bluuebunny/crossref_metadata_2025_split huggingface dataset:
Convergence of HIV and non-communicable disease epidemics: Geospatial mapping of the unmet health needs in a HIV Hyperendemic South African community
Authors: Cuadros Diego F , Devi Chayanika , Singh Urisha , Olivier Stephen , Castle Alison , Moosa Yumna , Edwards Johnathan A , Kim Hae-Young , Siedner Mark J. , Wong Emily B , Tanser Frank 
Published: March 2023

ABSTRACTBackgroundAs people living with HIV (PLHIV) are experiencing longer survival, the co-occurrence of HIV and non-communicable diseases has become a public health priority. In response to this emerging challenge, we aimed to characterize the spatial structure of convergence of chronic health conditions in a HIV hyperendemic community in KwaZulu-Natal, South Africa.MethodsWe utilized data from a comprehensive population-based disease survey conducted in KwaZulu-Natal, South Africa, which collected data on HIV, diabetes, and hypertension. We implemented a novel health needs scale to categorize participants as: diagnosed and well-controlled (Needs Score 1), diagnosed and sub-optimally controlled (Score 2), diagnosed but not engaged in care (Score 3), or undiagnosed and uncontrolled (Score 4). Scores 2-4 were indicative of unmet health needs. We explored the geospatial structure of unmet health needs using different spatial clustering methods.FindingsThe analytical sample comprised of 18,041 individuals. We observed a similar spatial structure for HIV among those with a combined needs Score 2-3 (diagnosed but uncontrolled) and Score 4 (undiagnosed and uncontrolled), with most PLHIV with unmet needs clustered in the southern peri-urban area, which was relatively densely populated within the surveillance area. Multivariate clustering analysis revealed a significant overlap of all three diseases in individuals with undiagnosed and uncontrolled diseases (unmet needs Score 4) in the southern part of the catchment area.InterpretationIn a HIV hyperendemic community in South Africa, areas with the highest needs for PLHIV with undiagnosed and uncontrolled disease are also areas with the highest burden of unmet needs for other chronic health conditions, such as diabetes and hypertension. The identification and prioritization of geographically clustered vulnerable communities with unmet health needs for both HIV and non-communicable diseases provide a basis for policy and implementation strategies to target communities with the highest health needs.FundingResearch reported in this publication was supported by the Fogarty International Center (R21 TW011687; D43 TW010543), the National Institute of Mental Health, the National Institute of Allergy and Infectious Diseases (K24 HL166024; T32 AI007433) of the National Institutes of Health, and Heart Lung and Blood Institute (K24 HL166024, T32 AI007433). The contents of this manuscript are solely the responsibility of the authors and do not necessarily represent the official views of the funders.
Tensor decomposition based feature extraction and classification to detect natural selection from genomic data
Authors: Amin Md Ruhul , Hasan Mahmudul , Arnab Sandipan Paul , DeGiorgio Michael 
Published: March 2023

AbstractInferences of adaptive events are important for learning about traits, such as human digestion of lactose after infancy and the rapid spread of viral variants. Early efforts toward identifying footprints of natural selection from genomic data involved development of summary statistic and likelihood methods. However, such techniques are grounded in simple patterns or theoretical models that limit the complexity of settings they can explore. Due to the renaissance in artificial intelligence, machine learning methods have taken center stage in recent efforts to detect natural selection, with strategies such as convolutional neural networks applied to images of haplotypes. Yet, limitations of such techniques include estimation of large numbers of model parameters under non-convex settings and feature identification without regard to location within an image. An alternative approach is to use tensor decomposition to extract features from multidimensional data while preserving the latent structure of the data, and to feed these features to machine learning models. Here, we adopt this framework and present a novel approach termedT-REx, which extracts features from images of haplotypes across sampled individuals using tensor decomposition, and then makes predictions from these features using classical machine learning methods. As a proof of concept, we explore the performance ofT-RExon simulated neutral and selective sweep scenarios and find that it has high power and accuracy to discriminate sweeps from neutrality, robustness to common technical hurdles, and easy visualization of feature importance. Therefore,T-RExis a powerful addition to the toolkit for detecting adaptive processes from genomic data.
The genetic composition of hybridMangifera
Authors: Warschefsky Emily J. , von Wettberg Eric J.B. 
Published: March 2023

AbstractHomoploid hybridization is known to play an important role in the evolution of plants, including many crop species, but can have different outcomes including introgression between parental taxa and the formation of new evolutionary lineages. We investigate the occurrence and consequences of hybridization between the economically important tree cropMangifera indica(mango) and two congeneric species in Southeast Asia. A total of 90 samples of the hybridM. odorataand its parental taxa,M. indica(mango) andM. foetida, along with 65 samples of a newly proposed hybrid,M. casturiand its putative parental taxa,M. indicaandM. quadrifida, were sampled and genotyped using restriction site associated DNA sequencing. For each hybrid, we assessed population structure and admixture and indices of genetic diversity, including multilocus linkage disequilibrium. We found no evidence of introgression betweenM. foetidaandM. indicacultivars from Southeast Asia, but find support for a hybrid origin ofM. casturi. Both hybrids show low levels of intraspecific genetic diversity and individuals have high genetic identity and significant multilocus linkage disequilibrium. For bothM. odorataandM. casturi, our results are consistent with hybrid lineages that have formed only a few times and have since been maintained clonally. While grafting may play a role in the continued propagation of these hybrids, we suggest that the ability ofM. odorataandM. casturito reproduce asexually through nucellar polyembryony has allowed the hybrids to persist independently of grafting.
EGFR inhibition in lung adenocarcinoma upregulates cell surface expression of the placental antigen ALPP and enhances efficacy of ALPP-ADC therapy
Authors: Chen Yihui , Hong Monica J. , Xu Hanwen , Vykoukal Jody , Park Soyoung , Cai Yining , León-Letelier Ricardo A. , Irajizad Ehsan , Hsiao Fu Chung , Dennison Jennifer B. , Ostrin Edwin J. , Fahrmann Johannes F. , Katayama Hiroyuki , Hanash Samir M. 
Published: March 2023

AbstractAlkaline phosphatase placental type (ALPP) and ALPPL2 are closely related and regulated GPI anchored proteins that are known to be expressed on the cell surface in some cancers, whereas normal tissue expression is largely limited to the placenta. Clinical utility of ALPP is potentially limited by heterogenous expression in tumors. Here, we assessed ALPP and ALPPL2 surfaceome protein levels in 158 cancer cell lines and mRNA expression levels in 10,967 tumors representing 32 cancer types from The Cancer Genome Atlas (TCGA), which revealed ALPP, and to a lesser extent ALPPL2, to be variably expressed in several cancer types including lung adenocarcinoma (LUAD). Surface expression of ALPP was confirmed by tissue microarray analysis of 204 lung tumors. Using LUAD as a model system, we demonstrated that treatment with EGFR inhibitors, or induction of cancer cell quiescence via nutrient deprivation greatly enhanced ALPP surface expression. Mechanistic studies revealed that enhancement of surface ALPP expression in LUAD following gefitinib treatment was mediated through repression of EGFR signaling and activation of the transcription factor FoxO3a, which was identified as an upstream transcriptional regulator of ALPP. Using xenograft models of LUAD, we further demonstrated that gefitinib treatment upregulates surface expression of ALPP in LUAD cells but not in normal tissues. Combination therapy with gefitinib and an ALPP antibody conjugated with Monomethylauristatin F (ALPP-ADC-MAF) resulted in superior anti-cancer efficacy compared with gefitinib or ALPP-ADC-MAF alone. Our findings support a novel combination treatment modality that boosts the efficacy of ALPP-ADC directed therapy.
Transcription Factor Co-Expression Mediates Lineage Priming for Embryonic and Extra-Embryonic Differentiation
Authors: Redó-Riveiro Alba , Al-Mousawi Jasmina , Linneberg-Agerholm Madeleine , Proks Martin , Perera Marta , Brickman Joshua M. 
Published: March 2023

AbstractIn early mammalian development, cleavage stage blastomeres and cells of the inner cell mass (ICM) of the blastocyst co-express embryonic and extra-embryonic transcriptional determinants. Using a double protein-based reporter we identify embryonic stem cells (ESC) that co-express the extra-embryonic factor GATA6 alongside the embryonic factor SOX2 in specific conditions. Based on single cell transcriptomics we find these population resemble unsegregated ICM, exhibiting enhanced differentiation potential for endoderm while maintaining epiblast competence and suggesting they represent an ideal model to determine how GATA6 and SOX2 influence each other’s DNA binding. To relate this binding to future fate, we describe a complete enhancer set in both ESCs and naïve extraembryonic endoderm stem cells and ask whether SOX2 and GATA6 recognize these elements in ICM-like ESC sub-population. Both factors support cooperative recognition in these lineages, with GATA6 bound alongside SOX2 on a fraction of pluripotency enhancers and SOX2 alongside GATA6 more extensively on endoderm enhancers. Our findings suggest that cooperative binding between these antagonistic factors both supports self-renewal and prepares progenitor cells for later differentiation.
Quantitative determination of fluorescence labeling implemented in cell cultures
Authors: Spagnolo Chiara Schirripa , Moscardini Aldo , Amodeo Rosy , Beltram Fabio , Luin Stefano 
Published: March 2023

AbstractBackgroundLabeling efficiency is a crucial parameter in fluorescence applications, especially when studying biomolecular interactions. Current approaches for estimating the yield of fluorescent labeling have critical drawbacks that usually lead them to be inaccurate or not quantitative.ResultsWe present a method to quantify fluorescent-labeling efficiency that addresses the critical issues marring existing approaches. The method operates in the same conditions of the target experiments by exploiting a ratiometric evaluation with two fluorophores used in sequential reactions. We show the ability of the protocol to extract reliable quantification for different fluorescent probes, reagents concentrations, reaction timing and to optimize labeling performance. As paradigm, we consider the labeling of the membrane-receptor TrkA through 4’-phosphopantetheinyl transferase Sfp in living cells, visualizing the results by TIRF microscopy. This investigation allows us to find conditions for demanding single and multi-color single-molecule studies requiring high degrees of labeling.ConclusionsThe developed method allows the quantitative determination and the optimization of staining efficiency in any labeling strategy based on stable reactions.
People with a tobacco use disorder misattribute non-drug cues as worse predictors of positive outcomes compared to drug cues
Authors: Kalhan Shivam , Schwartenbeck Philipp , Hester Robert , Garrido Marta I. 
Published: March 2023

AbstractAdaptive behaviours depend on dynamically updating internal representations of the world based on the ever-changing environmental contingencies. People with a substance use disorder (pSUD) show maladaptive behaviours with high persistence in drug-taking, despite severe negative consequences. We recently proposed a salience misattribution model for addiction (SMMA; Kalhan et al., (2021)), arguing that pSUD have aberrations in their updating processes where drug cues are misattributed as strong predictors of positive outcomes, but weaker predictors of negative outcomes. We also argue that conversely, non-drug cues are misattributed as weak predictors of positive outcomes, but stronger predictors of negative outcomes. However, these hypotheses need to be empirically tested. Here we used a multi-cue reversal learning task, with reversals in whether drug or non-drug cues are currently relevant in predicting the outcome (monetary win or loss). We show that compared to controls, people with a tobacco use disorder (pTUD), do form misaligned internal representations. We found that pTUD updated less towards learning the drug cue’s relevance in predicting a loss. Further, when neither drug nor non-drug cue predicted a win, pTUD updated more towards the drug cue being relevant predictors of that win. Our Bayesian belief updating model revealed that pTUD had a low estimated likelihood of non-drug cues being predictors of wins, compared to drug cues, which drove the misaligned updating. Overall, several hypotheses of the SMMA were supported, but not all. Our results implicate that strengthening the non-drug cue association with positive outcomes may help restore the misaligned internal representation in pTUD.
Comparative Genomics and Directed Evolution Reveal Genetic Determinants of Extreme UVC Radiation Tolerance in Bacteria Recovered from the Stratosphere
Authors: Ellington Adam J. , Schult Tyler J. , Reisch Christopher R. , Christner Brent C. 
Published: March 2023

AbstractAerosolized microbes surviving transport to and in the stratosphere endure extremes of low temperature, atmospheric pressure, and relative humidity, and high shortwave ultraviolet radiation flux. However, the genetic determinants for traits enabling resistance to the combination of stresses experienced by microbes in the high atmosphere have not been systematically investigated. In this study, we examined Proteobacteria and Actinobacteria isolated from the stratosphere (18 to 29 km ASL) and that demonstrated high tolerance to desiccation (15-25% RH) and UVC radiation (UVCR; λ= 254 nm). Closely related reference strains were more sensitive to UVCR than the stratospheric isolates, indicating that extreme resistance is not universally distributed in these phylogenetically related bacteria. Comparative genomic analyses revealed DNA repair and antioxidant defense genes in the isolates that are not possessed by the related reference strains, including genes encoding photolyase, DNA nucleases and helicases, and catalases. Directed evolution by repeated exposure to increasing doses of UVCR improved the LD90in a sensitive reference strain by ∼3.5-fold. The mutations acquired inCurtobacterium flaccumfaciens pv. flaccumfaciensstrain DSM 20129 incrementally increased its UVCR resistance, with the accumulation of 20 point mutations in protein coding genes increasing tolerance to a level approaching that of stratospheric isolateCurtobacteriumsp. L6-1. The genetic basis for the increased UVCR tolerance phenotypes observed is discussed, with a specific emphasis on the role of genes involved in DNA repair and detoxification of reactive oxygen species.ImportanceUltraviolet radiation is omnipresent in sunlight and has important biological effects on organisms. The stratosphere is the only location on Earth where microbes receive natural exposure to highly mutagenic wavelengths (<280 nm) of ultraviolet radiation. Genetic studies of bacteria from an environment that selects for extreme ultraviolet radiation resistant phenotypes has expanded what is known from studies of model species (e.g.,E. coli) and identified potentially novel protection and repair strategies. In addition to deepening understanding of ultraviolet radiation photobiology in atmospheric microbes and bacteria in general, these advancements are also highly relevant to astrobiology and space biology. The cold, dry, hypobaric, and high radiation environment of the stratosphere provides an earthly analog for thin extraterrestrial atmospheres (e.g., Mars) and is ideal for bioprospecting extremophile phenotypes that enable engineering of genetic stability and functionality in bio-based space life-support systems or any application where long-term persistence is desirable (e.g., biocontrol).
CT-based Machine Learning for Donor Lung Screening Prior to Transplantation
Authors: Ram Sundaresh , Verleden Stijn E , Kumar Madhav , Bell Alexander J. , Pal Ravi , Ordies Sofie , Vanstapel Arno , Dubbeldam Adriana , Vos Robin , Galban Stefanie , Ceulemans Laurens J. , Frick Anna E. , Van Raemdonck Dirk E. , Verschakelen Johny , Vanaudenaerde Bart M. , Verleden Geert M. , Lama Vibha N , Neyrinck Arne P. , Galban Craig J. 
Published: March 2023

ABSTRACTBackgroundAssessment and selection of donor lungs remains largely subjective and experience based. Criteria to accept or decline lungs are poorly standardized and are not compliant with the current donor pool. Using ex vivo CT images, we investigated the use of a CT-based machine learning algorithm for screening donor lungs prior to transplantation.MethodsClinical measures and ex-situ CT scans were collected from 100 cases as part of a prospective clinical trial. Following procurement, donor lungs were inflated, placed on ice according to routine clinical practice, and imaged using a clinical CT scanner prior to transplantation while stored in the icebox. We trained and tested a supervised machine learning method calleddictionary learning, which uses CT scans and learns specific image patterns and features pertaining to each class for a classification task. The results were evaluated with donor and recipient clinical measures.ResultsOf the 100 lung pairs donated, 70 were considered acceptable for transplantation (based on standard clinical assessment) prior to CT screening and were consequently implanted. The remaining 30 pairs were screened but not transplanted. Our machine learning algorithm was able to detect pulmonary abnormalities on the CT scans. Among the patients who received donor lungs, our algorithm identified recipients who had extended stays in the ICU and were at 19 times higher risk of developing CLAD within 2 years post-transplant.ConclusionsWe have created a strategy to ex vivo screen donor lungs using a CT-based machine learning algorithm. As the use of suboptimal donor lungs rises, it is important to have in place objective techniques that will assist physicians in accurately screening donor lungs to identify recipients most at risk of post-transplant complications.
Prognostic factors of clinical responses in patients with advanced-stage intrahepatic cholangiocarcinoma followingAtractylodes lanceaadministration: A phase 2A clinical trial
Authors: Saeheng Teerachat , Karbwang Juntra , Cheomung Anurak , Tongsiri Nisit , Plengsuriyakarn Tullayakorn , Na-Bangchang Kesara 
Published: March 2023

AbstractA statistical model is essential in determining the appropriate predictive indicators for therapies in many types of cancers. Predictors have been compared favorably to the traditional systems for many cancers. Thus, this study has been proposed as an alternative or a new standard approach. A recent study on the clinical efficacy ofAtractylodes lancea(Thunb) DC. (AL) revealed the higher clinical benefits in patients with advanced-stage intrahepatic cholangiocarcinoma (ICC) treated with AL compared with standard supportive care. we investigated the relationships between clinical efficacy and pharmacokinetic parameters of serum bioactivity of AL and its active constituent “atractylodin” and determined therapeutic ranges. Cox proportion hazard model and Receive Operating Characteristic (ROC) were applied to determine the cut-off values of AUC0-inf, Cmax, and Cavgassociated with therapeutic outcomes. Number-need to be treated (NNT) and relative risk (RR) was also applied to determine potential predictors. The AUC0-infof total AL bioactivity of> 96.71 µg*h/ml was identified as a promising predictor of disease prognosis,i.e., progression-free survival (PFS) and disease control rate (DCR). Cmaxof total AL bioactivity of>21.42 was identified as a predictor of the prognosis of death. The therapeutic range of total AL bioactivity for PFS and DCR is 14.48-65.8 µg/ml, and for overall survival is 10.97-65.8 µg/ml. The predictors of ICC disease prognosis were established based on the pharmacokinetics of total AL bioactivity. The information could be exploited to improve the clinical efficacy of AL in patients with advanced-stage ICC. These predictors will be validated in a phase 2B clinical study.
Insights into the comorbidity between type 2 diabetes and osteoarthritis
Authors: de S. V. Arruda Ana Luiza , Hartley April , Katsoula Georgia , Smith George Davey , Morris Andrew P. , Zeggini Eleftheria 
Published: March 2023

AbstractMultimorbidity is a rising public health challenge with important implications for health management and policy. The most common multimorbidity pattern is for the combination of cardiometabolic and osteoarticular diseases. Here, we study the genetic underpinning of the comorbidity between type 2 diabetes and osteoarthritis. We find genome-wide genetic correlation between the two diseases, and robust evidence for association signal colocalization at 18 genomic regions. We integrate multi-omics and functional information to resolve the colocalizing signals, and identify high-confidence effector genes, includingFTOandIRX3, which provide proof-of-concept insights into the epidemiologic link between obesity and both diseases. We find enrichment for lipid metabolism and skeletal formation pathways for signals underpinning the knee and hip osteoarthritis comorbidities with type 2 diabetes, respectively. Causal inference analysis identifies complex effects of tissue-specific gene expression on comorbidity outcomes. Our findings provide insights into the biological basis for the type 2 diabetes-osteoarthritis disease co-occurrence.