bluuebunny/crossref_metadata_2025_split huggingface dataset:
Novel Smartphone Based Free Flap Monitoring Tool Using Machine Learning
Authors: Provenzano Destie , Chandawarkar Akash , Caterson Edward 
Published: August 2022

AbstractFree flap monitoring is important to ensure early detection of arterial or venous failure to facilitate salvage. Our prior research has shown ability to magnify skin change as a result of skin changes. This study was undertaken to test the feasibility of detecting venous and arterial occlusion using a smartphone camera and pattern recognition (a simplistic implementation of a machine learning algorithm). Bilateral hands of seven patients were video recorded with various tourniquet pressures on one hand simulating no occlusion, venous occlusion, and arterial occlusion with the other hand as internal control. Video data resolved at an average iPhone camera quality of 33 fps was processed using the sci-kit learn library in Python to detect changes in color frequency between frames and then compared to the control hand. Comparing the test hand to the control hand allowed for the depiction of the “delta” that was sensitive enough to detect changes on a video without any additional augmentation. The average rate of change in red pixels between video frames was noticeably different compared to control for both arterial occlusion (1.06x greater) and venous occlusion (1.07x greater). A graphical representation depicted a clear relationship while an individual was undergoing occlusion (Fig 1). Our smartphone video capture and analysis facilitates visualization of skin perfusion and can distinguish between states of no occlusion, arterial occlusion, and venous occlusion. This study shows promise for the use of inexpensive smartphone monitoring in a clinical setting for accurate free flap monitoring.
Integrate multiscale module kernel for disease-gene discovery in biological networks
Authors: Xiang Ju , Meng Xiangmao , Zheng Ruiqing , Li Min 
Published: August 2022

AbstractBiomedical data mining is very important for the research of complex diseases, and disease-gene discovery is one of the most representative topics in this field. Multiscale module structure (MMS) that widely exists in biological networks can provide useful insight for disease research. However, how to effectively mine information in MMS to enhance the ability of disease-gene discovery is challenging. Thus, we propose a type of novel hybrid methods (HyMSMK) for disease-gene discovery by integrating multiscale module kernel (MSMK) derived from multiscale module profile (MSMP). We extract MSMP with local to global structural information from comprehensive human protein interactome by multiscale modularity optimization with exponential sampling, and construct MSMK by using the MSMP as a feature matrix, combining with the relative information content of features and kernel sparsification. Then, we present several fusion strategies integrating MSMK, including a probabilistic model for rank aggregation. By a series of experiments, we study the effect of the fusion strategies and kernel sparsification on HyMSMK, and demonstrate that HyMSMK outperforms the state-of-art network-based algorithms. These results confirm that MSMK is particularly helpful for disease-gene discovery, and the kernel sparsification can improve HyMSMK in storage space and computing speed. This may provide useful insights for the study and application of MMS.
“I see salt everywhere”: A qualitative examination of the utility of arts-based participatory workshops to study noncommunicable diseases in Tanzania and Malawi
Authors: Bissett Maria , Gray Cindy M , Abdulla Sharifa , Bunn Christopher , Crampin Amelia C , Dillip Angel , Gill Jason MR , Kaare Heri C , Kalima Sharon , Kambalu Elson , Lwanda John , Makoye Herbert F , Mtema Otiyela , Perry Mia , Strachan Zoë , Todd Helen , Mtenga Sally M 
Published: August 2022

AbstractThe burden of noncommunicable diseases (NCDs) including hypertension, diabetes and cancer, is rising in Sub-Saharan Africa countries like Tanzania and Malawi. This increase reflects complex interactions between diverse social, environmental, biological, and political factors. To intervene successfully, new approaches are therefore needed to understand how local knowledges and attitudes towards common NCDs influence health behaviours. This study compares the utility of using a novel arts-based participatory method and more traditional focus groups to generate new understandings of local knowledges, attitudes and behaviours towards NCDs and their risk factors. Single-gender arts-based participatory workshops and focus group discussions were conducted with local communities in Tanzania and Malawi. Thematic analysis compared workshop and focus group transcripts for depth of content and researcher-participant hierarchies. In addition, semiotic analysis examined the contribution of photographs of workshop activities to understanding participants’ experiences and beliefs about NCD risk factors. The arts-based participatory workshops produced in-depth, vivid, emotive narratives of participants’ beliefs about NCDs and their impact (e.g., “… it spreads all over your body and kills you - snake’s poison is similar to diabetes poison”), while the focus groups provided more basic accounts (e.g., “diabetes is a fast killer”). The workshops also empowered participants to navigate activities with autonomy, revealing their almost overwhelmingly negative beliefs about NCDs. However, enabling participants to direct the focus of workshop activities led to challenges, including the perpetuation of stigma (e.g., comparing smells associated with diabetes symptoms with sewage). Semiotic analysis of workshop photographs provided little additional insight beyond that gained from the transcripts. Arts-based participatory workshops are promising as a novel method to inform development of culturally relevant approaches to NCD prevention in Tanzania and Malawi. Future research should incorporate more structured opportunities for participant reflection during the workshops to minimise harm from any emerging stigma.
Integrative 4D-conformational mechanisms of single AdiC transporter molecules
Authors: Lewis John H. , Zhou Yufeng , Lu Zhe 
Published: August 2022

AbstractTo understand the mechanism of counter-transport of substrates by the amino-acid transporter AdiC, we used a state-of-the-art polarization-microscope to investigate conformation-specific changes of the emission polarization of a fluorophore attached to individual AdiC molecules. This capability enabled us to determine the lifetimes of two energetic states of each of AdiC’s four conformations in the absence and presence of its two natural substrates, totaling 24 states. From these lifetimes and relative state-to-state transition frequencies, we further determined 60 rate constants of all state transitions and the 4 KD values for the two substrates to interact with both sides of AdiC, quantitatively defining a 24-state model that satisfactorily predicts previously observed transporting behaviors of AdiC. Combining this temporal information and the existing structural information, we have successfully built a fully experiment-based integrative 4D-model to capture and exhibit the complex spatiotemporal mechanisms of a facilitated counter-transport of an amino acid and its metabolite. Thus, a combination of the present method and existing structural techniques serves as an effective means to help transition structural biology, which has thus far been highly successful in the investigation of individual static structures, to an integrative form of dynamic structural biology.
Diverse Strategies Utilized by Coronaviruses to Evade Antiviral Responses and Suppress Pyroptosis
Authors: Fu Xinyu , Xu Weilv , Yang Yang , Li Danyue , Shi Wen , Li Xinyue , Chen Nan , Lv Qian , Shi Yuhua , Xu Jidong , Yan Yuqi , Shi Fushan , Li Xiaoliang 
Published: August 2022

AbstractViral infection triggers inflammasome-mediated caspase-1 activation. Nevertheless, limited understanding exists regarding how viruses use the active caspase-1 to evade host immune response. Here, we use porcine epidemic diarrhea virus (PEDV) as a model of coronaviruses (CoVs) to illustrate the intricate regulation of CoVs to combat IFN-I signaling and pyroptosis. Our findings demonstrate that PEDV infection stabilizes caspase-1 expression via papain-like protease PLP2’s deubiquitinase activity and the enhanced stabilization of caspase-1 disrupts IFN-I signaling by cleaving RIG-I at D189 residue. 6-Thioguanine (6TG), a PLP2 inhibitor, plays a critical role in reversing the inhibitory effect of IFN-I via PLP2 and also inhibiting PEDV replication. Meanwhile, PLP2 can degrade GSDMD-p30 by removing its K27-linked ubiquitin chain at K275 to restrain pyroptosis. Papain-like proteases from other genera of CoVs (PDCoV and SARS-CoV-2) have the similar activity to degrade GSDMD-p30. We further demonstrate that SARS-CoV-2 N protein induced NLRP3 inflammasome activation also uses the active caspase-1 to counter IFN-I signaling by cleaving RIG-I. Therefore, our work unravels a novel antagonistic mechanism employed by CoVs to evade host antiviral response.
PhaeoEpiView: An epigenome browser of the newly assembled genome of the model diatom Phaeodactylum tricornutum
Authors: Wu Yue , Timothée Chaumier , Manirakiza Eric , Veluchamy Alaguraj , Tirichine Leila 
Published: August 2022

AbstractMotivationRecent advances in DNA sequencing technologies in particular of long reads type greatly improved genomes assembly leading to discrepancies between both published annotations and epigenome tracks which did not keep pace with new assemblies. This comprises the availability of accurate resources which penalizes the progress in research.ResultsHere, we used the latest improved telomere to telomere assembly of the model pennate diatom Phaeodactylum tricornutum to lift over the gene models from Phatr3, a previously annotated reference genome. We used the lifted genome annotation including genes and transposable elements to map the epigenome landscape, namely DNA methylation and post translational modifications of histones providing the community with PhaeoEpiView, a browser that allows the visualization of epigenome data as well as transcripts on an updated reference genome to better understand the biological significance of the mapped data on contiguous genome rather than a fragmented one. We updated previously published histone marks with a more accurate mapping using monoclonal antibodies instead of polyclonal and deeper sequencing. PhaeoEpiView will be continuously updated with the newly published epigenomic data making it the largest and richest epigenome browser of any stramenopile. We expect that PhaeoEpiView will be a standard tool for the coming era of molecular environmental studies where epigenetics holds a place of choice.AvailabilityPhaeoEpiView is available at: https://PhaeoEpiView.univ-nantes.fr
Yield Prediction Through Integration of Genetic, Environment, and Management Data Through Deep Learning
Authors: Kick Daniel R. , Wallace Jason G. , Schnable James C. , Kolkman Judith M. , Alaca Barış , Beissinger Timothy M. , Ertl David , Flint-Garcia Sherry , Gage Joseph L. , Hirsch Candice N. , Knoll Joseph E. , de Leon Natalia , Lima Dayane C. , Moreta Danilo , Singh Maninder P. , Weldekidan Teclemariam , Washburn Jacob D. 
Published: August 2022

AbstractAccurate prediction of the phenotypic outcomes produced by different combinations of genotypes, environments, and management interventions remains a key goal in biology with direct applications to agriculture, research, and conservation. The past decades have seen an expansion of new methods applied towards this goal. Here we predict maize yield using deep neural networks, compare the efficacy of two model development methods, and contextualize model performance using linear models, which are the conventional method for this task, and machine learning models We examine the usefulness of incorporating interactions between disparate data types. We find a deep learning model with interactions has the best average performance. Optimizing submodules for each datatype improved model performance relative to optimizing the whole model for all data types at once. Examining the effect of interactions in the best performing model revealed that including interactions altered the model’s sensitivity to weather and management features, including a reduction of the importance scores for timepoints expected to have limited physiological basis for influencing yield – those at the extreme end of the season, nearly 200 days post planting. Based on these results, deep learning provides a promising avenue for phenotypic prediction of complex traits in complex environments and a potential mechanism to better understand the influence of environmental and genetic factors.
In Silico Proteomics Approach Towards the Identification of Potential Novel Drug Targets Against Cryptococcus gattii
Authors: Robin Tanjin Barketullah , Rani Nurul Amin , Ahmed Nadim , Prome Anindita Ash , Bappy Md. Nazmul Islam , Ahmed Foeaz 
Published: August 2022

AbstractCryptococcosis is a condition caused by inhaling Cryptococcus gattii, the tiny fungus from the environment. It is thought that the pathogen C. gattii is clinically more virulent than C. neoformans and could be a vicious agent in coming decades. It can enter the host’s brain and harm human peripheral blood mononuclear cells’ DNA (PBMCs). It is vital to investigate potential alternative medications to treat this disease since global antifungal resistance preventing Cryptococci infections is on the rise, leading to treatment failure. In order to find effective novel drug targets for C. gattii, a comprehensive novel approach has been used in conjunction with in silico analysis. Among 6561 proteins of C. gattii we have found three druggable proteins (XP 003194316.1, XP 003197297.1, XP 003197520.1) after completing a series of steps including exclusion of paralogs, human homologs, non-essential and human microbiome homologs proteins. These three proteins are involved in pathogen specific pathways, and can be targeted for drugs to eliminate the pathogen from the host. The subcellular locations and their interactions with a high number of proteins also demonstrate their eligibility as potential drug targets. We have approached their secondary, tertiary model and docked them with 21 potential antifungal plant metabolites. From the molecular docking analysis, we found Amentoflavone, Baicalin, Rutin and Viniferin to be the most effective drugs to stop such proteins because of their increased binding affinity. Correspondingly, the drugs showed proper ADME properties and also analyzed to be safe (Figure 9, Table 6). Moreover, these potential drugs can successfully be used in the treatment of Cryptococcosis caused by the fungus Cryptococcus gattii. In vivo trail is highly recommended for further prospection.
Cannabis sativa oxylipin biosynthesis: Genome-wide characterization of lipoxygenase, allene oxide synthase, allene oxide cyclase, hydroperoxide lyase, and 12-oxo-phytodienoic acid reductase gene families
Authors: Borrego Eli J. , Robertson Mariah , Taylor James , Espinoza Elida 
Published: August 2022

AbstractCannabis sativa is a global multi-billion-dollar cash crop with numerous industrial uses, including in medicine and recreation where its value is largely owed to the production of pharmacological and psychoactive metabolites known as cannabinoids. Often underappreciated in this role, the lipoxygenase (LOX)-derived green leaf volatiles (GLVs), also known as the scent of cut grass, are the hypothetical origin of hexanoic acid, the initial substrate for cannabinoid biosynthesis. The LOX pathway is best known as the primary source of plant oxylipins, molecules analogous to the eicosanoids from mammalian systems. These molecules are chemically and functionally diverse group of fatty acid-derived signals that govern nearly all biological processes including plant defense and development. The interaction between oxylipin and cannabinoid biosynthetic pathways remains to be explored.Despite their unique importance in this crop, there has not been a comprehensive investigation focusing on the genes responsible for oxylipin biosynthesis in any Cannabis species. This study documents the first genome-wide catalogue of the Cannabis sativa oxylipin biosynthetic genes and identified 21 LOX, five allene oxide synthases (AOS), three allene oxide cyclases (AOC), one hydroperoxide lyase (HPL), and five 12-oxo-phytodienoic acid reductases (OPR). Gene collinearity analysis found chromosomal regions containing several isoforms maintained across Cannabis, Arabidopsis, and tomato. Promoter, expression, weighted co-expression genetic network, and functional enrichment analysis provide evidence of tissue- and cultivar-specific transcription and roles for distinct isoforms in oxylipin and cannabinoid biosynthesis.This knowledge facilitates future targeted approaches towards Cannabis crop improvement and for the manipulation of cannabinoid metabolism.
The Blood Proteome of Imminent Lung Cancer Diagnosis
Authors:   The Lung Cancer Cohort Consortium (LC3), Albanes Demetrius , Alcala Karine , Alcala Nicolas , Amos Christopher I. , Arslan Alan A. , Bassett Julie K. , Brennan Paul , Cai Qiuyin , Chen Chu , Feng Xiaoshuang , Freedman Neal , Guida Florence , Hung Rayjean J. , Hveem Kristian , Johansson Mikael , Johansson Mattias , Koh Woon-Puay , Langhammer Arnulf , Milne Roger L. , Muller David , Onwuka Justina , Sørgjerd Elin Pettersen , Robbins Hilary A. , Sesso Howard D. , Severi Gianluca , Shu Xiao-Ou , Sieri Sabina , Smith-Byrne Karl , Stevens Victoria , Tinker Lesley , Tjønneland Anne , Visvanathan Kala , Wang Ying , Wang Renwei , Weinstein Stephanie , Yuan Jian-Min , Zahed Hana , Zhang Xuehong , Zheng Wei 
Published: August 2022

AbstractIdentification of novel risk biomarkers may enhance early detection of smoking-related lung cancer. We measured 1,162 proteins in blood samples drawn at most three years before diagnosis in 731 smoking-matched case-control sets nested within six prospective cohorts from the US, Europe, Singapore, and Australia.We identified 36 proteins with replicable associations with risk of imminent lung cancer diagnosis (all p<4×10-5). These included several documented tumor markers (e.g. CA-125/MUC-16 and CEACAM5/CEA) but most had not been previously reported. The 36 proteins included several growth factors (e.g. HGF, IGFBP-1, IGFP-2), tumor necrosis factor-receptors (e.g. TNFRSF6B, TNFRSF13B), and chemokines and cytokines (e.g. CXL17, GDF-15, SCF). The odds ratio per standard deviation ranged from 1.31 for IGFBP-1 (95% CI: 1.17-1.47) to 2.43 for CEACAM5 (95% CI: 2.04-2.89). We mapped the 36 proteins to the hallmarks of cancer and found that proliferative signaling, tumor-promoting inflammation, and activation of invasion and metastasis were most frequently implicated.Statement of significanceAfter screening 1,162 proteins, we identified 36 markers of imminent smoking-related lung cancer diagnosis with a wide range of functions and relevance across the hallmarks of cancer. Forthcoming studies will address the extent to which these markers can discriminate future lung cancer cases and their utility for early detection.
Memory B cells and memory T cells induced by SARS-CoV-2 booster vaccination or infection show different dynamics and efficacy to the Omicron variant
Authors: Mise-Omata Setsuko , Ikeda Mari , Takeshita Masaru , Uwamino Yoshifumi , Wakui Masatoshi , Arai Tomoko , Yoshifuji Ayumi , Murano Kensaku , Siomi Haruhiko , Nakagawara Kensuke , Ohyagi Masaki , Ando Makoto , Hasegawa Naoki , Saya Hideyuki , Murata Mitsuru , Fukunaga Koichi , Namkoong Ho , Lu Xiuyuan , Yamasaki Sho , Yoshimura Akihiko 
Published: August 2022

AbstractAlthough BNT162b2 vaccination was shown to prevent infection and reduce COVID-19 severity, and the persistence of immunological memory generated by the vaccination has not been well elucidated. We evaluated memory B and T cell responses to the SARS-CoV-2 spike protein before and after the third BNT162b2 booster. Although the antibody titer against the spike receptor-binding domain (RBD) decreased significantly 8 months after the second vaccination, the number of memory B cells continued to increase, while the number of memory T cells decreased slowly. Memory B and T cells from unvaccinated infected patients showed similar kinetics. After the third vaccination, the antibody titer increased to the level of the second vaccination, and memory B cells increased at significantly higher levels before the booster, while memory T cells recovered close to the second vaccination levels. In memory T cells, the frequency of CXCR5+CXCR3+CCR6- cTfh1 was positively correlated with RBD-specific antibody-secreting B cells. Furthermore, T cell-dependent antibody production from reactivated memory B cells in vitro was correlated to the Tfh-like cytokine levels. For the response to variant RBDs, although 60%-80% of memory B cells could bind to the Omicron RBD, their binding affinity was low, while memory T cells show an equal response to the Omicron spike. Thus, the persistent presence of memory B and T cells will quickly upregulate antibody production and T cell responses after Omicron strain infection, which prevents severe illness and death due to COVID-19.
Development of an amplicon nanopore sequencing strategy for detection of mutations conferring intermediate resistance to vancomycin in Staphylococcus aureus strains
Authors: Moller Abraham G. , Petit Robert A. , Davis Michelle H. , Read Timothy D. 
Published: August 2022

AbstractStaphylococcus aureus is a major cause of bacteremia and other hospital-acquired infections. The cell-wall active antibiotic vancomycin is commonly used to treat both methicillin-resistant (MRSA) and sensitive (MSSA) infections, but vancomycin intermediate S. aureus (VISA) variants can arise through de novo mutations. Here we performed pilot experiments to develop a combined PCR/long-read sequencing-based method for detection of previously known VISA-causing mutations. We amplified 16 genes (walR, walK, rpoB, graR, graS, vraF, vraG, stpI, vraR, vraS, agrA, sarA, clpP, ccpA, prsA, and yvqF) known from prior studies to be associated with mutations responsible for VISA as 10 amplicons and sequenced amplicon pools as long-reads with Oxford Nanopore adapter ligation on Flongle flow cells. We then detected mutations by mapping reads against a parental consensus or known reference sequence and comparing called variants against a database of known VISA mutations from laboratory selection. There was high (>1000x) coverage of each amplicon in the pool, no relationship between amplicon length and coverage, and the ability to detect the causative mutation (walK 646C>G) in a VISA mutant derived from the USA300 strain (N384-3 from parental strain N384). Mixing mutant (N384-3) and parental (N384) DNA at various ratios from 0 to 1 mutant suggested a mutation detection threshold of roughly the average minor allele frequency of 6.5% at 95% confidence (two standard errors above mean mutation frequency). The study lays the groundwork for direct S. aureus antibiotic phenotype inference using rapid nanopore sequencing from clinical samples.ImportanceBacteremia mortality is known to increase rapidly with time after infection, making rapid diagnostics and treatment necessary. Successful treatment depends on correct administration of antibiotics based on knowledge of strain antibiotic susceptibility. Staphylococcus aureus is a major causative agent of bacteremia and is also increasingly antibiotic resistant. In this work, we develop a method to improve detection of a complex, polygenic antibiotic resistance phenotype in S. aureus, vancomycin-intermediate resistance (VISA) through long-read genomic sequencing of amplicons representing genes most commonly mutated in VISA selection. This method both speeds up VISA determination relative to purely culture-based detection and incorporates the most comprehensive database of VISA genetic determinants known to date.