bluuebunny/crossref_metadata_2025_split huggingface dataset:
STANDARD-DEVIATION BASED CONFORMITY INDEX FOR EVALUATING TREATMENT PLAN OF INTENSITY MODULATED RADIOTHERAPY IN LUNG CANCER
Authors: Qingya Pan , Yizhong Fan , Fuke Zhang , Shengjie Luan , Linzhao Tian , Yuling Lv 
Published: November 2022

AbstractThis paper attempts to find a new conformity index (CI) calculation method with slice and angle information for evaluating lung cancer radiation treatment plan. A total of 20 lung cancer patients in 2016–2019 were selected. Treatment plans were made for each patient. Parameters used in the process of making treatment plans were set the same. The CI and the standard-deviation based CI (SDCI) that contains angle and slice information were calculated. Comparison of results calculated with SDCI and CI were made. The results of the two methods for the patients showed the same trend. Different shapes of simulated dose distribution line shows SDCI can provide more detail information about the target area. Special shapes of simulated dose distribution line for SDCI showed inaccuracy in angle information. The parameter SDCI has more advantage towards the traditional CI for it can provide angle and slice information. However, more angles need to be calculated.
Standing genetic variation and chromosome differences drove rapid ecotype formation in a major malaria mosquito
Authors: Small Scott T. , Costantini Carlo , Sagnon N’Fale , Guelbeogo Moussa W. , Emrich Scott J. , Kern Andrew D. , Fontaine Michael C. , Besansky Nora J. 
Published: November 2022

AbstractSpecies distributed across heterogeneous environments often evolve locally adapted ecotypes, but understanding of the genetic mechanisms involved in their formation and maintenance in the face of gene flow is incomplete. In Burkina Faso, the major African malaria mosquitoAnopheles funestuscomprises two strictly sympatric and morphologically indistinguishable yet karyotypically differentiated forms reported to differ in ecology and behavior. However, knowledge of the genetic basis and environmental determinants ofAn. funestusdiversification was impeded by lack of modern genomic resources. Here, we applied deep whole-genome sequencing and analysis to test the hypothesis that these two forms are ecotypes differentially adapted to breeding in natural swamps versus irrigated rice fields. We demonstrate genome-wide differentiation despite extensive microsympatry, synchronicity, and ongoing hybridization. Demographic inference supports a split only ~1,300 years ago, closely following the massive expansion of domesticated African rice cultivation ~1,850 years ago. Regions of highest divergence, concentrated in chromosomal inversions, were under selection during lineage splitting, consistent with local adaptation. The origin of nearly all variation implicated in adaptation, including chromosomal inversions, substantially predates the ecotype split, suggesting that rapid adaptation was fueled mainly by standing genetic variation. Sharp inversion frequency differences likely facilitated adaptive divergence between ecotypes, both by suppressing recombination between opposing chromosomal orientations of the two ecotypes, and by maximizing recombination within the structurally monomorphic rice ecotype. Our results align with growing evidence from diverse taxa that rapid ecological diversification can arise from evolutionarily old structural genetic variants that modify genetic recombination.Significance StatementLocal adaptation to heterogeneous environments is pervasive, but its underlying genetic basis is incompletely understood. Within a major African malaria vector,An. funestus, are two chromosomally differentiated groups that are co-localized, morphologically indistinguishable, and reported to differ both in ecology and behavior relevant to malaria transmission and control. Progress in understanding the genetic basis and environmental determinants of vector diversification was impeded by the lack of modern genomic resources. Here we perform deep whole-genome sequencing on individuals from these groups, establishing that they are differentiated genome-wide in a manner consistent with recent ecotype formation associated with the exploitation of a new anthropogenic larval habitat. Such rapid malaria vector diversification was facilitated by standing genetic variation, including evolutionarily old chromosomal rearrangements.
gtexture: novel extension of image texture analysis to graphs and its application to cancer informatics
Authors: Barker-Clarke Rowan J , Weaver Davis , Scott Jacob G 
Published: November 2022

ABSTRACTObjectiveThe calculation of texture features, such as those derived by Haralicket al., has been traditionally limited to 2D-imaging data. We present the novel derivation of an extension to these texture features that can be applied to graphs and networks and set out to illustrate the potential of these metrics for use in cancer informatics.ApproachWe extend the pixel-based calculation of texture and generate analogous novel metrics for graphs and networks. The graph structures in question must have ordered or continuous node weights/attributes. To demonstrate the utility of these metrics in cancer biology, we demonstrate these metrics can distinguish different fitness landscapes, gene co-expression and regulatory networks, and protein interaction networks with both simulated and publicly available experimental gene expression data.Main ResultsWe demonstrate that texture features are informative of graph structure and analyse their sensitivity to discretization parameters and node label noise. We demonstrate that graph texture varies across multiple network types including fitness landscapes and large protein interaction networks with experimental expression data. We show the ability of these texture metrics, calculated on specific protein interaction subnetworks, to classify cell line expression by lineage, generating classifiers with 82% and 89% accuracy.SignificanceGraph texture features are a novel second order graph metric that can distinguish cancer types and topologies of evolutionary landscapes. It appears that no similar metrics currently exist and thus we open up the potential derivation of more metrics for the classification and analysis of network-structured data. This may be particularly useful in the complex setting of cancer, where large graph and network structures underlie the omics data generated. Network-based data underlies drug discovery, drug response prediction and single-cell dynamics and thus these metrics provide an additional tool in tackling these problems in cancer.
Improving protein secondary structure prediction by deep language models and transformer networks
Authors: Wu Tianqi , Cheng Weihang , Cheng Jianlin 
Published: November 2022

AbstractProtein secondary structure prediction is useful for many applications. It can be considered a language translation problem, i.e., translating a sequence of 20 different amino acids into a sequence of secondary structure symbols (e.g., alpha helix, beta strand, and coil). Here, we develop a novel protein secondary structure predictor called TransPross based on the transformer network and attention mechanism widely used in natural language processing to directly extract the evolutionary information from the protein language (i.e., raw multiple sequence alignment (MSA) of a protein) to predict the secondary structure. The method is different from traditional methods that first generate a MSA and then calculate expert-curated statistical profiles from the MSA as input. The attention mechnism used by TransPross can effectively capture long-range residue-residue interactions in protein sequences to predict secondary structures. Benchmarked on several datasets, TransPross outperforms the state-of-art methods. Moreover, our experiment shows that the prediction accuracy of TransPross positively correlates with the depth of MSAs and it is able to achieve the average prediction accuracy (i.e., Q3 score) above 80% for hard targets with few homologous sequences in their MSAs. TransPross is freely available athttps://github.com/BioinfoMachineLearning/TransPro
Multi-layer guilt-by-association-based drug repurposing by integrating clinical knowledge on biological heterogeneous networks
Authors: Bang Dongmin , Lim Sangsoo , Lee Sangseon , Kim Sun 
Published: November 2022

ABSTRACTComputational drug repurposing attempts to leverage rapidly accumulating high-throughput data to discover new indications for existing drugs, often by clarifying biological mechanisms with relevant genes. Leveraging the Guilt-by-association (GBA), the principle of “similar genes share similar functions,” we introducedclinicalneighbors of drug and disease entities while learning their mechanisms on thebiologicalnetwork. To overcome the hurdle of connecting drugs and diseases through large and dense gene-gene network and simultaneously realize the concept of “semantic multi-layer GBA”, we present a random walk-based algorithm with a novel clinical-knowledge guided teleport. As a result, drug-disease association prediction accuracy increased up to 8.7% compared to existing state-of-the-art models. In addition, exploration of the generated embedding space displays harmony between biological and clinical contexts. Through repurposing case studies for breast carcinoma and Alzheimer’s disease, we demonstrate the potential power of multi-layer GBA, a novel perspective for predicting clinical-level associations on heterogeneous biomedical networks.
Forecasting the numbers of disease vectors with deep learning
Authors: Ceia-Hasse Ana , Sousa Carla A. , Gouveia Bruna R. , Capinha César 
Published: November 2022

AbstractArboviral diseases such as dengue, Zika, chikungunya or yellow fever are a worldwide concern. The abundance of vector species plays a key role in the emergence of outbreaks of these diseases, so forecasting these numbers is fundamental in preventive risk assessment. Here we describe and demonstrate a novel approach that uses state-of-the-art deep learning algorithms to forecast disease vector numbers. Unlike classical statistical and machine learning methods, deep learning models use time series data directly as predictors and identify the features that are most relevant from a predictive perspective. We demonstrate the application of this approach to predict temporal trends in the number ofAedes aegyptimosquito eggs across Madeira Island for the period 2013 to 2019. Specifically, we apply the deep learning models to predict whether, in the following week, the number ofAe. aegyptieggs will remain unchanged, or whether it will increase or decrease, considering different percentages of change. We obtained high predictive accuracy for all years considered (mean AUC = 0.92 ± 0.05 sd). We also found that the preceding numbers of eggs is a highly informative predictor of future numbers. Linking our approach to disease transmission or importation models will contribute to operational, early warning systems of arboviral disease risk.
IFNγ drives neuroinflammation and demyelination in a mouse model of multiple system atrophy
Authors: Gallups Nicole J. , Childers Gabrielle M. , Webster Jhodi M. , Zane Asta , Yang Ya-Ting , Mudium Nikhita , Manfredsson Fredric P. , Kordower Jeffrey H. , Harms Ashley S. 
Published: November 2022

AbstractMultiple system atrophy (MSA) is a rare and fatal synucleinopathy characterized by insoluble alpha-synuclein (α-syn) cytoplasmic inclusions located within oligodendroglia. Neuroinflammation, demyelination, and neurodegeneration are correlated with areas of GCI pathology, however it is not known what specifically drives disease pathogenesis. Recently in a mouse model of MSA, CD4+ T cells have been shown to drive neuroinflammation and demyelination, however the mechanism by which this occurs also remains unclear. In this study we use genetic and pharmacological approaches in a novel model of MSA to show that the pro-inflammatory cytokine interferon gamma (IFNγ) drives neuroinflammation and demyelination. Furthermore, using an IFNγ reporter mouse, we found that infiltrating CD4+ T cells were the primary producers of IFNγ in response to α-syn overexpression in oligodendrocytes. Results from these studies indicate that IFNγ expression in CD4 T cells drives α-syn-mediated neuroinflammation and demyelination, and strategies to target IFNγ expression may be a potential disease modifying therapeutic strategy for MSA.
Genomic differentiation among European perch in the western Baltic Sea reflects colonisation history and local adaptation
Authors: Skovrind Mikkel , Pacheco George , Flindt Christensen Emil Aputsiaq , Gopalakrishnan Shyam , Fietz Katharina , Holm-Hansen Tore Hejl , Vieira Filipe Garrett , Krag Marcus Anders , Carl Henrik , Gilbert M Thomas P , Olsen Morten Tange , Møller Peter Rask 
Published: November 2022

AbstractEnvironmental variation across the distribution of wild species can lead to local adaptations. The Baltic Sea was formed when the Fenno-Scandian ice sheet retreated around 12 thousand years ago, creating a new brackish water habitat colonised by both marine and freshwater fish species. The European perch (Perca fluviatilis) is a predatory freshwater fish with a large geographical distribution across Eurasia, where it inhabits a wide range of environmental niches. In the Baltic Sea region it has even developed a specialised brackish water phenotype that can tolerate environmental salinity levels, which are lethal to the ancestral freshwater phenotype. However, very little is known about the colonisation history and underlying genomic mechanisms facilitating the colonisation and adaptation of perch to the Baltic Sea. Here, we use Genotyping-By-Sequencing data from six freshwater and six brackish water localities to disclose the evolutionary relationship between the freshwater and brackish water phenotype. Our results show that the brackish water perch phenotype occurs in multiple distinct genetic clusters. We find that gene flow between brackish water phenotypes with full access to the sea likely led to lower levels of differentiation and higher diversity than in freshwater phenotypes. Selection analyses suggest that genomic adaptation played a role in the colonisation of the Baltic Sea and that the top three regions under selection harbour salinity tolerance genes. We also find a link between the historic salinity of the Baltic Sea and the demographic history of the brackish water phenotypes and go on to discuss the implications of our findings for management of brackish water perch in the western Baltic sea.HighlightsGBS data from 12 perch populations, six with brackish and six with freshwater originColonisation history and differentiated gene flow shaped the current population structureThe brackish water ecotype was found in all three major genetic cladesTop three regions under selection harboured salinity tolerance genesSalinity influenced Ne during the formation of the Baltic SeaGraphical abstract
Engineered reporter phages for rapid detection ofEscherichia coli, Klebsiellaspp., andEnterococcusspp. in urine
Authors: Meile Susanne , Du Jiemin , Staubli Samuel , Grossman Sebastian , Koliwer-Brandl Hendrik , Piffaretti Pietro , Leitner Lorenz , Matter Cassandra I. , Baggenstos Jasmin , Hunold Laura , Milek Sonja , Gübeli Christian , Kozomara-Hocke Marko , Neumeier Vera , Botteon Angela , Klumpp Jochen , Marschall Jonas , McCallin Shawna , Zbinden Reinhard , Kessler Thomas M. , Loessner Martin J. , Dunne Matthew , Kilcher Samuel 
Published: November 2022

AbstractThe rapid detection and species-level differentiation of bacterial pathogens facilitates antibiotic stewardship and improves disease management. Here, we develop a rapid bacteriophage-based diagnostic assay to detect the most prevalent pathogens causing urinary tract infections:Escherichia coli, Klebsiellaspp., andEnterococcusspp. For each uropathogen, two virulent phages were genetically engineered to express a nanoluciferase reporter gene upon host infection. Using 206 patient urine samples, reporter phage-induced bioluminescence was quantified to identify bacteriuria and the assay was benchmarked against conventional urinalysis. Overall,E. coli, Klebsiellaspp., andEnterococcusspp. were each detected with high sensitivity (68%, 78%, 85%), specificity (99%, 99%, 99%), and accuracy (90%, 94%, 96%) at a resolution of ⩾103CFU/ml within 5 h. We further demonstrate how bioluminescence in urine can be used to predict phage antibacterial activity, demonstrating the future potential of reporter phages as companion diagnostics that guide patient-phage matching prior to therapeutic phage application.
Individual bat viromes reveal the co-infection, spillover and emergence risk of potential zoonotic viruses
Authors: Wang Jing , Pan Yuan-fei , Yang Li-fen , Yang Wei-hong , Luo Chu-ming , Wang Juan , Kuang Guo-peng , Wu Wei-chen , Gou Qin-yu , Xin Gen-yang , Li Bo , Luo Huan-le , Chen Yao-qing , Shu Yue-long , Guo Deyin , Gao Zi-Hou , Liang Guodong , Li Jun , Holmes Edward C. , Feng Yun , Shi Mang 
Published: November 2022

ABSTRACTBats are reservoir hosts for many zoonotic viruses. Despite this, relatively little is known about the diversity and abundance of viruses within bats at the level of individual animals, and hence the frequency of virus co-infection and inter-species transmission. Using an unbiased meta-transcriptomics approach we characterised the mammalian associated viruses present in 149 individual bats sampled from Yunnan province, China. This revealed a high frequency of virus co-infection and species spillover among the animals studied, with 12 viruses shared among different bat species, which in turn facilitates virus recombination and reassortment. Of note, we identified five viral species that are likely to be pathogenic to humans or livestock, including a novel recombinant SARS-like coronavirus that is closely related to both SARS-CoV-2 and SARS-CoV, with only five amino acid differences between its receptor-binding domain sequence and that of the earliest sequences of SARS-CoV-2. Functional analysis predicts that this recombinant coronavirus can utilize the human ACE2 receptor such that it is likely to be of high zoonotic risk. Our study highlights the common occurrence of inter-species transmission and co-infection of bat viruses, as well as their implications for virus emergence.
Cerebral malaria is regulated by host mediated changes inPlasmodiumgene expression
Authors: Cimperman Clare K. , Pena Mirna , Gokcek Sohret M. , Theall Brandon P. , Patel Meha V. , Sharma Anisha , Qi ChenFeng , Sturdevant Daniel , Miller Louis H. , Collins Patrick L , Pierce Susan K. , Akkaya Munir 
Published: November 2022

AbstractCerebral Malaria (CM), the deadliest complication ofPlasmodiuminfection, is a complex and unpredictable disease. However, our understanding of the host and parasite factors that cause CM is limited. Using a mouse model of CM, experimental CM (ECM), we performed a three-way comparison between: ECM susceptible C57BL/6 mice infected with ECM-causingPlasmodiumANKA (PbANKA) parasites (ANKA(C57BL/6)); ECM resistant Balb/c mice infected withPbANKA (ANKA(Balb/c)); and C57BL/6 mice infected withPbNK65 that does not cause ECM (NK65(C57BL/6)). All ANKA(C57BL/6)mice developed CM. In contrast in ANKA(Balb/c)and NK65(C57BL/6)infections do not result in CM and proceed similarly in terms of parasite growth, disease course and host immune response.. However, parasite gene expression in (ANKA(C57BL/6)) was remarkably different than in ANKA(Balb/c)but similar to the gene expression in NK65(C57BL/6). Thus,PbANKA has a ECM-specific gene expression profile that is only activated in susceptible hosts providing evidence that the host has a critical influence on the outcome of infection.
A simple and rapid CRISPR-Cas12a-based detection test for diastaticSaccharomyces cerevisiae
Authors: Uotila Ida , Krogerus Kristoffer 
Published: November 2022

AbstractDiastaticSaccharomyces cerevisiaeis a common contaminant in the brewing industry. Currently available detection methods are either time-coansuming or require specialized equipment. The aim of this study was to develop a new rapid and simple assay for the detection of diastatic yeast from beer and yeast samples. More specifically, we aimed to develop a simple and rapid assay that requires minimal laboratory equipment or training, and ideally yields results as accurate as PCR-based methods. The developed assay consisted of three main steps: DNA extraction, pre-amplification of DNA, and CRISPR-Cas12a-based detection and visualisation. We compared different preamplification and visualisation techniques, and the final assay involved a one-pot reaction where LAMP and Cas12a were consecutively used to pre-amplify and detect a fragment from theSTA1gene in a single tube. These reactions only required a heat block, a pipette, and a centrifuge. The assay result was then visualised on a lateral flow strip. We used the developed assay to monitor an intentionally contaminated beer fermentation, and it was shown to yield results as accurate as PCR using previously published primers. Furthermore, the assay yielded results in approx. 75 minutes starting from a beer sample. The developed assay therefore offers reliable and rapid quality control for breweries of all sizes and can be performed without any expensive laboratory equipment. We believe the assay will be particularly useful for smaller breweries that don’t already have well-equipped laboratories and are looking to implement better quality control.
Expression profiles of hypoxia-related genes of cancers originating from anatomically similar locations using TCGA database analysis
Authors: Bae Hye Lim , Jeong Kyeonghun , Yang Suna , Jun Hyeji , Kim Kwangsoo , Chai Young Jun 
Published: November 2022

AbstractHypoxia is a well-recognized characteristic of the tumor microenvironment of solid cancers. This study aimed to analyze hypoxia-related genes shared by groups based on tumor location. Nine hypoxia-related pathways from the Kyoto Encyclopedia of Genes and Genomes database or the Reactome database were selected, and 850 hypoxia-related genes were analyzed. Based on their anatomical locations, 14 tumor types were categorized into the following six groups. The group-specific genetic risk score was classified as high or low risk based on mRNA expression, and survival outcomes were evaluated. The risk scores in the Female reproductive group and Lung group were internally and externally validated. In the Female reproductive group, CDKN2A, FN1 and ITGA5, were identified as hub genes associated with poor prognosis, while IL2RB and LEF1 were associated with favorable prognosis. In the Lung group, ITGB1 and LDHA were associated with poor prognosis, and GLS2 was associated with favorable prognosis. Functional enrichment analysis showed that the Female reproductive group was enriched in terms related to cilia and skin, while the Lung group was enriched in terms related to cytokines and defense. This analysis may lead to better understanding of the mechanisms of cancer progression and facilitate establishing new biomarkers for prognosis prediction.