Miracle-dz/UniFAR-FTUs huggingface dataset:
Exchange Rate Dynamics, Energy Consumption, and Sustainable Environment in Pakistan: New Evidence From Nonlinear ARDL Cointegration

Pakistan's local currency has been devalued during different exchange regimes, which may substantially affect energy consumption and CO2 emissions. Therefore, this study investigates the effects of exchange rate depreciation on Pakistan's CO2 emissions and energy consumption from 1990–2018. We apply the nonlinear autoregressive distributed lag (ARDL) cointegration approach for the empirical analysis and found that exchange rate depreciation increases CO2 emissions and energy consumption in both the short and long runs. These results suggest that currency devaluation has an expansionary effect which enhances economic growth at the cost of high energy consumption and CO2 emissions. Therefore, the government needs regulations along with an exchange rate policy to control CO2 emissions. Moreover, the government should search for alternate energy resources such as renewable energy resources that meet the country's energy needs and mitigate CO2 emissions.
The Values of Systemic Immune-Inflammation Index and Neutrophil–Lymphocyte Ratio in the Localized Prostate Cancer and Benign Prostate Hyperplasia: A Retrospective Clinical Study

Purpose To evaluate the diagnostic values of systemic immune-inflammation index (SII) and neutrophil–lymphocyte ratio (NLR) in patients with localized prostate cancer (PCa). Methods Between January 2014 and December 2019, 117 patients with benign prostate hyperplasia (BPH) and 278 patients with localized PCa who underwent radical prostatectomy (RP) were included in this study. The inflammatory markers including SII, NLR, platelet–lymphocyte ratio (PLR), lymphocyte–monocyte ratio (LMR), lymphocyte ratio (LR), neutrophil ratio (NR), mean platelet volume (MPV), and red cell distribution (RDW) of these two groups were examined and analyzed. ROC curve analysis was performed to assess the discriminative ability of inflammatory markers and their combination with tPSA for PCa. The binary logistic regression model was used to evaluate the association between significant inflammatory markers and risk of PCa. Results The pathological results from RP specimen comprised 72 (25.90%) patients with pT1, 168 (60.43%) patients with pT2, and 38 (13.67%) patients with pT3. According to Student’s t test, patients with PCa had higher NLR (p = 0.034), SII (p = 0.008), and NR (p = 0.004), and lower LR (p = 0.025), MPV (p = 0.003), and TPV (p = 0.022) compared with patients with BPH; the distribution of age, PLR, LMR, RDW, f/t PSA ratio, and BMI did not show any significant differences. The AUC for NLR, SII, NR, and tPSA was 0.697 (p = 0.015), 0.719 (p < 0.001), 0.647 (p = 0.009), and 0.708 (p < 0.001), with threshold values of 1.6, 471.86, 65.15%, and 12.89 ng/ml, respectively. Patients were divided into two groups according to the threshold values, respectively. By using the multivariable logistic regression models, NLR ≥ 1.6 (OR, 2.731; 95% CI, 0.937–7.961, p = 0.042), SII ≥ 471.86 (OR, 1.274; 95% CI 0.473–3.433; p = 0.033), and PSA ≥ 12.89 ng/ml (OR, 1.443; 95% CI, 0.628–3.944; p = 0.014) were independent risk factors associated with PCa. The AUC for combination of NLR, SII, and NR with tPSA was 0.705 (p < 0.001), 0.725 (p < 0.001), and 0.704 (p < 0.001), respectively. Conclusion This study demonstrated that SII, NLR, and NR were all independent risk factors of PCa. These factors alone could provide better screen methods for PCa before biopsy. In addition, SII is a more powerful tool among these three inflammatory markers associated with PCa. Besides, combination of SII and NLR with tPSA had not much advantage compared with themselves alone.
Toward a Multimodal Computer-Aided Diagnostic Tool for Alzheimer’s Disease Conversion

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder. It is one of the leading sources of morbidity and mortality in the aging population AD cardinal symptoms include memory and executive function impairment that profoundly alters a patient’s ability to perform activities of daily living. People with mild cognitive impairment (MCI) exhibit many of the early clinical symptoms of patients with AD and have a high chance of converting to AD in their lifetime. Diagnostic criteria rely on clinical assessment and brain magnetic resonance imaging (MRI). Many groups are working to help automate this process to improve the clinical workflow. Current computational approaches are focused on predicting whether or not a subject with MCI will convert to AD in the future. To our knowledge, limited attention has been given to the development of automated computer-assisted diagnosis (CAD) systems able to provide an AD conversion diagnosis in MCI patient cohorts followed longitudinally. This is important as these CAD systems could be used by primary care providers to monitor patients with MCI. The method outlined in this paper addresses this gap and presents a computationally efficient pre-processing and prediction pipeline, and is designed for recognizing patterns associated with AD conversion. We propose a new approach that leverages longitudinal data that can be easily acquired in a clinical setting (e.g., T1-weighted magnetic resonance images, cognitive tests, and demographic information) to identify the AD conversion point in MCI subjects with AUC = 84.7. In contrast, cognitive tests and demographics alone achieved AUC = 80.6, a statistically significant difference (n = 669, p < 0.05). We designed a convolutional neural network that is computationally efficient and requires only linear registration between imaging time points. The model architecture combines Attention and Inception architectures while utilizing both cross-sectional and longitudinal imaging and clinical information. Additionally, the top brain regions and clinical features that drove the model’s decision were investigated. These included the thalamus, caudate, planum temporale, and the Rey Auditory Verbal Learning Test. We believe our method could be easily translated into the healthcare setting as an objective AD diagnostic tool for patients with MCI.
A Semi-Quantitative Assay to Measure Glycosaminoglycan Degradation by the Urinary Microbiota

Glycosaminoglycans (GAGs) are linear polysaccharides and are among the primary components of mucosal surfaces in mammalian systems. The GAG layer lining the mucosal surface of the urinary tract is thought to play a critical role in urinary tract homeostasis and provide a barrier against urinary tract infection (UTI). This key component of the host-microbe interface may serve as a scaffolding site or a nutrient source for the urinary microbiota or invading pathogens, but its exact role in UTI pathogenesis is unclear. Although members of the gut microbiota have been shown to degrade GAGs, the utilization and degradation of GAGs by the urinary microbiota or uropathogens had not been investigated. In this study, we developed an in vitro plate-based assay to measure GAG degradation and utilization and used this assay to screen a library of 37 urinary bacterial isolates representing both urinary microbiota and uropathogenic species. This novel assay is more rapid, inexpensive, and quantitative compared to previously developed assays, and can measure three of the major classes of human GAGs. Our findings demonstrate that this assay captures the well-characterized ability of Streptococcus agalactiae to degrade hyaluronic acid and partially degrade chondroitin sulfate. Additionally, we present the first known report of chondroitin sulfate degradation by Proteus mirabilis, an important uropathogen and a causative agent of acute, recurrent, and catheter-associated urinary tract infections (CAUTI). In contrast, we observed that uropathogenic Escherichia coli (UPEC) and members of the urinary microbiota, including lactobacilli, were unable to degrade GAGs.
Natural AMPK Activators in Cardiovascular Disease Prevention

Cardiovascular diseases (CVD), as a life-threatening global disease, is receiving worldwide attention. Seeking novel therapeutic strategies and agents is of utmost importance to curb CVD. AMP-activated protein kinase (AMPK) activators derived from natural products are promising agents for cardiovascular drug development owning to regulatory effects on physiological processes and diverse cardiometabolic disorders. In the past decade, different therapeutic agents from natural products and herbal medicines have been explored as good templates of AMPK activators. Hereby, we overviewed the role of AMPK signaling in the cardiovascular system, as well as evidence implicating AMPK activators as potential therapeutic tools. In the present review, efforts have been made to compile and update relevant information from both preclinical and clinical studies, which investigated the role of natural products as AMPK activators in cardiovascular therapeutics.
Fabrication of β-Ni(OH)2 Particles by Alkaline Etching Layered Double Hydroxides Precursor for Supercapacitor

We report the facile preparation of β-Ni(OH)2 particles by etching a NiAl- layered double hydroxides (NiAl-LDHs) precursor with KOH solution. The amphoteric Al3+ ions in LDHs crystal were selectively dissolved out by KOH solution and LDHs crystals were proposed to be in situ topologically transformed to form β-Ni(OH)2. Alkaline concentration has a great influence on the structure, morphology, specific surface area, and porous structure of the resulting samples. Compared to LDHs precursor and β-Ni(OH)2 prepared by a precipitation reaction, the sample etched in 10 M KOH solution has enhanced specific capacitance (829 F/g at 1 A/g), high rate capability (capacitance retention 57.3% with current density 8 A/g), and good charge/discharge stability. We suggested that the high accessible specific surface area and appropriate porous structure, which is conducive to full contact between active material and electrolyte, can improve the utilization rate of the active material to increase the rate capacity of the 10 M KOH-etched sample.
Effects of Hyperoxia on Aging Biomarkers: A Systematic Review

The effects of short-term hyperoxia on age-related diseases and aging biomarkers have been reported in animal and human experiments using different protocols; however, the findings of the studies remain conflicting. In this systematic review, we summarized the existing reports in the effects of short-term hyperoxia on age-related diseases, hypoxia-inducible factor 1α (HIF-1α), and other oxygen-sensitive transcription factors relevant to aging, telomere length, cellular senescence, and its side effects. This review was done as described in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline. A systematic search was done in PubMed, Google Scholar, and Cochrane Library and from the references of selected articles to identify relevant studies until May 2021. Of the total 1,699 identified studies, 17 were included in this review. Most of the studies have shown significant effects of short-term hyperoxia on age-related diseases and aging biomarkers. The findings of the studies suggest the potential benefits of short-term hyperoxia in several clinical applications such as for patients undergoing stressful operations, restoration of cognitive function, and the treatment of severe traumatic brain injury. Short-term hyperoxia has significant effects in upregulation or downregulation of transcription factors relevant to aging such as HIF-1α, nuclear factor kappa-light-chain-enhancer of activated B-cells (NF-kB), and nuclear factor (erythroid-derived 2)-like 2 (NRF2) among others. Short-term hyperoxia also has significant effects to increase antioxidant enzymes, and increase telomere length and clearance of senescent cells. Some of the studies have also reported adverse consequences including mitochondrial DNA damage and nuclear cataract formation depending on the dose and duration of oxygen exposure. In conclusion, short-term hyperoxia could be a feasible treatment option to treat age-related disease and to slow aging because of its ability to increase antioxidant enzymes, significantly increase telomere length and clearance of senescent cells, and improve cognitive function, among others. The reported side effects of hyperoxia vary depending on the dose and duration of exposure. Therefore, it seems that additional studies for better understanding the beneficial effects of short-term hyperoxia and for minimizing side effects are necessary for optimal clinical application.
Mechanical Properties and Deformation Mechanisms of Heterostructured High-Entropy and Medium-Entropy Alloys: A Review

Recently, heterostructured (HS) materials, consisting of hard and soft zones with dramatically different strengths, have been developed and received extensive attention because they have been reported to exhibit superior mechanical properties over those predicted by the rule of mixtures. Due to the accumulation of geometrically necessary dislocations during plastic deformation, a back stress is developed in the soft zones to increase the yield strength of HS materials, which also induce forward stress in the hard zones, and a global hetero-deformation induced (HDI) hardening to retain ductility. High-entropy alloys (HEAs) and medium-entropy alloys (MEAs) or multicomponent alloys usually contain three or more principal elements in near-equal atomic ratios and have been widely studied in the world. This review paper first introduces concepts of HS materials and HEAs/MEAs, respectively, and then reviewed emphatically the mechanical properties and deformation mechanisms of HS HEAs/MEAs. Finally, we discuss the prospect for industrial applications of the HS HEAs and MEAs.
Toward a Fundamental Understanding of the Role of Lignin in the Biorefinery Process

As one of the main components in biomass, lignin plays a vital role in the biorefinery industry. Its unique structural feature increases the dose of cellulases during enzymatic deconstruction and is an attractive resource for many high valued products. The inhibition of lignin on cellulases is proposed to occur in several ways, with the most studied being nonproductive enzyme binding, which is attributed to hydrogen bonding, hydrophobic and/or electrostatic interactions. This review provides a comprehensive review of how lignin is transformed during various pretreatment methods as well as how these changes impact the cellulases inhibition. Future pretreatment directions for decreased cellulases inhibition are also proposed.
Identification of Somatic Mutations From Bulk and Single-Cell Sequencing Data

Somatic mutations are DNA variants that occur after the fertilization of zygotes and accumulate during the developmental and aging processes in the human lifespan. Somatic mutations have long been known to cause cancer, and more recently have been implicated in a variety of non-cancer diseases. The patterns of somatic mutations, or mutational signatures, also shed light on the underlying mechanisms of the mutational process. Advances in next-generation sequencing over the decades have enabled genome-wide profiling of DNA variants in a high-throughput manner; however, unlike germline mutations, somatic mutations are carried only by a subset of the cell population. Thus, sensitive bioinformatic methods are required to distinguish mutant alleles from sequencing and base calling errors in bulk tissue samples. An alternative way to study somatic mutations, especially those present in an extremely small number of cells or even in a single cell, is to sequence single-cell genomes after whole-genome amplification (WGA); however, it is critical and technically challenging to exclude numerous technical artifacts arising during error-prone and uneven genome amplification in current WGA methods. To address these challenges, multiple bioinformatic tools have been developed. In this review, we summarize the latest progress in methods for identification of somatic mutations and the challenges that remain to be addressed in the future.
The Functional Polymorphism of DDAH2 rs9267551 Is an Independent Determinant of Arterial Stiffness

Background: The association of circulating asymmetric dimethylarginine (ADMA) levels with cardiovascular risk and arterial stiffness has been reportedly demonstrated, although the causal involvement of ADMA in the pathogenesis of these conditions is still debated. Dimethylaminohydrolase 2 (DDAH2) is the enzyme responsible for ADMA hydrolysis in the vasculature, and carriers of the polymorphism rs9267551 C in the 5′-UTR of DDAH2 have been reported to have higher DDAH2 expression and reduced levels of serum ADMA. Approach and Results: We genotyped rs9267551 in 633 adults of European ancestry and measured their carotid–femoral pulse wave velocity (cfPWV), the gold-standard method to estimate arterial stiffness. cfPWV resulted significantly lower in rs9267551 C allele carriers (Δ = −1.12 m/s, P < 0.01) after correction for age, sex and BMI, and a univariate regression showed that the presence of rs9267551 C variant was negatively associated with cfPWV (β = −0.110, P < 0.01). In a multivariable regression model, subjects carrying the rs9267551 C allele manifested significantly lower cfPWV than GG carriers (β = −0.098, P = 0.01) independently from several potential confounders. We measured circulating ADMA levels in a subset of 344 subjects. A mediation analysis revealed that the effect of DDAH2 rs9267551 genotype on cfPWV was mediated by the variation in ADMA levels. Conclusions: These evidences hint that the presence of rs9267551 C allele may explain, at least in part, a reduction in vessel rigidity as measured by cfPWV, and support the attribution of a causative role to ADMA in the pathogenesis of arterial stiffness.
The Elephant in The Room: AML Relapse Post Allogeneic Hematopoietic Cell Transplantation

Relapsed acute myeloid leukemia (AML) following allogeneic hematopoietic cell transplantation (allo-HCT) is an unfavorable event associated with a poor prognosis, particularly for patients with early relapses. It usually arises from resistant leukemic blasts that escaped both preparative chemotherapy regimen and the graft-versus-leukemia (GVL) effect. Independent from the choice of salvage treatment, only minority of patients can achieve durable remissions. In recent years, better understanding of the disease relapse biology post allo-HCT allowed the application of newer strategies that could induce higher rates of remission, and potential longer survival. Those strategies aim at optimizing drugs that have a direct anti-leukemia activity by targeting different oncogenic mutations, metabolism pathways or surface antigens, and concurrently enhancing the immune microenvironment to promote GVL effect. This review discusses the current treatment landscape of AML relapse post allo-HCT.
Community-oriented models and applications for the social web

The past few years have seen the rapid rise of all things “social” on the web from the growth of online social networks like Facebook, to user-contributed content sites like Flickr and YouTube, to social bookmarking services like Delicious, among many others. Whereas traditional approaches to organizing and accessing the web's massive amount of information have focused on content-based and link-based approaches, these social systems offer rich opportunities for user-based and community-based exploration and analysis of the web by building on the unprecedented access to the interests and perspectives of millions of users. We focus here on the challenge of modeling and mining social bookmarking systems, in which resources are enriched by large-scale socially generated metadata (“tags”) and contextualized by the user communities that are associated with the resources. Our hypothesis is that an underlying social collective intelligence is embedded in the uncoordinated actions of users on social bookmarking services, and that this social collective intelligence can be leveraged for enhanced web-based information discovery and knowledge sharing. Concretely, we posit the existence of underlying implicit communities in these social bookmarking systems that drive the social bookmarking process which can provide a foundation for community-based organization of web resources. To that end, we make three contributions: (1) First, we propose a pair of novel probabilistic generative models for describing and modeling community-oriented social bookmarking. We show how these models enable effective extraction of meaningful communities over large real-world social bookmarking services. (2) Second, we develop two frameworks for community-based web information browsing and search that are based on these community-oriented social bookmarking models. We show how both achieve improved discovery and exploration of the social web. (3) Third, we introduce a community evolution framework for studying and analyzing social bookmarking communities over time. We explore the temporal dimension of social bookmarking and explore the dynamics of community formation, evolution, and dissolution. By uncovering implicit communities, putting them to use in an application scenario (search and browsing), and analyzing them over time, this dissertation provides a foundation for the study of how social knowledge networks are self-organized, a deeper understanding and appreciation of the factors impacting collective intelligence, and the creation of new information access algorithms for leveraging these communities.
AlphaFold2-aware protein-DNA binding site prediction using graph transformer

Protein-DNA interactions play crucial roles in the biological systems, and identifying protein-DNA binding sites is the first step for mechanistic understanding of various biological activities (such as transcription and repair) and designing novel drugs. How to accurately identify DNA-binding residues from only protein sequence remains a challenging task. Currently, most existing sequence-based methods only consider contextual features of the sequential neighbors, which are limited to capture spatial information. Based on the recent breakthrough in protein structure prediction by AlphaFold2, we propose an accurate predictor, GraphSite, for identifying DNA-binding residues based on the structural models predicted by AlphaFold2. Here, we convert the binding site prediction problem into a graph node classification task and employ a transformer-based variant model to take the protein structural information into account. By leveraging predicted protein structures and graph transformer, GraphSite substantially improves over the latest sequence-based and structure-based methods. The algorithm is further confirmed on the independent test set of 181 proteins, where GraphSite surpasses the state-of-the-art structure-based method by 16.4% in area under the precision-recall curve and 11.2% in Matthews correlation coefficient, respectively. We provide the datasets, the predicted structures and the source codes along with the pre-trained models of GraphSite at https://github.com/biomed-AI/GraphSite. The GraphSite web server is freely available at https://biomed.nscc-gz.cn/apps/GraphSite.