Miracle-dz/UniFAR-FTUs huggingface dataset:
PFKFB3-Mediated Glycolysis Boosts Fibroblast Activation and Subsequent Kidney Fibrosis

Renal fibrosis, a hallmark of chronic kidney diseases, is driven by the activation of renal fibroblasts. Recent studies have highlighted the role of glycolysis in this process. Nevertheless, one critical glycolytic activator, 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase 3 (PFKFB3), remains unexplored in renal fibrosis. Upon reanalyzing the single-cell sequencing data from Dr. Humphreys’ lab, we noticed an upregulation of glycolysis, gluconeogenesis, and the TGFβ signaling pathway in myofibroblasts from fibrotic kidneys after unilateral ureter obstruction (UUO) or kidney ischemia/reperfusion. Furthermore, our experiments showed significant induction of PFKFB3 in mouse kidneys following UUO or kidney ischemia/reperfusion. To delve deeper into the role of PFKFB3, we generated mice with Pfkfb3 deficiency, specifically in myofibroblasts (Pfkfb3f/f/PostnMCM). Following UUO or kidney ischemia/reperfusion, a substantial decrease in fibrosis in the injured kidneys of Pfkfb3f/f/PostnMCM mice was identified compared to their wild-type littermates. Additionally, in cultured renal fibroblast NRK-49F cells, PFKFB3 was elevated upon exposure to TGFβ1, accompanied by an increase in α-SMA and fibronectin. Notably, this upregulation was significantly diminished with PFKFB3 knockdown, correlated with glycolysis suppression. Mechanistically, the glycolytic metabolite lactate promoted the fibrotic activation of NRK-49F cells. In conclusion, our study demonstrates the critical role of PFKFB3 in driving fibroblast activation and subsequent renal fibrosis.
Inter-annual variation of threshold wind speed for dust emission in Mongolia

Threshold wind speed for dust emission, which is the minimum wind speed required to lift dust particles into the atmosphere, largely varies according to land-cover and land-use changes (LCLUC) (e.g., soil wetness, vegetation cover, snow cover, soil freeze, cultivation, grazing). This means that we can recognize the variation of threshold speed as an index of LCLUC. Moreover, well recognition of the relation between LCLUC and variations of threshold speed contributes improvements of numerical dust models, which have been utilized for the evaluation of the impact of aeolian dust on climate.
Breast Milk Mesenchymal Stem Cells and/or Derived Exosomes Mitigated Adenine-Induced Nephropathy via Modulating Renal Autophagy and Fibrotic Signaling Pathways and Their Epigenetic Regulations

Chronic kidney disease (CKD), a global health concern, is highly prevalent among adults. Presently, there are limited therapeutic options to restore kidney function. This study aimed to investigate the therapeutic potential of breast milk mesenchymal stem cells (Br-MSCs) and their derived exosomes in CKD. Eighty adult male Sprague Dawley rats were randomly assigned to one of six groups, including control, nephropathy, nephropathy + conditioned media (CM), nephropathy + Br-MSCs, nephropathy + Br-MSCs derived exosomes (Br-MSCs-EXOs), and nephropathy + Br-MSCs + Br-MSCs-EXOs. Before administration, Br-MSCs and Br-MSCs-EXOs were isolated, identified, and labeled with PKH-26. SOX2, Nanog, and OCT3/4 expression levels in Br-MSCs and miR-29b, miR-181, and Let-7b in both Br-MSCs and Br-MSCs-EXOs were assayed. Twelve weeks after transplantation, renal function tests, oxidative stress, expression of the long non-coding RNA SNHG-7, autophagy, fibrosis, and expression of profibrotic miR-34a and antifibrotic miR-29b, miR-181, and Let-7b were measured in renal tissues. Immunohistochemical analysis for renal Beclin-1, LC3-II, and P62, Masson trichome staining, and histopathological examination of kidney tissues were also performed. The results showed that Br-MSCs expressed SOX2, Nanog, and OCT3/4, while both Br-MSCs and Br-MSCs-EXOs expressed antifibrotic miR-181, miR-29b, and Let-7b, with higher expression levels in exosomes than in Br-MSCs. Interestingly, the administration of Br-MSCs + EXOs, EXOs, and Br-MSCs improved renal function tests, reduced renal oxidative stress, upregulated the renal expression of SNHG-7, AMPK, ULK-1, Beclin-1, LC3, miR-29b, miR-181, Let-7b, and Smad-7, downregulated the renal expression of miR-34a, AKT, mTOR, P62, TGF-β, Smad-3, and Coli-1, and ameliorated renal pathology. Thus, Br-MSCs and/or their derived exosomes appear to reduce adenine-induced renal damage by secreting antifibrotic microRNAs and potentiate renal autophagy by modulating SNHG-7 expression.
Molecular Design of FRET Probes Based on Domain Rearrangement of Protein Disulfide Isomerase for Monitoring Intracellular Redox Status

Multidomain proteins can exhibit sophisticated functions based on cooperative interactions and allosteric regulation through spatial rearrangements of the multiple domains. This study explored the potential of using multidomain proteins as a basis for Förster resonance energy transfer (FRET) biosensors, focusing on protein disulfide isomerase (PDI) as a representative example. PDI, a well-studied multidomain protein, undergoes redox-dependent conformational changes, enabling the exposure of a hydrophobic surface extending across the b’ and a’ domains that serves as the primary binding site for substrates. Taking advantage of the dynamic domain rearrangements of PDI, we developed FRET-based biosensors by fusing the b’ and a’ domains of thermophilic fungal PDI with fluorescent proteins as the FRET acceptor and donor, respectively. Both experimental and computational approaches were used to characterize FRET efficiency in different redox states. In vitro and in vivo evaluations demonstrated higher FRET efficiency of this biosensor in the oxidized form, reflecting the domain rearrangement and its responsiveness to intracellular redox environments. This novel approach of exploiting redox-dependent domain dynamics in multidomain proteins offers promising opportunities for designing innovative FRET-based biosensors with potential applications in studying cellular redox regulation and beyond.
Using Household Living Aid for Entrepreneurship: What Does the B40 Think?

Cash transfers like household living aid given to the poor are initially for the recipients to fulfil their necessities. However, previous studies found that cash assistance can lift the household’s income by influencing self -employment and entrepreneurial activities. Thus, this study addresses the participation of the poor in entrepreneurship and unravels their perception of the entrepreneurship model using household living aid. Specifically, this qualitative study examines household living aid usage for entrepreneurship. Seven household living aid recipients and ten microentrepreneurs were interviewed using semi-structured interviews. From the analysis, most recipients believe that household living aid can be utilised for entrepreneurship. Six themes that confirm and disconfirm the propositions were identified. Even though the findings are varied, the potential use of household living aid for entrepreneurship is optimistic. The results can guide policymakers, financial institutions, and entrepreneurial training agencies in catering to the needs of the niche group. Ultimately, the bottom 40 group could benefit from this policy and assistance, while the government could enjoy the return on investment (ROI) in employment and reduce poverty among citizens. Implications for both research and practice are discussed.
The Pandemic Allocation of Ventilators Model Penalizes Infants with Bronchopulmonary Dysplasia

During the COVID-19 pandemic, institutions developed ventilator allocation models. In one proposed model, neonates compete with adults for ventilators using a scoring system. Points are given for conditions that increase one- and five-year (y) mortality. For example, comparable points were added for adult conditions with mortality of 71.3% and for neonates with moderate or severe bronchopulmonary dysplasia (mod/sBPD). We hypothesized that this model overestimates mortality in neonates with BPD and would penalize these infants unfairly. There was little information available on 1 y and 5 y mortality risk for mod/sBPD. To evaluate this allocation protocol, a retrospective chart review was performed on infants born ≥22 weeks and weighing <1500 g admitted to Rainbow Babies and Children’s Hospital in 2015 to identify babies with BPD. The main outcomes were 1 and 5 y mortality. In 2015, 28 infants were diagnosed with mod/s BPD based on NIH 2001 definition; 4 infants had modBPD and 24 had sBPD. All infants (100%) with modBPD survived to 5 y; 2 infants with sBPD died by 1 y (8%) and 22 survived (92%) to 1 y; 3 died (12.5%) by 5 y; and at least 13 survived (54%) to 5 y. Infants with mod/s BPD had lower-than-predicted 1 and 5 y mortality, suggesting the points assigned in the model are too high for these conditions. We believe this model would unfairly penalize these babies.
Scheduling Eight-Phase Urban Traffic Light Problems via Ensemble Meta-Heuristics and Q-Learning Based Local Search

This paper addresses urban traffic light scheduling problems (UTLSP) with eight phases. The objective is to minimize the total vehicle delay time by assigning traffic phases and phase-timing optimally. A novel hybrid algorithm framework by combining meta-heuristics with Q-learning is proposed to solve the UTLSP for the first time. First, a mathematical model is developed to describe UTLSP. Second, five meta-heuristics are employed and improved to solve the concerned problems. Based on the feature of UTLSP, five local search operators are developed to improve the exploitation performance of the meta-heuristics. Third, two Q-learning-based ensemble strategies are designed to select the premium local search operators during the meta-heuristics’ iterations. Finally, experiments are conducted on 10 cases with different scales. A total of 26 algorithms are compared for validation. Experimental results verify the effectiveness of the proposed ensemble strategies. Comparisons and discussions show that the improved water cycle algorithm with the first Q-learning strategy has the best competitiveness for solving the considered problems.
Evolutive screening of candidates for new materials using genetic algorithms and deep learning

— Different mechanisms are used for the discovery of materials. These include creating a material by trial-and-error process without knowing its properties. Other methods are based on computational simulations or mathematical and statistical approaches, such as Density Functional Theory (DFT). A well-known strategy combines elements to predict their properties and selects a set of those with the properties of interest. Carrying out exhaustive calculations to predict the properties of these found compounds may require a high computational cost. Therefore, there is a need to create methods for identifying materials with a desired set of properties while reducing the search space and, consequently, the computational cost. In this work, we present a genetic algorithm that can find a higher percentage of compounds with specific properties than state-of-the-art methods, such as those based on combinatorial screening. Both methods are compared in the search for ternary compounds in an unconstrained space, using a Deep Neural Network (DNN) to predict properties such as formation enthalpy, band gap, and stability; we will focus on formation enthalpy. As a result, we provide a genetic algorithm capable of finding up to 60% more compounds with atypical values of properties, using DNNs for their prediction.
Functional and Morphological Adaptations in the Heart of Children Aged 12–14 Years following Two Different Endurance Training Protocols

This study investigated the cardiac functional and the morphological adaptations because of two endurance training protocols. Untrained children (N = 30, age: 12–14 years) were divided into three groups (N = 10/group). The first group did not perform any session (CONTROL), the second performed ventilatory threshold endurance training (VTT) for 12 weeks (2 sessions/week) at an intensity corresponding to the ventilatory threshold (VT) and the third (IT) performed two sessions per week at 120% of maximal oxygen uptake (VO2max). Two other sessions (30 min running at 55–65% of VO2max) per week were performed in VVT and IT. Echocardiograms (Left Ventricular end Diastolic Diameter, LVEDd; Left Ventricular end Diastolic Volume, LVEDV; Stroke Volume, SV; Ejection Fraction, EF; Posterior Wall Thickness of the Left Ventricle, PWTLV) and cardiopulmonary ergospirometry (VO2max, VT, velocity at VO2max (vVO2max), time in vVO2max until exhaustion (Tlim) was conducted before and after protocols. Significant increases were observed in both training groups in LVEDd (VTT = 5%; IT = 3.64%), in LVEDV (VTT = 23.7%; ITT = 13.6%), in SV (VTT = 25%; IT = 16.9%) but not in PWTLV and EF, after protocols. No differences were noted in the CONTROL group. VO2max and VT increased significantly in both training groups by approximately 9% after training. Our results indicate that intensity endurance training does not induce meaningful functional and morphological perturbations in the hearts of children.
Robust Model Design by Comparative Evaluation of Clustering Algorithms

The K-means algorithm, widely used in cluster analysis, is a centroid-based clustering method known for its high efficiency and scalability. However, in realistic situations, the operating environment is susceptible to contamination issues caused by outliers and distribution departures, which may lead to clustering results from K-means that are distorted or rendered invalid. In this paper, we introduce three other alternative algorithms, including K-weighted-medians, K-weighted-L2-medians, and K-weighted-HLs, to address these issues under the consideration of data with weights. The impact of contamination is investigated by examining the estimation effects on optimal cluster centroids. We explore the robustness of the clustering algorithms from the perspective of the breakdown point, and then conduct experiments on simulated and real datasets to evaluate their performance using two new numerical metrics: relative efficiencies based on generalized variance and average Euclidean distance. The results demonstrate the effectiveness of the proposed K-weighted-HLs algorithm, surpassing other algorithms in scenarios involving both contamination issues.
Environmental Sampling Methods for Detection of Salmonella Infections in Laying Hens: A Systematic Review and Meta-Analysis

Salmonellosis is the second most commonly reported foodborne gastrointestinal infection in humans in the European Union (EU). Most outbreaks are caused by Salmonella Enteritidis, present in contaminated food products, particularly in egg and egg products. In recent years, an increase in the prevalence of Salmonella in laying hen flocks in the EU has been observed. For the effective control of infection, adequate detection is key. In laying hen flocks, the occurrence of Salmonella in the EU is monitored by the culture of environmental samples (dust, faeces, and boot swabs). The performance of sampling procedures described in the literature for the detection of Salmonella in laying hens was reviewed. In total, 924 abstracts were screened, resulting in the selection of 87 abstracts and 18 publications for qualitative and quantitative analyses, respectively. Sample sizes and sampling locations of faecal material and dust were variable and poorly described. Microbiological culture methods used to detect Salmonella were variably described in the literature and were often incomplete. Overall, the available literature indicates higher sensitivity of environmental versus individual hen matrices and points to differences in sensitivity between environmental matrices. For non-cage housing systems, boot swabs are the preferred samples, while for cage housing systems dust might be a more reliable sample.
Development of lateral flow assays to detect host proteins in cattle for improved diagnosis of bovine tuberculosis

Bovine tuberculosis (bTB), caused by Mycobacterium bovis (M. bovis) infection in cattle, is an economically devastating chronic disease for livestock worldwide. Efficient disease control measures rely on early and accurate diagnosis using the tuberculin skin test (TST) and interferon-gamma release assays (IGRAs), followed by culling of positive animals. Compromised performance of TST and IGRA, due to BCG vaccination or co-infections with non-tuberculous mycobacteria (NTM), urges improved diagnostics. Lateral flow assays (LFAs) utilizing luminescent upconverting reporter particles (UCP) for quantitative measurement of host biomarkers present an accurate but less equipment- and labor-demanding diagnostic test platform. UCP-LFAs have proven applications for human infectious diseases. Here, we report the development of UCP-LFAs for the detection of six bovine proteins (IFN-γ, IL-2, IL-6, CCL4, CXCL9, and CXCL10), which have been described by ELISA as potential biomarkers to discriminate M. bovis infected from naïve and BCG-vaccinated cattle. We show that, in line with the ELISA data, the combined PPDb-induced levels of IFN-γ, IL-2, IL-6, CCL4, and CXCL9 determined by UCP-LFAs can discriminate M. bovis challenged animals from naïve (AUC range: 0.87–1.00) and BCG-vaccinated animals (AUC range: 0.97–1.00) in this cohort. These initial findings can be used to develop a robust and user-friendly multi-biomarker test (MBT) for bTB diagnosis.
DEDPUL: Method for Mixture Proportion Estimation and Positive-Unlabeled Classification based on Density Estimation

This paper studies Positive-Unlabeled Classification, the problem of semi-supervised binary classification in the case when Negative (N) class in the training set is contaminated with instances of Positive (P) class. We develop a novel method (DEDPUL) that simultaneously solves two problems concerning the contaminated Unlabeled (U) sample: estimates the proportions of the mixing components (P and N) in U, and classifies U. By conducting experiments on synthetic and real-world data we favorably compare DEDPUL with current state-of-the-art methods for both problems. We introduce an automatic procedure for DEDPUL hyperparameter optimization. Additionally, we improve two methods in the literature and achieve DEDPUL level of performance with one of them.
An Iterative Sparse-Group Lasso

Abstract In high-dimensional supervised learning problems, sparsity constraints in the solution often lead to better performance and interpretability of the results. For problems in which covariates are grouped and sparse structure are desired, both on group and within group levels, the sparse-group lasso (SGL) regularization method has proved to be very efficient. Under its simplest formulation, the solution provided by this method depends on two weight parameters that control the penalization on the coefficients. Selecting these weight parameters represents a major challenge. In most of the applications of the SGL, this problem is left aside, and the parameters are either fixed based on a prior information about the data, or chosen to minimize some error function in a grid of possible values. However, an appropriate choice of the parameters deserves more attention, considering that it plays a key role in the structure and interpretation of the solution. In this sense, we present a gradient-free coordinate descent algorithm that automatically selects the regularization parameters of the SGL. We focus on a more general formulation of this problem, which also includes individual penalizations for each group. The advantages of our approach are illustrated using both real and synthetic datasets. Supplementary materials for this article are available online.
The roles of nurses in supporting health literacy: a scoping review

Introduction The importance of health literacy in achieving optimum health is highly significant, particularly in the nursing profession where it is an integral part of the roles and functions of nurses. Therefore, this scoping review aims to describe the roles of nurses in promoting patient health literacy and identify the determinant factors of health literacy in nursing practices. Methods An integrative search was conducted through four databases, namely, ScienceDirect, ProQuest, SAGE Journal, and PubMed, using various keyword combinations such as “health literacy,” “health information,” “patient health literacy,” “patient literacy,” and “nurses. ” Furthermore, the inclusion criteria employed were peer-reviewed articles focused on the nursing profession, explicitly discussing health literacy related to nursing, and including original studies, such as cross-sectional, quasi-experimental, and qualitative studies. The selected review articles were all published between 2017 and 2022. Results In total, 13 articles met the criteria and were applied in this scoping review. Most of these discuss health literacy related to nursing practice in clinical and community settings, as well as educational institutions. Health literacy is an essential aspect of professional nursing practice. Consequently, the supportive roles of nurses include acting as caregivers, facilitators, and educators to help patients overcome their literacy limitations and attain improved wellbeing. Conclusion Nurses can improve the health literacy skills of patients by making health information related to their illnesses easier to access, understand, evaluate, and use. They must also recognize various factors influencing health literacy and use the factors as opportunities to optimize health literacy improvement. A health literacy approach can be applied by nurses to solve health problems and improve the quality of care for patients.
Advertising Factors Affecting Purchase Intention of Sportswear Products for Batam City People

Purpose – This study aims to verify whether the variables in social media advertising affect the Purchase Intention of sportswear products mediated by the Attitudes toward Advertising variable. Methodology/ approach – This study uses a causal-comparative method. The population in this study is the people of Batam City who actively use social media. The research sample is Batam City people who have watched social media advertisements. The sample collected in this study numbered 305 respondents. The data is then processed using SmartPLS software. Findings – It is found that social media advertising variables, namely Entertainment, Informativeness, Emotional Appeal, and Creativity have a significant positive effect on Attitudes toward Advertising and indirectly influence Purchase Intention mediated by Attitudes toward Advertising. Novelty/value – This study differentiates itself from the previous research not discussing sportswear products during the Covid-19 pandemic in Batam City. Keywords: Entertainment, Informativeness, Credibility, Emotional Appeal, Creativity, Attitudes toward Advertising, Purchase Intention
Picturing Pity: Pitfalls and Pleasures in Cross-cultural Communication Image and Word in a North Cameroon Mission

Empirically, this book is based on an in-depth analysis of the published photographs taken by Norwegian evangelical missionaries in Northern Cameroon between 1925, the start of their activities, and 2005. Norway sent out more missionaries per capita than any other country in Europe. Marianne Gullestad's main contention is that the need to continuously justify their activities to donors in Europe has led to the creation and maintenance of specific ways of portraying Africans. The missionary visual rhetoric is both based on earlier visualizations and has over time established its own conventions which can now also be traced within secular fields of activity such as international development agencies, foreign policy, human relief organizations and the media. "Picturing Pity" takes part in the present "pictorial turn" in academic teaching and research, constituting visual images as an exciting site of conversation across disciplinary lines.