Annually, over 1 billion people sustain traumatic injuries, resulting in over 900,000 deaths in Africa and 6 million deaths globally. Timely response, intervention, and transportation in the prehospital setting reduce morbidity and mortality of trauma victims. Our objective was to describe the existing literature evaluating trauma morbidity and mortality outcomes as a function of prehospital care time to identify gaps in literature and inform future investigation.
 
  We performed a scoping review of published literature in MEDLINE. Results were limited to English language publications from 2009 to 2020. Included articles reported trauma outcomes and prehospital time. We excluded case reports, reviews, systematic reviews, meta-analyses, comments, editorials, letters, and conference proceedings. In total, 808 articles were identified for title and abstract review. Of those, 96 articles met all inclusion criteria and were fully reviewed. Higher quality studies used data derived from trauma registries. There wasations in Africa and LMICs.
 The existing literature disproportionately represents high-income settings and most commonly assessed in-hospital mortality as a function of crude prehospital time. Future studies should focus on how specific prehospital intervals impact morbidity outcomes (e.g., organ failure) and mortality at earlier time points (e.g., 3 or 7 days) to better reflect the effect of early prehospital resuscitation and transport. Trauma registries may be a tool to facilitate such research and may promote higher quality investigations in Africa and LMICs.
  Monoculture farming poses significant disease challenges, but fungus-farming termites are able to successfully keep their monoculture crop free from contamination by other fungi. It has been hypothesised that obligate gut passage of all plant substrate used to manure the fungal symbiont is key to accomplish this. Here we refute this hypothesis in the fungus-farming termite species Macrotermes bellicosus.
 
  We first used ITS amplicon sequencing to show that plant substrate foraged on by termite workers harbour diverse fungal communities, which potentially could challenge the farming symbiosis. Subsequently, we cultivated fungi from dissected sections of termite guts to show that fungal diversity does not decrease during gut passage. Therefore, we investigated if healthy combs harboured these undesirable fungal genera, and whether the presence of workers affected fungal diversity within combs. Removal of workers led to a surge in fungal diversity in combs, implying that termite defences must be responsible fove insurmountable.
  Identifying one or more biologically-active/native decoys from millions of non-native decoys is one of the major challenges in computational structural biology. The extreme lack of balance in positive and negative samples (native and non-native decoys) in a decoy set makes the problem even more complicated. Consensus methods show varied success in handling the challenge of decoy selection despite some issues associated with clustering large decoy sets and decoy sets that do not show much structural similarity. Recent investigations into energy landscape-based decoy selection approaches show promises. However, lack of generalization over varied test cases remains a bottleneck for these methods.
 
  We propose a novel decoy selection method, ML-Select, a machine learning framework that exploits the energy landscape associated with the structure space probed through a template-free decoy generation. The proposed method outperforms both clustering and energy ranking-based methods, all the while consistently offering better performance on varied test-cases.  https://www.selleckchem.com/  Moreover, ML-Select shows promising results even for the decoy sets consisting of mostly low-quality decoys.
 
  ML-Select is a useful method for decoy selection. This work suggests further research in finding more effective ways to adopt machine learning frameworks in achieving robust performance for decoy selection in template-free protein structure prediction.
 ML-Select is a useful method for decoy selection. This work suggests further research in finding more effective ways to adopt machine learning frameworks in achieving robust performance for decoy selection in template-free protein structure prediction.
  The RNA interference (RNAi) pathway is a gene regulation mechanism that utilizes small RNA (sRNA) and Argonaute (Ago) proteins to silence target genes. Our previous work identified a functional RNAi pathway in the protozoan parasite Entamoeba histolytica, including abundant 27 nt antisense sRNA populations which associate with EhAgo2-2 protein. However, there is lack of understanding about the sRNAs that are bound to two other EhAgos (EhAgo2-1 and 2-3), and the mechanism of sRNA regulation itself is unclear in this parasite. Therefore, identification of the entire pool of sRNA species and their sub-populations that associate with each individual EhAgo protein would be a major step forward.
 
  In the present study, we sequenced sRNA libraries from both total RNAs and EhAgo bound RNAs. We identified a new population of 31 nt sRNAs that results from the addition of a non-templated 3-4 adenosine nucleotides at the 3'-end of the 27 nt sRNAs, indicating a non-templated RNA-tailing event in the parasite. The relatiodification, which is the first such observation amongst single celled protozoan parasites. Our sRNA sequencing libraries provide the first comprehensive sRNA dataset for all three Entamoeba Ago proteins, which can serve as a useful database for the amoeba community.
 We identified a new population of sRNA with non-templated oligo-adenylation modification, which is the first such observation amongst single celled protozoan parasites. Our sRNA sequencing libraries provide the first comprehensive sRNA dataset for all three Entamoeba Ago proteins, which can serve as a useful database for the amoeba community.
  Drug repositioning has been an important and efficient method for discovering new uses of known drugs. Researchers have been limited to one certain type of collaborative filtering (CF) models for drug repositioning, like the neighborhood based approaches which are good at mining the local information contained in few strong drug-disease associations, or the latent factor based models which are effectively capture the global information shared by a majority of drug-disease associations. Few researchers have combined these two types of CF models to derive a hybrid model which can offer the advantages of both. Besides, the cold start problem has always been a major challenge in the field of computational drug repositioning, which restricts the inference ability of relevant models.
 
  Inspired by the memory network, we propose the hybrid attentional memory network (HAMN) model, a deep architecture combining two classes of CF models in a nonlinear manner. First, the memory unit and the attention mechanism are combined to generate a neighborhood contribution representation to capture the local structure of few strong drug-disease associations.