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Motivation The location, timing and abundance of gene expression (both mRNA and proteins) within a tissue define the molecular mechanisms of cell functions. Recent technology breakthroughs in spatial molecular profiling, including imaging-based technologies and sequencing-based technologies, have enabled the comprehensive molecular characterization of single cells while preserving their spatial and morphological contexts. This new bioinformatics scenario calls for effective and robust computational methods to identify genes with spatial patterns. Results We represent a novel Bayesian hierarchical model to analyze spatial transcriptomics data, with several unique characteristics. It models the zero-inflated and over-dispersed counts by deploying a zero-inflated negative binomial model that greatly increases model stability and robustness. Besides, the Bayesian inference framework allows us to borrow strength in parameter estimation in a de novo fashion. As a result, the proposed model shows competitive performances in accuracy and robustness over existing methods in both simulation studies and two real data applications. Availability and implementation The related R/C++ source code is available at https://github.com/Minzhe/BOOST-GP. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Molecular profiling technologies, such as genome sequencing and proteomics, have transformed biomedical research, but most such technologies require tissue dissociation, which leads to loss of tissue morphology and spatial information. Recent developments in spatial molecular profiling technologies have enabled the comprehensive molecular characterization of cells while keeping their spatial and morphological contexts intact. Molecular profiling data generate deep characterizations of the genetic, transcriptional and proteomic events of cells, while tissue images capture the spatial locations, organizations and interactions of the cells together with their morphology features. These data, together with cell and tissue imaging data, provide unprecedented opportunities to study tissue heterogeneity and cell spatial organization. This review aims to provide an overview of these recent developments in spatial molecular profiling technologies and the corresponding computational methods developed for analyzing such data. [ABSTRACT FROM AUTHOR]

• MOFs precursors were prepared via microwave irradiation and used as self-sacrificing template. • Fe, Co bimetal embedded nanoporous carbon (Fe-Co/NPC) was prepared by a rapid and scalable strategy. • Fe-Co/NPC composites showed excellent electrochemical performance as ideal electrocatalyst for OER. The development of highly efficient, stable, and low-cost non-noble-metal electrocatalysts for oxygen evolution reactions (OER) is a major challenge for facilitate the efficiency of green energy storage. Bimetallic oxides are considered promising candidates as the electrocatalysts for OER because of their remarkable electrocatalytic activity, good stability, and low cost. In this work, ZIF-67 precursors were prepared via microwave irradiation and used as a self-sacrificing template. We proposed a rapid and scalable strategy to prepare Fe, Co bimetal embedded nanoporous carbon (Fe-Co/NPC) polyhedron composites by thermal decomposition of Fe species incorporated ZIF-67 precursor. Benefiting from the distinctive 3D polyhedron structural and compositional advantages, Fe-Co/NPC with hierarchical porous structure showed excellent electrochemical performance as ideal electrode material for OER. The resulting Fe-Co/NPC displayed outstanding electrocatalytic activity for OER with appreciable onset potential (1.59 V (vs. RHE)), small Tafel slope (53.55 mV dec−1), low over-potential (396 mV) to reach 10 mA cm−2, and excellent durability with negligible loss in current density after 1000 cycles. The current work demonstrated new insight into the design and construction of 3D structured Fe-Co/NPC polyhedron catalysts with highly electrocatalytic activity and good stability for electrocatalysis applications. [ABSTRACT FROM AUTHOR]

Background: Previous cancer genomics studies focused on searching for novel oncogenes and tumor suppressor genes whose abundance is positively or negatively correlated with end-point observation, such as survival or tumor grade. This approach may potentially miss some truly functional genes if both its low and high modes have associations with end-point observation. Such genes act as both oncogenes and tumor suppressor genes, a scenario that is unlikely but theoretically possible. Results: We invented an Expectation-Maximization (EM) algorithm to divide patients into low-, middle- and high-expressing groups according to the expression level of a certain gene in both tumor and normal patients. We found one gene, ORMDL3, whose low and high modes were both associated with worse survival and higher tumor grade in breast cancer patients in multiple patient cohorts. We speculate that its tumor suppressor gene role may be real, while its high expression correlating with worse end-point outcome is probably due to the passenger event of the nearby ERBB2's amplification. Conclusions: The proposed EM algorithm can effectively detect genes having tri-modal distributed expression in patient groups compared to normal genes, thus rendering a new perspective on dissecting the association between genomic features and end-point observations. Our analysis of breast cancer datasets suggest that the gene ORMDL3 may have an unexploited tumor suppressive function. [ABSTRACT FROM AUTHOR]

Motivation Synergistic drug combinations are a promising approach to achieve a desirable therapeutic effect in complex diseases through the multi-target mechanism. However, in vivo screening of all possible multi-drug combinations remains cost-prohibitive. An effective and robust computational model to predict drug synergy in silico will greatly facilitate this process. Results We developed DIGREM (Drug-Induced Genomic Response models for identification of Effective Multi-drug combinations), an online tool kit that can effectively predict drug synergy. DIGREM integrates DIGRE, IUPUI_CCBB, gene set-based and correlation-based models for users to predict synergistic drug combinations with dose–response information and drug-treated gene expression profiles. Availability and implementation http://lce.biohpc.swmed.edu/drugcombination Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Circular RNAs are a special type of RNA that has recently attracted a lot of research interest in studying its formation and function. RNA binding proteins (RBPs) that bind circRNAs are important in these processes, but have been relatively less studied. CLIP-Seq technology has been invented and applied to profile RBP-RNA interactions on the genome-wide scale. While mRNAs are usually the focus of CLIP-Seq experiments, RBP-circRNA interactions could also be identified through specialized analysis of CLIP-Seq datasets. However, many technical difficulties are involved in this process, such as the usually short read length of CLIP-Seq reads. In this study, we created a pipeline called Clirc specialized for profiling circRNAs in CLIP-Seq data and analyzing the characteristics of RBP-circRNA interactions. In conclusion, to our knowledge, this is one of the first studies to investigate circRNAs and their binding partners through repurposing CLIP-Seq datasets, and we hope our work will become a valuable resource for future studies into the biogenesis and function of circRNAs. [ABSTRACT FROM AUTHOR]

This paper studies mechanical properties and energy damage evolution of fiber-reinforced cemented sulfur tailings (CSTB) backfill. The effects of fiber length and fiber content on the stress, toughness and failure properties of the CSTB were systematically revealed. In addition, the energy index evolution law was studied, and the energy damage evolution mechanism of CSTB was revealed. The results show that the deformation failure of fiber-reinforced CSTB mainly goes through four stages: initial crack compaction, linear elastic deformation, yield failure and post-peak failure. The peak stress and residual stress of the CSTB firstly increase and then decrease with the increase of fiber content and the addition of fiber can promote the change from brittle failure to ductile failure of the CSTB. Adding appropriate amount of fiber can improve the toughness of CSTB, and the influence degree of fiber length on the toughness index of CSTB is 6mm>12mm>3mm. The total strain energy increases linearly along the variation of fiber content, while the elastic strain energy and dissipated energy increase exponentially at the peak stress point. In the process of CSTB deformation and failure, "gentle—linear growth—slow growth—rapid decline" is for elastic strain energy, while "gentle—slow growth—rapid growth—linear growth" is for dissipation energy. The damage and failure of CSTB mainly experienced four stages: initial damage, slow growth of damage, accelerated damage and damage failure, and the damage evolution curve also showed the changing characteristics of "gentle—slow growth—rapid growth—linear growth". The CSTB without added fiber showed obvious "Y-type" and "linear-type" shear failure characteristics and the phenomenon of shear cracks penetrating the backfill appeared. No big shear crack occur when it is damaged, showing that the fiber addition restrain the crack growth and improve the overall crack resistance of the CSTB. Hydration products are obviously distributed on the surface of the fiber, which indicates that the fiber will be evenly dispersed in the CSTB and form a certain bonding force with the cement-tailings matrix, thus improving the overall mechanical properties of the CSTB. [ABSTRACT FROM AUTHOR]

Previous studies have confirmed that for cemented tailings backfill, mechanical properties are improved through the addition of fiber. However, for fiber-reinforced cemented sulfur tailings backfill (FRCSTB), physical and flow properties are still unknown. In this paper, the changes in fluidity, splitting tensile strength (STS) and uniaxial compressive strength (UCS) of cemented sulfur tailings backfill (CSTB) are analyzed in detail. Secondly, regarding the addition of glass fiber and polypropylene fiber, the changes in the fluidity, STS and UCS of the CSTB, resulting from the fiber length, fiber content and method of fiber addition used, were analyzed. Moreover, the relationship between the UCS and fiber content is established. Finally, the mechanism behind the influence of fiber and sulfur content on the mechanical properties of CSTB is revealed. The results indicate that with the increase in sulfur content, the fluidity of the tailings slurry exhibits exponential growth. During the process of increasing sulfur content, the UCS and STS of CSTB initially increase and then decrease, reaching maximum values at 12% sulfur content. Similarly, at a fiber content of 0.6%, the UCS and 28d STS of CSTB reach their maximum values. In terms of enhancing the mechanical properties of CSTB, the effectiveness of glass fibers surpasses that of polypropylene fibers. In addition, regarding the improvement of the UCS of CSTB, the mixed addition of fibers is obviously worse than that of fiber alone. However, in terms of enhancing the STS of CSTB, the mixed addition of fibers outperforms the single addition of polypropylene fiber. From a microscopic perspective, polypropylene and glass fiber are able to form strong cohesion with the cement–tailings matrix and effectively prevent the formation and expansion of pores and cracks. [ABSTRACT FROM AUTHOR]