bluuebunny/crossref_metadata_2025_split huggingface dataset:
Comprehensive survey of conserved RNA secondary structures in full-genome alignment of Hepatitis C virus
Authors: Triebel Sandra , Lamkiewicz Kevin , Ontiveros Nancy , Sweeney Blake , Stadler Peter F. , Petrov Anton I. , Niepmann Michael , Marz Manja 
Published: July 2024

AbstractHepatitis C virus (HCV) is a plus-stranded RNA virus that often chronically infects liver hepatocytes and causes liver cirrhosis and cancer. These viruses replicate their genomes employing error-prone replicases. Thereby, they routinely generate a large ‘cloud’ of RNA genomes (quasispecies) which—by trial and error—comprehensively explore the sequence space available for functional RNA genomes that maintain the ability for efficient replication and immune escape. In this context, it is important to identify which RNA secondary structures in the sequence space of the HCV genome are conserved, likely due to functional requirements. Here, we provide the first genome-wide multiple sequence alignment (MSA) with the prediction of RNA secondary structures throughout all representative full-length HCV genomes. We selected 57 representative genomes by clustering all complete HCV genomes from the BV-BRC database based on k-mer distributions and dimension reduction and adding RefSeq sequences. We include annotations of previously recognized features for easy comparison to other studies. Our results indicate that mainly the core coding region, the C-terminal NS5A region, and the NS5B region contain secondary structure elements that are conserved beyond coding sequence requirements, indicating functionality on the RNA level. In contrast, the genome regions in between contain less highly conserved structures. The results provide a complete description of all conserved RNA secondary structures and make clear that functionally important RNA secondary structures are present in certain HCV genome regions but are largely absent from other regions. Full-genome alignments of all branches of Hepacivirus C are provided in the supplement.
Myricetin mitigates motor disturbance and decreases neuronal ferroptosis in a rat model of Parkinson’s disease
Authors: Gu Si-Chun , Xie Zhi-Guo , Gu Min-Jue , Wang Chang-De , Xu Li-Min , Gao Chen , Yuan Xiao-Lei , Wu You , Hu Yu-Qing , Cao Yang , Ye Qing 
Published: July 2024

AbstractFerroptosis is an iron-dependent cell death form characterized by reactive oxygen species (ROS) overgeneration and lipid peroxidation. Myricetin, a flavonoid that exists in numerous plants, exhibits potent antioxidant capacity. Given that iron accumulation and ROS-provoked dopaminergic neuron death are the two main pathological hallmarks of Parkinson’s disease (PD), we aimed to investigate whether myricetin decreases neuronal death through suppressing ferroptosis. The PD models were established by intraperitoneally injecting 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) into rats and by treating SH-SY5Y cells with 1-methyl-4-phenylpyridinium (MPP+), respectively. Ferroptosis was identified by assessing the levels of Fe2+, ROS, malondialdehyde (MDA), and glutathione (GSH). The results demonstrated that myricetin treatment effectively mitigated MPTP-triggered motor impairment, dopamine neuronal death, and α-synuclein (α-Syn) accumulation in PD models. Myricetin also alleviated MPTP-induced ferroptosis, as evidenced by decreased levels of Fe2+, ROS, and MDA and increased levels of GSH in the substantia nigra (SN) and serum in PD models. All these changes were reversed by erastin, a ferroptosis activator. In vitro, myricetin treatment restored SH-SY5Y cell viability and alleviated MPP+-induced SH-SY5Y cell ferroptosis. Mechanistically, myricetin accelerated nuclear translocation of nuclear factor E2-related factor 2 (Nrf2) and subsequent glutathione peroxidase 4 (Gpx4) expression in MPP+-treated SH-SY5Y cells, two critical inhibitors of ferroptosis. Collectively, these data demonstrate that myricetin may be a potential agent for decreasing dopaminergic neuron death by inhibiting ferroptosis in PD.
Myco-fabricated silver nanoparticle by novel soil fungi from Saudi Arabian desert and antimicrobial mechanism
Authors: Mohammed Afrah E. , Korany Shereen M. , Sonbol Hana , Alhomaidi Eman A. , Alwakeel Suaad S. , Elbaz Reham M. 
Published: July 2024

AbstractBiological agents are getting a noticeable concern as efficient eco-friendly method for nanoparticle fabrication, from which fungi considered promising agents in this field. In the current study, two fungal species (Embellisia spp. and Gymnoascus spp.) were isolated from the desert soil in Saudi Arabia and identified using 18S rRNA gene sequencing then used as bio-mediator for the fabrication of silver nanoparticles (AgNPs). Myco-synthesized AgNPs were characterized using UV–visible spectrometry, transmission electron microscopy, Fourier transform infrared spectroscopy and dynamic light scattering techniques. Their antibacterial activity against Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus, and Klebsiella pneumoniae were investigated. In atrial to detect their possible antibacterial mechanism, Sodium dodecyl sulfate (SDS-PAGE) and TEM analysis were performed for Klebsiella pneumoniae treated by the myco-synthesized AgNPs. Detected properties of the fabricated materials indicated the ability of both tested fungal strains in successful fabrication of AgNPs having same range of mean size diameters and varied PDI. The efficiency of Embellisia spp. in providing AgNPs with higher antibacterial activity compared to Gymnoascus spp. was reported however, both indicated antibacterial efficacy. Variations in the protein profile of K. pneumoniae after treatments and ultrastructural changes were observed. Current outcomes suggested applying of fungi as direct, simple and sustainable approach in providing efficient AgNPs.
Machine learning prediction of methane, nitrogen, and natural gas mixture viscosities under normal and harsh conditions
Authors: Gomaa Sayed , Abdalla Mohamed , Salem Khalaf G. , Nasr Karim , Emara Ramadan , Wang Qingsheng , El-hoshoudy A. N. 
Published: July 2024

AbstractThe accurate estimation of gas viscosity remains a pivotal concern for petroleum engineers, exerting substantial influence on the modeling efficacy of natural gas operations. Due to their time-consuming and costly nature, experimental measurements of gas viscosity are challenging. Data-based machine learning (ML) techniques afford a resourceful and less exhausting substitution, aiding research and industry at gas modeling that is incredible to reach in the laboratory. Statistical approaches were used to analyze the experimental data before applying machine learning. Seven machine learning techniques specifically Linear Regression, random forest (RF), decision trees, gradient boosting, K-nearest neighbors, Nu support vector regression (NuSVR), and artificial neural network (ANN) were applied for the prediction of methane (CH4), nitrogen (N2), and natural gas mixture viscosities. More than 4304 datasets from real experimental data utilizing pressure, temperature, and gas density were employed for developing ML models. Furthermore, three novel correlations have developed for the viscosity of CH4, N2, and composite gas using ANN. Results revealed that models and anticipated correlations predicted methane, nitrogen, and natural gas mixture viscosities with high precision. Results designated that the ANN, RF, and gradient Boosting models have performed better with a coefficient of determination (R2) of 0.99 for testing data sets of methane, nitrogen, and natural gas mixture viscosities. However, linear regression and NuSVR have performed poorly with a coefficient of determination (R2) of 0.07 and − 0.01 respectively for testing data sets of nitrogen viscosity. Such machine learning models offer the industry and research a cost-effective and fast tool for accurately approximating the viscosities of methane, nitrogen, and gas mixture under normal and harsh conditions.
Heat absorption effect of magneto-natural convection flow in vertical concentric annuli with influence of radial and induced magnetic field
Authors: Yusuf Muhammad Muhammad , Auwal Lawan Muhammad , Gambo Yusuf Y. 
Published: July 2024

Abstract
This paper aims to study the natural convective magneto-hydrodynamic flow of fluid through vertical concentric annuli with iso-flux heating under the conditions of constant internal heat absorption and an induced magnetic field. By solving the set of dimensionless coupled governing equations, we were able to obtain exact expressions for the temperature field, velocity field, and induced magnetic field. We also managed to derive the formulas for skin friction, mass flux, and induced current density. We also examined the effects of non-dimensional parameters on skin friction and mass flux. For easy comprehension and interpretation, the results are provided graphically and in tabular form. The heat absorption parameter, the induced current density, the induced magnetic field, and velocity exhibit a negative trend as the Hartmann number (Ha) value increases. The induced magnetic field has the effect of raising both the induced current density and velocity profile. It is found that, when a fluid absorbs heat, the heat absorption parameter experiences reverse flow. For the heat-absorbing fluids, the radii ratio has the effect of increasing velocity, induced magnetic field, and induced current density. The numerical values of skin friction and mass flux at cylindrical walls increase (decrease) with increasing heat absorption parameter and generally it has decreasing tendency with increasing Hartmann number
Autophagy and ubiquitin-dependent proteolysis processes in left ventricular mass loss in pulmonary arterial hypertension
Authors: Hołda Mateusz K. , Raźny Urszula , Sordyl Maria , Góralska Joanna , Kapusta Maria , Słowińska-Solnica Krystyna , Wojtysiak Dorota , Lis Grzegorz , Solnica Bogdan , Kopeć Grzegorz , Hołda Jakub 
Published: July 2024

AbstractThe goal of this study was to evaluate the intensity of autophagy and ubiquitin-dependent proteolysis processes occurring in myocardium of left ventricle (LV) in subsequent stages of pulmonary arterial hypertension (PAH) to determine mechanisms responsible for LV mass loss in a monocrotaline-induced PAH rat model. LV myocardium samples collected from 32 Wistar rats were analyzed in an early PAH group (n = 8), controls time-paired (n = 8), an end-stage PAH group (n = 8), and their controls (n = 8). Samples were subjected to histological analyses with immunofluorescence staining, autophagy assessment by western blotting, and evaluation of ubiquitin-dependent proteolysis in the LV by immunoprecipitation of ubiquitinated proteins. Echocardiographic, hemodynamic, and heart morphometric parameters were assessed regularly throughout the experiment. Considerable morphological and hemodynamic remodeling of the LV was observed over the course of PAH. The end-stage PAH was associated with significantly impaired LV systolic function and a decrease in LV mass. The LC3B-II expression in the LV was significantly higher in the end-stage PAH group compared to the early PAH group (p = 0.040). The measured LC3B-II/LC3B-I ratios in the end-stage PAH group were significantly elevated compared to the controls (p = 0.039). Immunofluorescence staining showed a significant increase in the abundance of LC3 puncta in the end-stage PAH group compared to the matched controls. There were no statistically significant differences in the levels of expression of all ubiquitinated proteins when comparing both PAH groups and matched controls. Autophagy may be considered as the mechanism behind the LV mass loss at the end stage of PAH.
Effects of microplastics on reproductive characteristics and mechanisms of the marine rotifer Brachionus plicatilis
Authors: Seong Taekyoung , Yamamoto Sae , Nakatani Hisayuki , Yagi Mitsuharu , Kyozuka Yusaku , Satuito Glenn , Kim Hee-Jin 
Published: July 2024

AbstractMicroplastic pollution, especially secondary microplastics (MPs), poses a significant threat to marine ecosystems. Despite its prevalence, the impact of natural-aged MPs on marine organisms, hindered by collection challenges, remains poorly understood. This study focused on 1–3 μm natural-aged MPs collected from Japan's coastal sea, investigating their effects on the rotifer Brachionus plicatilis sensu stricto and its reproductive mechanisms. Rotifers exposed to varying MP concentrations (0, 20, and 200 particles/mL) over 14-day batch cultures exhibited reduced population growth and fertilization rates. Down-regulation of reproductive genes and up-regulation of oxidative stress-related genes were observed, indicating MP-induced disruptions. Enhanced activities of superoxide dismutase and acetylcholinesterase and elevated malondialdehyde levels further emphasized oxidative stress. These findings underscore the detrimental impact of MPs on rotifer reproductivity, shedding light on the underlying mechanisms.
Indicator metrics and temporal aggregations introduce ambiguities in water scarcity estimates
Authors: Wolkeba Fitsume T. , Mekonnen Mesfin M. , Brauman Kate A. , Kumar Mukesh 
Published: July 2024

AbstractWater scarcity is a global challenge affecting billions of people worldwide. This study systematically assesses differences in the estimation of the global population exposed to water scarcity based on 7 water scarcity indicators and 11 Environmental Flow Requirements (EFR) evaluated at various spatial and temporal resolutions. All indicators show an increase in water scarcity since 1901. However, considering monthly average water scarcity estimates spatially aggregated at the basin scale found 35% less population exposed than estimates based on a distributed grid over the landscape. Estimates temporally disaggregated to consider water scarcity for at least one month a year found 50% (tenfold) larger population exposed compared to average monthly (annual) estimates. The study illustrates that estimates of the impacts of water scarcity are an artifact of how water scarcity is defined and calculated. This suggests caution is needed when relying on a single method and emphasizes the importance of considering the diversity of factors that can influence estimates of impact when assessing water scarcity.
Impact of MTHFR gene polymorphism on the outcome of methotrexate treatment in a sample of Iraqi rheumatoid arthritis patients
Authors: Mutlak Qassim Mahdi , Kasim Ali Abdulhussain 
Published: July 2024

AbstractAnalyze the relationship between genetic variations in the MTHFR gene at SNPs (rs1801131 and rs1801133) and the therapy outcomes for Iraqi patients with rheumatoid arthritis (RA). The study was conducted on a cohort of 95 RA Iraqi patients. Based on their treatment response, the cohort was divided into two groups: the responder (47 patients) and the nonresponder (48 patients), identified after at least three months of methotrexate (MTX) treatment. A polymerase chain reaction-restriction fragment length polymorphism (PCR–RFLP) technique was employed to analyze the MTHFR variations, specifically at rs1801133 and rs1801131. Overall, rs1801131 followed both codominant and dominate models, in which in the codominant model, GG [OR (95% CI) 0.11 (0.022–0.553)] and TG [OR (95% CI) 0.106 (0.021–0.528)] predict responders compared to the TT genotype; meanwhile, for the dominate model, the presence of both GG and TG genotypes [OR (95% CI) 0.108 (0.023–0.507)] together predict responders compared to the TT genotype. The Ars1801133Grs1801131 haplotype was significantly associated with responders [OR (95% CI): 0.388 (0.208–0.723)], while the Grs1801133Trs1801131 haplotype was associated marginally with nonresponders [OR (95% CI) 1.980 (0.965–4.064)]. In the final multivariate analysis, GG/TGrs1801131 genotypes were independently related to responders after adjustment for patients, disease, and treatment characteristics, while TTrs1801131 genotypes were associated with nonresponders. The Iraqi RA patients showed genetic polymorphism in MTHFR gene rs1801131 with T carrier allele associated with nonresponders to MTX therapy. The rs1801131 followed both codominant and dominant models. The G-carried allele for rs1801131 showed an independent association with responder to MTX therapy after adjustment for patients, disease, and treatment characteristics.
Benefits and limits of biological nitrification inhibitors for plant nitrogen uptake and the environment
Authors: Kuppe Christian W. , Postma Johannes A. 
Published: July 2024

AbstractPlant growth and high yields are secured by intensive use of nitrogen (N) fertilizer, which, however, pollutes the environment, especially when N is in the form of nitrate. Ammonium is oxidized to nitrate by nitrifiers, but roots can release biological nitrification inhibitors (BNIs). Under what conditions does root-exudation of BNIs facilitate nitrogen N uptake and reduce pollution by N loss to the environment? We modeled the spatial–temporal dynamics of nitrifiers, ammonium, nitrate, and BNIs around a root and simulated root N uptake and net rhizosphere N loss over the plant’s life cycle. We determined the sensitivity of N uptake and loss to variations in the parameter values, testing a broad range of soil–plant-microbial conditions, including concentrations, diffusion, sorption, nitrification, population growth, and uptake kinetics. An increase in BNI exudation reduces net N loss and, under most conditions, increases plant N uptake. BNIs decrease uptake in the case of (1) low ammonium concentrations, (2) high ammonium adsorption to the soil, (3) rapid nitrate- or slow ammonium uptake by the plant, and (4) a slowly growing or (5) fast-declining nitrifier population. Bactericidal inhibitors facilitate uptake more than bacteriostatic ones. Some nitrification, however, is necessary to maximize uptake by both ammonium and nitrate transporter systems. An increase in BNI exudation should be co-selected with improved ammonium uptake. BNIs can reduce N uptake, which may explain why not all species exude BNIs but have a generally positive effect on the environment by increasing rhizosphere N retention.
Prolonged ventricular repolarization associated with mild cognitive impairment and white matter hyperintensities: a cross-sectional study
Authors: Mao Ming , Wei Yiran , Wang Chaoqun , Han Xiaolei , Liu Rui , Dong Yi , Song Lin , Cong Lin , Wang Yongxiang , Du Yifeng , Qiu Chengxuan 
Published: July 2024

AbstractProlonged ventricular repolarization has been associated with cardiovascular disease. We sought to investigate the association of prolonged ventricular repolarization with mild cognitive impairment (MCI) and the potential underlying neuropathological mechanisms in older adults. This cross-sectional study included 4328 dementia-free participants (age ≥ 65 years; 56.8% female) in the baseline examination of the Multidomain INterventions to delay dementia and Disability in rural China; of these, 989 undertook structural brain magnetic resonance imaging (MRI) scans. QT, QTc, JT, JTc, and QRS intervals were derived from 12-lead electrocardiograph. MCI, amnestic MCI (aMCI), and non-amnestic MCI (naMCI) were defined following the Petersen’s criteria. Volumes of gray matter (GM), white matter, cerebrospinal fluid, total white matter hyperintensities (WMH), periventricular WMH (PWMH), and deep WMH (DWMH) were automatically estimated. Data were analyzed using logistic and general linear regression models. Prolonged QT, QTc, JT, and JTc intervals were significantly associated with an increased likelihood of MCI and aMCI, but not naMCI (p < 0.05). In the MRI subsample, QT, QTc, JT, and JTc intervals were significantly associated with larger total WMH and PWMH volumes (p < 0.05), but not with DWMH volume. Statistical interactions were detected, such that prolonged QT and JT intervals were significantly associated with reduced GM volume only among participants with coronary heart disease or without APOE ε4 allele (p < 0.05). Prolonged ventricular repolarization is associated with MCI and cerebral microvascular lesions in a general population of older adults. This underlies the importance of cognitive assessments and brain MRI examination among older adults with prolonged QT interval.
Exploration and verification a 13-gene diagnostic framework for ulcerative colitis across multiple platforms via machine learning algorithms
Authors: Wang Jing , Li Lin , Chen Pingbo , He Chiyi , Niu Xiaoping 
Published: July 2024

AbstractUlcerative colitis (UC) is a chronic inflammatory bowel disease with intricate pathogenesis and varied presentation. Accurate diagnostic tools are imperative to detect and manage UC. This study sought to construct a robust diagnostic model using gene expression profiles and to identify key genes that differentiate UC patients from healthy controls. Gene expression profiles from eight cohorts, encompassing a total of 335 UC patients and 129 healthy controls, were analyzed. A total of 7530 gene sets were computed using the GSEA method. Subsequent batch correction, PCA plots, and intersection analysis identified crucial pathways and genes. Machine learning, incorporating 101 algorithm combinations, was employed to develop diagnostic models. Verification was done using four external cohorts, adding depth to the sample repertoire. Evaluation of immune cell infiltration was undertaken through single-sample GSEA. All statistical analyses were conducted using R (Version: 4.2.2), with significance set at a P value below 0.05. Employing the GSEA method, 7530 gene sets were computed. From this, 19 intersecting pathways were discerned to be consistently upregulated across all cohorts, which pertained to cell adhesion, development, metabolism, immune response, and protein regulation. This corresponded to 83 unique genes. Machine learning insights culminated in the LASSO regression model, which outperformed others with an average AUC of 0.942. This model's efficacy was further ratified across four external cohorts, with AUC values ranging from 0.694 to 0.873 and significant Kappa statistics indicating its predictive accuracy. The LASSO logistic regression model highlighted 13 genes, with LCN2, ASS1, and IRAK3 emerging as pivotal. Notably, LCN2 showcased significantly heightened expression in active UC patients compared to both non-active patients and healthy controls (P < 0.05). Investigations into the correlation between these genes and immune cell infiltration in UC highlighted activated dendritic cells, with statistically significant positive correlations noted for LCN2 and IRAK3 across multiple datasets. Through comprehensive gene expression analysis and machine learning, a potent LASSO-based diagnostic model for UC was developed. Genes such as LCN2, ASS1, and IRAK3 hold potential as both diagnostic markers and therapeutic targets, offering a promising direction for future UC research and clinical application.