bluuebunny/crossref_metadata_2025_split huggingface dataset:
Machine learning, artificial intelligence and the prediction of dementia
Authors: Merkin Alexander , Krishnamurthi Rita , Medvedev Oleg N. 
Published: December 2021

Purpose of review
Artificial intelligence and its division machine learning are emerging technologies that are increasingly applied in medicine. Artificial intelligence facilitates automatization of analytical modelling and contributes to prediction, diagnostics and treatment of diseases. This article presents an overview of the application of artificial intelligence in dementia research.

Recent findings
Machine learning and its branch Deep Learning are widely used in research to support in diagnosis and prediction of dementia. Deep Learning models in certain tasks often result in better accuracy of detection and prediction of dementia than traditional machine learning methods, but they are more costly in terms of run times and hardware requirements. Both machine learning and Deep Learning models have their own strengths and limitations. Currently, there are few datasets with limited data available to train machine learning models. There are very few commercial applications of machine learning in medical practice to date, mostly represented by mobile applications, which include questionnaires and psychometric assessments with limited machine learning data processing.

Summary
Application of machine learning technologies in detection and prediction of dementia may provide an advantage to psychiatry and neurology by promoting a better understanding of the nature of the disease and more accurate evidence-based processes that are reproducible and standardized.
CHRONOSIG: Digital Triage for Secondary Mental Healthcare using Natural Language Processing - rationale and protocol
Authors: Joyce Dan W , Kormilitzin Andrey , Hamer-Hunt Julia , James Anthony , Nevado-Holgado Alejo , Cipriani Andrea 
Published: December 2021

ABSTRACTBackgroundAccessing specialist secondary mental health care in the NHS in England requires a referral, usually from primary or acute care. Community mental health teams triage these referrals deciding on the most appropriate team to meet patients’ needs. Referrals require resource-intensive review by clinicians and often, collation and review of the patient’s history with services captured in their electronic health records (EHR). Triage processes are, however, opaque and often result in patients not receiving appropriate and timely access to care that is a particular concern for some minority and under-represented groups. Our project, funded by the National Institute of Health Research (NIHR) will develop a clinical decision support tool (CDST) to deliver accurate, explainable and justified triage recommendations to assist clinicians and expedite access to secondary mental health care.MethodsOur proposed CDST will be trained on narrative free-text data combining referral documentation and historical EHR records for patients in the UK-CRIS database. This high-volume data set will enable training of end-to-end neural network natural language processing (NLP) to extract ‘signatures’ of patients who were (historically) triaged to different treatment teams. The resulting algorithm will be externally validated using data from different NHS trusts (Nottinghamshire Healthcare, Southern Health, West London and Oxford Health). We will use an explicit algorithmic fairness framework to mitigate risk of unintended harm evident in some artificial intelligence (AI) healthcare applications. Consequently, the performance of the CDST will be explicitly evaluated in simulated triage team scenarios where the tool augments clinician’s decision making, in contrast to traditional “human versus AI” performance metrics.DiscussionThe proposed CDST represents an important test-case for AI applied to real-world process improvement in mental health. The project leverages recent advances in NLP while emphasizing the risks and benefits for patients of AI-augmented clinical decision making. The project’s ambition is to deliver a CDST that is scalable and can be deployed to any mental health trust in England to assist with digital triage.
Systemic lipid peroxidation and skeletal tissues metabolism in animals with simulated posttraumatic osteoarthrosis
Authors: Belova Svetlana V. , Zubavlenko Roman A. , Ulyanov Vladimir Yu. 
Published: December 2021

ABSTRACTThe role of individual factors in the progressing of degenerative and dystrophic changes of articular cartilage as the focus of pathologic changes in posttraumatic osteoarthrosis (PTOA) has been studied comprehensively. However, no single concept of pathogenetic mechanisms leading to the formation of permanent changes in the affected joint has been represented yet. Therefore, the investigation of lipid peroxidation and skeletal tissues metabolism in animals with simulated PTOA is a challenging and important issue.In 28 non-linear male rats (12 intact control animals and 16 experimental animals with simulated knee OA) we studied the lipid peroxidation rate by the level of lipid hydroperoxides, antioxidant system activity by the indicants of total antioxidant, and thiol statuses. The destruction processes in skeletal tissues were evaluated by the level of hyaluronan, a cartilaginous tissue marker, and subchondral bone turnover makers (fibroblast growth factor-23, osteoprotegerin, sclerostin, osteocalcin).In rats with simulated PTOA, we observed the activation of lipid peroxidation with the accumulation of lipid hydroperoxides in systemic blood flow along with the insignificant decrease of thiol status indicant while the normal antioxidant activity is maintained. We also revealed the significant increase of biopolymer hyaluronan (p<0.05) in a negative change of turnover regulation marker (the significant increase in fibroblast growth factor-23 (p<0.05) with the emerging trend for osteoprotegerin and sclerostin decrease) and bone formation marker (high osteocalcin) as compared to the intact controls.Our findings confirmed that animals with simulated PTOA featured rearranging of skeletal tissues as compared to intact animals. This process involved degenerative destruction of both chondral and osseous tissues along with profound prooxidant activity and relative incompetence of the thiol system while the indicants of general antioxidant activity remain normal. This suggests the importance of the analyzed markers in PTOA pathogenesis and determined the prospects of their use for the evaluation of therapy efficacy.
The Politics of Vaccine Hesitancy in the United States
Authors: Cao Jian , Ramirez Christina , Alvarez R. Michael 
Published: December 2021

AbstractObjectiveWhy are Americans COVID-19 vaccine hesitant? We test social science hypotheses for vaccine hesitancy, focusing on partisanship, trust in institutions, and social-demographic characteristics of registered voters.MethodsWe use survey data from a representative sample of American registered voters collected in November 2020 to study vaccine hesitancy, and the reasons for vaccine hesitancy, at a point in time before the vaccine was available and hence show underlying responses based on beliefs and not on clinical trial data. We use multivariate logistic regression models to test hypotheses on vaccine hesitancy.ResultsWe find that consistently similar groups of people tend to be vaccine hesitant. Specifically, Black voters, those between the ages of 45 and 64, female voters, voters without college degrees, voters not worried about the spread of COVID-19, and voters who are concerned about government and the CDC’s handling of the COVID-19 pandemic, were vaccine hesitant. We also provide intriguing results showing the nuanced reasons that the vaccine hesitant provide.ConclusionsOur analysis allows us to establish important baseline information from a social science perspective on vaccine hesitancy at a crucial time, right before COVID-19 vaccines were beginning to be made available to adult Americans. What emerges from our analysis is a nuanced perspective on vaccine hesitancy in the United States, from this important point in the history of the COVID-19 pandemic.
Imaging analysis to quantitate the Interplay of membrane and cytoplasm protein dynamics
Authors: Kislev N , Egozi M , Benayahu D 
Published: December 2021

AbstractPlasma membrane proteins are extremely important in cell signaling and cellular functions. Protein expression and localization alter in response to various signals in a way that is dependent on cell type and niche. Compartmental quantification of the expression of particular proteins is a very useful means of understanding their role in cellular processes. Immunofluorescence staining is frequently used to investigate the distribution of proteins of interest. Here, we present an imaging method for quantifying the membrane to cytoplasm ratio (MCR) of proteins analyzed at single-cell resolution. This technique provides a robust quantification of membrane proteins and contributes new insights into membrane expression dynamics. We have developed a protocol that uses immunostaining to assess protein expression according to the fluorescent cellular distribution and to compute the MCR. The method was applied to measure the MCR of glucose transporter 4 (GLUT4) in response to insulin in 3T3-L1 cells, an in-vitro model for adipocyte function and adipogenesis. The results revealed informative changes in the subcellular localization of GLUT4 following insulin induction. MCR analysis is a powerful imaging tool that can be generally applied to membrane proteins to provide a rapid and efficient quantitative analysis of protein distribution and sub-cellular processes in cells.
Application of Parallel Reaction Monitoring in 15N labeled Samples for Quantification
Authors: Reyes Andres V. , Shrestha Ruben , Baker Peter R. , Chalkley Robert J. , Xu Shou-Ling 
Published: December 2021

AbstractAccurate relative quantification is critical in proteomic studies. The incorporation of stable isotope 15N to plant-expressed proteins in vivo is a powerful tool for accurate quantification with a major advantage of reducing preparative and analytical variabilities. However, 15N labeling quantification has several challenges. Less identifications are often observed in the heavy labeled samples because of incomplete labeling, resulting in missing values in reciprocal labeling experiments. Inaccurate quantification can happen when there is contamination from co-eluting peptides or chemical noise in the MS1 survey scan. These drawbacks in quantification can be more pronounced in less abundant but biologically interesting proteins, which often have very few identified peptides. Here we demonstrate the application of parallel reaction monitoring (PRM) to 15N labeled samples on a high resolution, high mass accuracy Orbitrap mass spectrometer to achieve reliable quantification even of low abundance proteins in samples.
The ENDS of assumptions; an online tool for the Epistemic Nonparametric Drug-response Scoring
Authors: Amiryousefi Ali , Williams Bernardo , Jafari Mohieddin , Tang Jing 
Published: December 2021

AbstractMotivationThe drugs sensitivity analysis is often elucidated from drug dose-response curves. These curves capture the degree of cell viability (or inhibition) over a range of induced drugs, often with parametric assumptions that are rarely validated.ResultsWe present a class of nonparametric models for the curve fitting and scoring of drug dose-responses. To allow a more objective representation of the drug sensitivity, these epistemic models devoid of any parametric assumptions attached to the linear fit, allow the parallel indexing such as IC50 and AUC. Specifically, three nonparametric models including Spline, Monotonic, and Bayesian (npS, npM, npB) and the parametric Logistic (pL) are implemented. Other indices including Maximum Effective Dose (MED) and Drug-response Span Gradient (DSG) pertinent to the npS are also provided to facilitate the interpretation of the fit. The collection of these models are implemented in an online app, standing as useful resource for drug dose-response curve fitting and analysis.AvailabilityThe ENDS is freely available online at https://irscope.shinyapps.io/ENDS/ and source codes can be obtained from https://github.com/AmiryousefiLab/ENDS.Supplementary informationSupplementary data are available at Bioinformatics and https://irscope.shinyapps.io/ENDS/Contactali.amiryousefi@helisnki.fi; jing.tang@helisnki.fi.ContributionsAA conceived the study and developed the models, AA and BW adopted and implemented the methods, JT provided the funding, AA, BW, MJ, and JT wrote the paper.
CaveCrawler: An interactive analysis suite for cavefish bioinformatics
Authors: Perry Annabel , McGaugh Suzanne E. , Keene Alex C. , Blackmon Heath 
Published: December 2021

AbstractThe growing use of genomics data in diverse animal models provides the basis for identifying genomic and transcriptional differences across species and contexts. Databases containing genomic and functional data have played critical roles in the development of numerous genetic models but are lacking for most emerging models of evolution. There is a rapidly expanding use of genomic, transcriptional, and functional genetic approaches to study diverse traits of the Mexican tetra, Astyanax mexicanus. This species exists as two morphs, eyed surface populations and at least 30 blind cave populations, providing a system to study convergent evolution. We have generated a web-based analysis suite that integrates datasets from different studies to identify how gene transcription and genetic markers of selection differ between populations and across experimental contexts. Results can be processed with other analysis platforms including Gene Ontology (GO) to enable biological inference from cross-study patterns and identify future avenues of research. Furthermore, the framework that we have built A. mexicanus can readily applied to other emerging model systems.
Neoisoptera repetitively colonised Madagascar after the Middle Miocene climatic optimum
Authors: Wang Menglin , Hellemans Simon , Buček Aleš , Kanao Taisuke , Arora Jigyasa , Clitheroe Crystal , Rafanomezantsoa Jean-Jacques , Fisher Brian L. , Scheffrahn Rudolf , Sillam-Dussès David , Roisin Yves , Šobotník Jan , Bourguignon Thomas 
Published: December 2021

AbstractMadagascar is home to many endemic plant and animal species owing to its ancient isolation from other landmasses. This unique fauna includes several lineages of termites, a group of insects known for their key role in organic matter decomposition in many terrestrial ecosystems. How and when termites colonised Madagascar remains unknown. In this study, we used 601 mitochondrial genomes, 93 of which were generated from Madagascan samples, to infer the global historical biogeography of Neoisoptera, a lineage containing upwards of 80% of described termite species. Our results indicate that Neoisoptera colonised Madagascar between seven to ten times independently during the Miocene, between 8.4-16.6 Ma (95% HPD: 6.1-19.9 Ma). This timing matches that of the colonization of Australia by Neoisoptera. Furthermore, the taxonomic composition of the Neoisopteran fauna of Madagascar and Australia are strikingly similar, with Madagascar harbouring an additional two lineages absent from Australia. Therefore, akin to Australia, Neoisoptera colonised Madagascar during the global expansion of grasslands, possibly helped by the ecological opportunities arising from the spread of this new biome.
NRF2-dependent epigenetic regulation can promote the hybrid epithelial/mesenchymal phenotype
Authors: Jia Wen , Jolly Mohit Kumar , Levine Herbert 
Published: December 2021

AbstractThe epithelial-mesenchymal transition (EMT) is a cellular process critical for wound healing, cancer metastasis and embryonic development. Recent efforts have identified the role of hybrid epithelial/mesenchymal states, having both epithelial and mesehncymal traits, in enabling cancer metastasis and resistance to various therapies. Also, previous work has suggested that NRF2 can act as phenotypic stability factor to help stablize such hybrid states. Here, we incorporate a phenomenological epigenetic feedback effect into our previous computational model for EMT signaling. We show that this type of feedback can stabilize the hybrid state as compared to the fully mesenchymal phenotype if NRF2 can influence SNAIL at an epigenetic level, as this link makes transitions out of hybrid state more difficult. However, epigenetic regulation on other NRF2-related links do not significantly change the EMT dynamics. Finally, we considered possible cell division effects in our epigenetic regulation model, and our results indicate that the degree of epigenetic inheritance does not appear to be a critical factor for the hybrid E/M state stabilizing behavior of NRF2.
Coupling adipose tissue architecture and metabolism via cytoophidia
Authors: Liu Jingnan , Zhang Yuanbing , Zhou Youfang , Wang Qiao-Qi , Ding Kang , Zhao Suwen , Lu Pengfei , Liu Ji-Long 
Published: December 2021

ABSTRACTTissue architecture determines its unique physiology and function. How these properties are intertwined has remained unclear. Here, we show that the metabolic enzyme CTP synthase (CTPS) form filamentous structures termed cytoophidia along the adipocyte cortex in Drosophila adipose tissue. Interestingly, loss of cytoophidia, whether due to reduced CTPS expression or a point mutation that specifically abrogates its polymerization ability, leads to downregulated Collagen-Integrin signaling, weakened adipocyte adhesion, and defective adipose architecture. Strikingly, CTPS specifically binds with Integrin subunit α2, which influences Integrin function and Collagen IV deposition. cytoophidia promote Collagen IV mRNA expression and thus its extracellular deposition to strengthen adipocyte adhesion. Remarkably, Collagen IV-Integrin signaling reciprocally regulates cytoophidium formation at a post-translational level. Together, we demonstrate that a positive feedback signaling loop containing both cytoophidia and Integrin adhesion complex couples tissue architecture and metabolism in the fly adipose.
A novelPhytophthora sojaeeffector PsFYVE1 modulates transcription and alternative splicing of immunity related genes by targeting host RZ-1A protein
Authors: Lu Xinyu , Yang Zitong , Song Wen , Si Jierui , Yin Zhiyuan , Jing Maofeng , Shen Danyu , Dou Daolong 
Published: December 2021

AbstractOomycete pathogens secrete many effectors to manipulate plant immunity and promote infection. However, relatively few effector types have been well characterized. In this study, members of a FYVE domain-containing protein family that is highly expanded in oomycetes were systematically identified, and one secreted protein, PsFYVE1, was selected for further study. PsFYVE1 enhancedPhytophthorainfection inNicotiana benthamianaand was necessary forP. sojaevirulence. The FYVE domain of PsFYVE1 had PI3P-binding activity that depended on four conservative amino acid residues. Furthermore, PsFYVE1 targeted RNA-binding proteins RZ-1A/1B/1C inN. benthamianaand soybean, and silencing ofNbRZ-1A/1B/1Cgenes attenuates plant immunity. NbRZ-1A was associated with spliceosome that included three important components, NbGRP7, NbGRP8, and NbU1-70K. Notably, PsFYVE1 could disrupt NbRZ-1A–NbGRP7 interaction. RNA-seq and subsequent experimental analysis demonstrated that PsFYVE1 and NbRZ-1A not only co-regulated transcription ofNbHCT,NbEIN2, andNbSUS4genes but also modulated pre-mRNA alternative splicing (AS) of theNbNSL1gene, which participated in plant immunity. Collectively, these findings indicate that the FYVE domain-containing protein family includes potential new effector types and also highlight that plant pathogen effectors can regulate plant immunity related genes at both transcription and AS levels to promote disease.Author summaryMany plant pathogenic oomycetes secrete effector proteins into plants to facilitate infection. Discovering potential repertoire of novel effectors and corresponding molecular mechanisms are major themes in the study of oomycete–plant interactions. Here, we characterized a FYVE domain-containing protein (PsFYVE1) inP. sojae. PsFYVE1 carries a functional secretory signal peptide and is a virulence-essential effector forP. sojaeinfection. We demonstrated that PsFYVE1 interacted with a class of plant RNA-binding proteins, including soybean GmRZ-1A/1B/1C andN. benthamianaNbRZ-1A/1B/1C. Silencing ofNbRZ-1A/1B/1Cproteins increasedPhytophthorainfection and suppressed plant defense. Furthermore, NbRZ-1A interacted with the spliceosome components, and PsFYVE1 disrupted association between NbRZ-1A and spliceosome component NbGRP7. We examined the global transcription and alternative splicing (AS) changes regulated by PsFYVE1 and NbRZ-1A, which indicated that PsFYVE1 and NbRZ-1A co-regulated transcription and pre-mRNA AS of immunity-related genes. Thus, this study identifies a novel virulence-related effector fromP. sojaeand a class of positive regulators of plant immunity, and reveals a detailed mechanism of effector-medicated transcription and AS regulation during pathogen–plant interactions.
Accurate sequence-dependent coarse-grained model for conformational and elastic properties of double-stranded DNA
Authors: Assenza Salvatore , Pérez Rubén 
Published: December 2021

AbstractWe introduce MADna, a sequence-dependent coarse-grained model of double-stranded DNA (dsDNA), where each nucleotide is described by three beads localized at the sugar and base moieties, and at the phosphate group. The sequence dependence is included by considering a step-dependent parameterization of the bonded interactions, which are tuned in order to reproduce the values of key observables obtained from exhaustive atomistic simulations from literature. The predictions of the model are benchmarked against an independent set of all-atom simulations, showing that it captures with high fidelity the sequence dependence of conformational and elastic features beyond the single step considered in its formulation. A remarkably good agreement with experiments is found for both sequence-averaged and sequence-dependent conformational and elastic features, including the stretching and torsion moduli, the twist-stretch and twist-bend couplings, the persistence length and the helical pitch. Overall, for the inspected quantities, the model has a precision comparable to atomistic simulations, hence providing a reliable coarse-grained description for the rationalization of singlemolecule experiments and the study of cellular processes involving dsDNA. Owing to the simplicity of its formulation, MADna can be straightforwardly included in common simulation engines.