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In this paper, we study a geometrical inequality conjecture which states that for any four points on a hemisphere with the unit radius, the largest sum of distances between the points is 4 + 4 2 , the best configuration is a regular square inscribed to the equator, and for any five points, the largest sum is 5 5 + 2 5 and the best configuration is the regular pentagon inscribed to the equator. We prove that the conjectured configurations are local optimal, and construct a rectangular neighborhood of the local maximum point in the related feasible set, whose size is explicitly determined, and prove that (1): the objective function is bounded by a quadratic polynomial which takes the local maximum point as the unique critical point in the neighborhood, and (2): the remaining part of the feasible set can be partitioned into a finite union of a large number of very small cubes so that on each small cube, the conjecture can be verified by estimating the objective function with exact numerical computation. We also explain the method for constructing the neighborhoods and upper-bound quadratic polynomials in detail and describe the computation process outside the constructed neighborhoods briefly. [ABSTRACT FROM AUTHOR]

Corona Virus Disease 2019 (COVID-19) not only causes respiratory system damage, but also imposes strain on the cardiovascular system. Vascular endothelial cells and cardiomyocytes play an important role in cardiac function. The aberrant expression of genes in vascular endothelial cells and cardiomyocytes can lead to cardiovascular diseases. In this study, we sought to explain the influence of respiratory syndrome coronavirus 2 (SARS-CoV-2) infection on the gene expression levels of vascular endothelial cells and cardiomyocytes. We designed an advanced machine learning-based workflow to analyze the gene expression profile data of vascular endothelial cells and cardiomyocytes from patients with COVID-19 and healthy controls. An incremental feature selection method with a decision tree was used in building efficient classifiers and summarizing quantitative classification genes and rules. Some key genes, such as MALAT1, MT-CO1, and CD36, were extracted, which exert important effects on cardiac function, from the gene expression matrix of 104,182 cardiomyocytes, including 12,007 cells from patients with COVID-19 and 92,175 cells from healthy controls, and 22,438 vascular endothelial cells, including 10,812 cells from patients with COVID-19 and 11,626 cells from healthy controls. The findings reported in this study may provide insights into the effect of COVID-19 on cardiac cells and further explain the pathogenesis of COVID-19, and they may facilitate the identification of potential therapeutic targets. [ABSTRACT FROM AUTHOR]

[Display omitted] • "Yardang-controlled dune" is systematically illustrated with morphology and dynamics. • In three patterns, dunes form before, beside and behind the yardangs. • CFD simulations reveal that the yardang-induced low-velocity winds shape the dunes. • Remote sensing measurements support the CFD, which agrees with three patterns. • The aspect ratios of the streamlined yardangs control the linear dune formation. The yardang-dune coexistence and the control of yardangs on dune formation reveal the joint action of wind erosion and deposition. Yardangs exhibit distinctive characteristics of streamlining and clustering, which apply unique and complicated impacts on the wind field and sand transport. However, previous studies lack systematic investigation on how yardangs exert control over dune morphology, and the intricate wind dynamics and mechanisms involved remain unclear. In this study, the "yardang-controlled dune" is systematically illustrated, supported by thorough studies of the yardang-controlled dune morphology and dynamics. Through the monitoring of the 6722 km2 dunes in the Qaidam Basin (QB), three typical yardang-dune coexistence patterns are identified and summarized: Pattern I-windward dune, crescentic dunes form on the upwind sides of the sparsely arranged yardangs; Pattern II-corridor dune, crescentic dunes form beside the narrowly arranged yardangs in the corridors and migrate; Pattern III-leeward dune, linear dune forms on the downwind side from isolated yardang tail. Computational fluid dynamics (CFD) modeling is used to analyze the dynamics and mechanisms of the three patterns and the corresponding real yardang topographies. CFD simulations show that with the control of yardangs, the winds in the locations corresponding to the dunes have significantly low velocities, leading to the wind entrainment decrease, sand accumulation and subsequent dune formation. CFD also reveals that the aspect ratios of yardangs control the formations of wind shadows and linear dunes on the yardang tails. The simulation results agree well with the actual landforms observed in the QB of the three patterns. Additionally, the dune leeward slope width and migration velocity statistics based on remote sensing provide further support for the observations and simulations. This study sheds light on the intricate influences of yardangs on wind deposition, underscoring their role in shaping the landform of dunes. [ABSTRACT FROM AUTHOR]

Southwest Indian Ridge (SWIR) is a typical ultra-slow spreading ridge which has been extensively studied based on geophysical survey, geochemical sampling and morphological feature extraction. In this paper, based on the high-resolution multibeam bathymetric data, we extract seven types of topography, including fault, axial rift, axial volcanic ridge (AVR), axial trough, AVR's remnant, volcanic cone and oceanic core complex (OCC). We perform quantitative morphotectonic statistic, verifying the relations between the topography and magmato-tectonic mechanism of SWIR 49–50°E. The analyzed results of flank topography show that the average seafloor depth of the southern flank is shallower than that of the northern flank. The Total Cumulative Length of the faults in the northern and southern flank is 624.6 km and 948.2 km. The number, Total density, Average Length and Maximum Length of the faults in the southern flank are higher than those in the northern flank. An OCC and detachment faults locate in the southern flank. The asymmetry in flank topography indicates the asymmetric spreading mode of the study area. The analyzed results of the axial topography show that AVRs and troughs develop in the rift. The rift is narrower and shallower in the AVR regions, and wider and deeper in the trough regions. These observations support the partition of magmatic and less-magmatic stretching in the study area, and indicate the relation between the variation of rift's depth-width and the magmato-tectonic accretion. • Topographic indices reveal the magmato-tectonic mechanism quantitatively. • Asymmetry of faults in two flanks indicates the asymmetric spreading of the region. • Depth, width and dip of axial rift are related with the magmato-tectonic accretion. [ABSTRACT FROM AUTHOR]

Geospatial data, especially remote sensing (RS) data, are of significant importance for public services and production activities. Expertise is critical in processing raw data, generating geospatial information, and acquiring domain knowledge and other remote sensing applications. However, existing geospatial service platforms are more oriented towards the professional users in the implementation process and final application. Building appropriate geographic applications for non-professionals remains a challenge. In this study, a geospatial data service architecture is designed that links desktop geographic information system (GIS) software and cloud-based platforms to construct an efficient user collaboration platform. Based on the scalability of the platform, four web apps with different themes are developed. Data in the fields of ecology, oceanography, and geology are uploaded to the platform by the users. In this pilot phase, the gap between non-specialized users and experts is successfully bridged, demonstrating the platform's powerful interactivity and visualization. The paper finally evaluates the capability of building spatial data infrastructures (SDI) based on GeoNode and discusses the current limitations. The support for three-dimensional data, the improvement of metadata creation and management, and the fostering of an open geo-community are the next steps. [ABSTRACT FROM AUTHOR]

Summary Bisulfite sequencing (BS-seq) is currently the gold standard for measuring genome-wide DNA methylation profiles at single-nucleotide resolution. Most analyses focus on mean CpG methylation and ignore methylation states on the same DNA fragments [DNA methylation haplotypes (mHaps)]. Here, we propose mHap, a simple DNA mHap format for storing DNA BS-seq data. This format reduces the size of a BAM file by 40- to 140-fold while retaining complete read-level CpG methylation information. It is also compatible with the Tabix tool for fast and random access. We implemented a command-line tool, mHapTools, for converting BAM/SAM files from existing platforms to mHap files as well as post-processing DNA methylation data in mHap format. With this tool, we processed all publicly available human reduced representation bisulfite sequencing data and provided these data as a comprehensive mHap database. Availability and implementation https://jiantaoshi.github.io/mHap/index.html. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

The human papillomavirus (HPV), a common virus that infects the reproductive tract, may lead to malignant changes within the infection area in certain cases and is directly associated with such cancers as cervical cancer, anal cancer, and vaginal cancer. Identification of novel HPV infection related genes can lead to a better understanding of the specific signal pathways and cellular processes related to HPV infection, providing information for the development of more efficient therapies. In this study, several novel HPV infection related genes were predicted by a computation method based on the known genes involved in HPV infection from HPVbase. This method applied the algorithm of random walk with restart (RWR) to a protein-protein interaction (PPI) network. The candidate genes were further filtered by the permutation and association tests. These steps eliminated genes occupying special positions in the PPI network and selected key genes with strong associations to known HPV infection related genes based on the interaction confidence and functional similarity obtained from published databases, such as STRING, gene ontology (GO) terms and KEGG pathways. Our study identified 104 novel HPV infection related genes, a number of which were confirmed to relate to the infection processes and complications of HPV infection, as reported in the literature. These results demonstrate the reliability of our method in identifying HPV infection related genes. This article is part of a Special Issue entitled: Accelerating Precision Medicine through Genetic and Genomic Big Data Analysis edited by Yudong Cai & Tao Huang. [ABSTRACT FROM AUTHOR]

Deoxyribonucleic acid (DNA) methylation (DNAm) is an important epigenetic mechanism that plays a role in chromatin structure and transcriptional regulation. Elucidating the relationship between DNAm and gene expression is of great importance for understanding its role in transcriptional regulation. The conventional approach is to construct machine-learning-based methods to predict gene expression based on mean methylation signals in promoter regions. However, this type of strategy only explains about 25% of gene expression variation, and hence is inadequate in elucidating the relationship between DNAm and transcriptional activity. In addition, using mean methylation as input features neglects the heterogeneity of cell populations that can be reflected by DNAm haplotypes. We here developed TRAmaHap, a novel deep-learning framework that predicts gene expression by utilizing the characteristics of DNAm haplotypes in proximal promoters and distal enhancers. Using benchmark data of human and mouse normal tissues, TRAmHap shows much higher accuracy than existing machine-learning based methods, by explaining 60~80% of gene expression variation across tissue types and disease conditions. Our model demonstrated that gene expression can be accurately predicted by DNAm patterns in promoters and long-range enhancers as far as 25 kb away from transcription start site, especially in the presence of intra-gene chromatin interactions. [ABSTRACT FROM AUTHOR]

Background: Multiple myeloma (MM) is a clinically and biologically heterogeneous plasma‐cell malignancy. Despite extensive research, disease heterogeneity and relapse remain a big challenge in MM therapeutics. We tried to dissect this disease and identify novel biomarkers for patient stratification and treatment outcome prediction by applying single‐cell technology. Methods: We performed single‐cell RNA sequencing (scRNA‐seq) and variable‐diversity‐joining regions‐targeted sequencing (scVDJ‐seq) concurrently on bone marrow samples from a cohort of 18 patients with newly diagnosed MM (NDMM; n = 12) or refractory/relapsed MM (RRMM; n = 6). We analysed the malignant clonotypes using scVDJ‐seq data and conducted data integration and cell‐type annotation through the CCA algorithm based on gene expression profiling. Furthermore, we identified disease status‐specific genes and modules by comparison of NDMM and RRMM datasets and explored the findings in a larger MM cohort from the MMRF CoMMpass study. Results: We found that all the myeloma cells in either diagnosed or relapsed samples were dominated by a major clone, with a few subclones in several samples (n = 5). Next, we investigated the universal transcriptional features of myeloma cells and identified eight meta‐programs correlated with this disease, especially meta‐programs 1 and 8 (M1 and M8), which were the most significant and related to cell cycle and stress response, respectively. Furthermore, we classified the malignant plasma cells into eight clusters and found that the cell numbers in clusters 2/6/7 were exclusively higher in relapsed samples. Besides, we identified several attractive candidates for biomarkers (e.g. SMAD1 and STMN1) associated with disease progression and relapse in our dataset and related to overall survival in the CoMMpass dataset. Conclusions: Our data provide insights into the heterogeneity of MM as well as highlight the relevance of intra‐tumour heterogeneity and discover novel biomarkers that might be a potent therapy. [ABSTRACT FROM AUTHOR]

The morphological characteristics of yardangs are the direct evidence that reveals the wind and fluvial erosion for lacustrine sediments in arid areas. These features can be critical indicators in reconstructing local wind directions and environment conditions. Thus, the fast and accurate extraction of yardangs is key to studying their regional distribution and evolution process. However, the existing automated methods to characterize yardangs are of limited generalization that may only be feasible for specific types of yardangs in certain areas. Deep learning methods, which are superior in representation learning, provide potential solutions for mapping yardangs with complex and variable features. In this study, we apply Mask region-based convolutional neural networks (Mask R-CNN) to automatically delineate and classify yardangs using very high spatial resolution images from Google Earth. The yardang field in the Qaidam Basin, northwestern China is selected to conduct the experiments and the method yields mean average precisions of 0.869 and 0.671 for intersection of union (IoU) thresholds of 0.5 and 0.75, respectively. The manual validation results on images of additional study sites show an overall detection accuracy of 74%, while more than 90% of the detected yardangs can be correctly classified and delineated. We then conclude that Mask R-CNN is a robust model to characterize multi-scale yardangs of various types and allows for the research of the morphological and evolutionary aspects of aeolian landform. [ABSTRACT FROM AUTHOR]

Mapping dyke and dyke-related fractures is important for magma propagation and emplacement mechanisms analysis. Here, we propose a semi-automatic workflow for dyke and fracture mapping using unmanned aerial vehicle (UAV)-based photogrammetric data. Our workflow integrates mean-shift and GrabCut in one cut (GCOC) algorithms to segment dykes, and harnesses the phase symmetry algorithm and a series of follow-up actions to detect fractures. Sijiao Island of Zhejiang Province (SE China) is examined as a case study to validate the approaches. With our workflow, the generated dyke map attains more than a 97% recall rate when compared with the results of expert interpretation, and the ratio of the number of detected false fracture segments to the number of total segments is less than 18%. The dyke strike and fracture strike calculation match well with field measurements. Moreover, by combining visual interpretation and the resulting geometric information, our results manifest that dyke magma invaded along pre-existing fractures to form dykes on Sijiao Island, and the difference between dyke fractures and host rock fractures may be because of the effect of major changes of regional stress. This workflow has great potential for high-resolution terrestrial mapping. • Integrating mean-shift and GrabCut in one cut algorithms improves the accuracy of dyke segmentation. • Use of the phase symmetry algorithm and a series of subsequent actions decreases the detection error rate of fractures. • Semi-automatic extraction of dykes and fractures ensures the objectivity and efficiency of data analysis. • The generated maps contribute to determining the dyke intrusion model of Sijiao Island. [ABSTRACT FROM AUTHOR]