Miracle-dz/UniFAR-FTUs huggingface dataset:
Electron temperature measurement by a helium line intensity ratio method in helicon plasmas

Electron temperature measurements in helicon plasmas are difficult. The presence of intense rf fields in the plasma complicates the interpretation of Langmuir probe measurements. Furthermore, the non-negligible ion temperature in the plasma considerably shortens the lifetime of conventional Langmuir probes. A spectroscopic technique based on the relative intensities of neutral helium lines is used to measure the electron temperature in the HELIX (Hot hELicon eXperiment) plasma [P. A. Keiter et al., Phys. Plasmas 4, 2741 (1997)]. This nonintrusive diagnostic is based on the fact that electron impact excitation rate coefficients for helium singlet and triplet states differ as a function of the electron temperature. The different aspects related to the validity of this technique to measure the electron temperature in rf generated plasmas are discussed in this paper. At low plasma density (ne⩽1011 cm−3), this diagnostic is believed to be very reliable since the population of the emitting level can be easily e.
Characterization of the Ensemble of Lignin-Remodeling DyP-Type Peroxidases from Streptomyces coelicolor A3(2)

Lignin is Nature’s major source of aromatic chemistry and is by many seen as the green entry-point alternative to the fossil-based chemical industry. Due to its chemically recalcitrant structure, the utilization of lignin is challenging, wherein enzymes might be the key to overcome this challenge. Here, we focus on the characterization of dye-decolorizing peroxidases from Streptomyces coelicolor A3(2) (ScDyPs) in the context of enzymatic modification of organosolv lignins from aspen and Miscanthus × giganteus. In this study, we show that the ScDyPB can remodel organosolv lignins from grassy biomass, leading to higher molecular weight species, while ScDyPAs can deconstruct hardwood lignin, leading to an overall reduction in its molecular weight. Additionally, we show that ScDyPB is effective in polymerizing low-molecular-weight phenolics, leading to their removal from the solution.
Enhancement of terahertz electromagnetic wave emission from an undoped GaAs∕n-type GaAs epitaxial layer structure

We have investigated the emission of the terahertz electromagnetic wave from an undoped GaAs (200nm)∕n-type GaAs (3μm) epitaxial layer structure (i-GaAs∕n-GaAs structure), where the doping concentration of the n-GaAs layer is 3×1018cm−3. It is found that the first-burst amplitude of terahertz wave of the i-GaAs∕n-GaAs sample is remarkably larger than that of a n-GaAs crystal, which means that the i-GaAs layer enhances the terahertz emission intensity. The first-burst amplitude of the i-GaAs∕n-GaAs sample, by tuning the pump-beam energy to the higher energy side, exceeds that of an i-InAs crystal that is known as one of the most intense terahertz emitters. We, therefore, conclude that the i-GaAs∕n-GaAs structure is useful to obtain intense terahertz emission.
Measurable Residual Disease (MRD) as a Surrogate Efficacy-Response Biomarker in AML

In acute myeloid leukemia (AML) many patients experience relapse, despite the achievement of morphological complete remission; therefore, conventional morphologic criteria are currently considered inadequate for assessing the quality of the response after treatment. Quantification of measurable residual disease (MRD) has been established as a strong prognostic marker in AML and patients that test MRD negative have lower relapse rates and better survival than those who test positive. Different techniques, varying in their sensitivity and applicability to patients, are available for the measurement of MRD and their use as a guide for selecting the most optimal post-remission therapy is an area of active investigation. Although still controversial, MRD prognostic value promises to support drug development serving as a surrogate biomarker, potentially useful for accelerating the regulatory approval of new agents. In this review, we will critically examine the methods used to detect MRD and its potential role as a study endpoint.
Prediction and sensitivity analysis of self compacting concrete slump flow by random forest algorithm

Self-compacting concrete (SCC) is a construction material with many advantages, including high performance and the capacity to self-compact without mechanical vibration. As a result, SCC is widely used in construction, especially at locations where concrete structures are difficult to construct. Filling ability is one of the three basic requirements that must be met when designing the SCC mix. The slump flow (SF) is used to determine the SCC mixture's filling capacity. As a result, it is critical to estimate this number fast and precisely. The purpose of this study is to propose the use of a random forest (RF) model to predict the SF of SCC and to assess the effect of input parameters on output parameters. The study constructed the RF model using a dataset of 507 experimental results collected, which is the biggest data collection compared to previous studies on this subject. Additionally, a 10-fold cross-validation approach is used to improve the model's prediction performance. As a result, the performance assessment criteria for the testing dataset have values of RMSE = 59.5664 mm, MAE = 32.4483 mm, and R = 0.8614, respectively. This result shows that the RF model is an effective tool in predicting the SF of SCC.
Network Pharmacological Analysis of a New Herbal Combination Targeting Hyperlipidemia and Efficacy Validation In Vitro

The network pharmacology (NP) approach is a valuable novel methodology for understanding the complex pharmacological mechanisms of medicinal herbs. In addition, various in silico analysis techniques combined with the NP can improve the understanding of various issues used in natural product research. This study assessed the therapeutic effects of Arum ternata (AT), Poria cocos (PC), and Zingiber officinale (ZO) on hyperlipidemia after network pharmacologic analysis. A protein–protein interaction (PPI) network of forty-one key targets was analyzed to discover core functional clusters of the herbal compounds. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and gene ontology (GO) term enrichment analysis identified significant categories of hypolipidemic mechanisms. The STITCH database indicated a high connection with several statin drugs, deduced by the similarity in targets. AT, PC, and ZO regulated the genes related to the energy metabolism and lipogenesis in HepG2 cells loaded with free fatty acids (FFAs). Furthermore, the mixture of three herbs had a combinational effect. The herbal combination exerted superior efficacy compared to a single herb, particularly in regulating acetyl-CoA carboxylase (ACC) and carnitine palmitoyltransferase 1 (CPT-1). In conclusion, the network pharmacologic approach was used to assess potential targets of the herbal combination for treatment. Experimental data from FFA-induced HepG2 cells suggested that the combination of AT, PC, and ZO might attenuate hyperlipidemia and its associated hepatic steatosis.
Unsupervised Domain Adaptation Semantic Segmentation of High-Resolution Remote Sensing Imagery With Invariant Domain-Level Prototype Memory

Semantic segmentation is a key technique involved in automatic interpretation of high-resolution remote sensing (HRS) imagery and has drawn much attention in the remote sensing community. Deep convolutional neural networks (DCNNs) have been successfully applied to the HRS imagery semantic segmentation task due to their hierarchical representation ability. However, the heavy dependence on a large number of training data with dense annotation and the sensitiveness to the variation of data distribution severely restrict the potential application of DCNNs for the semantic segmentation of HRS imagery. This study proposes a novel unsupervised domain adaptation semantic segmentation network (MemoryAdaptNet) for the semantic segmentation of HRS imagery. MemoryAdaptNet constructs an output space adversarial learning scheme to bridge the domain distribution discrepancy between the source domain and the target domain and to narrow the influence of domain shift. Specifically, we embed an invariant feature memory module to store invariant domain-level prototype information because the features obtained from adversarial learning only tend to represent the variant feature of current limited inputs. This module is integrated by a category attention-driven invariant domain-level memory aggregation module to current pseudo-invariant feature for further augmenting the representations. An entropy-based pseudo label filtering strategy is used to update the memory module with high-confident pseudo-invariant feature of current target images. Extensive experiments under three cross-domain tasks indicate that our proposed MemoryAdaptNet is remarkably superior to the state-of-the-art methods. Our code is available at https://github.com/RS-CSU/MemoryAdaptNet-master.
SOLAR X-RAY JETS, TYPE-II SPICULES, GRANULE-SIZE EMERGING BIPOLES, AND THE GENESIS OF THE HELIOSPHERE

From Hinode observations of solar X-ray jets, Type-II spicules, and granule-size emerging bipolar magnetic fields in quiet regions and coronal holes, we advocate a scenario for powering coronal heating and the solar wind. In this scenario, Type-II spicules and Alfvén waves are generated by the granule-size emerging bipoles (EBs) in the manner of the generation of X-ray jets by larger magnetic bipoles. From observations and this scenario, we estimate that Type-II spicules and their co-generated Alfvén waves carry into the corona an area-average flux of mechanical energy of ∼7 × 105 erg cm−2 s−1. This is enough to power the corona and solar wind in quiet regions and coronal holes, and therefore indicates that the granule-size EBs are the main engines that generate and sustain the entire heliosphere.
Content-addressability and learning in neural networks

The content-addressability of patterns stored in Ising-spin neural network models with symmetric interactions is studied. Numerical results from simulations on the ICL distributed array processor (DAP) involving systems with up to 2048 neurons are presented. Behaviour consistent with finite-size scaling, characteristic of a first-order phase transition, is shown to be exhibited by the basins of attraction of the stored patterns both in the case of the Hopfield model and for systems using a local iterative learning algorithm designed to optimise the basins of attraction. Estimates are obtained for the critical minimum overlaps which an input pattern must have with a stored pattern in order to successfully retrieve it.
Dynamo regime transition among Sun-like stars in M 34 - A time evolution model of X-ray activity on the main sequence

Context. The X-ray emission from late-type stars in open clusters exhibits two kinds of dependences on stellar rotation. While fast rotators have a relatively constant X-ray emission level, slower rotators show a decline of their X-ray emission with decreasing rotation rate. The physical significance of the transition between these two X-ray emission regimes is a matter of debate. During the ∼500 Myr period of stellar evolution that separates the Pleiades from the Hyades, late-type stars such as those present in the M 34 open cluster undergo significant changes in their rotation rates. These could affect the dynamo processes in their interiors, possibly altering their surface magnetic fluxes. Aims. The purpose of the present study is to look for the X-ray signatures of such possible modifications, to search for correlations with rotation and stellar parameters, and to propose a parameterization of the X-ray activity evolution on the main sequence. The aim is to provide observational constraints on the time evolution of dynamo processes in the interiors of late-type stars. Methods. The distributions of stellar X-ray luminosities vs. rotation periods and Rossby numbers of a sample of M 34 late-type stars are compared with rotation-activity relationships established in a large sample of cluster members and field stars. A model of X-ray activity evolution is developed by combining an X-ray activity-rotation relationship with a recent model of stellar rotation evolution on the main sequence. The distribution of stellar X-ray luminosities in M 34 is compared with this model. Results. A correlation is observed between the saturated and non-saturated regime of X-ray emission and the C- and I-rotational sequences that have been observed in M 34 from extensive rotational periods surveys. M 34 sample stars show a steep transition in X-ray to bolometric luminosity ratio between the C-sequence and gap stars that emit close to the 10 −3 saturation level, and the I-sequence stars, whose LX/Lbol ratio is significantly lower for similar values of the Rossby number. A comparison between X-ray emission vs. mass distribution in M 34 and the X-ray luminosity evolution model suggests that the transition between the saturated and non-saturated regime of X-ray emission occurs in M 34 cluster members depending on their convective turnover time and period of rotation. Conclusions. I argue that the drop of (LX/Lbol) by one order of magnitude observed in M 34 around a Rossby number of 0.3 is indicative of a change in dynamo efficiency. I conclude that the transition from the saturated to the non-saturated regime of X-ray emission among main-sequence stars is the result of a dynamo regime transition, possibly between a turbulent dynamo and an interface-type dynamo.
Closed-form expressions for uniform currents on a wedge illuminated by TM plane wave

Closed-form expressions for nonuniform currents on a perfectly conducting, infinite wedge illuminated by a transverse magnetic plane wave are presented. The expressions are derived by requiring that they agree with the current predicted by the eigenfunction solution close to the edge and J.B. Keller's geometrical theory of diffraction (1962) far from the edge. The angle of incidence is arbitrary and the expressions remain uniformly valid even for glancing angles of incidence when the geometrical optics boundaries are in the vicinity of the wedge faces. The formulas presented are simple, involving Fresnel functions with complex arguments. These functions can be expressed in terms of complimentary error functions which may be computed using standard subroutine packages. Exact expressions for nonuniform currents are available for the two special cases of half-planes and infinite planes. Closed-form expressions for the axial electric field, and hence all the field components in the vicinity of the wedge axes, are also obtained. Currents computed using expressions obtained are compared with currents computed from the eigenfunction solution of the wedge, with good agreement throughout. >
The Association of Social Support and Loneliness with Symptoms of Depression, Anxiety, and Posttraumatic Stress during the COVID-19 Pandemic: A Meta-Analysis

Background: Research suggests that changes in social support and loneliness have affected mental disorder symptoms during the COVID-19 pandemic. However, there are a lack of studies comparing the robustness of these associations. Aims: The aims were to estimate the strength of the associations of loneliness and social support with symptoms of depression, anxiety, and posttraumatic stress during the COVID-19 pandemic (2020–2022) in the general population. Method: The method entailed a systematic review and random-effects meta-analysis of quantitative studies. Results: Seventy-three studies were included in the meta-analysis. The pooled correlations of the effect size of the association of loneliness with symptoms of depression, anxiety, and posttraumatic stress were 0.49, 0.40, and 0.38, respectively. The corresponding figures for social support were 0.29, 0.19, and 0.18, respectively. Subgroup analyses revealed that the strength of some associations could be influenced by the sociodemographic characteristics of the study samples, such as age, gender, region, and COVID-19 stringency index, and by methodological moderators, such as sample size, collection date, methodological quality, and the measurement scales. Conclusions: Social support had a weak association with mental disorder symptoms during the COVID-19 pandemic while the association with loneliness was moderate. Strategies to address loneliness could be highly effective in reducing the impact of the pandemic on social relationships and mental health.
Chloroquine Inhibition of Autophagy Enhanced the Anticancer Effects of Listeria monocytogenes in Melanoma

Listeria monocytogenes has been shown to exhibit antitumor effects. However, the mechanism remains unclear. Autophagy is a cellular catabolic process that mediates the degradation of unfolded proteins and damaged organelles in the cytosol, which is a double-edged sword in tumorigenesis and treatment outcome. Tumor cells display lower levels of basal autophagic activity than normal cells. This study examined the role and molecular mechanism of autophagy in the antitumor effects induced by LM, as well as the combined antitumor effect of LM and the autophagy inhibitor chloroquine (CQ). We investigated LM-induced autophagy in B16F10 melanoma cells by real-time PCR, immunofluorescence, Western blotting, and transmission electron microscopy and found that autophagic markers were increased following the infection of tumor cells with LM. The autophagy pathway in B16F10 cells was blocked with the pharmacological autophagy inhibitor chloroquine, which led to a significant increase in intracellular bacterial multiplication in tumor cells. The combination of CQ and LM enhanced LM-mediated cancer cell death and apoptosis compared with LM infection alone. Furthermore, the combination of LM and CQ significantly inhibited tumor growth and prolonged the survival time of mice in vivo, which was associated with the increased colonization and accumulation of LM and induced more cell apoptosis in primary tumors. The data indicated that the inhibition of autophagy by CQ enhanced LM-mediated antitumor activity in vitro and in vivo and provided a novel strategy to improving the anticancer efficacy of bacterial treatment.
Gut Microbiota Deficiency Exacerbates Liver Injury in Bile Duct Ligated Mice via Inflammation and Lipid Metabolism

Bile components play a critical role in maintaining gut microbiota homeostasis. In cholestasis, bile secretion is impaired, leading to liver injury. However, it remains to be elucidated whether gut microbiota plays a role in cholestatic liver injury. Here, we performed a sham operation and bile duct ligation (BDL) in antibiotic-induced microbiome depleted (AIMD) mice and assessed liver injury and fecal microbiota composition in these mice. Significant reductions in gut microbiota richness and diversity were found in AIMD-sham mice when compared to sham controls. Three-day BDL leads to great elevation of plasma ALT, ALP, total bile acids, and bilirubin where reduced diversity of the gut microbiota was also found. AIMD further aggravated cholestatic liver injury evidenced by significantly higher levels of plasma ALT and ALP, associated with further reduced diversity and increased Gram-negative bacteria in gut microbiota. Further analyses revealed increased levels of LPS in the plasma of AIMD-BDL mice where elevated expression of inflammatory genes and decreased expression of hepatic detoxification enzymes were also found in liver when compared to the BDL group. These findings indicate that gut microbiota plays a critical role in cholestatic liver injury. Maintaining its homeostasis may alleviate liver injury in patients with cholestasis.
EUROPEAN ORGANIZATION FOR NUCLEAR RESEARCH

Recent theoretical scenarios propose that quantum gravity effects may manifest at LEP energies by means of gravitons that couple to Standard Model particles and propagate into extra space dimensions. These predictions are checked against the most recent experimental results on photon, W and Z pair production. No deviations from the Standard Model expectations are found and limits of the order of 1TeV on the scale of these models are set. Submitted to Physical Review On leave of absence from INFN–Sezione di Napoli, Italy. E-mail: Salvatore.Mele@cern.ch E-mail: Eusebio.Sanchez@cern.ch
Lower Expression of CFTR Is Associated with Higher Mortality in a Meta-Analysis of Individuals with Colorectal Cancer

Simple Summary The ion channel gene CFTR is a tumor suppressor in colorectal cancer. It is well-established that individuals with cystic fibrosis, caused by biallelic germline mutations in CFTR, are at increased risk of developing colorectal cancer. A population of colorectal cancer patients with no known relationship to cystic fibrosis expresses reduced levels of CFTR in their tumors. This study aimed to determine if this population experienced increased mortality compared to those expressing higher levels of CFTR. Three independent datasets containing 1177 colorectal cancer patients were analyzed using Cox proportional hazards regression. Analysis of each study individually and meta-analysis of all three revealed an association between reduced CFTR expression and increased mortality. This association is potentially clinically significant because individuals with low CFTR expression may benefit from more aggressive treatment. Additionally, molecular therapies developed to treat cystic fibrosis by increasing CFTR activity may be applicable for colorectal cancer tumors expressing low levels of CFTR. Abstract Individuals with cystic fibrosis (CF), caused by biallelic germline mutations in the cystic fibrosis transmembrane conductance regulator (CFTR), have higher risk and earlier onset of colorectal cancer (CRC). A subset of CRC patients in the non-CF population expresses low levels of tumoral CFTR mRNA which may also cause decreased CFTR activity. To determine the consequences of reduced CFTR expression in this population, we investigated association of tumoral CFTR expression with overall and disease-specific mortality in CRC patients. CFTR mRNA expression, clinical factors and survival data from 1177 CRC patients reported in the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus studies GSE39582 and GSE17538 were included. Log-transformed and z-normalized [mean = 0, standard deviation (SD) = 1] CFTR expression values were modeled as quartiles or dichotomized at the median. Univariate and multivariable Cox proportional hazards regression models were used to estimate hazard ratios (HR) and 95% confidence intervals (CI) for overall and disease-specific mortality in individual studies and meta-analyses. Analyses of each of the three individual datasets showed a robust association of decreased CFTR expression with increased mortality. In meta-analyses adjusted for stage at diagnosis, age and sex, CFTR expression was inversely associated with risk of overall death [pooled HR (95% CI): 0.70 (0.57–0.86)] and disease-specific death [pooled HR (95% CI): 0.68 (0.47–0.99)]. Associations did not differ by stage at diagnosis, age, or sex. Meta-analysis of overall death stratified by microsatellite instable (MSI) versus microsatellite stable (MSS) status indicated potential interaction between MSI/MSS status and CFTR expression, (p-interaction: 0.06). The findings from these three datasets support the hypothesis that low CFTR expression is associated with increased CRC mortality.