Miracle-dz/UniFAR-FTUs huggingface dataset:
A novel calculation strategy for optimized prediction of the reduction of electrochemical window at anode

The reduction of the electrochemical window (EW) of electrolytes plays a significant role in assessing their compatibility with the anode in lithium-ion batteries. However, the accurate calculation of the reduction of EW is still challenging due to missing the solvation effects, condensation effects, kinetic factors, and the passivation on anodes. The theoretical prediction of the intrinsic and apparent EW is confirmed by a comprehensive experimental analysis of ethylene carbonate-dimethyl carbonate (EC-DMC) electrolytes, combining linear sweep voltammetry (LSV) and gas chromatography-mass spectrometry (GC-MS). The proposed novel kinetic normal distribution theory model can quantitatively explain the current density from LSV and affirm acetaldehyde (MeCHO) as one of the primary reduction products of EC. The solvent effect restricts the intrinsic EW of EC-DMC without lithium salt to 2.6 V (vs. Li+/Li) arising from the Marcus–Gerischer theory and the passivation of MeCHO on the anode broadens the apparent EW to 0.3 V (vs. Li+/Li) arising from the normal distribution of the lowest unoccupied molecular orbital (LUMO) for MeCHO produced by thermal motion. In addition, the passivation on the anode depends intensively on the lithium salt, resulting in more complicated influences on the apparent EW.
Computational modeling of sigmoid functionally graded material (SFGM) plate

The first-order shear deformation theory (FSDT) was used to explore the natural frequency response of functionally graded piezoelectric plates subjected to static electrical and mechanical strain in this present study. A monomorph model for a functionally graded piezoelectric plate with material properties that change according to sigmoid law with respect to plate thickness has been considered. A three-dimensional finite element model with a free tetrahedral element mesh was created using COMSOL 4.2 Multiphysics® software, with each node having three degrees of freedom. Variations in the FGPM plate’s free vibration boundary conditions, composition, and geometry have all been investigated. In free vibration analysis, non-dimensional natural frequency of FGPM plate initially decreasing considerably and then remaining almost constant with the increase in volume fraction index when material property graded by power law. When material properties are varied by sigmoid law, with an increase in volume fraction index, the non-dimensional natural frequency of FGPM plates remains virtually constant. FGPM plates have a lower non-dimensional natural frequency if the thickness to width ratio is greater. Non-dimensional natural frequencies of Clamped-Clamped FGPM plates (C–C–C–C) are greater compared to Clamped-Free FGPM (C–F–C–F) and Simply Supported Free FGPM (S–F–S–F).
Probing the Circumgalactic Medium with Cosmic Microwave Background Polarization Statistical Anisotropy

As cosmic microwave background (CMB) photons traverse the universe, anisotropies can be induced via Thomson scattering (proportional to the electron density; optical depth) and inverse Compton scattering (proportional to the electron pressure; thermal Sunyaev–Zel’dovich effect). Measurements of anisotropy in optical depth τ and Compton y parameters are imprinted by the galaxies and galaxy clusters and are thus sensitive to the thermodynamic properties of the circumgalactic medium and intergalactic medium. We use an analytic halo model to predict the power spectrum of the optical depth (τ τ), the cross-correlation between the optical depth and the Compton y parameter (τ y), and the cross-correlation between the optical depth and galaxy clustering (τ g), and compare this model to cosmological simulations. We constrain the optical depths of halos at z ≲ 3 using a technique originally devised to constrain patchy reionization at a higher redshift range. The forecasted signal-to-noise ratio is 2.6, 8.5, and 13, respectively, for a CMB-S4-like experiment and a Vera C. Rubin Observatory–like optical survey. We show that a joint analysis of these probes can constrain the amplitude of the density profiles of halos to 6.5% and the pressure profiles to 13%. These constraints translate to astrophysical parameters, such as the gas mass fraction, f g, which can be constrained to 5.3% uncertainty at z ∼ 0. The cross-correlations presented here are complementary to other CMB and galaxy cross-correlations since they do not require spectroscopic galaxy redshifts and are another example of how such correlations are a powerful probe of the astrophysics of galaxy evolution.
The Variance of Fractional Partial Differential Equation of Distributed Order

The aim of this paper is to express variance of fractional partial differential equation of distributed order, where the Caputo fractional derivative is considered of order 1 0 ≤ <α . This expression is achieved by using a function transform, Fourier and Laplace transforms. In particular the asymptotic behavior of the fundamental solution and its variance are studied. As special case the variance of the fundamental solution of time fractional diffusion equation is obtained.
Future Biomarkers for Infection and Inflammation in Rheumatoid Arthritis

Abstract Rheumatoid arthritis (RA) increases the susceptibility to a variety of infections that are often difficult to diagnose and can be asymptomatic or symptoms are atypical. Usually, this is a great challenge for rheumatologists, because it is difficult to distinguish infection and aseptic inflammation at an early stage. Prompt diagnosis and treatment of bacterial infections in immunosuppressed individuals is critical for clinicians, and early exclusion of infection allows for specific treatment of inflammatory diseases and avoids the unnecessary use of antibiotics. However, for patients with clinically suspected infection, traditional laboratory markers are not specific for bacterial infection and cannot be used to distinguish outbreaks from infections. Therefore, new infection markers that can distinguish infection from underlying disease are urgently needed for clinical practice. Here, we review the novel biomarkers in RA patients with infection. These biomarkers include presepsin, serology and haematology, as well as neutrophils, T cells, and natural killer cells. Meanwhile, we discuss meaningful biomarkers that distinguish infection from inflammation and develop novel biomarkers for clinical applications, allowing clinicians to make better decisions when diagnosing and treating RA.
Evaluation of vacuum packaging for extending the shelf life of Sardinian fermented sausage

Salsiccia sarda or Sardinian fermented sausage is a traditional dry-fermented sausage included in the list of traditional food products of Sardinia (Italy). At the request of some producing plants, the possibility of extending the shelf life of the vacuum-packed product up to 120 days was evaluated. Manufacturing of 90 samples, representing 3 different batches of Sardinian fermented sausage was carried out in two producing plants (A and B). In the packaged product and subsequently every 30 days for four months (T0, T30, T60, T120), the following analyses were conducted on all samples: physicochemical characteristics, total aerobic mesophilic count, Enterobacteriaceae count, detection of Listeria monocytogenes, Salmonella spp. , mesophilic lactic acid bacteria, and coagulase-positive Staphylococci. Moreover, surfaces in contact and surfaces not in contact with food were sampled in both producing plants. Sensory profile analysis was also performed for every analysis time. At the end of the extended shelf life, pH values were equal to 5.90±0.11 (producing plant A) and 5.61±0.29 (producing plant B). Water activity mean values at T120 were 0.894±0.02 (producing plant A) and 0.875±0.01 (producing plant B). L. monocytogenes was detected in 73.3% (33/45) of the samples from producing plant A, with mean levels of 1.12±0.76 log10 CFU/g. In producing plant B, L. monocytogenes was never detected. Enterobacteriaceae were detected in 91.1% (41/45) of samples in producing plant A with mean values of 3.15±1.21 log10 CFU/g, and in 35.5% (16/45) samples in producing plant B samples with mean values of 0.72±0.86 log10 CFU/g. Salmonella and Staphylococcus aureus were never detected. Regarding environmental samples, the sites that were most contaminated by L. monocytogenes were the bagging table (contact surface) and processing room floor drains (non-contact surface) with a prevalence of 50% each (8/16 positive samples for both sampling sites). Sensory analysis results showed that at T30 the overall sensory quality was at its highest;moreover, the visual-tactile aspect, the olfactory characteristics, the gustatory aspects, and the texture showed significant differences in samples throughout the shelf life, with a decreased intensity at 120 days of storage. Overall, the quality and sensory acceptance of the vacuumpacked Sardinian fermented sausage was not affected until 120 days of shelf-life. However, the possible contamination by L. monocytogenes calls attention to the hygienic management of the entire technological process. The environmental sampling was confirmed as a useful verification tool during control.
Bayesian modeling of multivariate loss reserving data based on scale mixtures of multivariate normal distributions: estimation and case influence diagnostics

Abstract One of the most important problems in general insurance is estimating the loss reserve distribution. In this article, we develop Bayesian multivariate loss reserving models for cases where losses and random effects are assumed to be distributed under the scale mixtures of multivariate normal (SMMN) distributions. This class of distributions, which contains heavy-tailed multivariate distributions such as student’s t, Pearson type VII, variance-gamma, slash and contaminated normal distributions, can be often used for robust inferences; when the assumptions of normality become questionable. The hierarchical structure of the SMMN representation has the advantage that under a Bayesian paradigm, the parameter estimation is simplified by sampling from multivariate normal distribution using Markov Chain Monte Carlo (MCMC) methods. A Bayesian case deletion influence diagnostics based on q-divergence measures is also presented. Further, simulated and real data sets are analyzed, where we show that the models under the Pearson type VII and the variance-gamma distributions outperform the usual normal models.
Safety assessment of the process CCH CIRCULARPET, based on the NGR technology, used to recycle post‐consumer PET into food contact materials

Abstract The EFSA Panel on Food Contact Materials, Enzymes and Processing Aids (CEP) assessed the safety of the recycling process CCH CIRCULARPET (EU register number RECYC284), which uses the NGR technology. The input is washed and dried poly(ethylene terephthalate) (PET) flakes mainly originating from collected post‐consumer PET containers, with no more than 5% PET from non‐food consumer applications. The flakes are dried (step 2), melted in an extruder (step 3) and decontaminated during a melt‐state polycondensation step ■■■■■ (step 4). In step 5, the material is granulated. Having examined the challenge test provided, the Panel concluded that the melt‐state polycondensation (step 4) is critical in determining the decontamination efficiency of the process. The operating parameters to control the performance of the critical step are the pressure, the temperature, the residence time (depending on the mass and throughput of the melt) and the characteristics of the reactor. It was demonstrated that this recycling process is able to ensure that the level of migration of potential unknown contaminants into food is below the conservatively modelled migration of 0.1 μg/kg food. Therefore, the Panel concluded that the recycled PET obtained from this process is not of safety concern when used at up to 100% for the manufacture of materials and articles for contact with all types of foodstuffs, including drinking water, for long‐term storage at room temperature, with or without hotfill. The final articles made of this recycled PET are not intended to be used in microwave and conventional ovens and such uses are not covered by this evaluation.
Modulation of Immunological, Biochemical, and Histopathological Changes of Airway Remodeling by Withania somnifera in an Experimental Model of Allergic Asthma in Rats

Objectives Airway remodeling in asthma involves chronic inflammation associated with structural changes, which result in severe airflow limitation and very few therapeutic options. Thus, the present study was designed to experimentally evaluate the ameliorative effects of Withania somnifera (WS) root extract against Ovalbumin (OVA)-induced airway remodeling in a rat model of asthma. Methods Wistar rats were immunized (i.p) and challenged (aerosol) with ovalbumin (OVA), and the effects of WS extract were investigated on the development and progress of airway remodeling by assessing immunological, biochemical, and histological changes in these rats. Results OVA-immunization and challenge in rats resulted in significant increases in the levels of IL-13, 8-OhdG, TGF-β, hydroxyproline, and periostin in bronchoalveolar lavage fluid (BALF) and serum/lung homogenate compared to normal control (saline only) rats, and these changes were attenuated after WS extract (200 and 400 mg/kg), as well as dexamethasone (DEX, 1 mg/kg) pretreatments. Further, WS extract attenuated histopathological changes and maintained lung integrity. In herb-drug interactions, sub-threshold doses of WS extract and DEX showed synergistic effects on all parameters studied as compared to either form of monotherapy. Conclusion These results indicated that WS exerted significant protective effects against airway remodeling in the experimental model by modulating inflammatory and fibrotic cytokines, and could have the potential for developing a therapeutic alternative/adjunct for the treatment of airway remodeling of bronchial asthma.
In Silico Studies of Indole Derivatives as Antibacterial Agents

Objectives Molecular docking and QSAR studies of indole derivatives as antibacterial agents. Methods In this study, we used a multiple linear regressions (MLR) approach to construct a 2D quantitative structure activity relationship of 14 reported indole derivatives. It was performed on the reported antibacterial activity data of 14 compounds based on theoretical chemical descriptors to construct statistical models that link structural properties of indole derivatives to antibacterial activity. We have also performed molecular docking studies of same compounds by using Maestro module of Schrodinger. A set the molecular descriptors like hydrophobic, geometric, electronic and topological characters were calculated to represent the structural features of compounds. The conventional antibiotics sultamicillin and ampicillin were not used in the model development since their structures are different from those of the created compounds. Biological activity data was first translated into pMIC values (i.e. –log MIC) and used as a dependent variable in QSAR investigation. Results Compounds with high electronic energy and dipole moment were effective antibacterial agents against S. aureus, indole derivatives with lower κ2 values were excellent antibacterial agents against MRSA standard strain, and compounds with lower R value and a high 2χv value were effective antibacterial agents against MRSA isolate. Conclusion Compounds 12 and 2 showed better binding score against penicillin binding protein 2 and penicillin binding protein 2a respectively.
Cardiovascular disease risk in women with hyperandrogenism, oligomenorrhea/menstrual irregularity or polycystic ovaries (components of polycystic ovary syndrome): a systematic review and meta-analysis

Abstract Aims Prior meta-analyses indicate polycystic ovary syndrome (PCOS) is associated with cardiovascular diseases (CVDs), but have high statistical heterogeneity, likely because PCOS is a heterogenous syndrome diagnosed by having any two of the three components: hyperandrogenism, oligomenorrhea/menstrual irregularity or polycystic ovaries. Several studies report higher risk of CVDs from individual PCOS components, but a comprehensive assessment of how each component contributes to CVD risk is lacking. This study aims to assess CVD risk for women with one of the PCOS components. Methods and results A systematic review and meta-analysis of observational studies was conducted. PubMed, Scopus, and Web of Science were searched without restrictions in July 2022. Studies meeting inclusion criteria examined the association between PCOS components and risk of a CVD. Two reviewers independently assessed abstracts and full-text articles, and extracted data from eligible studies. Where appropriate, relative risk (RR) and 95% confidence interval (CI) were estimated by random-effects meta-analysis. Statistical heterogeneity was assessed using the I2 statistic. Twenty-three studies, including 346 486 women, were identified. Oligo-amenorrhea/menstrual irregularity was associated with overall CVD (RR = 1.29, 95%CI = 1.09–1.53), coronary heart disease (CHD) (RR = 1.22, 95%CI = 1.06–1.41), and myocardial infarction (MI) (RR = 1.37, 95%CI = 1.01–1.88) but not cerebrovascular disease. These results were broadly consistent even after further adjustment for obesity. There was mixed evidence for the role of hyperandrogenism in CVDs. No studies examined polycystic ovaries as an independent exposure for CVD risk. Conclusion Oligo-amenorrhea/menstrual irregularity is associated with greater risk of overall CVD, CHD, and MI. More research is needed to assess the risks associated with hyperandrogenism or polycystic ovaries.
Comparative analysis of machine learning approaches for predicting respiratory virus infection and symptom severity

Respiratory diseases are among the major health problems causing a burden on hospitals. Diagnosis of infection and rapid prediction of severity without time-consuming clinical tests could be beneficial in preventing the spread and progression of the disease, especially in countries where health systems remain incapable. Personalized medicine studies involving statistics and computer technologies could help to address this need. In addition to individual studies, competitions are also held such as Dialogue for Reverse Engineering Assessment and Methods (DREAM) challenge which is a community-driven organization with a mission to research biology, bioinformatics, and biomedicine. One of these competitions was the Respiratory Viral DREAM Challenge, which aimed to develop early predictive biomarkers for respiratory virus infections. These efforts are promising, however, the prediction performance of the computational methods developed for detecting respiratory diseases still has room for improvement. In this study, we focused on improving the performance of predicting the infection and symptom severity of individuals infected with various respiratory viruses using gene expression data collected before and after exposure. The publicly available gene expression dataset in the Gene Expression Omnibus, named GSE73072, containing samples exposed to four respiratory viruses (H1N1, H3N2, human rhinovirus (HRV), and respiratory syncytial virus (RSV)) was used as input data. Various preprocessing methods and machine learning algorithms were implemented and compared to achieve the best prediction performance. The experimental results showed that the proposed approaches obtained a prediction performance of 0.9746 area under the precision-recall curve (AUPRC) for infection (i.e., shedding) prediction (SC-1), 0.9182 AUPRC for symptom class prediction (SC-2), and 0.6733 Pearson correlation for symptom score prediction (SC-3) by outperforming the best leaderboard scores of Respiratory Viral DREAM Challenge (a 4.48% improvement for SC-1, a 13.68% improvement for SC-2, and a 13.98% improvement for SC-3). Additionally, over-representation analysis (ORA), which is a statistical method for objectively determining whether certain genes are more prevalent in pre-defined sets such as pathways, was applied using the most significant genes selected by feature selection methods. The results show that pathways associated with the ‘adaptive immune system’ and ‘immune disease’ are strongly linked to pre-infection and symptom development. These findings contribute to our knowledge about predicting respiratory infections and are expected to facilitate the development of future studies that concentrate on predicting not only infections but also the associated symptoms.
Validation of the shotgun metabarcoding approach for comprehensively identifying herbal products containing plant, fungal, and animal ingredients

Identifying plant, fungal, and animal ingredients in a specific mixture remains challenging during the limitation of PCR amplification and low specificity of traditional methods. Genomic DNA was extracted from mock and pharmaceutical samples. Four type of DNA barcodes were generated from shotgun sequencing dataset with the help of a local bioinformatic pipeline. Taxa of each barcode was assigned by blast to TCM-BOL, BOLD, and GenBank. Traditional methods including microscopy, thin layer chromatography (TLC), and high-performance liquid chromatography (HPLC) were carried out according to Chinese pharmacopoeia. On average, 6.8 Gb shotgun reads were sequenced from genomic DNA of each sample. Then, 97, 11, 10, 14, and one operational taxonomic unit (OTU) were generated for ITS2, psbA-trnH, rbcL, matK, and COI, respectively. All the labeled ingredients including eight plant, one fungal, and one animal species were successfully detected in both the mock and pharmaceutical samples, in which Chebulae Fructus, Poria, and Fritilariae Thunbergia Bulbus were identified via mapping reads to organelle genomes. In addition, four unlabeled plant species were detected from pharmaceutical samples, while 30 genera of fungi, such as Schwanniomyces, Diaporthe, Fusarium were detected from mock and pharmaceutical samples. Furthermore, the microscopic, TLC, and HPLC analysis were all in accordance with the standards stipulated by Chinese Pharmacopoeia. This study indicated that shotgun metabarcoding could simultaneously identified plant, fungal, and animal ingredients in herbal products, which has the ability to serve as a valuable complement to traditional methods.