Combining genome-wide association study and transcriptome analysis to identify molecular markers and genetic basis of population-asynchronous ovarian development in Coilia nasus

Coilia nasus,a migratory fish species found in the middle and lower reaches of the Yangtze River and along offshore areas of China,possesses considerable aquacultural and economic potential.However,the species faces challenges due to significant variation in the gonadal development rate among females,resulting in inconsistent ovarian maturation times at the population level,an extended reproductive period,and limitations on fish growth rate due to ovarian prematurity.In the present study,we combined genome-wide association study(GWAS)and comparative transcriptome analysis to investigate the potential single nucleotide polymorphisms(SNPs)and candidate genes associated with population-asynchronous ovarian development in C.nasus.Genotyping of the female population based on whole-genome resequencing yielded 2120695 high-quality SNPs,39 of which were suggestively associated with ovarian development.Of note,a significant SNP peak on LG21 containing 30 suggestively associated SNPs was identified,with cpne5a determined as the causal gene of the peak.Therefore,single-marker and haplotype association analyses were performed on cpne5a,revealing four genetic markers(P<0.05)and seven haplotypes(r2>0.9)significantly associated with the phenotype.Comparative transcriptome analysis of precociously and normally maturing individuals screened out 29 and 426 overlapping differentially expressed genes in the brain and ovary,respectively,between individuals of different body sizes.Integrating the GWAS and transcriptome analysis results,this study identified genes and pathways related to hypothalamic-pituitary-gonadal axis hormone secretion,extracellular matrix,angiogenesis,and gap junctions involved in population-asynchronous ovarian development.The insights gained from this study provide a basis for a deeper understanding of the molecular mechanisms underlying ovarian development in fish and may facilitate the genetic breeding of C.nasus strains exhibiting population-synchronous ovarian development in the future.

The Role and Place of Artificial Neural Network Architectures Structural Redundancy in the Input Data Prototypes and Generalization Development

Neural Networks (NN) are the functional unit of Deep Learning and are known to mimic the behavior of the human brain to solve complex data-driven problems. Whenever we train our own neural networks, we need to take care of something called the generalization of the neural network. The performance of Artificial Neural Networks (ANN) mostly depends upon its generalization capability. In this paper, we propose an innovative approach to enhance the generalization capability of artificial neural networks (ANN) using structural redundancy. A novel perspective on handling input data prototypes and their impact on the development of generalization, which could improve to ANN architectures accuracy and reliability is described.

Rapid Prototype Development Approach for Genetic Programming

Genetic Programming (GP) is an important approach to deal with complex problem analysis and modeling, and has been applied in a wide range of areas. The development of GP involves various aspects, including design of genetic operators, evolutionary controls and implementations of heuristic strategy, evaluations and other mechanisms. When designing genetic operators, it is necessary to consider the possible limitations of encoding methods of individuals. And when selecting evolutionary control strategies, it is also necessary to balance search efficiency and diversity based on representation characteristics as well as the problem itself. More importantly, all of these matters, among others, have to be implemented through tedious coding work. Therefore, GP development is both complex and time-consuming. To overcome some of these difficulties that hinder the enhancement of GP development efficiency, we explore the feasibility of mutual assistance among GP variants, and then propose a rapid GP prototyping development method based on πGrammatical Evolution (πGE). It is demonstrated through regression analysis experiments that not only is this method beneficial for the GP developers to get rid of some tedious implementations, but also enables them to concentrate on the essence of the referred problem, such as individual representation, decoding means and evaluation. Additionally, it provides new insights into the roles of individual delineations in phenotypes and semantic research of individuals.

Anethole improves the developmental competence of porcine embryos by reducing oxidative stress via the sonic hedgehog signaling pathway

Background Anethole(AN)is an organic antioxidant compound with a benzene ring and is expected to have a positive impact on early embryogenesis in mammals.However,no study has examined the effect of AN on porcine embryonic development.Therefore,we investigated the effect of AN on the development of porcine embryos and the underlying mechanism.Results We cultured porcine in vitro-fertilized embryos in medium with AN(0,0.3,0.5,and 1 mg/mL)for 6 d.AN at 0.5 mg/mL significantly increased the blastocyst formation rate,trophectoderm cell number,and cellular survival rate compared to the control.AN-supplemented embryos exhibited significantly lower reactive oxygen species levels and higher glutathione levels than the control.Moreover,AN significantly improved the quantity of mitochondria and mitochondrial membrane potential,and increased the lipid droplet,fatty acid,and ATP levels.Interestingly,the levels of proteins and genes related to the sonic hedgehog(SHH)signaling pathway were significantly increased by AN.Conclusions These results revealed that AN improved the developmental competence of porcine preimplantation embryos by activating SHH signaling against oxidative stress and could be used for large-scale production of high-quality porcine embryos.

GAMYB transcription factor LoMYB65 from lily plays a vital role in pollen development

Lily(Lilium spp.)is an important horticultural crop,but its use is limited due to serious pollen contamination problems.There are many studies on pollen development in model plants,but few on flower crops such as lilies.Gibberellin(GA)is a large class of hormones and plays an important role in plant vegetative growth and reproductive development.GAMYB is a group of the R2R3-MYB family upregulated by gibberellin,and plays an important role in anther development.Here,we isolated a novel GAMYB,named LoMYB65,from lily,which was closely related to the AtMYB65 and AtMYB33 in Arabidopsis.Fluorescence quantitative PCR results showed that LoMYB65 was mainly expressed in lily anthers.LoMYB65 could be activated by 288μmol·L^(-1)GA3treatment and the LoMYB65 protein was located in the nucleus and cytoplasm,and had transactivation in yeast and tobacco leaf cells.The conserved motif within 226 amino acids of the C-terminal of LoMYB65 contributed to its transactivation.Overexpression of LoMYB65 caused dwarf phenotype,unnormal tapetum development,less seeds of siliques in transgenic Arabidopsis plants,the transgenic plants showed partly male sterile.Simultaneously,silencing of LoMYB65 with VIGS(Virus Induced Gene Silencing)in lily anthers caused unnormal pollen development and reduced the pollen amount.Overexpression of LoMYB65 in Arabidopsis and silencing of LoMYB65 in lily resulted in decreased pollen counts,so we speculate that LoMYB65 may be dose-dependent.Overall,these findings suggest that LoMYB65 may play an important role in anther development and pollen formation in lily.LoMYB65 may provide a useful candidate gene for pollenless breeding of lily.

Research on Coordinated Development and Optimization of Distribution Networks at All Levels in Distributed Power Energy Engineering

The uncertainty of distributed generation energy has dramatically challenged the coordinated development of distribution networks at all levels.This paper focuses on the multi-time-scale regulation model of distributed generation energy under normal conditions.The simulation results of the example verify the self-optimization characteristics and the effectiveness of real-time dispatching of the distribution network control technology at all levels under multiple time scales.

Exploring the moderating role of financial development in environmental Kuznets curve for South Africa:fresh evidence from the novel dynamic ARDL simulations approach

The extant literature has produced mixed evidence on the relationship between finan-cial development and ecological sustainability.This work addresses this conundrum by investigating financial development’s direct and indirect consequences on ecologi-cal quality utilizing the environmental Kuznets curve(EKC)methodological approach.Our empirical analysis is based on the novel dynamic autoregressive distributed lag simulations approach for South Africa between 1960 and 2020.The results,which used five distinct financial development measures,demonstrate that financial develop-ment boosts ecological integrity and environmental sustainability over the long and short terms.In the instance of South Africa,we additionally confirm the validity of the EKC theory.More importantly,the outcomes of the indirect channels demonstrate that financial development increases energy usage’s role in causing pollution while attenuating the detrimental impacts of economic growth,trade openness,and foreign direct investment on ecological quality.Moreover,the presence of an inadequate financial system is a requirement for the basis of the pollution haven hypothesis(PHH),which we examine using trade openness and foreign direct investment variables.PHH for both of these variables disappears when financial development crosses specified thresholds.Finally,industrial value addition destroys ecological quality while tech-nological innovation enhances it.This research provides some crucial policy recom-mendations and fresh perspectives for South Africa as it develops national initiatives to support ecological sustainability and reach its net zero emissions goal.

Hormonal and epigenetic regulation of root responses to salinity stress

Salinity stress is a major environmental stress affecting crop productivity,and its negative impact on global food security is only going to increase,due to current climate trends.Salinity tolerance was present in wild crop relatives but significantly weakened during domestication.Regaining it back requires a good understanding of molecular mechanisms and traits involved in control of plant ionic and ROS homeostasis.This review summarizes our current knowledge on the role of major plant hormones(auxin,cytokinins,abscisic acid,salicylic acid,and jasmonate)in plants adaptation to soil salinity.We firstly discuss the role of hormones in controlling root tropisms,root growth and architecture(primary root elongation,meristematic activity,lateral root development,and root hairs formation).Hormone-mediated control of uptake and sequestration of key inorganic ions(sodium,potassium,and calcium)is then discussed followed by regulation of cell redox balance and ROS signaling in salt-stressed roots.Finally,the role of epigenetic alterations such as DNA methylation and histone modifications in control of plant ion and ROS homeostasis and signaling is discussed.This data may help develop novel strategies for breeding and cultivating salt-tolerant crops and improving agricultural productivity in saline regions.

Advances in understanding epigenetic regulation of plant trichome development: a comprehensive review

Plant growth and development are controlled by a complex gene regulatory network,which is currently a focal point of research.It has been established that epigenetic factors play a crucial role in plant growth.Trichomes,specialized appendages that arise from epidermal cells,are of great significance in plant growth and development.As a model system for studying plant development,trichomes possess both commercial and research value.Epigenetic regulation has only recently been implicated in the development of trichomes in a limited number of studies,and microRNA-mediated post-transcriptional regulation appears to dominate in this context.In light of this,we have conducted a review that explores the interplay between epigenetic regulations and the formation of plant trichomes,building upon existing knowledge of hormones and transcription factors in trichome development.Through this review,we aim to deepen our understanding of the regulatory mechanisms underlying trichome formation and shed light on future avenues of research in the field of epigenetics as it pertains to epidermal hair growth.

Conventional Geothermal Systems and Unconventional Geothermal Developments: An Overview

This paper provides an overview of conventional geothermal systems and unconventional geothermal developments as a common reference is needed for discussions between energy professionals. Conventional geothermal systems have the heat, permeability and fluid, requiring only drilling down to °C, normal heat flow or decaying radiogenic granite as heat sources, and used in district heating. Medium-temperature (MT) 100°C - 190°C, and high-temperature (HT) 190°C - 374°C resources are mostly at plate boundaries, with volcanic intrusive heat source, used mostly for electricity generation. Single well capacities are °C - 500°C) and a range of depths (1 m to 20 Km), but lack permeability or fluid, thus requiring stimulations for heat extraction by conduction. HVAC is 1 - 2 m deep and shallow geothermal down to 500 m in wells, both capturing °C, with °C are either advanced by geothermal developers at <7 Km depth (Enhanced Geothermal Systems (EGS), drilling below brittle-ductile transition zones and under geothermal fields), or by the Oil & Gas industry (Advanced Geothermal Systems, heat recovery from hydrocarbon wells or reservoirs, Superhot Rock Geothermal, and millimeter-wave drilling down to 20 Km). Their primary aim is electricity generation, relying on closed-loops, but EGS uses fractures for heat exchange with earthquake risks during fracking. Unconventional approaches could be everywhere, with shallow geothermal already functional. The deeper and hotter unconventional alternatives are still experimental, overcoming costs and technological challenges to become fully commercial. Meanwhile, the conventional geothermal resources remain the most proven opportunities for investments and development.

Empirical Analysis of Neural Networks-Based Models for Phishing Website Classification Using Diverse Datasets

Phishing attacks pose a significant security threat by masquerading as trustworthy entities to steal sensitive information,a problem that persists despite user awareness.This study addresses the pressing issue of phishing attacks on websites and assesses the performance of three prominent Machine Learning(ML)models—Artificial Neural Networks(ANN),Convolutional Neural Networks(CNN),and Long Short-Term Memory(LSTM)—utilizing authentic datasets sourced from Kaggle and Mendeley repositories.Extensive experimentation and analysis reveal that the CNN model achieves a better accuracy of 98%.On the other hand,LSTM shows the lowest accuracy of 96%.These findings underscore the potential of ML techniques in enhancing phishing detection systems and bolstering cybersecurity measures against evolving phishing tactics,offering a promising avenue for safeguarding sensitive information and online security.

Development and technology status of energy storage in depleted gas reservoirs

Utilizing energy storage in depleted oil and gas reservoirs can improve productivity while reducing power costs and is one of the best ways to achieve synergistic development of"Carbon Peak–Carbon Neutral"and"Underground Resource Utiliza-tion".Starting from the development of Compressed Air Energy Storage(CAES)technology,the site selection of CAES in depleted gas and oil reservoirs,the evolution mechanism of reservoir dynamic sealing,and the high-flow CAES and injection technology are summarized.It focuses on analyzing the characteristics,key equipment,reservoir construction,application scenarios and cost analysis of CAES projects,and sorting out the technical key points and existing difficulties.The devel-opment trend of CAES technology is proposed,and the future development path is scrutinized to provide reference for the research of CAES projects in depleted oil and gas reservoirs.

Regional Economic Development Trend Prediction Method Based on Digital Twins and Time Series Network

At present,the interpretation of regional economic development(RED)has changed from a simple evaluation of economic growth to a focus on economic growth and the optimization of economic structure,the improvement of economic relations,and the change of institutional innovation.This article uses the RED trend as the research object and constructs the RED index to conduct the theoretical analysis.Then this paper uses the attention mechanism based on digital twins and the time series network model to verify the actual data.Finally,the regional economy is predicted according to the theoretical model.The specific research work mainly includes the following aspects:1)This paper introduced the development status of research on time series networks and economic forecasting at home and abroad.2)This paper introduces the basic principles and structures of long and short-term memory(LSTM)and convolutional neural network(CNN),constructs an improved CNN-LSTM model combined with the attention mechanism,and then constructs a regional economic prediction index system.3)The best parameters of the model are selected through experiments,and the trained model is used for simulation experiment prediction.The results show that the CNN-LSTM model based on the attentionmechanism proposed in this paper has high accuracy in predicting regional economies.

BMPRⅡ^(+)neural precursor cells isolated and characterized from organotypic neurospheres:an in vitro model of human fetal spinal cord development

Roof plate secretion of bone morphogenetic proteins(BMPs)directs the cellular fate of sensory neurons during spinal cord development,including the formation of the ascending sensory columns,though their biology is not well understood.Type-ⅡBMP receptor(BMPRⅡ),the cognate receptor,is expressed by neural precursor cells during embryogenesis;however,an in vitro method of enriching BMPRⅡ^(+)human neural precursor cells(hNPCs)from the fetal spinal cord is absent.Immunofluorescence was undertaken on intact second-trimester human fetal spinal cord using antibodies to BMPRⅡand leukemia inhibitory factor(LIF).Regions of highest BMPRⅡ^(+)immunofluorescence localized to sensory columns.Parenchymal and meningeal-associated BMPRⅡ^(+)vascular cells were identified in both intact fetal spinal cord and cortex by co-positivity with vascular lineage markers,CD34/CD39.LIF immunostaining identified a population of somas concentrated in dorsal and ventral horn interneurons,mirroring the expression of LIF receptor/CD118.A combination of LIF supplementation and high-density culture maintained culture growth beyond 10 passages,while synergistically increasing the proportion of neurospheres with a stratified,cytoarchitecture.These neurospheres were characterized by BMPRⅡ^(+)/MAP2ab^(+/–)/βⅢ-tubulin^(+)/nestin^(–)/vimentin^(–)/GFAP^(–)/NeuN^(–)surface hNPCs surrounding a heterogeneous core ofβⅢ-tubulin^(+)/nestin^(+)/vimentin^(+)/GFAP^(+)/MAP2ab^(–)/NeuN^(–)multipotent precursors.Dissociated cultures from tripotential neurospheres contained neuronal(βⅢ-tubulin^(+)),astrocytic(GFAP+),and oligodendrocytic(O4+)lineage cells.Fluorescence-activated cell sorting-sorted BMPRⅡ^(+)hNPCs were MAP2ab^(+/–)/βⅢ-tubulin^(+)/GFAP^(–)/O4^(–)in culture.This is the first isolation of BMPRⅡ^(+)hNPCs identified and characterized in human fetal spinal cords.Our data show that LIF combines synergistically with high-density reaggregate cultures to support the organotypic reorganization of neurospheres,characterized by surface BMPRⅡ^(+)hNPCs.Our study has provided a new methodology for an in vitro model capable of amplifying human fetal spinal cord cell numbers for>10 passages.Investigations of the role BMPRⅡplays in spinal cord development have primarily relied upon mouse and rat models,with interpolations to human development being derived through inference.Because of significant species differences between murine biology and human,including anatomical dissimilarities in central nervous system(CNS)structure,the findings made in murine models cannot be presumed to apply to human spinal cord development.For these reasons,our human in vitro model offers a novel tool to better understand neurodevelopmental pathways,including BMP signaling,as well as spinal cord injury research and testing drug therapies.

Image Recognition Model of Fraudulent Websites Based on Image Leader Decision and Inception-V3 Transfer Learning

The fraudulent website image is a vital information carrier for telecom fraud.The efficient and precise recognition of fraudulent website images is critical to combating and dealing with fraudulent websites.Current research on image recognition of fraudulent websites is mainly carried out at the level of image feature extraction and similarity study,which have such disadvantages as difficulty in obtaining image data,insufficient image analysis,and single identification types.This study develops a model based on the entropy method for image leader decision and Inception-v3 transfer learning to address these disadvantages.The data processing part of the model uses a breadth search crawler to capture the image data.Then,the information in the images is evaluated with the entropy method,image weights are assigned,and the image leader is selected.In model training and prediction,the transfer learning of the Inception-v3 model is introduced into image recognition of fraudulent websites.Using selected image leaders to train the model,multiple types of fraudulent websites are identified with high accuracy.The experiment proves that this model has a superior accuracy in recognizing images on fraudulent websites compared to other current models.

CMAES-WFD:Adversarial Website Fingerprinting Defense Based on Covariance Matrix Adaptation Evolution Strategy

Website fingerprinting,also known asWF,is a traffic analysis attack that enables local eavesdroppers to infer a user’s browsing destination,even when using the Tor anonymity network.While advanced attacks based on deep neural network(DNN)can performfeature engineering and attain accuracy rates of over 98%,research has demonstrated thatDNNis vulnerable to adversarial samples.As a result,many researchers have explored using adversarial samples as a defense mechanism against DNN-based WF attacks and have achieved considerable success.However,these methods suffer from high bandwidth overhead or require access to the target model,which is unrealistic.This paper proposes CMAES-WFD,a black-box WF defense based on adversarial samples.The process of generating adversarial examples is transformed into a constrained optimization problem solved by utilizing the Covariance Matrix Adaptation Evolution Strategy(CMAES)optimization algorithm.Perturbations are injected into the local parts of the original traffic to control bandwidth overhead.According to the experiment results,CMAES-WFD was able to significantly decrease the accuracy of Deep Fingerprinting(DF)and VarCnn to below 8.3%and the bandwidth overhead to a maximum of only 14.6%and 20.5%,respectively.Specially,for Automated Website Fingerprinting(AWF)with simple structure,CMAES-WFD reduced the classification accuracy to only 6.7%and the bandwidth overhead to less than 7.4%.Moreover,it was demonstrated that CMAES-WFD was robust against adversarial training to a certain extent.

Structural characteristics and influencing factors of spatial correlation network for regional high-quality development in China

On the basis of measuring the regional high-quality development in China from 2011 to 2020,this study uses gravity model to build spatial correlation network,and uses social network analysis method to analyze the structural characteristics and influencing factors of correlation network.The results are shown as follows.First,from 2011 to 2020,the level of regional high-quality development in China is rising gradually,and the discrete characteristics between regions are gradually obvious,showing a step-like distribution structure decreasing from east to west.Second,the network density of regional high-quality development is generally low and tends to decline,but it has strong stability and correlation strength.Third,the spatial correlation network has an obvious core-edge structure.Shanghai is always at the center of the network and plays a significant intermediary role,while Qinghai and Xinjiang are always at the edge of the network.Fourth,the regional high-quality development association network can be divided into four major sectors:main benefit,net benefit,net spillover,and broker,showing the spatial correlation characteristics of inter-plate contact and intra-plate agglomeration.Fifth,the level of economic development,the level of urbanization and geographical proximity have a significant impact on the formation of regional high-quality development correlation network.

Development Methodologies for Network Softwarization: A Comparison of DevOps, NetOps, and Verification

This white paper explores three popular development methodologies for network softwarization: DevOps, NetOps, and Verification. The paper compares and contrasts the strengths and weaknesses of each approach and provides recommendations for organizations looking to adopt network softwarization.

FE65:a hub for neurodevelopment

FE65,initially identified as a binding partner of amyloid precursor protein(APP),is an adaptor protein enriched in the brain and regulated during development.FE65 belongs to the FE65 protein family.This family is comprised of three members,FE65,FE65 like-1(FE65L1),and FE65 like-2(FE65L2).

Constructing a standard system of city sustainable development assessment technology based on the SDGs

Given the rapid development of China’s new urbanization,cities with different locations and varying functional positioning,resource endowments,and development stages have insufficient scientific and applicable technical tools for implementing the United Nations Sustainable Development Goals(SDGs).City managers and policymakers must urgently establish SDG benchmarks to diagnose city development.Moreover,successful experiences from similar cities regarding sustainable development and self-improvement must be learned from to promote diversified,sustainable development across the country.Furthermore,emerging technologies such as artificial intelligence,the Internet of Things,big data and 5G are widely used in smart cities.Therefore,there is a growing need for“knowledge-based,personalized and intelligent”technologies to support monitoring,evaluation,and decision-making processes facilitating sustainable development in cities.This paper uses standardization as the theoretical support and technical basis.This approach can help clarify the sustainable development processes in China and clarify the evaluation results of and provide data on horizontal city comparisons,which can be used to develop evaluation technology for sustainable development in cities and construct a standardized system.The results provide a standard framework for intelligent assessment and decision-making regarding cities’sustainable development capabilities in China.Evaluating major international standardization institutions reveals that the practices of Chinese national standards should be fully absorbed and integrated to guide the evaluation of smart,resilient,and low-carbon cities.To this end,an indicator library of city sustainable development is proposed to provide standard evaluation technology methods.Finally,analyzing the response relationship of the indicator library to SDGs reveals the need for a standardized knowledge map of sustainable development assessment techniques and methods from the perspective of integrated management for sustainable development in cities.