Miracle-dz/UniFAR-FTUs huggingface dataset:
Machine learning method for shale gas adsorption capacity prediction and key influencing factors evaluation

Shale gas plays a pivotal role in the global energy landscape, emphasizing the need for accurate shale gas-in-place (GIP) prediction to facilitate effective production planning. Adsorbed gas in shale, the primary form of gas storage under reservoir conditions, is a critical aspect of this prediction. In this study, a machine learning Gaussian process regression (GPR) model for methane adsorption prediction was established and validated using published experimental data. Five typical variables, i.e., total organic carbon (TOC), clay minerals, temperature, pressure, and moisture were considered, which were derived from the Marine shale of the Longmaxi formation in the Sichuan Basin through correlation analysis. The performance of the GPR model was compared with the widely used an extreme gradient boosting model. It turned out that our GPR model had better accuracy for predicting methane adsorption in shale with an average relative error of less than 3%. Furthermore, a variance-based sensitivity analysis method in conjunction with kernel density estimation theory was employed to conduct a global sensitivity analysis, quantifying the nonlinear influence of each variable methane adsorption. The findings indicate that TOC is the most significant factor affecting methane adsorption, while clay minerals have a limited direct impact but can enhance their influence through interactions with other influencing factors. Finally, based on the GPR model, a GIP prediction method was proposed that eliminates the need for calculating the density of the adsorbed phase. These findings are expected to extend the shale gas reserve assessment methodologies and offer valuable insight for further exploring the adsorption mechanisms of shale gas.
Development and deployment of a nationwide predictive model for chronic kidney disease progression in diabetic patients

Aim Chronic kidney disease (CKD) is a major complication of diabetes and a significant disease burden on the healthcare system. The aim of this work was to apply a predictive model to identify high-risk patients in the early stages of CKD as a means to provide early intervention to avert or delay kidney function deterioration. Materials and methods Using the data from the National Diabetes Database in Singapore, we applied a machine-learning algorithm to develop a predictive model for CKD progression in diabetic patients and to deploy the model nationwide. Results Our model was rigorously validated. It outperformed existing models and clinician predictions. The area under the receiver operating characteristic curve (AUC) of our model is 0.88, with the 95% confidence interval being 0.87 to 0.89. In recognition of its higher and consistent accuracy and clinical usefulness, our CKD model became the first clinical model deployed nationwide in Singapore and has been incorporated into a national program to engage patients in long-term care plans in battling chronic diseases. The risk score generated by the model stratifies patients into three risk levels, which are embedded into the Diabetes Patient Dashboard for clinicians and care managers who can then allocate healthcare resources accordingly. Conclusion This project provided a successful example of how an artificial intelligence (AI)-based model can be adopted to support clinical decision-making nationwide.
Cycle of heavy metals in the soil of livestock farms in an acidic zone of the central region of Thailand

The purpose of the present study was to examine the connections between heavy metal concentrations in the soil, pasture grass, and livestock manure on farms located in an acidic soil zone of central Thailand. Samples of the soil, grass, and livestock manure were collected from three farms, and were tested for Cd, Pb, As, and Hg. Multiple indicators were employed to assess the pollution levels of the farm soils. The results showed that the amounts of heavy metals in the soil were in the order Pb > Hg > Cd > As; in grass the order was Pb > As > Cd > Hg, and in livestock manure this was Pb > As > Hg > Cd. The soil pH level influenced the quantities of Hg and Ag in the soil. Concerning individual indicators, Igeo and CF indices showed that Hg was present at high levels in farm soils, but an integrated indicator PLI was less than 1, suggesting that soil in the farms was not significantly contaminated, in contrast to the individual indicators. Heavy metal accumulation depended on the species of grass, and the element content in manure was higher than in grass used for feeding the animals, including supplements given to animals by farmers. However, the levels of Cd in the livestock farms demonstrated a relationship among soil, grass, and manure. The heavy metals in manure from animal farms were below the thresholds at which they could be utilized as a soil amendments for organic farming or for safe food production. However, the heavy metals in the farm soil could possibly be dispersed to other locations.
The determinants of COVID-induced brain dysfunctions after SARS-CoV-2 infection in hospitalized patients

The severity of the pandemic and its consequences on health and social care systems were quite diverse and devastating. COVID-19 was associated with an increased risk of neurological and neuropsychiatric disorders after SARS-CoV-2 infection. We did a cross-sectional study of 3 months post-COVID consequences of 178 Cuban subjects. Our study has a unique CUBAN COVID-19 cohort of hospitalized COVID-19 patients and healthy subjects. We constructed a latent variable for pre-health conditions (PHC) through Item Response Theory (IRT) and for post-COVID neuropsychiatric symptoms (Post-COVID-NPS) through Factor Analysis (FA). There seems to be a potential causal relationship between determinants of CIBD and post-COVID-NPS in hospitalized COVID-19 patients. The causal relationships accessed by Structural Equation Modeling (SEM) revealed that PHC (p < 0.001) and pre-COVID cognitive impairments (p < 0.001) affect the severity of COVID-19 patients. The severity of COVID-19 eventually results in enhanced post-COVID-NPS (p < 0.001), even after adjusting for confounders (age, sex, and pre-COVID-NPS). The highest loadings in PHC were for cardiovascular diseases, immunological disorders, high blood pressure, and diabetes. On the other hand, sex (p < 0.001) and pre-COVID-NPS including neuroticism (p < 0.001), psychosis (p = 0.005), cognition (p = 0.036), and addiction (p < 0.001) were significantly associated with post-COVID-NPS. The most common neuropsychiatric symptom with the highest loadings includes pain, fatigue syndrome, autonomic dysfunctionalities, cardiovascular disorders, and neurological symptoms. Compared to healthy people, COVID-19 patients with pre-health comorbidities or pre-neuropsychiatric conditions will have a high risk of getting severe COVID-19 and long-term post-COVID neuropsychiatric consequences. Our study provides substantial evidence to highlight the need for a complete neuropsychiatric follow-up on COVID-19 patients (with severe illness) and survivors (asymptomatic patients who recovered).
Legality rating and corporate efﬁciency: evidence from a conditional nonparametric frontier analysis

Promoting legality and productivity is a central issue in modern economies. In this paper, we investigate the implications of a public policy that aims at achieving these goals through the adoption of the so-called legality rating (LR). The latter reduces corporate risk uncertainty by abating asymmetric information between companies that use good legal, ﬁscal and ethical practices and the credit system. We match companies whose legal and ethical practices have been certiﬁed by the Italian government— through the assignment of the LR—with a sample of unrated ﬁrms, and apply recent advances in nonparametric frontier analysis to assess the economic performance. We highlight a positive relationship between legality rating and ﬁrms’ production efﬁ-ciency. Our ﬁndings also show that legality rating policy is an effective tool to enhance inter-regional technological catching-up of businesses.
Working Paper Series Why European banks adjust their dividend payouts?

Economic literature suggests that banks change their dividend payouts for three main reasons. They may be willing to signal good future profitability to shareholders to address information asymmetry, or use dividends to mitigate the agency costs, or could come under pressure from prudential supervisors and regulators to retain earnings. The COVID-19 pandemic led to introduction of sector-wide recommendation by regulators to suspend dividend payouts in view of prevailing large uncertainty. Using a panel data approach for two samples of listed and unlisted European banks, this paper provides evidence that, over a decade and a half preceding the pandemic, bank dividend payouts were adjusted in line with the three motivations found in the literature. The results are robust to selection of alternative variables representing these motivations. Banks are found not to discount expectations about future economic conditions or their own profitability when making payouts. Simulations shown in the paper suggest that, in the absence of supervisory recommendations, banks would likely have reduced the payouts only slightly in the first year of the pandemic.
Evaluating Riboglow-FLIM probes for RNA sensing

We recently developed Riboglow-FLIM, where we genetically tag and track RNA molecules in live cells through measuring the fluorescence lifetime of a small molecule probe that binds the RNA tag. Here, we systematically and quantitatively evaluated key elements of Riboglow-FLIM that may serve as the foundation for Riboglow-FLIM applications and further tool development efforts. Our investigation focused on measuring changes in fluorescence lifetime of representative Riboglow-FLIM probes with different linkers and fluorophores in different environments. In vitro measurements revealed distinct lifetime differences among the probe variants as a result of different linker designs and fluorophore selections. To expand on the platform's versatility, probes in a wide variety of mammalian cell types were examined using fluorescence lifetime imaging microscopy (FLIM), and possible effects on cell physiology were evaluated by metabolomics. The results demonstrated that variations in lifetime were dependent on both probe and cell type. Interestingly, distinct differences in lifetime values were observed between cell lines, while no overall change in cell health was measured. These findings underscore the importance of probe selection and cellular environment when employing Riboglow-FLIM for RNA detection, serving as a foundation for future tool development and applications across diverse fields and biological systems.
Socio-demographic characteristics and dietary pattern of community-dwelling adults in Abia State, Nigeria

Objective: Identification of dietary patterns and their association with socio-demographic factors.Design: Community-based cross-sectional study designSetting: Urban and rural communities in Abia State, NigeriaParticipants: Eight hundred and sixty-eight (868) male and female adults aged 20 to 59 yearsMethods: Identification of Dietary patterns (DP) by Principal Component Analysis (PCA) based on the consumption of 10 food groups, assessed using a 7-day qualitative food frequency questionnaire. Bivariate and multivariate logistic regression analyses evaluated the association between identified patterns and socio-economic factors.Results: Two dietary patterns ‘traditional and convenience DPs were identified, explaining 52% of the total variance. The traditional DP was loaded with starchy staples, vegetable soups/sauces, and animal proteins. The convenience DP was characterised by high factor loading of processed cereals, carbonated drinks and alcoholic beverages. Larger households (>3) had lower odds of adhering to high traditional DP [AOR =0.633; 95% CI (0.429-0.934); p = 0.021]. Females [AOR =1.586; 95% CI (1.104-2.279); p = 0.013] and middle-aged adults (AOR = 1.750; 95% CI (1.075-2.848);p = 0.024] were more likely to adhere to the convenience DP, whereas, the odds of adhering to the convenience pattern was lower among adults residing in rural areas [AOR =0.3161.586; 95% CI (0.219-0.456); p = 0.001]. Conclusion: Socio-economic variables (age, gender, household size and place of residence) were associated with dietary patterns among community dwellers in Nigeria.
Optimisation of osteosynthesis positioning in mandibular body fracture management using finite element analysis

Background: This proof of principle study aims to investigate the applicability of finite element analysis (FEA) in Oral and Maxillofacial (OMF) surgery, by studying the effect of mandibular body height and osteosynthesis positio - ning on unilateral mandibular body fractures based on Champy’s technique. Material and methods: Mandibles made of polyurethane foam (Synbone®), with heights of 18, 14, and 10 mm were used to create a FEA model with a unilateral straight-line fracture, fixated with a standard commercially available 6-hole 2 mm titanium miniplate. Two different FEA programs were used for the comparison, namely: Solidworks and Comsol Multiphysics. The FEA outcomes were compared with a series of mechanical tests with polymeric models fixed in a customised device and loaded onto a mechanical test bench. Results: First, the study illustrated that the optimal plate position appeared to be the upper border. Second, lower mandibular height increases instability and requires a stronger osteosynthesis system. Conclusions: FEA’s and polymeric model testing outcomes of unilateral non-comminuted fractures were highly comparable with current opinions of mandibular fracture management. The promising out - come of this study makes it worthwhile to do more extensive analysis in order to determine whether FEA alone is sufficient for optimisation of fracture management.
Effect of sarcopenia on survival in patients after pancreatic surgery: a systematic review and meta-analysis

Background Numerous studies have reported sarcopenia to be associated with unfavorable outcomes in patients who have undergone pancreatectomy. Therefore, in this meta-analysis, we examined the relationship between sarcopenia and survival after pancreatic surgery. Methods PubMed, Embase, and Cochrane Library were searched for studies that examined the association between sarcopenia and survival after pancreatic surgery from the inception of the database until June 1, 2023. Hazard ratio (HR) for overall survival (OS) and/or progression-free survival (PFS) of sarcopenia and pancreatic surgery were extracted from the selected studies and random or fixed-effect models were used to summarize the data according to the heterogeneity. Publication bias was assessed using Egger’s linear regression test and a funnel plot. Results Sixteen studies met the inclusion criteria. For 13 aggregated univariate and 16 multivariate estimates, sarcopenia was associated with decreased OS (univariate analysis: HR 1.69, 95% CI 1.48–1.93; multivariate analysis: HR 1.69; 95% CI 1.39–2.05, I2 = 77.4%). Furthermore, sarcopenia was significantly associated with poor PFS of pancreatic resection (Change to univariate analysis: HR 1.74, 95% CI 1.47–2.05; multivariate analysis: HR 1.54; 95% CI 1.23–1.93, I2 = 63%). Conclusion Sarcopenia may be a significant prognostic factor for a shortened survival following pancreatectomy since it is linked to an elevated risk of mortality. Further studies are required to understand how sarcopenia affects long-term results after pancreatic resection. Systematic review registrationRegistration ID: CRD42023438208 https://www.crd. york.ac. uk/PROSPERO/#recordDetails.
The efficacy and safety of acupoint catgut embedding therapy for depression: a protocol for systematic review and meta-analysis

Background Depression is a common public health problem, characterized by persistent low mood, lack of pleasure and exhaustion. Conventional treatments such as antidepressants and psychotherapy have some limitations, including variable efficacy, adverse side effects and high costs. Acupoint catgut embedding (ACE) therapy, as a subtype of acupuncture, has gained increasing clinical application due to its long-term effects, higher patient compliance, and cost-effectiveness. This study aims to conduct a meta-analysis to evaluate the efficacy and safety of ACE for depression. Methods Electronic searches will be conducted in 12 databases (both in English and Chinese databases), encompassing from inception to April 2022, without language restrictions. Randomized controlled trials (RCTs) that involve ACE for treating depression will be included. The primary outcome measures will include the response rate, Hamilton Depression Rating Scale (HAMD), Beck Depression Inventory (BDI), and Traditional Chinese Medicine Symptom Scale (TCMSSS). The secondary outcome measure will include Quality of Life scale score (QoL) and the incidence of adverse events. Results will be presented as risk ratios for dichotomous data and mean differences for continuous data. Two reviewers will independently conduct study selection, data extraction, and quality assessment. The methodological quality of eligible studies will be evaluated according to the criteria specified by the Cochrane Handbook for Systematic Reviews of Interventions (Version 5.1.0). Meta-analysis will be performed by RevMan 5.3 software. Discussion Due to the limitations, a safer, high-efficacy and non-pharmacological intervention with minimal side effects is required for treating depression. ACE has the advantages of longer-lasting effects, improved patient compliance, and reduced treatment costs. This protocol represents a meta-analysis and systematic review, aiming to present the current evidence regarding the efficacy and safety of ACE for depression. It seeks to provide clinicians with a theoretical basis and valuable references for complementary and alternative medicine therapies in their treatment approaches. Systematic review registration https://www.crd. york.ac. uk/PROSPERO/#recordDetails, Identifier CRD42022325966.
A method to determine the location of local mine earthquake source by using coal fracture electromagnetic signal

The accurate prediction of direction of coal or rock fracture source and the determination of major disaster area can make prevention measures of coal or rock dynamic disasters more effective.Based on the electromagnetic effect regularity of coal or rock deformation and fracture and the basic theory of electromagnetic wave,a method to determine the location of local mine earthquake source using the energy of electromagnetic signal is proposed. The effectiveness of the proposed method is verified through electromagnetic radiation directional receiving experiment in laboratory and field test of mine earthquake location in No.10mine of Pingdingshan Tian'an Coal Mining Corp.Ltd.Results show that Electromagnetic signal intensity varies with the stress changes.The electromagnetic signal energy of two orthogonal antennas is respectively in direct proportion to the cosine square of their receiving angles.Thus,the location of source can be calculated according to the proportional relationship.Furthermore,Electromagnetic signal location method is consistent with static index method in predicting risk of local area.The research results can provide basis for the determination of major disaster area of the coal and rock dynamic disasters.
Range Estimation and Range-Doppler Imaging Using Signed Measurements in LFMCW Radar

We consider range estimation and range-Doppler imaging using signed measurements of a radar system. Known time-varying thresholds are investigated for taking the signed measurements through one-bit sampling, and are compared with unknown dithering with known probability density function. The maximum likelihood (ML) approach is considered for signal parameter estimation. Since the ML algorithm is computationally prohibitive, a relaxation-based approach, referred to as the One-Bit RELAX algorithm, is used for signal parameter estimation. The conventional RELAX algorithm proposed for high-resolution sampling is also considered for comparison purposes. Moreover, a model-order selection tool, namely the Bayesian information criterion, is used to determine the number of scatterers within the scene of interest. Both numerical and experimental examples are provided to demonstrate the performance of the proposed approaches.
Estrogen-Dependent Epigenetic Regulation of Soluble Epoxide Estrogen-Dependent Epigenetic Regulation of Soluble Epoxide Hydrolase via DNA Methylation Hydrolase via DNA Methylation

To elucidate molecular mechanisms responsible for the sexually dimorphic phenotype of soluble epoxide hydrolase (sEH) expression, we tested the hypothesis that female-specific down-regulation of sEH expression is driven by estrogen-dependent methylation of the Ephx2 gene. Mesenteric arteries isolated from male, female, ovariectomized female (OV), and OV with estrogen replacement (OVE) mice, as well as the human cell line (HEK293T) were used. Methylation-specific PCR and bisulfite genomic sequencing analysis indicate significant increases in DNA/CG methylation in vessels of female and OVE compared with those of male and OV mice. The same increase in CG methylation was also observed in male vessels incubated with a physiological concentration of 17 β -estradiol (17 β -E 2 ) for 48 hours. All vessels that displayed increases in CG methylation were concomitantly associated with decreases in their Ephx2 mRNA and protein, suggesting a methylation-induced gene silencing. Transient transfection assays indicate that the activity of Ephx2 promoter-coding luciferase was significantly attenuated in HEK293T cells treated with 17 β -E 2 , which was prevented by additional treatment with an estrogen receptor antagonist (ICI). ChIP analysis indicates significantly reduced binding activities of transcription factors (including SP1, AP-1, and NF-κ B with their binding elements located in the Ephx2 promoter) in vessels of female mice and human cells treated with 17 β -E 2 , responses that were prevented by ICI and Decitabine (DNA methyltransferase inhibitor), respectively. In conclusion, estrogen/estrogen receptor-dependent methylation of the promoter of Ephx2 gene silences sEH expression, which is involved in specific transcription factor-directed regulatory pathways.