary biology allows us to understand how the variation in gene expression we see today came to be and to predict how it is most likely to evolve in the future.
As one of the important concepts in conventional quantitative genetics and breeding, genetic gain can be defined as the amount of increase in performance that is achieved annually through artificial ...selection. To develop products that meet the increasing demand of mankind, especially for food and feed, in addition to various industrial uses, breeders are challenged to enhance the potential of genetic gain continuously, at ever higher rates, while they close the gaps that remain between the yield potential in breeders’ demonstration trials and the actual yield in farmers’ fields. Factors affecting genetic gain include genetic variation available in breeding materials, heritability for traits of interest, selection intensity, and the time required to complete a breeding cycle. Genetic gain can be improved through enhancing the potential and closing the gaps, which has been evolving and complemented with modern breeding techniques and platforms, mainly driven by molecular and genomic tools, combined with improved agronomic practice. Several key strategies are reviewed in this article. Favorable genetic variation can be unlocked and created through molecular and genomic approaches including mutation, gene mapping and discovery, and transgene and genome editing. Estimation of heritability can be improved by refining field experiments through well-controlled and precisely assayed environmental factors or envirotyping, particularly for understanding and controlling spatial heterogeneity at the field level. Selection intensity can be significantly heightened through improvements in the scale and precision of genotyping and phenotyping. The breeding cycle time can be shortened by accelerating breeding procedures through integrated breeding approaches such as marker-assisted selection and doubled haploid development. All the strategies can be integrated with other widely used conventional approaches in breeding programs to enhance genetic gain. More transdisciplinary approaches, team breeding, will be required to address the challenge of maintaining a plentiful and safe food supply for future generations. New opportunities for enhancing genetic gain, a high efficiency breeding pipeline, and broad-sense genetic gain are also discussed prospectively.
Abstract
Background
Statistical model building requires selection of variables for a model depending on the model’s aim. In descriptive and explanatory models, a common recommendation often met in ...the literature is to include all variables in the model which are assumed or known to be associated with the outcome independent of their identification with data driven selection procedures. An open question is, how reliable this assumed “background knowledge” truly is. In fact, “known” predictors might be findings from preceding studies which may also have employed inappropriate model building strategies.
Methods
We conducted a simulation study assessing the influence of treating variables as “known predictors” in model building when in fact this knowledge resulting from preceding studies might be insufficient. Within randomly generated preceding study data sets, model building with variable selection was conducted. A variable was subsequently considered as a “known” predictor if a predefined number of preceding studies identified it as relevant.
Results
Even if several preceding studies identified a variable as a “true” predictor, this classification is often false positive. Moreover, variables not identified might still be truly predictive. This especially holds true if the preceding studies employed inappropriate selection methods such as univariable selection.
Conclusions
The source of “background knowledge” should be evaluated with care. Knowledge generated on preceding studies can cause misspecification.
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•Lack of interspecific morphological but clear genetic differences in the Phymaturus patagonicus clade.•Evidence of natural selection pressures acting on an adaptive optimum.•Evidence ...of how different attributes of a group of species vary at different rates.
During the speciation process sibling lineages accumulate differences in time (e.g. genetic, morphological, and/or ecological). Phenotypic traits such as size or shape, however, could experience rapid changes or show stasis depending on their role in survival and reproduction. The clade Phymaturus patagonicus includes 26 species characterized by a conservative morphology, and all inhabit rock crevice microhabitats in arid environments. In this study we quantify levels of morphological divergence (size and shape) among the multiple species relative to interspecific molecular divergence, and show that most species have not diverged significantly in size and/or shape to permit unambiguous species diagnosis with morphological data alone. The influence of stabilizing selection for an adaptive optimum in body size and head shape was detected for 13 of the 16 variables analyzed in an Ornstein-Uhlenbeck model. The strict dependence of these species to rock-crevice microenvironments likely explains the observed morphological stasis across the many species of the Phymaturus patagonicus group.
‘Living fossils’ are testimonies of long-term sustained ecological success, but how demographic history and natural selection contributed to their survival, resilience, and persistence in the face of ...Quaternary climate fluctuations remains unclear.
To better understand the interplay between demographic history and selection in shaping genomic diversity and evolution of such organisms, we assembled the whole genome of Cercidiphyllum japonicum, a widespread East Asian Tertiary relict tree, and resequenced 99 individuals of C. japonicum and its sister species, Cercidiphyllum magnificum (Central Japan).
We dated this speciation event to the mid-Miocene, and the intraspecific lineage divergence of C. japonicum (China vs Japan) to the Early Pliocene. Throughout climatic upheavals of the late Tertiary/Quaternary, population bottlenecks greatly reduced the genetic diversity of C. japonicum. However, this polymorphism loss was likely counteracted by, first, long-term balancing selection at multiple chromosomal and heterozygous gene regions, potentially reflecting overdominance, and, second, selective sweeps at stress response and growth-related genes likely involved in local adaptation.
Our findings contribute to a better understanding of how living fossils have survived climatic upheaval and maintained an extensive geographic range; that is, both types of selection could be major factors contributing to the species’ survival, resilience, and persistence.
The prevention of intrusion is deemed to be a cornerstone of network security. Although excessive work has been introduced on network intrusion detection in the last decade, finding an Intrusion ...Detection Systems (IDS) with potent intrusion detection mechanism is still highly desirable. One of the leading causes of the high number of false alarms and a low detection rate is the existence of redundant and irrelevant features of the datasets, which are used to train the IDSs. To cope with this problem, we proposed a double Particle Swarm Optimization (PSO)-based algorithm to select both feature subset and hyperparameters in one process. The aforementioned algorithm is exploited in the pre-training phase for selecting the optimized features and model’s hyperparameters automatically. In order to investigate the performance differences, we utilized three deep learning models, namely, Deep Neural Networks (DNN), Long Short-Term Memory Recurrent Neural Networks (LSTM-RNN), and Deep Belief Networks (DBN). Furthermore, we used two common IDS datasets in our experiments to validate our approach and show the effectiveness of the developed models. Moreover, many evaluation metrics are used for both binary and multiclass classifications to assess the model’s performance in each of the datasets. Finally, intensive quantitative, Friedman test, and ranking methods analyses of our results are provided at the end of this paper. Experimental results show a significant improvement in network intrusion detection when using our approach by increasing Detection Rate (DR) by 4% to 6% and reducing False Alarm Rate (FAR) by 1% to 5% from the corresponding values of same models without pre-training on the same dataset.
•LAE-Net goes beyond the limitations of previous works and explores the relationship between image characteristics and image quality from the data itself, so as to guide the high-quality image ...generation.•The entropy-inspired kernel-selection convolution can adaptively adjust the receptive field size according to its spatial frequency characterized by information entropy.•The illumination attention transfer sub-net can simultaneously sense global consistency and local details, thereby adjusting the refined features.•LAE-Net can balance different local enhancement requirements of properties of light intensity, detail presentation and color fidelity, and produce high-quality and visual-pleasing normal light images.
In the low-light enhancement task, one of the major challenges lies in how to balance the image enhancement properties of light intensity, detail presentation and color fidelity. In natural scenes, the multi-distribution of frequency and illumination characteristics in the spatial domain makes the balance more difficult. To solve this problem, we propose a Locally-Adaptive Embedding Network, namely LAE-Net, to realize high-quality low-light image enhancement with locally-adaptive kernel selection and feature adaptation for multi-distribution issues. Specifically, for the frequency multi-distribution, we rethink the spatial-frequency characteristic of human eyes, experimentally explore the relationship among the receptive field size, the image spatial frequency and the light enhancement properties, and propose an Entropy-Inspired Kernel-Selection Convolution, where each neuron can adaptively adjust the receptive field size according to its spatial frequency characterized by information entropy. For the illumination multi-distribution, we propose an Illumination Attentive Transfer subnet, where the neurons can simultaneously sense global consistency and local details, and accordingly hint where to focus the efforts on, thereby adjusting the refined features. Extensive experiments with ablation analysis show the effectiveness of our method and the proposed method outperforms many related state-of-the-art techniques on four benchmark datasets: MEF, LIME, NPE and DICM.
Evolution in novel environments Magnoli, Susan M.; Lau, Jennifer A.
Ecology (Durham),
October 2020, Volume:
101, Issue:
10
Journal Article
Peer reviewed
Open access
When populations colonize new habitats, they are likely to experience novel environmental conditions, and as a consequence may experience strong selection. While selection and the resulting ...evolutionary responses may have important implications for establishment success in colonizing populations, few studies have estimated selection in such scenarios. Here we examined evidence of selection in recently established plant populations in two prairie restorations in close proximity (< 15 km apart) using two approaches: (1) we tested for evidence of past selection on a suite of traits in two Chamaecrista fasciculata populations by comparing the restored populations to each other and their shared source population in common gardens to quantify evolutionary responses and (2) we measured selection in the field. We found evidence of past selection on flowering time, specific leaf area, and root nodule production in one of the populations, but detected contemporary selection on only one trait (plant height). Our findings demonstrate that while selection can occur in colonizing populations, resulting in significant trait differences between restored populations in fewer than six generations, evolutionary responses differ across even nearby populations sown with the same source population. Because contemporary measures of selection differed from evolutionary responses to past selection, our findings also suggest that selection likely differs over the early stages of succession that characterize young prairies.
In this paper, performances of joint transmit and receive antenna selection (JTRAS), transmit antenna selection/maximal ratio combining (TAS/MRC), and transmit antenna selection/receive antenna ...selection (TAS/RAS) techniques are examined in a unified manner in the (42) presence of feedback errors. Exact and closed-form outage probability, moments, moment-generating function, ergodic capacity, and symbol error probability (SEP) expressions are derived for flat Nakagami-m fading channels. In addition, to obtain diversity order and array gain of the investigated techniques in the presence of feedback errors, asymptotic outage probability and SEP expressions are also derived. Analytical results are validated by Monte Carlo simulations. Results show that the diversity order is significantly reduced in the presence of feedback errors, whereas all the systems provide full diversity order for a perfect feedback channel.
To establish an immunocompetent TCR repertoire that is useful yet harmless to the body, a de novo thymocyte repertoire generated through the rearrangement of genes that encode TCR is shaped in the ...thymus through positive and negative selection. The affinity between TCRs and self-peptides associated with MHC molecules determines the fate of developing thymocytes. Low-affinity TCR engagement with self-peptide-MHC complexes mediates positive selection, a process that primarily occurs in the thymic cortex. Massive efforts exerted by many laboratories have led to the characterization of peptides that can induce positive selection. Moreover, it is now evident that protein degradation machineries unique to cortical thymic epithelial cells play a crucial role in the production of MHC-associated self-peptides for inducing positive selection. This review summarizes current knowledge on positive selection-inducing self-peptides and Ag processing machineries in cortical thymic epithelial cells. Recent studies on the role of positive selection in the functional tuning of T cells are also discussed.
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