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Existing circular RNA (circRNA) databases have become essential for transcriptomics. However, most are unsuitable for mining in-depth information for candidate circRNA prioritization. To address this, we integrate circular transcript collections to develop the circAtlas database based on 1070 RNA-seq samples collected from 19 normal tissues across six vertebrate species. This database contains 1,007,087 highly reliable circRNAs, of which over 81.3% have been assembled into full-length sequences. We profile their expression pattern, conservation, and functional annotation. We describe a novel multiple conservation score, co-expression, and regulatory networks for circRNA annotation and prioritization. CircAtlas can be accessed at http://circatlas.biols.ac.cn/ .

Several species of herbivores feed on maize in field and storage setups, making the development of multiple insect resistance a critical breeding target. In this study, an association mapping panel of 341 tropical maize lines was evaluated in three field environments for resistance to fall armyworm (FAW), whilst bulked grains were subjected to a maize weevil (MW) bioassay and genotyped with Diversity Array Technology’s single nucleotide polymorphisms (SNPs) markers. A multi-locus genome-wide association study (GWAS) revealed 62 quantitative trait nucleotides (QTNs) associated with FAW and MW resistance traits on all 10 maize chromosomes, of which, 47 and 31 were discovered at stringent Bonferroni genome-wide significance levels of 0.05 and 0.01, respectively, and located within or close to multiple insect resistance genomic regions (MIRGRs) concerning FAW, SB, and MW. Sixteen QTNs influenced multiple traits, of which, six were associated with resistance to both FAW and MW, suggesting a pleiotropic genetic control. Functional prioritization of candidate genes (CGs) located within 10–30 kb of the QTNs revealed 64 putative GWAS-based CGs (GbCGs) showing evidence of involvement in plant defense mechanisms. Only one GbCG was associated with each of the five of the six combined resistance QTNs, thus reinforcing the pleiotropy hypothesis. In addition, through in silico co-functional network inferences, an additional 107 network-based CGs (NbCGs), biologically connected to the 64 GbCGs, and differentially expressed under biotic or abiotic stress, were revealed within MIRGRs. The provided multiple insect resistance physical map should contribute to the development of combined insect resistance in maize.

Software for large enterprises such as the enterprise resource planning (ERP) is more likely to be developed by a team of software developers where the functional requirements (FRs) are distributed in parallel developers. Therefore, development of pre-requisite FRs must be carefully timed to see which requirement is to be implemented first by assigning priority to some FRs over others, so that FRs can be made available on time to parallel developers. This research proposes a hybrid prioritization technique of minimal spanning trees (MST) and AHP called the spanning analytic hierarchical process (SAHP) for FRs prioritization by exploiting MST capability to prioritize large size software FRs with smaller pairwise comparisons but with more consistent results. Using Numerical Assignment (NA) technique, prioritized FRs from SAHP are assigned to priority groups such that top-priority groups contain high-priority FRs and low-priority groups contain low-priority FRs. low-priority group of FRs are dependent on high-priority groups. As a result, within each priority group, inter-dependencies in FRs are reduced for parallel developers. The proposed technique is evaluated on FRs of ODOO ERP and the results showed that SAHP reduces estimation time of parallel developers as compared to AHP and other techniques.

Background/Objectives: Population-specific genomic data are essential for understanding hepatocellular carcinoma (HCC) biology, particularly in underrepresented regions. This study aimed to perform exploratory single-nucleotide polymorphism (SNP)-array-based profiling of HCC tumor samples from Indonesian patients and to prioritize candidate functional variants using a systematic in silico framework. Methods: This retrospective cross-sectional study included 15 resected HCC cases with available formalin-fixed paraffin-embedded (FFPE) tumor tissue. Genome-wide SNP genotyping was performed using the Illumina Asian Screening Array. Following quality control and filtering, variants were annotated using the Ensembl Variant Effect Predictor. A case-only functional prioritization approach incorporating multiple in silico prediction tools was applied, followed by gene-level burden aggregation. Results: After multistep filtering, 11 samples and 104 prioritized variants were retained for analysis. Variants consisted predominantly of splice-region, missense, and regulatory changes. Gene-level burden analysis identified Nuclear Autoantigenic Sperm Protein (NASP, rs775916096) as the highest-ranked candidate gene, while G protein-coupled receptor 78 (GPR78, rs558447540) emerged as a secondary candidate with predicted functional annotations but currently limited biological evidence in HCC. Given the tumor-only design without matched normal tissue, the prioritized variants cannot be distinguished from rare germline variants. Conclusions: This exploratory SNP-array study provides a hypothesis-generating framework for functional variant prioritization in Indonesian HCC. NASP and GPR78 represent preliminary candidates that require validation in larger cohorts with matched normal tissue and sequencing-based confirmation.

Aim The ecosystem functions and services of coral reefs are critical for coastal communities worldwide. Due to conservation resource limitation, species need to be prioritized to protect desirable properties of biodiversity, such as functional diversity (FD), which has been associated with greater ecosystem functioning but is difficult to quantify directly. Selecting species to maximize phylogenetic diversity (PD) has been shown to indirectly capture FD in certain other taxa but not corals. Here, we test this hypothesis, the “phylogenetic gambit”, on corals within global marine protected areas (MPAs). Location Global coral reefs. Methods Based on the global distributions of reef corals, a complete species‐level phylogeny and trait data, we compared the FD of coral assemblages within MPAs when selected to maximize PD versus FD for assemblages selected randomly. The relationships between PD and FD were also tested as predictors of surrogacy. We then used coral FD and PD to perform spatial prioritization of reefs for protection and assessed the congruence between the two approaches. Results Selecting assemblages to maximize PD captured significantly more FD than a random subset of species for 83.1% of all selection scenarios across MPAs and would protect on average 18.7% more FD than random selection. Spatial prioritization analyses showed some mismatches between PD‐ and FD‐optimized planning units, particularly in the Tropical Western Atlantic, but the high degree of overlap between the optimizations for other reef regions lends further credence to the PD‐maximizing strategy in conserving coral FD. Main Conclusions A PD‐maximizing strategy generally protects greater FD of coral assemblages relative to random selection of species, suggesting that the “phylogenetic gambit” is valid for reef corals. There are risks, however, and the mismatches between PD‐maximized and FD‐maximized MPA networks highlight specific shortcomings of the PD‐maximization approach. Nevertheless, in data‐deficient circumstances, maximizing PD may provide a viable alternative.

Aim Rare species make substantial contributions to coastal ecosystem functions. Functional rarity (FR) and phylogenetic rarity (PR) are important features for biodiversity conservation. This work aimed to discuss the necessity and reasonableness of conserving fish FR and PR in coastal seas. Location China. Methods By compiling historical fish investigation data, joint species distribution modelling (JSDM) was applied to model fish communities in coastal China Seas. Biogeographic patterns of FR and PR were explored, and the effectiveness of current MPA networks in terms of match/mismatch with the hotspots of rarity was assessed. Results A total of 44 functionally rare species and 22 phylogenetically rare species were identified. Six of these species were both functionally and phylogenetically rare, and only one was listed as endangered on the Red List of the International Union for Conservation of Nature (IUCN). Functional rarity hotspots covered 10.27% of the coastal areas, which geographically converged in the southern and eastern coast of Taiwan, the Yangtze River Estuary and the Yellow River Estuary. Phylogenetic rarity hotspots only covered 3.06% of the coastal areas, which were sporadically distributed in the coastal East China Sea, the Bohai Sea and the northern Yellow Sea. Current marine‐protected areas (MPAs) only represented 16.16% of the FR hotspots and 20.48% of the PR hotspots, indicating substantial mismatched areas between the MPAs and the hotspots of FR and PR. Main Conclusions Only considering threatened species in conservation practices will omit functionally and phylogenetically rare species because FR and PR are not necessarily correlated with species threat status on the IUCN Red List. Functional rarity hotspots do not necessarily overlap with PR hotspots, and current MPAs mismatch the majority of these areas. We therefore advocate that conservation prioritization and expansion of MPA networks should account for FR and PR both at the species and site levels.

Aim Conservation planning and prioritization generally have focused on protecting taxa based on assessments of their long‐term persistence or on protecting habitats and sites with high species richness. An implicit assumption of these approaches is that species are equally different from each other. We propose metrics for conservation planning and prioritization that include consideration of differences among taxa in their functional characteristics to ensure long‐term maintenance of ecosystem functioning and services. Innovation We define metrics of functional distinctiveness, irregularity and singularity for a species. Functional distinctiveness is the mean distance in trait space of a species to all other species in a community. Functional irregularity is the variation in the proportional distances of a focal species to all other species based on a Hill function. Functional singularity is the product of those two metrics. These metrics can be weighted based on proportional abundance, biomass or frequency of occurrence. The metrics can be used to prioritize particular species for conservation based on their functional characteristics or to identify functionally distinct priority areas for conservation using the mean functional distinctiveness, irregularity and singularity of a set of species in an area. The metrics can be compared to the species richness of that area, thereby identifying areas that might have low species richness, but whose species are especially functionally distinct, providing important information of conservation relevance. Main conclusions Applying these metrics to data on the global distributions of parrots, we identified species that are not of current conservation concern because they are geographically widespread, but which might be prioritized due to their functional singularity (e.g., the scarlet macaw). We also identified areas that are species poor and not generally considered noteworthy for their parrot fauna, but that contain a fauna that is functionally singular (e.g., Chile). Together, these metrics broaden the criteria used for conservation prioritization.

Plants are fascinating and complex organisms. A comprehensive understanding of the organization, function and evolution of plant genes is essential to disentangle important biological processes and to advance crop engineering and breeding strategies. The ultimate aim in deciphering complex biological processes is the discovery of causal genes and regulatory mechanisms controlling these processes. The recent surge of omics data has opened the door to a system-wide understanding of the flow of biological information underlying complex traits. However, dealing with the corresponding large data sets represents a challenging endeavor that calls for the development of powerful bioinformatics methods. A popular approach is the construction and analysis of gene networks. Such networks are often used for genome-wide representation of the complex functional organization of biological systems. Network based on similarity in gene expression are called (gene) co-expression networks. One of the major application of gene co-expression networks is the functional annotation of unknown genes. Constructing co-expression networks is generally straightforward. In contrast, the resulting network of connected genes can become very complex, which limits its biological interpretation. Several strategies can be employed to enhance the interpretation of the networks. A strategy in coherence with the biological question addressed needs to be established to infer reliable networks. Additional benefits can be gained from network-based strategies using prior knowledge and data integration to further enhance the elucidation of gene regulatory relationships. As a result, biological networks provide many more applications beyond the simple visualization of co-expressed genes. In this study we review the different approaches for co-expression network inference in plants. We analyse integrative genomics strategies used in recent studies that successfully identified candidate genes taking advantage of gene co-expression networks. Additionally, we discuss promising bioinformatics approaches that predict networks for specific purposes.

•We use machine learning models in a relatively large resting-state fMRI data sample.•Functional connectivity predicts the individualized tendency of self-prioritization.•Interaction among default mode, cognitive control, and salience networks contributes.•Some subcortical regions like the thalamus also play an essential role.•Results support the fundamental-self hypothesis and advance a new neural model. Although the self has traditionally been viewed as a higher-order mental function by most theoretical frameworks, recent research advocates a fundamental self hypothesis, viewing the self as a baseline function of the brain embedded within its spontaneous activities, which dynamically regulates cognitive processing and subsequently guides behavior. Understanding this fundamental self hypothesis can reveal where self-biased behaviors emerge and to what extent brain signals at rest can predict such biased behaviors. To test this hypothesis, we investigated the association between spontaneous neural connectivity and robust self-bias in a perceptual matching task using resting-state functional magnetic resonance imaging (fMRI) in 348 young participants. By decoding whole-brain connectivity patterns, the support vector regression model produced the best predictions of the magnitude of self-bias in behavior, which was evaluated via a nested cross-validation procedure. The out-of-sample generalizability was further authenticated using an external dataset of older adults. The functional connectivity results demonstrated that self-biased behavior was associated with distinct connections between the default mode, cognitive control, and salience networks. Consensus network and computational lesion analyses further revealed contributing regions distributed across six networks, extending to additional nodes, such as the thalamus, whose role in self-related processing remained unclear. These results provide evidence that self-biased behavior derives from spontaneous neural connectivity, supporting the fundamental self hypothesis. Thus, we propose an integrated neural network model of this fundamental self that synthesizes previous theoretical models and portrays the brain mechanisms by which the self emerges at rest internally and regulates responses to the external environment.

Intellectual disability (ID) can be caused by non-genetic and genetic factors, the latter being responsible for more than 1700 ID-related disorders. The broad ID phenotypic and genetic heterogeneity, as well as the difficulty in the establishment of the inheritance pattern, often result in a delay in the diagnosis. It has become apparent that massive parallel sequencing can overcome these difficulties. In this review we address: (i) ID genetic aetiology, (ii) clinical/medical settings testing, (iii) massive parallel sequencing, (iv) variant filtering and prioritization, (v) variant classification guidelines and functional studies, and (vi) ID diagnostic yield. Furthermore, the need for a constant update of the methodologies and functional tests, is essential. Thus, international collaborations, to gather expertise, data and resources through multidisciplinary contributions, are fundamental to keep track of the fast progress in ID gene discovery.