bluuebunny/crossref_metadata_2025_split huggingface dataset:
Adherence to antiretroviral therapy in people living with HIV with moderate or severe mental disorder
Authors: Arashiro Priscilla , Maciel Camila Guadeluppe , Freitas Fernanda Paes Reis , Koch Gabriel Serrano Ramires , da Cunha João Cesar Pereira , Stolf Anderson Ravy , Paniago Anamaria Mello Miranda , de Medeiros Márcio José , Santos-Pinto Cláudia Du Bocage , de Oliveira Everton Falcão 
Published: March 2023

AbstractHuman immunodeficiency virus (HIV) infection remains a serious public health concern, with an estimated 38 million people living with HIV (PLHIV). PLHIV are often affected by mental disorders at higher rate than the general population. One challenge in the control and prevention of new HIV infections is adherence to antiretroviral therapy (ART), with PLHIV with mental disorders having seemingly lower adherence than PLHIV without mental disorders. This cross-sectional study assessed adherence to ART in PLHIV with mental disorders who attended the Psychosocial Care Network health facilities in Campo Grande, Mato Grosso do Sul, Brazil, from January 2014 to December 2018. Data from health and medical databases were used to describe clinical–epidemiological profiles and adherence to ART. To assess the associated factors (potential risk or predisposing factors) with ART adherence, we used logistic regression model. Adherence was extremely low (16.4%). Factors associated with poor adherence were lack of clinical follow-up, particularly in middle-aged PLHIV. Other apparently associated factors were living on the streets and having suicidal ideation. Our findings reinforce the need for improvements in the care for PLHIV with mental disorders, especially in the integration between specialized mental health and infectious disease health facilities.
Photodegradation of a bacterial pigment and resulting hydrogen peroxide release enable coral settlement
Authors: Petersen Lars-Erik , Kellermann Matthias Y. , Fiegel Laura J. , Nietzer Samuel , Bickmeyer Ulf , Abele Doris , Schupp Peter J. 
Published: March 2023

AbstractThe global degradation of coral reefs is steadily increasing with ongoing climate change. Yet coral larvae settlement, a key mechanism of coral population rejuvenation and recovery, is largely understudied. Here, we show how the lipophilic, settlement-inducing bacterial pigment cycloprodigiosin (CYPRO) is actively harvested and subsequently enriched along the ectoderm of larvae of the scleractinian coralLeptastrea purpura. A light-dependent reaction transforms the CYPRO molecules through photolytic decomposition and provides a constant supply of hydrogen peroxide (H2O2), leading to attachment on the substrate and metamorphosis into a coral recruit. Micromolar concentrations of H2O2in seawater also resulted in rapid metamorphosis, but without prior larval attachment. We propose that the morphogen CYPRO is responsible for initiating attachment while simultaneously acting as a molecular generator for the comprehensive metamorphosis of pelagic larvae. Ultimately, our approach opens a novel mechanistic dimension to the study of chemical signaling in coral settlement and provides unprecedented insights into the role of infochemicals in cross-kingdom interactions.
MicroRNAs and cytokines as potential predictive biomarkers for COVID-19 disease progression
Authors: Mohamed Hatem A. , Abdelkafy Aya Eid , Khairy Rasha M. M. , Abdelraheim Salama R. , Kamel Bothina Ahmed , Marey Heba 
Published: March 2023

AbstractHost microRNAs can influence the cytokine storm associated SARS-CoV-2 infection and proposed as biomarkers for COVID-19 disease. In the present study, serum MiRNA-106a and miRNA-20a were quantified by real time-PCR in 50 COVID-19 patients hospitalized at Minia university hospital and 30 healthy volunteers. Profiles of serum inflammatory cytokines (TNF-α, IFN-γ, and IL-10) and TLR4 were analyzed by Eliza in patients and controls. A highly significant decrease (P value = 0.0001) in the expressions of miRNA-106a and miRNA-20a was reported in COVID-19 patients compared to controls. A significant decrease in the levels of miRNA-20a was also reported in patients with lymphopenia, patients having chest CT severity score (CSS) > 19 and in patients having O2 saturation less than 90%. Significantly higher levels of TNF-α, IFN-γ, IL-10 and TLR4 were reported in patients compared to controls. IL-10 and TLR4 levels were significantly higher in patients having lymphopenia. TLR-4 level was higher in patients with CSS > 19 and in patients with hypoxia. Using univariate logistic regression analysis, miRNA-106a, miRNA-20a, TNF-α, IFN-γ, IL-10 and TLR4 were identified as good predictors of disease. Receiver operating curve showed that the downregulation of miRNA-20a in patients having lymphopenia, patients with CSS > 19 and patients with hypoxia could be a potential biomarker with AUC = 0.68 ± 0.08, AUC = 0.73 ± 0.07 and AUC = 0.68 ± 0.07 respectively. Also, ROC curve showed accurate association between the increase of serum IL-10 and TLR-4 and lymphopenia among COVID-19 patients with AUC = 0.66 ± 0.08 and AUC = 0.73 ± 0.07 respectively. ROC curve showed also that serum TLR-4 could be a potential marker for high CSS with AUC = 0.78 ± 0.06. A negative correlation was detected between miRNA-20a with TLR-4 (r = − 0.30, P value = 0.03). We concluded that, miR-20a, is a potential biomarker of COVID-19 severity and blockade of IL-10 and TLR4 may constitute a novel therapy for COVID-19 patients.
Assessment of ITER radiation environment during the remote-handling operation of In-Vessel components with D1SUNED
Authors: Martínez-Albertos P. , Sauvan P. , Loughlin M. J. , Le Tonqueze Y. , Juárez R. 
Published: March 2023

AbstractDuring ITER operational life, a remote-handled cask will be used to transfer In-Vessel components to the Hot Cell for maintenance, storage and decommissioning purposes. Due to the distribution of penetrations for system allocation in the facility, the radiation field of each transfer operation presents a high spatial variability; all operations must be studied independently for workers and electronics protection. In this paper, we present a fully representative approach to describe the radiation environment during the complete remote-handling scenario of In-Vessel components in the ITER facility. The impact of all relevant radiation sources during different stages of the operation is addressed. As-built structures and 2020 baseline designs are considered to produce the most detailed neutronics model of the Tokamak Complex, the 400,000-tonne civil structure hosting the tokamak, up to date. Novel capabilities of the D1SUNED code have allowed to compute the integral dose, the dose rate and the photon-induced neutron flux of both moving and static radiation sources. Time bins are included in the simulations to compute the dose rate caused by In-Vessel components at all positions along the transfer. The time evolution of the dose rate is built in video format with a 1-m resolution, especially valuable for hot-spots identification.
A deep learning architecture for energy service demand estimation in transport sector for Shared Socioeconomic Pathways
Authors: Joshi Siddharth , Ó Gallachóir Brian , Glynn James 
Published: March 2023

AbstractMeeting current global passenger and freight transport energy service demands accounts for 20% of annual anthropogenic CO2emissions, and mitigating these emissions remains a considerable challenge for climate policy. Pursuant to this, energy service demands play a critical role in the energy systems and integrated assessment models but fail to get the attention they warrant. This study introduces a novel custom deep learning neural network architecture (called TrebuNet) that mimics the physical process of firing a trebuchet to model the nuanced dynamics inherent in energy service demand estimation. Here we show, how TrebuNet is designed, trained, and used to estimate transport energy service demand. We find that the TrebuNet architecture shows superior performance compared with traditional multivariate linear regression and state of the art methods like densely connected neural network, Recurrent Neural Network, and Gradient Boosted machine learning algorithms when evaluated for regional demand projection for all modes of transport demands at short, decadal, and medium-term time horizons. Finally, TrebuNet introduces a framework to project energy service demand for regions having multiple countries spanning different socio-economic development pathways which can be replicated for wider regression-based task for timeseries having non-uniform variance.
Comparative study of three plant-derived extracts as new management strategies against Spodoptera littoralis (Boisd.) (Lepidoptera: Noctuidae)
Authors: Hussein Hanaa S. , Salem Mohamed Z. M. , Soliman Ahmed M. , Eldesouky Sahar E. 
Published: March 2023

AbstractFinding innovative eco-friendly agents for pest control may be aided by investigating the plant-derived extracts’ properties on economic pests. Therefore, the insecticidal, behavioral, biological and biochemical effects of Magnolia grandiflora (Magnoliaceae) leaf water and methanol extracts, Schinus terebinthifolius (Anacardiaceae) wood methanol extract, and Salix babylonica (Salicaceae) leaf methanol extract in comparison with a reference insecticide novaluron against S. littoralis were evaluated. The extracts were analyzed by High-Performance Liquid Chromatography (HPLC). The most abundant phenolic compounds were 4-hydroxybenzoic acid (7.16 mg/mL) and ferulic acid (6.34 mg/mL) in M. grandiflora leaf water extract; catechol (13.05 mg/mL), ferulic acid (11.87 mg/mL), and chlorogenic acid (10.33 mg/mL) in M. grandiflora leaf methanol extract; ferulic acid (14.81 mg/mL), caffeic acid (5.61 mg/mL), and gallic acid (5.07 mg/mL) In the S. terebinthifolius extract; cinnamic acid (11.36 mg/mL), and protocatechuic acid (10.33 mg/mL) In the methanol extract from S. babylonica extract. S. terebinthifolius extract had a highly toxic effect against second larvae after 96 h and eggs with LC50 values of 0.89 and 0.94 mg/L, respectively. Despite M. grandiflora extracts didn’t show any toxicity against S. littoralis stages, they had an attractant effect on fourth- and second larvae, with feeding deterrence values of − 2.7% and − 6.7%, respectively, at 10 mg/L. S. terebinthifolius extract significantly reduced the percentage of pupation, adult emergence, hatchability, and fecundity, with values of 60.2%, 56.7%, 35.3%, and 105.4 eggs/female, respectively. Novaluron and S. terebinthifolius extract drastically inhibited the activities of α-amylase and total proteases to 1.16 and 0.52, and 1.47 and 0.65 ΔOD/mg protein/min, respectively. In the semi-field experiment, the residual toxicity of tested extracts on S. littoralis gradually decreased over time compared to novaluron. These findings indicate that extract from S. terebinthifolius is a promising insecticidal agent against S. littoralis.
Predicting anterior cruciate ligament failure load with T2* relaxometry and machine learning as a prospective imaging biomarker for revision surgery
Authors: Flannery Sean W. , Beveridge Jillian E. , Proffen Benedikt L. , Walsh Edward G. ,   BEAR Trial Team, Ecklund Kirsten , Micheli Lyle J. , Owens Brett D. , Fadale Paul D. , Hulstyn Michael J. , Costa Meggin Q. , Chrostek Cynthia , Sanborn Ryan M. , Sant Nicholas J. , Yen Yi-Meng , Proffen Benedikt L. , Kramer Dennis E. , Murray Martha M. , Kiapour Ata M. , Fleming Braden C. , Kramer Dennis E. , Murray Martha M. , Kiapour Ata M. , Fleming Braden C. 
Published: March 2023

AbstractNon-invasive methods to document healing anterior cruciate ligament (ACL) structural properties could potentially identify patients at risk for revision surgery. The objective was to evaluate machine learning models to predict ACL failure load from magnetic resonance images (MRI) and to determine if those predictions were related to revision surgery incidence. It was hypothesized that the optimal model would demonstrate a lower mean absolute error (MAE) than the benchmark linear regression model, and that patients with a lower estimated failure load would have higher revision incidence 2 years post-surgery. Support vector machine, random forest, AdaBoost, XGBoost, and linear regression models were trained using MRI T2* relaxometry and ACL tensile testing data from minipigs (n = 65). The lowest MAE model was used to estimate ACL failure load for surgical patients at 9 months post-surgery (n = 46) and dichotomized into low and high score groups via Youden’s J statistic to compare revision incidence. Significance was set at alpha = 0.05. The random forest model decreased the failure load MAE by 55% (Wilcoxon signed-rank test: p = 0.01) versus the benchmark. The low score group had a higher revision incidence (21% vs. 5%; Chi-square test: p = 0.09). ACL structural property estimates via MRI may provide a biomarker for clinical decision making.
Numerical exploration of forced convection hydromagnetic hyperbolic tangent nanofluid flow over a permeable wedge with melting heat transfer
Authors: Endalew Mehari Fentahun , Sarkar Subharthi 
Published: March 2023

AbstractIn this communication, the joint impacts of the process of melting as well as wedge angle entity on hydromagnetic hyperbolic tangent nanofluid flow owing to permeable wedge-shaped surface in the incidence of suspended nanoparticles along with radiation, Soret and Dufour numbers are scrutinized. The mathematical model which represents the system consists of a system of highly non-linear coupled partial differential equations. These equations are solved using a finite-difference-based MATLAB solver which implements the Lobatto IIIa collocation formula and is fourth-order accurate. Further, the comparison of computed results is carried out with the previously reported articles and outstanding conformity is recorded. Emerged physical entities affecting the bearings of tangent hyperbolic MHD nanofluid velocity, distribution of temperature, and concentration of nanoparticles are visualized in graphs. In another line, shearing stress, the surface gradient of heat transfer, and volumetric rate of concentration are recorded in tabular form. Most interestingly, momentum boundary layer thickness and thicknesses of thermal as well as solutal boundary layers enhance with an increment of Weissenberg number. Moreover, an increment on tangent hyperbolic nanofluid velocity and decrement on the thickness of momentum boundary layer is visualized for the increment of numerical values of power-law index entity, which can determine the behavior of shear-thinning fluids.This study has applications for coating materials used in chemical engineering, such as strong paints, aerosol manufacturing, and thermal treatment of water-soluble solutions.
Transformers for cardiac patient mortality risk prediction from heterogeneous electronic health records
Authors: Antikainen Emmi , Linnosmaa Joonas , Umer Adil , Oksala Niku , Eskola Markku , van Gils Mark , Hernesniemi Jussi , Gabbouj Moncef 
Published: March 2023

AbstractWith over 17 million annual deaths, cardiovascular diseases (CVDs) dominate the cause of death statistics. CVDs can deteriorate the quality of life drastically and even cause sudden death, all the while inducing massive healthcare costs. This work studied state-of-the-art deep learning techniques to predict increased risk of death in CVD patients, building on the electronic health records (EHR) of over 23,000 cardiac patients. Taking into account the usefulness of the prediction for chronic disease patients, a prediction period of six months was selected. Two major transformer models that rely on learning bidirectional dependencies in sequential data, BERT and XLNet, were trained and compared. To our knowledge, the presented work is the first to apply XLNet on EHR data to predict mortality. The patient histories were formulated as time series consisting of varying types of clinical events, thus enabling the model to learn increasingly complex temporal dependencies. BERT and XLNet achieved an average area under the receiver operating characteristic curve (AUC) of 75.5% and 76.0%, respectively. XLNet surpassed BERT in recall by 9.8%, suggesting that it captures more positive cases than BERT, which is the main focus of recent research on EHRs and transformers.
Cluster analysis of plasma cytokines identifies two unique endotypes of children with asthma in the pediatric intensive care unit
Authors: Cottrill Kirsten A. , Rad Milad G. , Ripple Michael J. , Stephenson Susan T. , Mohammad Ahmad F. , Tidwell Mallory , Kamaleswaran Rishikesan , Fitzpatrick Anne M. , Grunwell Jocelyn R. 
Published: March 2023

AbstractChildren with life-threatening asthma exacerbations who are admitted to a pediatric intensive care unit (PICU) are a heterogeneous group with poorly studied inflammatory features. We hypothesized that distinct clusters of children with asthma in a PICU would be identified based on differences in plasma cytokine levels and that these clusters would have differing underlying inflammation and asthma outcomes within 1 year. Plasma cytokines and differential gene expression were measured in neutrophils isolated from children admitted to a PICU for asthma. Participants were clustered by differential plasma cytokine abundance. Gene expression differences were compared by cluster and pathway over-representation analysis was performed. We identified two clusters in 69 children with no clinical differences. Cluster 1 (n = 41) had higher cytokines compared to Cluster 2 (n = 28). Cluster 2 had a hazard ratio of 2.71 (95% CI 1.11–6.64) compared to Cluster 1 for time to subsequent exacerbation. Gene expression pathways that differed by cluster included interleukin-10 signaling; nucleotide-binding domain, leucine rich repeat containing receptor (NLR signaling); and toll-like receptor (TLR) signaling. These observations suggest that a subset of children may have a unique pattern of inflammation during PICU hospitalization that might require alternative treatment approaches.
Impact of the Russia–Ukraine armed conflict on water resources and water infrastructure
Authors: Shumilova Oleksandra , Tockner Klement , Sukhodolov Alexander , Khilchevskyi Valentyn , De Meester Luc , Stepanenko Sergiy , Trokhymenko Ganna , Hernández-Agüero Juan Antonio , Gleick Peter 
Published: March 2023

AbstractThe armed conflict between Ukraine and Russia that began in late February 2022 has far-reaching environmental consequences, especially regarding water resources and management. Here we analysed the multifaceted impacts of the military actions on freshwater resources and water infrastructure during the first three months of the conflict. We identified the nature of the impacts, the kind of pressures imposed on the water sector and the negative consequences for the availability and quality of freshwater resources for the civilian population. Our results showed that many water infrastructures such as dams at reservoirs, water supply and treatment systems and subsurface mines have been impacted or are at risk from military actions. Continuation of the conflict will have multiple negative sustainability implications not only in Ukraine but also on a global scale, hampering achievement of clean water and sanitation, conservation and sustainable use of water resources, and energy and food security.
Visualizing enteric nervous system activity through dye-free dynamic full-field optical coherence tomography
Authors: Durand Tony , Paul-Gilloteaux Perrine , Gora Michalina , Laboudie Lara , Coron Emmanuel , Neveu Isabelle , Neunlist Michel , Naveilhan Philippe 
Published: March 2023

AbstractMajor advances have been achieved in imaging technologies but most methodological approaches currently used to study the enteric neuronal functions rely on exogenous contrast dyes that can interfere with cellular functions or survival. In the present paper, we investigated whether full-field optical coherence tomography (FFOCT), could be used to visualize and analyze the cells of the enteric nervous system. Experimental work on whole-mount preparations of unfixed mouse colons showed that FFOCT enables the visualization of the myenteric plexus network whereas dynamic FFOCT enables to visualize and identify in situ individual cells in the myenteric ganglia. Analyzes also showed that dynamic FFOCT signal could be modified by external stimuli such veratridine or changes in osmolarity. These data suggest that dynamic FFOCT could be of great interest to detect changes in the functions of enteric neurons and glia in normal and disease conditions.
Quantum chemistry rules retinoid biology
Authors: Hammerling Ulrich , Kim Youn-Kyung , Quadro Loredana 
Published: March 2023

AbstractThis Perspective discusses how retinol catalyzes resonance energy transfer (RET) reactions pivotally important for mitochondrial energy homeostasis by protein kinase C δ (PKCδ). PKCδ signals to the pyruvate dehydrogenase complex, controlling oxidative phosphorylation. The PKCδ-retinol complex reversibly responds to the redox potential of cytochrome c, that changes with the electron transfer chain workload. In contrast, the natural retinoid anhydroretinol irreversibly activates PKCδ. Its elongated conjugated-double-bond system limits the energy quantum absorbed by RET. Consequently, while capable of triggering the exergonic activating pathway, anhydroretinol fails to activate the endergonic silencing path, trapping PKCδ in the ON position and causing harmful levels of reactive oxygen species. However, physiological retinol levels displace anhydroretinol, buffer cyotoxicity and potentially render anhydroretinol useful for rapid energy generation. Intriguingly, apocarotenoids, the primary products of the mitochondrial β-carotene,9'-10'-oxygenase, have all the anhydroretinol-like features, including modulation of energy homeostasis. We predict significant conceptual advances to stem from further understanding of the retinoid-catalyzed RET.
Efficient end-to-end learning for cell segmentation with machine generated weak annotations
Authors: Shrestha Prem , Kuang Nicholas , Yu Ji 
Published: March 2023

AbstractAutomated cell segmentation from optical microscopy images is usually the first step in the pipeline of single-cell analysis. Recently, deep-learning based algorithms have shown superior performances for the cell segmentation tasks. However, a disadvantage of deep-learning is the requirement for a large amount of fully annotated training data, which is costly to generate. Weakly-supervised and self-supervised learning is an active research area, but often the model accuracy is inversely correlated with the amount of annotation information provided. Here we focus on a specific subtype of weak annotations, which can be generated programmably from experimental data, thus allowing for more annotation information content without sacrificing the annotation speed. We designed a new model architecture for end-to-end training using such incomplete annotations. We have benchmarked our method on a variety of publicly available datasets, covering both fluorescence and bright-field imaging modality. We additionally tested our method on a microscopy dataset generated by us, using machine-generated annotations. The results demonstrated that our models trained under weak supervision can achieve segmentation accuracy competitive to, and in some cases, surpassing, state-of-the-art models trained under full supervision. Therefore, our method can be a practical alternative to the established full-supervision methods.