Miracle-dz/UniFAR-FTUs huggingface dataset:
The expression profiles of circular RNAs and competing endogenous RNA networks in intrahepatic cholangiocarcinoma

Introduction: Intrahepatic cholangiocarcinoma (iCCA) is a heterogeneous entity with diverse etiologies, morphologies, and clinical outcomes, but our knowledge of its epidemiology and carcinogenesis is very limited. Materials and methods: The expression patterns of circRNAs were explored in iCCA tissues and corresponding adjacent normal ones, denoted by (iCCA) and (iCCAP), respectively, using high-throughput sequencing. Results: A total of 117 differential expressed (DE) circRNAs were identified. Based on the parental transcripts of circRNAs, these DE circRNAs were related to several important GO terms and were enriched in important pathways. Two circRNA-mediated ceRNA networks were constructed and many important metabolic pathways related to mRNAs were regulated by DE circRNAs via miRNAs. Conclusion: Our study revealed the DE circRNAs in the iCCA tissues compared with iCCAP ones, suggesting that circRNAs may play crucial roles in the pathogenesis of iCCA.
On the linearity of tracer bias around voids

The large-scale structure of the Universe can be observed only via luminous tracers of the dark matter. However, the clustering statistics of tracers are biased and depend on various properties, such as their host-halo mass and assembly history. On very large scales, this tracer bias results in a constant offset in the clustering amplitude, known as linear bias. Towards smaller non-linear scales, this is no longer the case and tracer bias becomes a complicated function of scale and time. We focus on tracer bias centred on cosmic voids, i.e. depressions of the density field that spatially dominate the Universe. We consider three types of tracers: galaxies, galaxy clusters and active galactic nuclei, extracted from the hydrodynamical simulation Magneticum Pathfinder. In contrast to common clustering statistics that focus on auto-correlations of tracers, we find that void-tracer cross-correlations are successfully described by a linear bias relation. The tracer-density profile of voids can thus be related to their matter-density profile by a single number. We show that it coincides with the linear tracer bias extracted from the large-scale auto-correlation function and expectations from theory, if sufficiently large voids are considered. For smaller voids we observe a shift towards higher values. This has important consequences on cosmological parameter inference, as the problem of unknown tracer bias is alleviated up to a constant number. The smallest scales in existing data sets become accessible to simpler models, providing numerous modes of the density field that have been disregarded so far, but may help to further reduce statistical errors in constraining cosmology.
Cognitive tasks propagate the neural entrainment in response to a visual 40 Hz stimulation in humans

Introduction Alzheimer's disease is one of the great challenges in the coming decades, and despite great efforts, a widely effective disease-modifying therapy in humans remains elusive. One particular promising non-pharmacological therapy that has received increased attention in recent years is based on the Gamma ENtrainment Using Sensory stimulation (GENUS), a high-frequency neural response elicited by a visual and/or auditory stimulus at 40 Hz. While this has shown to be effective in animal models, studies on human participants have reported varying success. The current work hypothesizes that the varying success in humans is due to differences in cognitive workload during the GENUS sessions. Methods We recruited a cohort of 15 participants who underwent a scalp-EEG recording as well as one epilepsy patient who was implanted with 50 subdural surface electrodes over temporo-occipital and temporo-basal cortex and 14 depth contacts that targeted the hippocampus and insula. All participants completed several GENUS sessions, in each of which a different cognitive task was performed. Results We found that the inclusion of a cognitive task during the GENUS session not only has a positive effect on the strength and extent of the gamma entrainment, but also promotes the propagation of gamma entrainment to additional neural areas including deep ones such as hippocampus which were not recruited when no cognitive task was required from the participants. The latter is of particular interest given that the hippocampal complex is considered to be one of the primary targets for AD therapies. Discussion This work introduces a possible improvement strategy for GENUS therapy that might contribute to increasing the efficacy of the therapy or shortening the time needed for the positive outcome.
Multiport converters for incorporating solar photovoltaic system with battery storage: A pilot survey towards modern influences, challenges and future scenarios

The increasing significance of renewable power systems with diverse sources has produced an unexpected demand for electronic converters to integrate and simultaneously control, various energy resources, and storage devices. The voltage-current characteristics and the voltage levels of storage, as well as energy generating systems, are naturally diverse from those of loads. Hence, converters are employed to transform the energy from the renewable power plants to meet the total power demand, to enable the renewable energy system to use Maximum Power Point Tracking algorithm, to enhance the dynamic and static characteristics of the system, and to integrate the energy storage devices to resolve the issue of the irregularity of the load demand and unstable characteristics of the renewable sources. The implementation of a Multiport DC/DC converter (MDC) is a viable solution to increase the system efficiency and power density. The conventional MDC contains 1) DC unidirectional input ports to connect the renewable energy generating system; 2) two-way input ports to interface battery like storage devices; and 3) output ports to interface the load. Recently, numerous multiport converter configurations have been developed and described in the literature. Each of these reported MDCs has distinct architecture and working mechanism, which leads to a diverse level of intricacies, different component count, different performance, and reliability. This paper reviews various configurations of MDCs that have been introduced by different research communities to integrate solar energy with Battery Storage System (BSS). Different MDCs topologies such as partially-isolated, isolated, non-isolated configurations are discussed according to their physical structures and other aspects. This article can be employed as a guideline to select the appropriate configuration to match the certain condition of a system.
Application of multi-regression machine learning algorithms to solve ocean water mass mixing in the Atlantic Ocean

The distribution of any non-conservative variable in the deep open ocean results from the circulation and mixing of water masses (WMs) of contrasting origin and from the initial preformed composition, modified during ongoing simultaneous biological and/or geochemical processes. Estimating the contribution of the WMs composing a sample is useful to trace the distribution of each water mass and to quantitatively separate the physical (mixing) and biogeochemical components of the variability of any, non- conservative variable (e.g., dissolved organic carbon, prokaryote biomass) in the ocean. Other than potential temperature and salinity, additional semi-conservative and non-conservative variables have been used to solve the mixing of more than three water masses using Optimum Multi-Parameter (OMP) approaches. Successful application of an OMP analysis requires knowledge of the characteristics of the water masses in their source regions as well as their circulation and mixing patterns. Here, we propose the application of multi-regression machine learning models to solve ocean water mass mixing. The models tested were trained using the solutions from OMP analyses previously applied to samples from cruises in the Atlantic Ocean. Extremely Randomized Trees algorithm yielded the highest score (R2 = 0.9931; mse = 0.000227). Our model allows solving the mixing of water masses in the Atlantic Ocean using potential temperature, salinity, latitude, longitude and depth. Therefore, basic hydrographic data collected during typical research cruises or autonomous systems can be used as input variables and provide results in real time. The model can be fed with new solutions from compatible OMP analyses as well as with new water masses not previously considered in it. Our tool will provide knowledge on water mass composition and distribution to a broader community of marine scientists not specialized in OMP analysis and/or in the oceanography of the studied area. This will allow a quantitative analysis of the effect of water mass mixing on the variables or processes under study.
Derivation of Hydrodynamics for Multi-Relaxation Time Lattice Boltzmann using the Moment-Approach

A general analysis of the hydrodynamic limit of multi-relaxation time lattice Boltzmann models is presented. We examine multi-relaxation time BGK collision operators that are constructed similarly to those for the MRT case, however, without explicitly moving into a moment space representation. The corresponding 'moments' are derived as left eigenvectors of said collision operator in velocity space. Consequently we can, in a representation independent of the chosen base velocity set, generate the conservation equations. We find a significant degree of freedom in the choice of the collision matrix and the associated basis which leaves the collision operator invariant. Therefore we can explain why MRT implementations in the literature reproduce identical hydrodynamics despite being based on different orthogonalization relations.
Green finance, green energy, and green revolution: An impulse response function of food security in developing economies

Recently, it has been observed that fossil fuel utilization increased to manage the issue of food insecurity; however, it has increased the risk of environmental degradation and land availability. Therefore, the role of a farmer is to ensure food security (FS). With the assistance of the green revolution (GR), green finance (GF) and green energy have increased manifold. For this purpose, the current study utilized a time-series dataset between 2071 and 2019 to evaluate the relationship between green finance, green energy, and the green revolution with food security. This objective has been considered with modern econometric methods, including the Johansen cointegration model and impulse response. As a result, the Johansen model affirmed cointegration between the green revolution, green finance, and green energy with food security. According to the results, food security will rise by 0.17%, 2.05%, 0.006%, and 0.023%, with a 1% increase in fossil fuel utilization, green finance, improved seeds, and water availability, respectively. Furthermore, based on the diagnostic statistics, the Johansen model’s credibility, stability, and reliability were corrected. In light of these findings and the impulse response function, all relevant factors will favor future food security. Finally, this study suggested that the public and commercial sectors should invest significantly in R&D to produce organic chemicals and agricultural methods that preserve soil fertility and reduce environmental degradation.
A multifaceted and feasible prognostic model of amino acid metabolism-related genes in the immune response and tumor microenvironment of head and neck squamous cell carcinomas

We investigated the role of amino acid metabolism (AAM) in head and neck squamous cell carcinoma (HNSCC) tissues to explore its prognostic value and potential therapeutic strategies. A risk score based on four AAM-related genes (AMG) was constructed that could predict the prognosis of HNSCC. These four genes were up-regulated in HNSCC tissues and might act as oncogenes. Internal validation in The Cancer Genome Atlas (TCGA) by bootstrapping showed that patients with high-risk scores had a poorer prognosis than patients with low-risk scores, and this was confirmed in the Gene Expression Omnibus (GEO) cohort. There were also differences between the high-risk and low-risk groups in clinical information and different anatomical sites such as age, sex, TNM stage, grade stage, surgery or no surgery, chemotherapy, radiotherapy, no radiotherapy, neck lymph node dissection or not, and neck lymphovascular invasion, larynx, overlapping lesion of lip, and oral cavity and pharynx tonsil of overall survival (OS). Immune-related characteristics, tumor microenvironment (TME) characteristics, and immunotherapy response were significantly different between high- and low-risk groups. The four AMGs were also found to be associated with the expression of markers of various immune cell subpopulations. Therefore, our comprehensive approach revealed the characterization of AAM in HNSCC to predict prognosis and guide clinical therapy.
Comprehensive analysis of key m5C modification-related genes in type 2 diabetes

Background: 5-methylcytosine (m5C) RNA methylation plays a significant role in several human diseases. However, the functional role of m5C in type 2 diabetes (T2D) remains unclear. Methods: The merged gene expression profiles from two Gene Expression Omnibus (GEO) datasets were used to identify m5C-related genes and T2D-related differentially expressed genes (DEGs). Least-absolute shrinkage and selection operator (LASSO) regression analysis was performed to identify optimal predictors of T2D. After LASSO regression, we constructed a diagnostic model and validated its accuracy. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted to confirm the biological functions of DEGs. Gene Set Enrichment Analysis (GSEA) was used to determine the functional enrichment of molecular subtypes. Weighted gene co-expression network analysis (WGCNA) was used to select the module that correlated with the most pyroptosis-related genes. Protein-protein interaction (PPI) network was established using the STRING database, and hub genes were identified using Cytoscape software. The competitive endogenous RNA (ceRNA) interaction network of the hub genes was obtained. The CIBERSORT algorithm was applied to analyze the interactions between hub gene expression and immune infiltration. Results: m5C-related genes were significantly differentially expressed in T2D and correlated with most T2D-related DEGs. LASSO regression showed that ZBTB4 could be a predictive gene for T2D. GO, KEGG, and GSEA indicated that the enriched modules and pathways were closely related to metabolism-related biological processes and cell death. The top five genes were identified as hub genes in the PPI network. In addition, a ceRNA interaction network of hub genes was obtained. Moreover, the expression levels of the hub genes were significantly correlated with the abundance of various immune cells. Conclusion: Our findings may provide insights into the molecular mechanisms underlying T2D based on its pathophysiology and suggest potential biomarkers and therapeutic targets for T2D.
SeDeM tool-driven full factorial design for osmotic drug delivery of tramadol HCl: Formulation development, physicochemical evaluation, and in-silico PBPK modeling for predictive pharmacokinetic evaluation using GastroPlus™

The study is based on using SeDeM expert system in developing controlled-release tramadol HCl osmotic tablets and its in-silico physiologically based pharmacokinetic (PBPK) modeling for in-vivo pharmacokinetic evaluation. A Quality by Design (QbD) based approach in developing SeDEM-driven full factorial osmotic drug delivery was applied. A 24 Full-factorial design was used to make the trial formulations of tramadol HCl osmotic tablets using NaCl as osmogen, Methocel K4M as rate controlling polymer, and avicel pH 101 as diluent. The preformulation characteristics of formulations (F1-F16) were determined by applying SeDeM Expert Tool. The formulation was optimized followed by in-vivo predictive pharmacokinetic assessment using PBPK “ACAT” model of GastroPlus™. The FTIR results showed no interaction among the ingredients. The index of good compressibility (ICG) values of all trial formulation blends were ≥5, suggesting direct compression is the best-suited method. Formulation F3 and F4 were optimized based on drug release at 2, 10, and 16 h with a zero-order kinetic release (r 2 = 0.992 and 0.994). The SEM images confirmed micropores formation on the surface of the osmotic tablet after complete drug release. F3 and F4 were also stable (shelf life 29.41 and 23.46 months). The in vivo simulation of the pharmacokinetics of the PBPK in-silico model revealed excellent relative bioavailability of F3 and F4 with reference to tramadol HCl 50 mg IR formulations. The SeDeM expert tool was best utilized to evaluate the compression characteristics of selected formulation excipients and their blends for direct compression method in designing once-daily osmotically controlled-release tramadol HCl tablets. The in-silico GastroPlus™ PBPK modeling provided a thorough pharmacokinetic assessment of the optimized formulation as an alternative to tramadol HCl in vivo studies.
Validation of in silico biomarkers for drug screening through ordinal logistic regression

Since the Comprehensive in vitro Proarrhythmia Assay (CiPA) initiation, many studies have suggested various in silico features based on ionic charges, action potentials (AP), or intracellular calcium (Ca) to assess proarrhythmic risk. These in silico features are computed through electrophysiological simulations using in vitro experimental datasets as input, therefore changing with the quality of in vitro experimental data; however, research to validate the robustness of in silico features for proarrhythmic risk assessment of drugs depending on in vitro datasets has not been conducted. This study aims to verify the availability of in silico features commonly used in assessing the cardiac toxicity of drugs through an ordinal logistic regression model and three in vitro datasets measured under different experimental environments and with different purposes. We performed in silico drug simulations using the Tomek-Ohara Rudy (ToR-ORD) ventricular myocyte model and computed 12 in silico features comprising six AP features, four Ca features, and two ion charge features, which reflected the effect and characteristics of each in vitro data for CiPA 28 drugs. We then compared the classific performances of ordinal logistic regressions according to these 12 in silico features and used in vitro datasets to validate which in silico feature is the best for assessing the proarrhythmic risk of drugs at high, intermediate, and low levels. All 12 in silico features helped determine high-risky torsadogenic drugs, regardless of the in vitro datasets used in the in silico simulation as input. In the three types of in silico features, AP features were the most reliable for determining the three Torsade de Pointes (TdP) risk standards. Among AP features, AP duration at 50% repolarization (APD50) was the best when individually using in silico features per in vitro dataset. In contrast, the AP repolarization velocity (dVm/dtMax_repol) was the best when merging all in silico features computed through three in vitro datasets.
Natural variation in growth and leaf ion homeostasis in response to salinity stress in Panicum hallii

Soil salinity can negatively impact plants growth, development and fitness. Natural plant populations restricted to coastal environments may evolve in response to saline habitats and therefore provide insights into the process of salinity adaptation. We investigated the growth and physiological responses of coastal and inland populations of Panicum hallii to experimental salinity treatments. Coastal genotypes demonstrated less growth reduction and superior ion homeostasis compared to the inland genotypes in response to saline conditions, supporting a hypothesis of local adaptation. We identified several QTL associated with the plasticity of belowground biomass, leaf sodium and potassium content, and their ratio which underscores the genetic variation present in this species for salinity responses. Genome-wide transcriptome analysis in leaf and root tissue revealed tissue specific overexpression of genes including several cation transporters in the coastal genotype. These transporters mediate sodium ion compartmentalization and potassium ion retention and thus suggests that maintenance of ionic homeostasis of the coastal genotypes might be due to the regulation of these ion transporters. These findings contribute to our understanding of the genetics and molecular mechanisms of salinity adaptation in natural populations, and widens the scope for genetic manipulation of these candidate genes to design plants more resilient to climate change.
Andrographolide contributes to spinal cord injury repair via inhibition of apoptosis, oxidative stress and inflammation

Background: Spinal cord injury (SCI) is a common disorder of the central nervous system with considerable socio-economic burden. Andrographolide (Andro), the main active component of Andrographis paniculata, has exhibited neuroprotective effects in different models of neurological diseases. The aim of this study was to evaluate the neuroprotective effects of Andro against SCI and explore the related mechanisms. Methods: SCI was induced in rats by the Allen method, and the modeled animals were randomly divided into sham-operated, SCI, SCI + normal saline (NS) and SCI + Andro groups. The rats were injected intraperitoneally with Andro (1 mg/kg) or the same volume of NS starting day one after the establishment of the SCI model for 28 consecutive days. Post-SCI tissue repair and functional recovery were evaluated by measuring the spinal cord water content, footprint tests, Basso-Beattie-Bresnahan (BBB) scores, hematoxylin-eosin (HE) staining and Nissl staining. Apoptosis, oxidative stress and inflammation, as well as axonal regeneration and remyelination were analyzed using suitable markers. The in vitro model of SCI was established by treating cortical neurons with H2O2. The effects of Andro on apoptosis, oxidative stress and inflammation were evaluated as indicated. Results: Andro treatment significantly improved tissue repair and functional recovery after SCI by reducing apoptosis, oxidative stress and inflammation through the nuclear factor E2-related factor 2/heme oxygenase-1 (Nrf-2/HO-1) and nuclear factor-kappa B (NF-κB) signaling pathways. Furthermore, Andro treatment promoted M2 polarization of the microglial cells and contributed to axonal regeneration and remyelination to improve functional recovery after SCI. In addition, Andro also attenuated apoptosis, oxidative stress and inflammation in H2O2-stimulated cortical neurons in vitro. Conclusion: Andro treatment alleviated SCI by reducing apoptosis, oxidative stress and inflammation in the injured tissues and cortical neurons, and promoted axonal regeneration and remyelination for functional recovery.