HBeeID: a molecular tool that identifies honey bee subspecies from different geographic populations.

BACKGROUND: Honey bees are the principal commercial pollinators. Along with other arthropods, they are increasingly under threat from anthropogenic factors such as the incursion of invasive honey bee subspecies, pathogens and parasites. Better tools are needed to identify bee subspecies. Genomic data for economic and ecologically important organisms is increasing, but in its basic form its practical application to address ecological problems is limited. RESULTS: We introduce HBeeID a means to identify honey bees. The tool utilizes a knowledge-based network and diagnostic SNPs identified by discriminant analysis of principle components and hierarchical agglomerative clustering. Tests of HBeeID showed that it identifies African, Americas-Africanized, Asian, and European honey bees with a high degree of certainty even when samples lack the full 272 SNPs of HBeeID. Its prediction capacity decreases with highly admixed samples. CONCLUSION: HBeeID is a high-resolution genomic, SNP based tool, that can be used to identify honey bees and screen species that are invasive. Its flexible design allows for future improvements via sample data additions from other localities.

Effects of spatial variability in vegetation phenology, climate, landcover, biodiversity, topography, and soil property on soil respiration across a coastal ecosystem.

Coastal terrestrial-aquatic interfaces (TAIs) are crucial contributors to global biogeochemical cycles and carbon exchange. The soil carbon dioxide (CO2) efflux in these transition zones is however poorly understood due to the high spatiotemporal dynamics of TAIs, as various sub-ecosystems in this region are compressed and expanded by complex influences of tides, changes in river levels, climate, and land use. We focus on the Chesapeake Bay region to (i) investigate the spatial heterogeneity of the coastal ecosystem and identify spatial zones with similar environmental characteristics based on the spatial data layers, including vegetation phenology, climate, landcover, diversity, topography, soil property, and relative tidal elevation; (ii) understand the primary driving factors affecting soil respiration within sub-ecosystems of the coastal ecosystem. Specifically, we employed hierarchical clustering analysis to identify spatial regions with distinct environmental characteristics, followed by the determination of main driving factors using Random Forest regression and SHapley Additive exPlanations. Maximum and minimum temperature are the main drivers common to all sub-ecosystems, while each region also has additional unique major drivers that differentiate them from one another. Precipitation exerts an influence on vegetated lands, while soil pH value holds importance specifically in forested lands. In croplands characterized by high clay content and low sand content, the significant role is attributed to bulk density. Wetlands demonstrate the importance of both elevation and sand content, with clay content being more relevant in non-inundated wetlands than in inundated wetlands. The topographic wetness index significantly contributes to the mixed vegetation areas, including shrub, grass, pasture, and forest. Additionally, our research reveals that dense vegetation land covers and urban/developed areas exhibit distinct soil property drivers. Overall, our research demonstrates an efficient method of employing various open-source remote sensing and GIS datasets to comprehend the spatial variability and soil respiration mechanisms in coastal TAI. There is no one-size-fits-all approach to modeling carbon fluxes released by soil respiration in coastal TAIs, and our study highlights the importance of further research and monitoring practices to improve our understanding of carbon dynamics and promote the sustainable management of coastal TAIs.

Developing a cluster-based approach for deciphering complexity in individuals with neurodevelopmental differences.

Objective: Individuals with neurodevelopmental disorders such as global developmental delay (GDD) present both genotypic and phenotypic heterogeneity. This diversity has hampered developing of targeted interventions given the relative rarity of each individual genetic etiology. Novel approaches to clinical trials where distinct, but related diseases can be treated by a common drug, known as basket trials, which have shown benefits in oncology but have yet to be used in GDD. Nonetheless, it remains unclear how individuals with GDD could be clustered. Here, we assess two different approaches: agglomerative and divisive clustering. Methods: Using the largest cohort of individuals with GDD, which is the Deciphering Developmental Disorders (DDD), characterized using a systematic approach, we extracted genotypic and phenotypic information from 6,588 individuals with GDD. We then used a k-means clustering (divisive) and hierarchical agglomerative clustering (HAC) to identify subgroups of individuals. Next, we extracted gene network and molecular function information with regard to the clusters identified by each approach. Results: HAC based on phenotypes identified in individuals with GDD revealed 16 clusters, each presenting with one dominant phenotype displayed by most individuals in the cluster, along with other minor phenotypes. Among the most common phenotypes reported were delayed speech, absent speech, and seizure. Interestingly, each phenotypic cluster molecularly included several (3-12) gene sub-networks of more closely related genes with diverse molecular function. k-means clustering also segregated individuals harboring those phenotypes, but the genetic pathways identified were different from the ones identified from HAC. Conclusion: Our study illustrates how divisive (k-means) and agglomerative clustering can be used in order to group individuals with GDD for future basket trials. Moreover, the result of our analysis suggests that phenotypic clusters should be subdivided into molecular sub-networks for an increased likelihood of successful treatment. Finally, a combination of both agglomerative and divisive clustering may be required for developing of a comprehensive treatment.

PEG-induced physiological drought for screening winter wheat genotypes sensitivity - integrated biochemical and chlorophyll a fluorescence analysis.

This study aimed to screen different winter wheat genotypes at the onset of metabolic changes induced by water deficit to comprehend possible adaptive features of photosynthetic apparatus function and structure to physiological drought. The drought treatment was the most influential variable affecting plant growth and relative water content, and genotype variability determined with what intensity varieties of winter wheat seedlings responded to water deficit. PEG-induced drought, as expected, changed phenomenological energy fluxes and the efficiency with which an electron is transferred to final PSI acceptors. Based on the effect size, fluorescence parameters were grouped to represent photochemical parameters, that is, the donor and acceptor side of PSII (PC1); the thermal phase of the photosynthetic process, or the electron flow around PSI, and the chain of electrons between PSII and PSI (PC2); and phenomenological energy fluxes per cross-section (PC3). Furthermore, four distinct clusters of genotypes were discerned based on their response to imposed physiological drought, and integrated analysis enabled an explanation of their reactions' specificity. The most reliable JIP-test parameters for detecting and comparing the drought impact among tested genotypes were the variable fluorescence at K, L, I step, and PITOT. To conclude, developing and improving screening methods for identifying and evaluating functional relationships of relevant characteristics that are useful for acclimation, acclimatization, and adaptation to different types of drought stress can contribute to the progress in breeding research of winter wheat drought-tolerant lines.

Classification of air quality zones and fine particulate matter sensitive areas by risk assessment approach.

Many studies have shown that fine particulate matter can cause health problems. Thus, effectively controlling fine particulate matter concentration is an important issue around the world. The Taiwan Environmental Protection Administration (TWEPA) divides Taiwan into seven air quality zones based on counties and cities for managing air quality and analyzing pollution transmission. However, this artificial division by administrative areas relatively poorly match natural conditions and topographical and geographic factors and hence poorly represent air quality characteristics. This study proposes an air quality sensitive map analysis framework, which uses hierarchical agglomerative clustering with empirical orthogonal function and analysis of variance methods, to provide more detailed, reasonable, and township-level air quality zones incorporating the different spatial-temporal characteristics over the region. The risk concept is introduced to evaluate PM2.5 risk sensitivity for each administrative district, combining three aspects: hazard (PM2.5 exceedance probability), exposure (population density of sensitive groups), and vulnerability (average wind speed). Considering air quality spatial-temporal characteristics, Taiwan can be optimally divided into 14 air quality zones. PM2.5 risk is highest for western inland towns than western coastal towns, with eastern regions exhibiting least risk. Adopting the proposed air quality zones and clarifying high risk areas allows PM2.5 causes to be identified for different air quality zones. This allows a targeted control strategy for high risk areas to effectively improve domestic air quality. The proposed model also provides powerful reference for environmental management and environmental impact assessment for future construction and development.

Hybrid learning method based on feature clustering and scoring for enhanced COVID-19 breath analysis by an electronic nose.

Breath pattern analysis based on an electronic nose (e-nose), which is a noninvasive, fast, and low-cost method, has been continuously used for detecting human diseases, including the coronavirus disease 2019 (COVID-19). Nevertheless, having big data with several available features is not always beneficial because only a few of them will be relevant and useful to distinguish different breath samples (i.e., positive and negative COVID-19 samples). In this study, we develop a hybrid machine learning-based algorithm combining hierarchical agglomerative clustering analysis and permutation feature importance method to improve the data analysis of a portable e-nose for COVID-19 detection (GeNose C19). Utilizing this learning approach, we can obtain an effective and optimum feature combination, enabling the reduction by half of the number of employed sensors without downgrading the classification model performance. Based on the cross-validation test results on the training data, the hybrid algorithm can result in accuracy, sensitivity, and specificity values of (86 ± 3)%, (88 ± 6)%, and (84 ± 6)%, respectively. Meanwhile, for the testing data, a value of 87% is obtained for all the three metrics. These results exhibit the feasibility of using this hybrid filter-wrapper feature-selection method to pave the way for optimizing the GeNose C19 performance.

Image-level trajectory inference of tau pathology using variational autoencoder for Flortaucipir PET.

PURPOSE: Alzheimer's disease (AD) studies revealed that abnormal deposition of tau spreads in a specific spatial pattern, namely Braak stage. However, Braak staging is based on post mortem brains, each of which represents the cross section of the tau trajectory in disease progression, and numerous studies were reported that do not conform to that model. This study thus aimed to identify the tau trajectory and quantify the tau progression in a data-driven approach with the continuous latent space learned by variational autoencoder (VAE). METHODS: A total of 1080 [18F]Flortaucipir brain positron emission tomography (PET) images were collected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. VAE was built to compress the hidden features from tau images in latent space. Hierarchical agglomerative clustering and minimum spanning tree (MST) were applied to organize the features and calibrate them to the tau progression, thus deriving pseudo-time. The image-level tau trajectory was inferred by continuously sampling across the calibrated latent features. We assessed the pseudo-time with regard to tau standardized uptake value ratio (SUVr) in AD-vulnerable regions, amyloid deposit, glucose metabolism, cognitive scores, and clinical diagnosis. RESULTS: We identified four clusters that plausibly capture certain stages of AD and organized the clusters in the latent space. The inferred tau trajectory agreed with the Braak staging. According to the derived pseudo-time, tau first deposits in the parahippocampal and amygdala, and then spreads to the fusiform, inferior temporal lobe, and posterior cingulate. Prior to the regional tau deposition, amyloid accumulates first. CONCLUSION: The spatiotemporal trajectory of tau progression inferred in this study was consistent with Braak staging. The profile of other biomarkers in disease progression agreed well with previous findings. We addressed that this approach additionally has the potential to quantify tau progression as a continuous variable by taking a whole-brain tau image into account.

Pharmacological treatment profiles in the FACE-BD cohort: Treatment description and complete data for bipolar subtypes.

In the current study, we provide the list of pharmacological interventions applied during the one-year follow-up period of the Pharmacological treatment profiles in the FACE-BD cohort study. These data show the treatments used in the new clusters formed in this previous study and also in usual bipolarity subtypes. The proportion of each treatment used during the follow-up was calculated. Days on each treatment were also included in this dataset. The complete clinical and paraclinical data analyzed for clusters and bipolar subtypes were included in this dataset. Socio-demographic self-administered and clinician-administered scales, clinical evaluation during the follow-up, psychiatric and somatic comorbidities, and blood tests are shown in this material.

Identifying Distinct High Unmet-Need Phenotypes and Their Associated Bladder Cancer Patient Demographic, Clinical, Psychosocial, and Functional Characteristics: Results of Two Clustering Methods.

OBJECTIVES: We explored phenotypes of high unmet need of patients with bladder cancer and their associated patient demographic, clinical, psychosocial, and functional characteristics. DATA SOURCES: Patients (N=159) were recruited from the Bladder Cancer Advocacy Network and completed an online survey measuring unmet needs (BCNAS-32), quality of life (FACT-Bl), anxiety and depression (HADS), coping (BRIEF Cope), social support (SPS), and self-efficacy beliefs (GSE). Hierarchical agglomerative (HA) and partitioning clustering (PC) analyses were used to identify and confirm high unmet-need phenotypes and their associated patient characteristics. Results showed a two-cluster solution; a cluster of patients with high unmet needs (17% and 34%, respectively) and a cluster of patients with low-moderate unmet needs (83% and 66%, respectively). These two methods showed moderate agreement (κ=0.57) and no significant differences in patient demographic and clinical characteristics between the two groups. However, the high-need group identified by the HA clustering method had significantly higher psychological (81 vs 66, p < .05), health system (93 vs 74, p < .001), daily living (93 vs 74, P < .001), sexuality (97 vs 69, P < .001), logistics (84 vs 69, P < .001), and communication (90 vs 76, P < .001) needs. This group also had worse quality of life and emotional adjustment and lower personal and social resources (P < .001) compared with the group identified by the PC method. CONCLUSION: A significant proportion of patients with bladder cancer continues to have high unique but inter-related phenotypes of needs based on the HA clustering method. IMPLICATIONS FOR NURSING PRACTICE: Identifying characteristics of the most vulnerable patients will help tailor support programs to assist these patients with their unmet needs.

Obtaining the potential number of object models/atlases needed in medical image analysis.

Medical image processing and analysis operations, particularly segmentation, can benefit a great deal from prior information encoded to capture variations over a population in form, shape, anatomic layout, and image appearance of objects. Model/atlas-based methods are extant in medical image segmentation. Although multi-atlas/ multi-model methods have shown improved accuracy for image segmentation, if the atlases/models do not cover representatively the distinct groups, then the methods may not be generalizable to new populations. In a previous study, we have given an answer to address the following problem at image level: How many models/ atlases are needed for optimally encoding prior information to address the differing body habitus factor in a population? However, the number of models for different objects may be different, and at the image level, it may not be possible to infer the number of models needed for each object. So, the modified question to which we are now seeking an answer to in this paper is: How many models/ atlases are needed for optimally encoding prior information to address the differing body habitus factor for each object in a body region? To answer this question, we modified our method in the previous study for seeking the optimum grouping for a given population of images but focusing on the individual objects. We present our results on head and neck computed tomography (CT) scans of 298 patients.

Adjacency-constrained hierarchical clustering of a band similarity matrix with application to genomics.

BACKGROUND: Genomic data analyses such as Genome-Wide Association Studies (GWAS) or Hi-C studies are often faced with the problem of partitioning chromosomes into successive regions based on a similarity matrix of high-resolution, locus-level measurements. An intuitive way of doing this is to perform a modified Hierarchical Agglomerative Clustering (HAC), where only adjacent clusters (according to the ordering of positions within a chromosome) are allowed to be merged. But a major practical drawback of this method is its quadratic time and space complexity in the number of loci, which is typically of the order of 10 4 to 10 5 for each chromosome. RESULTS: By assuming that the similarity between physically distant objects is negligible, we are able to propose an implementation of adjacency-constrained HAC with quasi-linear complexity. This is achieved by pre-calculating specific sums of similarities, and storing candidate fusions in a min-heap. Our illustrations on GWAS and Hi-C datasets demonstrate the relevance of this assumption, and show that this method highlights biologically meaningful signals. Thanks to its small time and memory footprint, the method can be run on a standard laptop in minutes or even seconds. AVAILABILITY AND IMPLEMENTATION: Software and sample data are available as an R package, adjclust, that can be downloaded from the Comprehensive R Archive Network (CRAN).

How many models/atlases are needed as priors for capturing anatomic population variations?

Many medical image processing and analysis operations can benefit a great deal from prior information encoded in the form of models/atlases to capture variations over a population in form, shape, anatomic layout, and image appearance of objects. However, two fundamental questions have not been addressed in the literature: "How many models/atlases are needed for optimally encoding prior information to address the differing body habitus factor in that population?" and "Images of how many subjects in the given population are needed to optimally harness prior information?" We propose a method to seek answers to these questions. We assume that there is a well-defined body region of interest and a subject population under consideration, and that we are given a set of representative images of the body region for the population. After images are trimmed to the exact body region, a hierarchical agglomerative clustering algorithm partitions the set of images into a specified number of groups by using pairwise image (dis)similarity as a cost function. Optionally the images may be pre-registered among themselves prior to this partitioning operation. We define a measure called Residual Dissimilarity (RD) to determine the goodness of each partition. We then ascertain how RD varies as a function of the number of elements in the partition for finding the optimum number(s) of groups. Breakpoints in this function are taken as the recommended number of groups/models/atlases. Our results from analysis of sizeable CT data sets of adult patients from two body regions - thorax (346) and head and neck (298) - can be summarized as follows. (1) A minimum of 5 to 8 groups (or models/atlases) seems essential to properly capture information about differing anatomic forms and body habitus. (2) A minimum of 150 images from different subjects in a population seems essential to cover the anatomical variations for a given body region. (3) In grouping, body habitus variations seem to override differences due to other factors such as gender, with/without contrast enhancement in image acquisition, and presence of moderate pathology. This method may be helpful for constructing high quality models/atlases from a sufficiently large population of images and in optimally selecting the training image sets needed in deep learning strategies.

Hierarchical clustering reveals unique features in the diel dynamics of metabolites in the CAM orchid Phalaenopsis.

Crassulacean acid metabolism (CAM) is a major adaptation of photosynthesis that involves temporally separated phases of CO2 fixation and accumulation of organic acids at night, followed by decarboxylation and refixation of CO2 by the classical C3 pathway during the day. Transitory reserves such as soluble sugars or starch are degraded at night to provide the phosphoenolpyruvate (PEP) and energy needed for initial carboxylation by PEP carboxylase. The primary photosynthetic pathways in CAM species are well known, but their integration with other pathways of central C metabolism during different phases of the diel light-dark cycle is poorly understood. Gas exchange was measured in leaves of the CAM orchid Phalaenopsis 'Edessa' and leaves were sampled every 2 h during a complete 12-h light-12-h dark cycle for metabolite analysis. A hierarchical agglomerative clustering approach was employed to explore the diel dynamics and relationships of metabolites in this CAM species, and compare these with those in model C3 species. High levels of 3-phosphoglycerate (3PGA) in the light activated ADP-glucose pyrophosphorylase, thereby enhancing production of ADP-glucose, the substrate for starch synthesis. Trehalose 6-phosphate (T6P), a sugar signalling metabolite, was also correlated with ADP-glucose, 3PGA and PEP, but not sucrose, over the diel cycle. Whether or not this indicates a different function of T6P in CAM plants is discussed. T6P levels were low at night, suggesting that starch degradation is regulated primarily by circadian clock-dependent mechanisms. During the lag in starch degradation at dusk, carbon and energy could be supplied by rapid consumption of a large pool of aconitate that accumulates in the light. Our study showed similarities in the diel dynamics and relationships between many photosynthetic metabolites in CAM and C3 plants, but also revealed some major differences reflecting the specialized metabolic fluxes in CAM plants, especially during light-dark transitions and at night.

Machine learning-based identification of endogenous cellular microRNA sponges against viral microRNAs.

A "miRNA sponge" is an artificial oligonucleotide-based miRNA inhibitor containing multiple binding sites for a specific miRNA. Each miRNA sponge can bind and sequester several miRNA copies, thereby decreasing the cellular levels of the target miRNA. In addition to developing artificial miRNA sponges, scientists have sought endogenous RNA transcripts and found that long non-coding RNAs, competing endogenous RNAs, pseudogenes, circular RNAs, and coding RNAs could act as miRNA sponges under precise conditions. Here we present a computational approach for the prediction of endogenous human miRNA sponge candidates targeting viral miRNAs derived from pathogenic human viruses. Viral miRNA binding sites were predicted using a newly-developed machine learning-based method, and candidate interactions between miRNAs and sponge RNAs were experimentally validated using luciferase reporter assay, western blot analysis, and flow cytometry. We found that BX649188.1 functions as a potential natural miRNA sponge against kshv-miR-K12-7-3p.

Context-driven automatic subgraph creation for literature-based discovery.

BACKGROUND: Literature-based discovery (LBD) is characterized by uncovering hidden associations in non-interacting scientific literature. Prior approaches to LBD include use of: (1) domain expertise and structured background knowledge to manually filter and explore the literature, (2) distributional statistics and graph-theoretic measures to rank interesting connections, and (3) heuristics to help eliminate spurious connections. However, manual approaches to LBD are not scalable and purely distributional approaches may not be sufficient to obtain insights into the meaning of poorly understood associations. While several graph-based approaches have the potential to elucidate associations, their effectiveness has not been fully demonstrated. A considerable degree of a priori knowledge, heuristics, and manual filtering is still required. OBJECTIVES: In this paper we implement and evaluate a context-driven, automatic subgraph creation method that captures multifaceted complex associations between biomedical concepts to facilitate LBD. Given a pair of concepts, our method automatically generates a ranked list of subgraphs, which provide informative and potentially unknown associations between such concepts. METHODS: To generate subgraphs, the set of all MEDLINE articles that contain either of the two specified concepts (A, C) are first collected. Then binary relationships or assertions, which are automatically extracted from the MEDLINE articles, called semantic predications, are used to create a labeled directed predications graph. In this predications graph, a path is represented as a sequence of semantic predications. The hierarchical agglomerative clustering (HAC) algorithm is then applied to cluster paths that are bounded by the two concepts (A, C). HAC relies on implicit semantics captured through Medical Subject Heading (MeSH) descriptors, and explicit semantics from the MeSH hierarchy, for clustering. Paths that exceed a threshold of semantic relatedness are clustered into subgraphs based on their shared context. Finally, the automatically generated clusters are provided as a ranked list of subgraphs. RESULTS: The subgraphs generated using this approach facilitated the rediscovery of 8 out of 9 existing scientific discoveries. In particular, they directly (or indirectly) led to the recovery of several intermediates (or B-concepts) between A- and C-terms, while also providing insights into the meaning of the associations. Such meaning is derived from predicates between the concepts, as well as the provenance of the semantic predications in MEDLINE. Additionally, by generating subgraphs on different thematic dimensions (such as Cellular Activity, Pharmaceutical Treatment and Tissue Function), the approach may enable a broader understanding of the nature of complex associations between concepts. Finally, in a statistical evaluation to determine the interestingness of the subgraphs, it was observed that an arbitrary association is mentioned in only approximately 4 articles in MEDLINE on average. CONCLUSION: These results suggest that leveraging the implicit and explicit semantics provided by manually assigned MeSH descriptors is an effective representation for capturing the underlying context of complex associations, along multiple thematic dimensions in LBD situations.