Across two phylogeographic breaks: Quaternary evolutionary history of a mountain aspen (Populus rotundifolia) in the Hengduan Mountains

Biogeographical barriers to gene flow are central to plant phylogeography.In East Asia,plant distribution is greatly influenced by two phylogeographic breaks,the Mekong-Salween Divide and Tanaka-Kaiyong Line,however,few studies have investigated how these barriers affect the genetic diversity of species that are distributed across both.Here we used 14 microsatellite loci and four chloroplast DNA fragments to examine genetic diversity and distribution patterns of 49 populations of Populus rotundifolia,a species that spans both the Mekong-Salween Divide and the Tanaka-Kaiyong Line in southwestern China.Demographic and migration hypotheses were tested using coalescent-based approaches.Limited historical gene flow was observed between the western and eastern groups of P.rotundifolia,but substantial flow occurred across both the Mekong-Salween Divide and Tanaka-Kaiyong Line,manifesting in clear admixture and high genetic diversity in the central group.Wind-borne pollen and seeds may have facilitated the dispersal of P.rotundifolia following prevalent northwest winds in the spring.We also found that the Hengduan Mountains,where multiple genetic barriers were detected,acted on the whole as a barrier between the western and eastern groups of P.rotundifolia.Ecological niche modeling suggested that P.rotundifolia has undergone range expansion since the last glacial maximum,and demographic reconstruction indicated an earlier population expansion around 600 Ka.The phylogeographic pattern of P.rotundifolia reflects the interplay of biological traits,wind patterns,barriers,niche differentiation,and Quaternary climate history.This study emphasizes the need for multiple lines of evidence in understanding the Quaternary evolution of plants in topographically complex areas.

Wind-dispersed seeds blur phylogeographic breaks:The complex evolutionary history of Populus lasiocarpa around the Sichuan Basin

The strength of phylogeographic breaks can vary among species in the same area despite being subject to the same geological and climate history due to differences in biological traits.Several important phylogeographic breaks exist around the Sichuan Basin in Southwest China but few studies have focused on wind-dispersed plants.Here,we investigated the phylogeographic patterns and the evolutionary history of Populus lasiocarpa,a wind-pollinated and wind-dispersed tree species with a circum-Sichuan Basin distribution in southwest China.We sequenced and analyzed three plastid DNA fragments(ptDNA) and eight nuclear microsatellites(nSSRs) of 265 individuals of P.lasiocarpa from 21 populations spanning the entire distribution range.Distribution patterns based on nSSR data revealed that there are three genetic groups in P.lasiocarpa.This is consistent with the three phylogeographic breaks(Sichuan Basin,the Kaiyong Line and the 105°E line),where the Sichuan basin acts as the main barrier to gene flow between western and eastern groups.However,the distribution pattern based on ptDNA haplotypes poorly matched the phylogeographic breaks,and wind-dispersed seeds may be one of the main contributing factors.Species distribution modelling suggested a larger potential distribution in the last glacial maximum with a severe bottleneck during the last interglacial.A DIYABC model also suggested a population contraction and expansion for both western and eastern lineages.These results indicate that biological traits are likely to affect the evolutionary history of plants,and that nuclear molecular markers,which experience higher levels of gene flow,might be better indicators of phylogeographic breaks.

Genome resequencing reveals the evolutionary history of garlic reproduction traits

The propagation of cultivated garlic relies on vegetative cloves,thus flowers become non-essential for reproduction in this species,driving the evolution of reproductive feature-derived traits.To obtain insights into the evolutionary alteration of reproductive traits in the clonally propagated garlic,the evolutionary histories of two main reproduction-related traits,bolting and flower differentiation,were explored by genome analyses using 134 accessions displaying wide diversity in these two traits.Resequencing identified 272.8 million variations in the garlic genome,198.0million of which represent novel variants.Population analysis identified five garlic groups that have evolved into two clades.Gene expression,single-cell transcriptome sequencing,and genome-wide trait association analyses have identified numerous candidates that correlate with reproductive transition and flower development,some of which display distinct selection signatures.Selective forces acting on the B-box zinc finger protein-encoding Asa2G00291.1,the global transcription factor group E protein-encoding Asa5G01527.1,and VERNALIZATION INSENSITIVE 3-like Asa3G03399.1 appear to be representative of the evolution of garlic bolting.Plenty of novel genomic variations and trait-related candidates represent valuable resources for biological studies of garlic.Numerous selective signatures from genes associated with the two chosen reproductive traits provide important insights into the evolutionary history of reproduction in this clonally propagated crop.

On the evolutionary trail of MagRs

Magnetic sense,or termed magnetoreception,has evolved in a broad range of taxa within the animal kingdom to facilitate orientation and navigation.MagRs,highly conserved A-type iron-sulfur proteins,are widely distributed across all phyla and play essential roles in both magnetoreception and iron-sulfur cluster biogenesis.However,the evolutionary origins and functional diversification of MagRs from their prokaryotic ancestor remain unclear.In this study,MagR sequences from 131 species,ranging from bacteria to humans,were selected for analysis,with 23 representative sequences covering species from prokaryotes to Mollusca,Arthropoda,Osteichthyes,Reptilia,Aves,and mammals chosen for protein expression and purification.Biochemical studies revealed a gradual increase in total iron content in MagRs during evolution.Three types of MagRs were identified,each with distinct iron and/or iron-sulfur cluster binding capacity and protein stability,indicating continuous expansion of the functional roles of MagRs during speciation and evolution.This evolutionary biochemical study provides valuable insights into how evolution shapes the physical and chemical properties of biological molecules such as MagRs and how these properties influence the evolutionary trajectories of MagRs.

A high-quality Bougainvillea genome provides new insights into evolutionary history and pigment biosynthetic pathways in the Caryophyllales

Bougainvillea is a perennial ornamental shrub that is highly regarded in ornamental horticulture around the world.However,the absence of genome data limits our understanding of the pathways involved in bract coloration and breeding.Here,we report a chromosome-level assembly of the giga-genome of Bougainvillea×buttiana‘Mrs Butt’,a cultivar thought to be the origin of many other Bougainvillea cultivars.The assembled genome is∼5 Gb with a scaffold N50 of 151756278 bp and contains 86572 genes which have undergone recent whole-genome duplication.We confirmed that multiple rounds of whole-genome multiplication have occurred in the evolutionary history of the Caryophyllales,reconstructed the relationship in the Caryophyllales at whole genome level,and found discordance between species and gene trees as the result of complex introgression events.We investigated betalain and anthocyanin biosynthetic pathways and found instances of independent evolutionary innovations in the nine different Caryophyllales species.To explore the potential formation mechanism of diverse bract colors in Bougainvillea,we analyzed the genes involved in betalain and anthocyanin biosynthesis and found extremely low expression of ANS and DFR genes in all cultivars,which may limit anthocyanin biosynthesis.Our findings indicate that the expression pattern of the betalain biosynthetic pathway did not directly correlate with bract color,and a higher expression level in the betalain biosynthetic pathway is required for colored bracts.This improved understanding of the correlation between gene expression and bract color allows plant breeding outcomes to be predicted with greater certainty.

Evolutionary Neural Architecture Search and Its Applications in Healthcare

Most of the neural network architectures are based on human experience,which requires a long and tedious trial-and-error process.Neural architecture search(NAS)attempts to detect effective architectures without human intervention.Evolutionary algorithms(EAs)for NAS can find better solutions than human-designed architectures by exploring a large search space for possible architectures.Using multiobjective EAs for NAS,optimal neural architectures that meet various performance criteria can be explored and discovered efficiently.Furthermore,hardware-accelerated NAS methods can improve the efficiency of the NAS.While existing reviews have mainly focused on different strategies to complete NAS,a few studies have explored the use of EAs for NAS.In this paper,we summarize and explore the use of EAs for NAS,as well as large-scale multiobjective optimization strategies and hardware-accelerated NAS methods.NAS performs well in healthcare applications,such as medical image analysis,classification of disease diagnosis,and health monitoring.EAs for NAS can automate the search process and optimize multiple objectives simultaneously in a given healthcare task.Deep neural network has been successfully used in healthcare,but it lacks interpretability.Medical data is highly sensitive,and privacy leaks are frequently reported in the healthcare industry.To solve these problems,in healthcare,we propose an interpretable neuroevolution framework based on federated learning to address search efficiency and privacy protection.Moreover,we also point out future research directions for evolutionary NAS.Overall,for researchers who want to use EAs to optimize NNs in healthcare,we analyze the advantages and disadvantages of doing so to provide detailed guidance,and propose an interpretable privacy-preserving framework for healthcare applications.

Historical biogeography and evolutionary diversification of Lilium(Liliaceae): New insights from plastome phylogenomics

Here, we infer the historical biogeography and evolutionary diversification of the genus Lilium. For this purpose, we used the complete plastomes of 64 currently accepted species in the genus Lilium(14plastomes were newly sequenced) to recover the phylogenetic backbone of the genus and a timecalibrated phylogenetic framework to estimate biogeographical history scenarios and evolutionary diversification rates of Lilium. Our results suggest that ancient climatic changes and geological tectonic activities jointly shaped the distribution range and drove evolutionary radiation of Lilium, including the Middle Miocene Climate Optimum(MMCO), the late Miocene global cooling, as well as the successive uplift of the Qinghai-Tibet Plateau(QTP) and the strengthening of the monsoon climate in East Asia during the late Miocene and the Pliocene. This case study suggests that the unique geological and climatic events in the Neogene of East Asia, in particular the uplift of QTP and the enhancement of monsoonal climate, may have played an essential role in formation of uneven distribution of plant diversity in the Northern Hemisphere.

Enhancing Cancer Classification through a Hybrid Bio-Inspired Evolutionary Algorithm for Biomarker Gene Selection

In this study,our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization(GWO)with Harris Hawks Optimization(HHO)for feature selection.Themotivation for utilizingGWOandHHOstems fromtheir bio-inspired nature and their demonstrated success in optimization problems.We aimto leverage the strengths of these algorithms to enhance the effectiveness of feature selection in microarray-based cancer classification.We selected leave-one-out cross-validation(LOOCV)to evaluate the performance of both two widely used classifiers,k-nearest neighbors(KNN)and support vector machine(SVM),on high-dimensional cancer microarray data.The proposed method is extensively tested on six publicly available cancer microarray datasets,and a comprehensive comparison with recently published methods is conducted.Our hybrid algorithm demonstrates its effectiveness in improving classification performance,Surpassing alternative approaches in terms of precision.The outcomes confirm the capability of our method to substantially improve both the precision and efficiency of cancer classification,thereby advancing the development ofmore efficient treatment strategies.The proposed hybridmethod offers a promising solution to the gene selection problem in microarray-based cancer classification.It improves the accuracy and efficiency of cancer diagnosis and treatment,and its superior performance compared to other methods highlights its potential applicability in realworld cancer classification tasks.By harnessing the complementary search mechanisms of GWO and HHO,we leverage their bio-inspired behavior to identify informative genes relevant to cancer diagnosis and treatment.

Evolutionary Optimization Methods for High-Dimensional Expensive Problems:A Survey

Evolutionary computation is a rapidly evolving field and the related algorithms have been successfully used to solve various real-world optimization problems.The past decade has also witnessed their fast progress to solve a class of challenging optimization problems called high-dimensional expensive problems(HEPs).The evaluation of their objective fitness requires expensive resource due to their use of time-consuming physical experiments or computer simulations.Moreover,it is hard to traverse the huge search space within reasonable resource as problem dimension increases.Traditional evolutionary algorithms(EAs)tend to fail to solve HEPs competently because they need to conduct many such expensive evaluations before achieving satisfactory results.To reduce such evaluations,many novel surrogate-assisted algorithms emerge to cope with HEPs in recent years.Yet there lacks a thorough review of the state of the art in this specific and important area.This paper provides a comprehensive survey of these evolutionary algorithms for HEPs.We start with a brief introduction to the research status and the basic concepts of HEPs.Then,we present surrogate-assisted evolutionary algorithms for HEPs from four main aspects.We also give comparative results of some representative algorithms and application examples.Finally,we indicate open challenges and several promising directions to advance the progress in evolutionary optimization algorithms for HEPs.

Integrating high-volume molecular and morphological data into the evolutionary studies of Allium

The genus Allium(Amaryllidaceae),which includes economically important plants such as onions,garlic,and leeks,is one of the most species-rich and diverse genera of monocotyledon plants in the Northern Hemisphere(Govaerts et al.,2021),with approximately 1000 species.The evolution of Allium is characterized by ecological diversification,with most species preferring open.

Editorial Commentary:The History of Controlling and Treating Infectious Diseases in Ancient China

I recently read the article by LIU et all entitled The History of Controlling and Treating Infectious Diseases in Ancient China with great interest.The paper comprehensively summarizes ancient Chinese approaches to combat epidemics,elucidating them through the lens of three key elements of infectious diseases.Summarizing thousands of years of records detailing major infectious disease occurrences across various dynasties is no small feat.

Evolutionary dynamics of tax-based strong altruistic reward andpunishment in a public goods game

In public goods games, punishments and rewards have been shown to be effective mechanisms for maintaining individualcooperation. However, punishments and rewards are costly to incentivize cooperation. Therefore, the generation ofcostly penalties and rewards has been a complex problem in promoting the development of cooperation. In real society,specialized institutions exist to punish evil people or reward good people by collecting taxes. We propose a strong altruisticpunishment or reward strategy in the public goods game through this phenomenon. Through theoretical analysis and numericalcalculation, we can get that tax-based strong altruistic punishment (reward) has more evolutionary advantages thantraditional strong altruistic punishment (reward) in maintaining cooperation and tax-based strong altruistic reward leads toa higher level of cooperation than tax-based strong altruistic punishment.

Evolutionary game dynamics of combining two different aspiration-driven update rules in structured populations

In evolutionary games,most studies on finite populations have focused on a single updating mechanism.However,given the differences in individual cognition,individuals may change their strategies according to different updating mechanisms.For this reason,we consider two different aspiration-driven updating mechanisms in structured populations:satisfied-stay unsatisfied shift(SSUS)and satisfied-cooperate unsatisfied defect(SCUD).To simulate the game player’s learning process,this paper improves the particle swarm optimization algorithm,which will be used to simulate the game player’s strategy selection,i.e.,population particle swarm optimization(PPSO)algorithms.We find that in the prisoner’s dilemma,the conditions that SSUS facilitates the evolution of cooperation do not enable cooperation to emerge.In contrast,SCUD conditions that promote the evolution of cooperation enable cooperation to emerge.In addition,the invasion of SCUD individuals helps promote cooperation among SSUS individuals.Simulated by the PPSO algorithm,the theoretical approximation results are found to be consistent with the trend of change in the simulation results.

Evolutionary Multi/Many-Objective Optimisation via Bilevel Decomposition

Decomposition of a complex multi-objective optimisation problem(MOP)to multiple simple subMOPs,known as M2M for short,is an effective approach to multi-objective optimisation.However,M2M facilitates little communication/collaboration between subMOPs,which limits its use in complex optimisation scenarios.This paper extends the M2M framework to develop a unified algorithm for both multi-objective and manyobjective optimisation.Through bilevel decomposition,an MOP is divided into multiple subMOPs at upper level,each of which is further divided into a number of single-objective subproblems at lower level.Neighbouring subMOPs are allowed to share some subproblems so that the knowledge gained from solving one subMOP can be transferred to another,and eventually to all the subMOPs.The bilevel decomposition is readily combined with some new mating selection and population update strategies,leading to a high-performance algorithm that competes effectively against a number of state-of-the-arts studied in this paper for both multiand many-objective optimisation.Parameter analysis and component analysis have been also carried out to further justify the proposed algorithm.

Optimizing Deep Learning for Computer-Aided Diagnosis of Lung Diseases: An Automated Method Combining Evolutionary Algorithm, Transfer Learning, and Model Compression

Recent developments in Computer Vision have presented novel opportunities to tackle complex healthcare issues,particularly in the field of lung disease diagnosis.One promising avenue involves the use of chest X-Rays,which are commonly utilized in radiology.To fully exploit their potential,researchers have suggested utilizing deep learning methods to construct computer-aided diagnostic systems.However,constructing and compressing these systems presents a significant challenge,as it relies heavily on the expertise of data scientists.To tackle this issue,we propose an automated approach that utilizes an evolutionary algorithm(EA)to optimize the design and compression of a convolutional neural network(CNN)for X-Ray image classification.Our approach accurately classifies radiography images and detects potential chest abnormalities and infections,including COVID-19.Furthermore,our approach incorporates transfer learning,where a pre-trainedCNNmodel on a vast dataset of chest X-Ray images is fine-tuned for the specific task of detecting COVID-19.This method can help reduce the amount of labeled data required for the task and enhance the overall performance of the model.We have validated our method via a series of experiments against state-of-the-art architectures.

From Research on TCM Images to Historiography of TCM Images——Comments on History of Chinese Image Culture:Medical Image Volume

1 Introduction.Chinese medicine has a long and rich history,dating back to the classics of the Qin and Han dynasties and extending to the integration of Chinese and Western medicine in the modern era.The vast amount of literature and scholarly works in this field makes it essential to thoroughly study the history of traditional Chinese medicine(TCM)in order to understand its development path throughout the ages and boost innovation based on tradition.This is why the sages emphasized the importance of“classifying the works into different schools and tracing back to their origins”(辨章学术,考镜源流).

Correlation of personality with individual reproductive success in shrub-nesting birds depends on their life history style

Two questions in the research of animal personality—whether there is a correlation between a personality trait and individual reproductive success,and what is the genetic basis underlying a personality trait—remain unresolved.We addressed these two questions in three shrub-nesting birds,the Azure-winged Magpie(Cyanopica cyanus,AM),White-collared Blackbird(Turdus albocinctus,WB),and Brown-cheeked Laughingthrush(Trochalopteron henrici,BL).The personality type of an individual was first identified according to its response to a territorial intruder.Then,we compared the fleeing distance,breeding parameters,and differential expressed genes(DEGs) in the brain transcriptome between bold and shy breeders.In the three species,bold breeders exhibited more aggressiveness towards an intruder of their territory than did shy breeders.The reproductive success of bold breeders was significantly higher than that of shy breeders in AM but not in WB and BL.The three species shared one DEG,crabp1,which was up-regulated in bold relative to in shy individuals.By regulating the expression of corticotropin-releasing hormone,higher crabp1 gene expression can decrease cellular response to retinoic acid.Therefore,bold individuals are insensitive to external stresses and able to exhibit more aggressiveness to intruders than their shier counterparts.Aggressiveness is beneficial to bold individuals in AM but not in WB and BL because the former could evoke neighbors to make the same response of defending against intruders but the latter could not.Although a personality trait may have the same genetic basis across species,its correlation with reproductive success depends largely on the life history style of a species.

Evolutionary Variational YOLOv8 Network for Fault Detection in Wind Turbines

Deep learning has emerged in many practical applications,such as image classification,fault diagnosis,and object detection.More recently,convolutional neural networks(CNNs),representative models of deep learning,have been used to solve fault detection.However,the current design of CNNs for fault detection of wind turbine blades is highly dependent on domain knowledge and requires a large amount of trial and error.For this reason,an evolutionary YOLOv8 network has been developed to automatically find the network architecture for wind turbine blade-based fault detection.YOLOv8 is a CNN-backed object detection model.Specifically,to reduce the parameter count,we first design an improved FasterNet module based on the Partial Convolution(PConv)operator.Then,to enhance convergence performance,we improve the loss function based on the efficient complete intersection over the union.Based on this,a flexible variable-length encoding is proposed,and the corresponding reproduction operators are designed.Related experimental results confirmthat the proposed approach can achieve better fault detection results and improve by 2.6%in mean precision at 50(mAP50)compared to the existing methods.Additionally,compared to training with the YOLOv8n model,the YOLOBFE model reduces the training parameters by 933,937 and decreases the GFLOPS(Giga Floating Point Operations Per Second)by 1.1.

Constraints Separation Based Evolutionary Multitasking for Constrained Multi-Objective Optimization Problems

Constrained multi-objective optimization problems(CMOPs)generally contain multiple constraints,which not only form multiple discrete feasible regions but also reduce the size of optimal feasible regions,thus they propose serious challenges for solvers.Among all constraints,some constraints are highly correlated with optimal feasible regions;thus they can provide effective help to find feasible Pareto front.However,most of the existing constrained multi-objective evolutionary algorithms tackle constraints by regarding all constraints as a whole or directly ignoring all constraints,and do not consider judging the relations among constraints and do not utilize the information from promising single constraints.Therefore,this paper attempts to identify promising single constraints and utilize them to help solve CMOPs.To be specific,a CMOP is transformed into a multitasking optimization problem,where multiple auxiliary tasks are created to search for the Pareto fronts that only consider a single constraint respectively.Besides,an auxiliary task priority method is designed to identify and retain some high-related auxiliary tasks according to the information of relative positions and dominance relationships.Moreover,an improved tentative method is designed to find and transfer useful knowledge among tasks.Experimental results on three benchmark test suites and 11 realworld problems with different numbers of constraints show better or competitive performance of the proposed method when compared with eight state-of-the-art peer methods.

A New Perspective in the Study of Medical History through Visuals

In pre-modern cultural history,both textual and visual documentation played crucial roles,each possessing its own narrative logic.Historically,text has been a dominant medium for cultural documentation,whereas images,which predate text,have often been undervalued.