Diversified Selection Research Articles

Photothermal therapy based on magnetic nanoparticles in cancer

Photothermal therapy (PTT) is an extensively used kind of cancer treatment based on thermal energy, which has been technically mature relative to other emerging nanotechnologies. Initially, single-phase magnetic nanoparticles (NPs) have been used for PTT due to their excellent biocompatibility and photothermal properties. With the boom of research, various types of composite-phase NPs have been developed to improve the efficiency and broaden the application range of PTT. The synergistic effect of PTT and other therapies have also gradually attracted the attention of researchers in achieving enhanced tumor elimination. However, the intrinsic obstacles of PTT have restricted the widespread clinical application, and the vague pathological mechanisms of PTT have also made it more difficult to promote clinical translation. In this Perspective, the current research progress of PTT including a diversified selection of magnetic NPs prospects toward the clinic, and the potential pathological mechanism will be overviewed. Meanwhile, the potential outlook of open challenges will be discussed for the utility of PTT.

ADES: A New Ensemble Diversity-Based Approach for Handling Concept Drift

Beyond applying machine learning predictive models to static tasks, a significant corpus of research exists that applies machine learning predictive models to streaming environments that incur concept drift. With the prevalence of streaming real-world applications that are associated with changes in the underlying data distribution, the need for applications that are capable of adapting to evolving and time-varying dynamic environments can be hardly overstated. Dynamic environments are nonstationary and change with time and the target variables to be predicted by the learning algorithm and often evolve with time, a phenomenon known as concept drift. Most work in handling concept drift focuses on updating the prediction model so that it can recover from concept drift while little effort has been dedicated to the formulation of a learning system that is capable of learning different types of drifting concepts at any time with minimum overheads. This work proposes a novel and evolving data stream classifier called Adaptive Diversified Ensemble Selection Classifier (ADES) that significantly optimizes adaptation to different types of concept drifts at any time and improves convergence to new concepts by exploiting different amounts of ensemble diversity. The ADES algorithm generates diverse base classifiers, thereby optimizing the margin distribution to exploit ensemble diversity to formulate an ensemble classifier that generalizes well to unseen instances and provides fast recovery from different types of concept drift. Empirical experiments conducted on both artificial and real-world data streams demonstrate that ADES can adapt to different types of drifts at any given time. The prediction performance of ADES is compared to three other ensemble classifiers designed to handle concept drift using both artificial and real-world data streams. The comparative evaluation performed demonstrated the ability of ADES to handle different types of concept drifts. The experimental results, including statistical test results, indicate comparable performances with other algorithms designed to handle concept drift and prove their significance and effectiveness.

Examination of Fixed Income Securities Pooled with the S&P Index for Retirement Diversification: From Convertibles to Treasuries to Cash Alternatives

We examine the returns of retirement portfolios containing a diversified stock selection and various fixed income combinations to determine the best risk return tradeoffs for the 10 years 2010 -2019. The classic 60/40 Equity/Debt holds the track record for delivering returns while reducing risk. For comparison, we test for the optimal mix and 70/30, 80/20 among others; for the 10 years in the study. The lowest risk return combinations for a 60/40 mix were found; with Municipal Securities dominating followed by Preferred Stocks, US High Yield, and Convertibles. The worst performing 60/40 combinations were made with International Bonds, Treasury Bills, and Government Bonds excluding U.S.

Advocating for Minority Inclusion in Clinical Trials: A Call for Representation and Justice.

Off-label prescribing of medications has been a long-standing practice in pediatric medicine because, in the types of studies regarding safety, efficacy, and pharmacokinetics needed for labeling, researchers often only enroll adult subjects.1 The Best Pharmaceuticals for Children Act (BPCA) and Pediatric Research Equity Act have significantly increased the number of pediatric trials submitted to the US Food and Drug Administration for drug labeling in the past 10 years.1,2 Prioritizing minority enrollment in BPCA-funded clinical trials will increase the generalizability of scientific findings within the broader pediatric population.3In this issue of Pediatrics, Abdel-Rahman et al4 present new findings on the representation of racial and ethnic minorities participating in federally BPCA-funded pediatric studies. Pediatric enrollment in BPCA-funded studies remained comparable to or higher than expected in all minority groups, except Asian Americans. Despite observing some racial and ethnic differences in enrollment on the basis of geography, study type, and degree of study burden, in the findings from this report, it is suggested that overall, in BPCA-sponsored pediatric studies, there are no racial or ethnic biases or disparities in subject enrollment. However, as the authors note, we suspect that these studies were likely not sufficiently powered to reveal such differences between racial and ethnic groups, especially given the inherent incomplete capture of information on race and ethnicity. Although race and ethnicity are social constructs and crude proxies to account for the substantial heterogeneity of mixed ancestry, in their continued use in clinical trials, researchers have attempted to create standard terminology, and, in many historical studies, researchers have found clinically significant differences in pharmacokinetics and pharmacodynamics on the basis of these measures.5Historically, Black and Hispanic subjects are underrepresented in clinical trials.6 Lack of representation hinders our ability to discern factors that may influence a drug’s performance on the basis genetic ancestry and excludes the presence of particular racial and ethnic groups from inclusion within the full scientific body of evidence.7 Well-known barriers to subject enrollment in clinical trials include language preference, health literacy, geography, transportation, cost, and resources.8 In addition, the willingness of racial and ethnic minorities to participate in clinical trials has been significantly compromised by mistrust, historical abuses, and religious or cultural differences.9 Both concepts are further challenged in the pediatric population because of the ethical, clinical, and logistic considerations unique to children.10Although the current findings on BPCA-sponsored studies are encouraging, equitable demographic distribution of subjects enrolled in clinical trials should reflect our minimum standards of scientific rigor.11 In the adult literature, researchers discuss how stark underrepresentation of racial and ethnic minorities has limited access to potentially life-sustaining cancer drug treatments for eligible subjects at an increased risk for disease.12 In addition, drug therapies for adults with cardiac failure have been shown to perform differently on the basis of race and ethnicity.13 Therefore, the inclusion of racial and ethnic minorities in clinical trials should be free of bias and remains an issue of equity and justice.14 We suspect that there may be similar racial and ethnic differences in the performance of certain medications in pediatric populations; however, current trials may be underpowered to detect these differences. On the basis of findings from adult trials, developing strategies to increase and maintain minority inclusion in pediatric clinical trials remains imperative.Consideration of minority inclusion and subject enrollment should be tackled at the onset of clinical trial design and should include ensuring diversified BPCA site selection. Equitable demographic distribution of participants should be prioritized in such a way that a study is powered to reveal racial and ethnic differences. Furthermore, underscoring diversity and representation within the local study personnel is critical for both patient-oriented design and outreach. Finally, emphasizing the need for diversity and inclusion through community outreach and engagement regarding clinical trial participation is a key component to robust enrollment.The lack of racial and ethnic bias in patient enrollment in BPCA-funded sites is a positive sign, but historical evidence beckons caution. The call for diversity, equity, and inclusion remains vital to ensure that such findings are continuously sustained and integrated into the fabric of our scientific process.

Neural Style Palette: A Multimodal and Interactive Style Transfer From a Single Style Image

Despite the myriad of attributes found in a single style image, existing neural style transfer methods produce outputs with limited variety–typically only a single realization of the style image. They also do not provide an easy way to control the stylization process, limiting the creative freedom of users. In this paper, we propose Neural Style Palette ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NSP</i> ), a method for interactively generating a variety of stylized images from only a single style input. Our approach allows human influence in the stylization process, a design inspired by Hybrid Human-Artificial Intelligence. Like a color palette, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NSP</i> enables a meaningful interaction by presenting a collection of sub-textures, which we also refer to as anchor styles, that act as a visual guide for the users. These anchor styles capture different attributes in the single style image that the users can creatively blend to create their desired realizations. To offer a diversified selection in the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NSP</i> , we constrain the anchor styles to be distant from one another while maintaining faithfulness to the original style image. This is possible through our two proposed novel losses: a style-separation loss that encourages the sub-textures to be distinct and a unification loss to ensure that the sub-textures center around the original style while encouraging additional diversity. We perform several experiments to prove the effectiveness of our method and generalize to improve existing methods.

Gold-Catalyzed Conversion of Highly Strained Compounds.

The past decade has witnessed the golden age of homogeneous gold-catalyzed reactions, especially those that involve the transformation of highly strained molecules into complex molecular architectures. Gold catalysts, with unique electronic properties and catalytic abilities, have elevated versatile reaction modes through π-interaction induced activation. On the basis of increasing research interest in this topic, together with the significant development of various ligands, including phosphine ligands and azacyclic or noncyclic carbene ligands, the understanding of the catalytic function of gold catalysts has become much deeper and more comprehensive. Different reaction needs thus could be adapted by a novel gold catalyst with a diversified ligand selection. Furthermore, the whole evolution of the gold catalysis on synthetic methodologies has realized and expanded its application into natural product synthesis as well as the potentiality of drug discovery, which endows this ancient metal with a magnificent renaissance. The reactivity of strained small ring molecules with high tension has always been an important research topic in organic chemistry. When the highly strained small ring is linked with a π-electron rich moiety or contains a heteroatom, the gold activation of the π-system or coordination with the heteroatom can initiate a cascade reaction, usually followed by ring opening or expansion. These processes can result in the rapid construction of complex and distinct molecular structures, many of which feature in biologically important molecules. In this review, we will mainly summarize the advances on diverse reaction types and molecular constructions accomplished by homogeneous gold catalysis using highly strained substrates, including methylenecyclopropanes (MCPs), vinylidenecyclopropanes (VDCPs), cyclopropenes as well as aziridine- and epoxide-containing molecules, focusing on the last 10 years. For functionalized alkynyl cyclopropanes, several early inspiring and elegant examples will be described in this review for systematically understanding these transformations.

Artistic Reflection on Artificial Intelligence Digital Painting

Material media is the premise and foundation of artistic creation. It can not only determine the classification of works of art, judge the different aesthetic styles of works of art, but also have a profound impact on the thinking and techniques of creators. With the development of computer technology and the rise of artificial intelligence, AI technology is widely used in various fields, including painting. However, the mechanical nature of AI painting’s own expressive techniques limits its diversified selection of painting materials and the setting of brush parameters, and its works of art are unable to achieve the vivid and humanized expressive style of traditional painting. As a product of science and technology, with the continuous development of science and technology and the passage of time, the artistic content of works will increase. Digital technology should be regarded as a tool and means for artistic creation. Only the perfect combination of digital technology and traditional painting forms can be conducive to the innovative development of art and the sustainable development of science and technology and humanities. Based on this, this article analyzes how to dialectically look at the integration of AI technology and traditional painting art forms.

Engineering Nanoparticles toward the Modulation of Emerging Cancer Immunotherapy.

Cancer immunotherapy is a new therapeutic strategy to fight cancer by activating the patients' own immune system. At present, immunotherapy approaches such as cancer vaccines, immune checkpoint blockade (ICB), adoptive cell transfer (ACT), monoclonal antibodies (mAbs) therapy, and cytokines therapy have therapeutic potential in preclinical and clinical applications. However, the intrinsic limitations of conventional immunotherapy are difficulty of precise dosage control, insufficient enrichment in tumor tissues, partial immune response silencing or hyperactivity, and high cost. Engineering nanoparticles (NPs) have been emerging as a promising multifunctional platform to enhance conventional immunotherapy due to their intrinsic immunogenicity, convenient delivery function, controlled surface chemistry activity, multifunctional modifying potential, and intelligent targeting. This review presents the recent progress reflected by engineering NPs, including the diversified selection of functionalized NPs, the superiority of engineering NPs for enhancing conventional immunotherapy, and NP-mediated multiscale strategies for synergistic therapy consisting of compositions and their mechanism. Finally, the perspective on multifunctional NP-based cancer immunotherapy for boosting immunomodulation is discussed, which reveals the expanding landscape of engineering NPs in clinical translation.

Use of Collective Expertise as a Tool to Reinforce Food Safety Management in Africa

The Erasmus+ project (2017-2020) entitled Societal Challenges and Governance of African Universities: the case of ALIments in Morocco, the Democratic Republic of the Congo and Senegal (DAfrAli) seeks to strengthen the governance capacity of African Higher Education Institutions to mobilize their resources in order to respond to major societal challenges in relation to external stakeholders. A work package consisted of organizing three workshops to use Collective Expertise as a tool for the identification of societal risks, in the area of food safety. These three workshops were conducted in Morocco, in Senegal and in Democratic Republic of Congo. The exercise was performed by country academics with the contribution of the European project partners. &#x0D; &#x0D; Collective Expertise gave results that demonstrated that, with a careful and diversified selection of experts, this methodology can have a deep importance to list the food hazards in a country. The results obtained can induce changes in university curricula, showed the social impacts of food safety, unveiled research needs and training needs for different agents in the food sector and above all the impact in food policy in a country. The collective expertise approach of the determination of hazards also permitted to discuss possible organization models for food risk management in the 3 countries.

Osmotic pressure as driving force for recovering ionic liquids from aqueous solutions

An emerging challenge occurs for recovering ionic liquids (ILs) because of their exponential growth in academic and industrial applications. We report for the first time to reclaim water-soluble ILs from their aqueous solutions by osmotically driving water to spontaneously permeate through semi-permeable membranes. Theoretical simulation indicates a weak hydrability of ILs and a tendency to form micelles in water. We thus demonstrate a significant difference in osmotic pressures between the aqueous solutions of ILs and inorganic salts. Four categories of broadly representative ILs are continuously recovered from their diluent aqueous solutions (~1 wt%) with semi-permeable membranes. Eventually, a record-high concentration up to 58–78 wt% is achieved in this work. Our strategy integrates superiorities of low energy consumption, universal applicability toward ILs, diversified selection of semi-permeable membranes, almost unattenuated water flux and high rejection to ions, hopefully providing new insights into the authentic “green” industrial applications of ILs.

Boosting Feature Matching Accuracy with Pairwise Affine Estimation.

Local image feature matching lies in the heart of many computer vision applications. Achieving high matching accuracy is challenging when significant geometric difference exists between the source and target images. The traditional matching pipeline addresses the geometric difference by introducing the concept of support region. Around each feature point, the support region defines a neighboring area characterized by estimated attributes like scale, orientation, affine shape, etc. To correctly assign support region is not an easy job, especially when each feature is processed individually. In this paper, we propose to estimate the relative affine transformation for every pair of to-be-compared features. This "tailored" measurement of geometric difference is more precise and helps improve the matching accuracy. Our pipeline can be incorporated into most existing 2D local image feature detectors and descriptors. We comprehensively evaluate its performance with various experiments on a diversified selection of benchmark datasets. The results show that the majority of tested detectors/descriptors gain additional matching accuracy with proposed pipeline.

Helping consumers to overcome information overload with a diversified online review subset

Redundant online reviews often have a negative impact on the efficiency of consumers’ decision-making in their online shopping. A feasible solution for business analytics is to select a review subset from the original review corpus for consumers, which is called review selection. This study aims to address the diversified review selection problem, and proposes an effective review selection approach called Simulated Annealing-Diversified Review Selection (SA-DRS) that considers the semantic relationship of review features and the content diversity of selected reviews simultaneously. SA-DRS first constructs a feature taxonomy by utilizing the Latent Dirichlet Allocation (LDA) topic model and the Word2vec model to measure the topic relation and word context relation. Based on the established feature taxonomy, the similarity between each pair of reviews is defined and the review quality is estimated as well. Finally, diversified, high-quality reviews are selected heuristically by SA-DRS in the spirit of the simulated annealing method, forming the selected review subset. Extensive experiments are conducted on real-world e-commerce platforms to demonstrate the effectiveness of SA-DRS compared to other extant review selection approaches.

Genome wide molecular evolution analysis of begomoviruses reveals unique diversification pattern in coat protein gene of Old World and New World viruses.

Begomoviruses (Family-Geminiviridae) are plant infecting single stranded DNA viruses known to evolve very fast. Here, we have analysed the DNA-A sequences of 302 begomoviruses reported as 'type isolates' from different countries following the list of International Committee on Taxonomy of Viruses till 2017. Phylogenetic analysis was performed which revealed two major evolutionarily distinct groups namely Old World (OW) and New World (NW) viruses. Our work present evidence that cp gene has varied degree of diversification among the viruses reported from NW and OW. The NW viruses are more conserved in their cp gene sequences than that of OW viruses irrespective of host plant families. Further analysis reveals that cp gene differs in its recombination pattern among OW and NW viruses whereas rep gene is highly recombination prone in both OW and NW viruses. The sequence conservation in cp gene in NW viruses is a result of meagre recombination and subsequent low substitution rate in comparison to OW viruses. Our results demonstrated that the cp gene in NW viruses is less likely to possess nuclear localisation sequences than OW cp gene. Further we present evidence that the NW-cp is under the influence of strong purifying selection. We propose that the precoat protein (pcp) gene present exclusively in the 5' of cp gene in OW viruses is highly diversified and strong positive selection working on pcp gene might be attributing largely to the diversity of OW-cp gene.

Rapid and Efficient Bug Assignment Using ELM for IOT Software

The reliable implementation of software in an Internet system directly influences information transmission especially for the Internet of Things (IoT) system. Once defects in the system are found, the communication between things and things and the interaction between people and things in the IoT will be greatly affected. Therefore, rapid and effective defect assignment to the right developer is the key to ensuring software quality and bringing down time consumption in the IoT software life cycle. However, as the size of the software becomes increasing larger, the requirement of users grows, and a large number of software bugs will be found every day. It is difficult for managers to assign the software defects to the appropriate developers. In this paper, a novel hybrid method based on a diversified feature selection and an ensemble extreme learning machine (ELM) is proposed. First, the useful information is extracted from defect reports; then, the data are preprocessed to establish a vector space model; and the diversified feature selection is preprocessed in order to select a smallest set of representative non-redundant features with maximal statistical information. Finally, an ensemble GA-based ELM training classifier is used. Experimental results show that, compared to SVM, C4.5, NaiveBayes, and KNN classifiers, the proposed ELM-based bug triage approach with representative feature selection techniques in this paper significantly improves the efficiency and the effectiveness of bug triages.

Evolutionary Insights Based on SNP Haplotypes of Red Pericarp, Grain Size and Starch Synthase Genes in Wild and Cultivated Rice.

The origin and domestication of rice has been a subject of considerable debate in the post-genomic era. Rice varieties have been categorized based on isozyme and DNA markers into two broad cultivar groups, Indica and Japonica. Among other well-known cultivar groups Aus varieties are closer to Indica and Aromatic varieties including Basmati are closer to Japonica, while deep-water rice varieties share kinship to both Indica and Japonica cultivar groups. Here, we analyzed haplotype networks and phylogenetic relationships in a diverse set of genotypes including Indian Oryza nivara/Oryza rufipogon wild rice accessions and representative varieties of four rice cultivar groups based on pericarp color (Rc), grain size (GS3) and eight different starch synthase genes (GBSSI, SSSI, SSIIa, SSIIb, SSIIIa, SSIIIb, SSIVa, and SSIVb). Aus cultivars appear to have the most ancient origin as they shared the maximum number of haplotypes with the wild rice populations, while Indica, Japonica and Aromatic cultivar groups showed varying phylogenetic origins of these genes. Starch synthase genes showed higher variability in cultivated rice than wild rice populations, suggesting diversified selection during and after domestication. O. nivara/O. rufipogon wild rice accessions belonging to different sub-populations shared common haplotypes for all the 10 genes analyzed. Our results support polyphyletic origin of cultivated rice with a complex pattern of migration of domestication alleles from wild to different rice cultivar groups. The findings provide novel insights into evolutionary and domestication history of rice and will help utilization of wild rice germplasm for genetic improvement of rice cultivars.

Allele Mining and Selective Patterns of Pi9 Gene in a Set of Rice Landraces from India.

Allelic variants of the broad-spectrum blast resistance gene, Pi9 (nucleotide binding site-leucine-rich repeat region) have been analyzed in Indian rice landraces. They were selected from the list of 338 rice landraces phenotyped in the rice blast nursery at central Rainfed Upland Rice Research Station, Hazaribag. Six of them were further selected on the basis of their resistance and susceptible pattern for virulence analysis and selective pattern study of Pi9 gene. The sequence analysis and phylogenetic study illustrated that such sequences are vastly homologous and clustered into two groups. All the blast resistance Pi9 alleles were grouped into one cluster, whereas Pi9 alleles of susceptible landraces formed another cluster even though these landraces have a low level of DNA polymorphisms. A total number of 136 polymorphic sites comprising of transitions, transversions, and insertion and deletions (InDels) were identified in the 2.9 kb sequence of Pi9 alleles. Lower variation in the form of mutations (77) (Transition + Transversion), and InDels (59) were observed in the Pi9 alleles isolated from rice landraces studied. The results showed that the Pi9 alleles of the selected rice landraces were less variable, suggesting that the rice landraces would have been exposed to less number of pathotypes across the country. The positive Tajima’s D (0.33580), P > 0.10 (not significant) was observed among the seven rice landraces, which suggests the balancing selection of Pi9 alleles. The value of synonymous substitution (-0.43337) was less than the non-synonymous substitution (0.78808). The greater non-synonymous substitution than the synonymous means that the coding region, mainly the leucine-rich repeat domain was under diversified selection. In this study, the Pi9 gene has been subjected to balancing selection with low nucleotide diversity which is different from the earlier reports, this may be because of the closeness of the rice landraces, cultivated in the same region, and under low pathotype pressure.

Diversified models for portfolio selection based on uncertain semivariance

ABSTRACTSince the financial markets are complex, sometimes the future security returns are represented mainly based on experts’ estimations due to lack of historical data. This paper proposes a semivariance method for diversified portfolio selection, in which the security returns are given subjective to experts’ estimations and depicted as uncertain variables. In the paper, three properties of the semivariance of uncertain variables are verified. Based on the concept of semivariance of uncertain variables, two types of mean-semivariance diversified models for uncertain portfolio selection are proposed. Since the models are complex, a hybrid intelligent algorithm which is based on 99-method and genetic algorithm is designed to solve the models. In this hybrid intelligent algorithm, 99-method is applied to compute the expected value and semivariance of uncertain variables, and genetic algorithm is employed to seek the best allocation plan for portfolio selection. At last, several numerical examples are presented to illustrate the modelling idea and the effectiveness of the algorithm.

Investment Market Environment and Decision Making for Equity Portfolio Selection

Decision making for portfolio selection is based on financial theory of modern portfolio and capital asset pricing model that have shaped way in which academics and practitioners analyze investment performance. Most of investors act rationally and consider all available information in the decision making process for efficiency reflecting all available information for security prices. This study aimed to examine the relationship between equity investment market environment and its portfolio selection decision-making in Arusha. This study employed descriptive and correlation research design. Purposive sampling method was applied to select 48 participants. A questionnaire was administered to collect data. Findings revealed that there was very high chance of the investors to make income out of their investments. Majority of respondents reported that market equity investment was very adequate to guarantee income from investment (mean = 3.63), predict investment income for all the periods of investment (mean = 3.25), short term investment that was within 5 years (mean = 3.75), access to equity investment information (mean = 4), making proper decision on equity investment information (mean = 3.29) and was with adequate managerial skills (mean = 3.13). Results show that high return on investment (mean = 4.25) was extremely adequate for equity investment portfolio selection, diversification (mean = 3.33) and imitation and following (mean = 3.88) were very adequate for equity investment portfolio selection, investment flexibility (mean = 3.04) and investment status and prestige (mean = 3.13) were adequate for equity investment portfolio selection. There was significant relationship between market equtiy investment environment and diversified investment portfolio selection decision. This study may serve as an eye opener to investors and mangers of companies to plan for business projects and equity investments in Arusha. There is a need to provide investors with information flow from the Bank of Tanzania and other financial institutions on investments.

A Review on the Ensemble Framework for Sentiment Analysis

Sentiment analysis is an important task for the automated classification of positive and negative opinions by a machine. The approaches to this task can either be rule-based or machine learning, the later being the current trend due to its automaticity and versatility. An ensemble framework based on machine learning classifiers diversifies the text data and learning process in order to produce accurate sentiment classification. In this review paper, we highlight the various parts to consider when creating an ensemble of machine learning classifiers for sentiment analysis. Starting from the selection of suitable text features until the training of the machine learner, they all influence the accuracy of the system. The ensemble framework improves the accuracy of the system based on the concept that stronger classifiers compensate for the performance of weak classifiers. Concluding thought to the paper is the inclusion of diversified feature selection method into the ensemble framework to select useful features and reduce size of feature set.

Host selection in insects: reproductive interference shapes behavior of ovipositing females

AbstractIn nature, closely related species often utilize different host species, but it is still unclear what factors contribute to the evolution and maintenance of such diversified host selection. In this review, I describe how negative interspecific mating interactions (reproductive interference) can shape host selection by animals, focusing mainly on phytophagous and predatory insects. First, I explain an important premise of this hypothesis, which is that the adult reproductive site is the same as the feeding site for the offspring. Next, I describe several mathematical models and well‐studied empirical systems to show that reproductive interference can sufficiently drive and maintain different host selection between phylogenetically related species. Then, I argue for the first time that reproductive interference can cause an oviposition preference in insects that is not optimal for the survival and development of the offspring, as a result of maternal adaptive behavior that maximizes the mother's own fitness. Furthermore, I argue that in insects, reproductive interference probably shapes oviposition behavior before the female alights on the host (e.g., habitat preference), without affecting post‐alighting decision making. I would like to emphasize that these two arguments represent the novel approach to clarify the unrevealed pattern of complex insect oviposition behavior.