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Large-scale metagenomic assemblies have uncovered thousands of new species greatly expanding the known diversity of microbiomes in specific habitats. To investigate the roles of these uncultured species in human health or the environment, researchers need to incorporate their genome assemblies into a reference database for taxonomic classification. However, this procedure is hindered by the lack of a well-curated taxonomic tree for newly discovered species, which is required by current metagenomics tools. Here we report DeepMicrobes, a deep learning-based computational framework for taxonomic classification that allows researchers to bypass this limitation. We show the advantage of DeepMicrobes over state-of-the-art tools in species and genus identification and comparable accuracy in abundance estimation. We trained DeepMicrobes on genomes reconstructed from gut microbiomes and discovered potential novel signatures in inflammatory bowel diseases. DeepMicrobes facilitates effective investigations into the uncharacterized roles of metagenomic species.Erythroid-specific miR-451a and miR-486-5p are two of the most dominant microRNAs (miRNAs) in human peripheral blood. In small RNA sequencing libraries, their overabundance reduces diversity as well as complexity and consequently causes negative effects such as missing detectability and inaccurate quantification of low abundant miRNAs. Here we present a simple, cost-effective and easy to implement hybridization-based method to deplete these two erythropoietic miRNAs from blood-derived RNA samples. By utilization of blocking oligonucleotides, this method provides a highly efficient and specific depletion of miR-486-5p and miR-451a, which leads to a considerable increase of measured expression as well as detectability of low abundant miRNA species. selleck chemicals llc The blocking oligos are compatible with common 5' ligation-dependent small RNA library preparation protocols, including commercially available kits, such as Illumina TruSeq and Perkin Elmer NEXTflex. Furthermore, the here described method and oligo design principle can be easily adapted to target many other miRNA molecules, depending on context and research question.N6-adenosine methylation (m6A) is the most abundant internal RNA modification in eukaryotes, and affects RNA metabolism and non-coding RNA function. Previous studies suggest that m6A modifications in mammals occur on the consensus sequence DRACH (D = A/G/U, R = A/G, H = A/C/U). However, only about 10% of such adenosines can be m6A-methylated, and the underlying sequence determinants are still unclear. Notably, the regulation of m6A modifications can be cell-type-specific. In this study, we have developed a deep learning model, called TDm6A, to predict RNA m6A modifications in human cells. For cell types with limited availability of m6A data, transfer learning may be used to enhance TDm6A model performance. We show that TDm6A can learn common and cell-type-specific motifs, some of which are associated with RNA-binding proteins previously reported to be m6A readers or anti-readers. In addition, we have used TDm6A to predict m6A sites on human long non-coding RNAs (lncRNAs) for selection of candidates with high levels of m6A modifications. The results provide new insights into m6A modifications on human protein-coding and non-coding transcripts.The in-depth study of protein-protein interactions (PPIs) is of key importance for understanding how cells operate. Therefore, in the past few years, many experimental as well as computational approaches have been developed for the identification and discovery of such interactions. Here, we present UniReD, a user-friendly, computational prediction tool which analyses biomedical literature in order to extract known protein associations and suggest undocumented ones. As a proof of concept, we demonstrate its usefulness by experimentally validating six predicted interactions and by benchmarking it against public databases of experimentally validated PPIs succeeding a high coverage. We believe that UniReD can become an important and intuitive resource for experimental biologists in their quest for finding novel associations within a protein network and a useful tool to complement experimental approaches (e.g. mass spectrometry) by producing sorted lists of candidate proteins for further experimental validation. UniReD is available at http//bioinformatics.med.uoc.gr/unired/.Assessing similarity is highly important for bioinformatics algorithms to determine correlations between biological information. A common problem is that similarity can appear by chance, particularly for low expressed entities. This is especially relevant in single-cell RNA-seq (scRNA-seq) data because read counts are much lower compared to bulk RNA-seq. Recently, a Bayesian correlation scheme that assigns low similarity to genes that have low confidence expression estimates has been proposed to assess similarity for bulk RNA-seq. Our goal is to extend the properties of the Bayesian correlation in scRNA-seq data by considering three ways to compute similarity. First, we compute the similarity of pairs of genes over all cells. Second, we identify specific cell populations and compute the correlation in those populations. Third, we compute the similarity of pairs of genes over all clusters, by considering the total mRNA expression. We demonstrate that Bayesian correlations are more reproducible than Pearson correlations. Compared to Pearson correlations, Bayesian correlations have a smaller dependence on the number of input cells. We show that the Bayesian correlation algorithm assigns high similarity values to genes with a biological relevance in a specific population. We conclude that Bayesian correlation is a robust similarity measure in scRNA-seq data.Lactobacillus crispatus is a common inhabitant of both healthy poultry gut and human vaginal tract, and the absence of this species has been associated with a higher risk of developing infectious diseases. In this study, we analyzed 105 L. crispatus genomes isolated from a variety of ecological niches, including the human vaginal tract, human gut, chicken gut and turkey gut, to shed light on the genetic and functional features that drive evolution and adaptation of this important species. We performed in silico analyses to identify the pan and core genomes of L. crispatus, and to reveal the genomic differences and similarities associated with their origins of isolation. Our results demonstrated that, although a significant portion of the genomic content is conserved, human and poultry L. crispatus isolates evolved to encompass different genomic features (e.g. carbohydrate usage, CRISPR-Cas immune systems, prophage occurrence) in order to thrive in different environmental niches. We also observed that chicken and turkey L. crispatus isolates can be differentiated based on their genomic information, suggesting significant differences may exist between these two poultry gut niches. These results provide insights into host and niche-specific adaptation patterns in species of human and animal importance.Introduction Cross country is a popular high school and collegiate sport with a high rate of running-related injuries (RRI). Among high school runners, higher step rates have been associated with greater running experience and decreased body height, and lower step rates have been prospectively associated with increased risk of shin RRI. These associations have not been reported in collegiate cross country runners. The purpose of this study was to compare step rates between collegiate and high school cross country runners. Secondary objectives included determining if step rates in collegiate runners were related to experience and anthropometric variables, and whether their self-selected step rates were prospectively related to lower extremity RRI. Materials and methods Twenty-nine NCAA Division III collegiate cross country runners (13 females, mean ± SD age 19.7 ± 1.3 years) completed a survey and ran at their self-selected speed. Step rate was assessed with Polar RCX5 wristwatches and S3+ Stride Sensors™ on tnce was partially explained by higher self-selected running speeds. Thus, variations in step rate between high school and collegiate runners may be expected based on experience, speed, and body mass.Hemorrhagic transformation (HT) following ischemia is one complication which worsens stroke outcome. During and after ischemia-reperfusion, persistent reduction of brain pH occurs. In a recent study, we found that GPR68 functions as a neuronal proton receptor and mediates a protective pathway in brain ischemia. Here, we asked whether GPR68 contributes HT after ischemia. At 24 hr after transient middle cerebral artery occlusion (tMCAO), 58% of the wild-type (WT) mice exhibited some degrees of mild HT. At 72 hr, 95% of the WT showed HT with 42% exhibited large "parenchymal" type hemorrhage. In the GPR68-/- mice, there was a trend of increase in both the incidence and severity of HT at both time points. Mice with severe hemorrhage exhibited significantly larger infarct than those with no to mild hemorrhage. Next, we compared % infarct of GPR68-/- vs WT based on their HT categories. GPR68 deletion increased % infarct when the HT severity is mild. In contrast, for mice exhibiting large area HT, the two genotypes had no difference in % infarct. These data showed that GPR68-dependent signaling leads to protection when HT is mild.[This corrects the article PMC7293940.].Public payers around the world are increasingly using cost-effectiveness thresholds (CETs) to assess the value-for-money of an intervention and make coverage decisions. However, there is still much confusion about the meaning and uses of the CET, how it should be calculated, and what constitutes an adequate evidence base for its formulation. One widely referenced and used threshold in the last decade has been the 1-3 GDP per capita, which is often attributed to the Commission on Macroeconomics and WHO guidelines on Choosing Interventions that are Cost Effective (WHO-CHOICE). For many reasons, however, this threshold has been widely criticised; which has led experts across the world, including the WHO, to discourage its use. This has left a vacuum for policy-makers and technical staff at a time when countries are wanting to move towards Universal Health Coverage . This article seeks to address this gap by offering five practical options for decision-makers in low- and middle-income countries that can be used instead of the 1-3 GDP rule, to combine existing evidence with fair decision-rules or develop locally relevant CETs. It builds on existing literature as well as an engagement with a group of experts and decision-makers working in low, middle and high income countries.Here we report a case of central retinal vein occlusion that developed after using tranexamic acid. A 46-year-old female not known to have any previous illness came to the ophthalmology clinic complaining of sudden blurring of the vision in her left eye for almost 1 month, for which it is advised that tranexamic acid should be discontinued.To report the therapeutic efficacy and results of an accidentally injected intralenticular sustained-release dexamethasone implant (Ozurdex) in a patient with macular edema secondary to diabetic macular edema. Dexamethasone intravitreal implant is an approved formulation in the management of macular edema. The most common adversarial effects are an elevation of intraocular pressure (IOP) and cataract, but the unintentional injection of the dexamethasone implant into the lens is a particularly rare event.We report a case of a 72-year-old man treated for resistant Diabetic macular edema (DME) with dexamethasone intravitreal implant (Ozurdex, Allergan, Inc., Irvine, CA, USA) in which the technique was complicated by accidental implantation into the lens body and review the management. The patient underwent phacoemulsification of the lens, removal of the Ozurdex, intravitreal Avastin injection, and implant of a one-piece lens in the posterior capsule.