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Organic-inorganic hybrid halide perovskite is a significant semiconductor material in photonics. Their outstanding optoelectronic characteristics have been reported, increasing energy conversion efficiency of perovskite solar battery by up to 25.5%, which is expected to be major competitors in silicon industry. According to the fundamentals of physics, a viable candidate for a light emitter must really be a superior direct band gap material. Even though there are a lot of papers on perovskite-based light emitting devices in this area, the corresponding laser devices are still lack of research. Here, since the first publication of lasing in hybrid perovskites in the 1990s, great effort has been made especially in 2014. Halide perovskites have the potential to revolutionize the nanophotonics field due to their solution-processed gain medium, almost defect-free semiconductor formation, high luminous efficiency, flexibility of nanostructure, excellent stability, and wide wavelength tunability. This article highlights the important researches on the optical gain from diverse hybrid halide perovskite material and the future challenges of lasing.

An integrated battery system, which integrates solar power and rechargeable battery in the same unit, is recognized as a propective solution for the shortage and inefficiency of power energy. Herein, a hybrid S/N719 dye cathode is proposed in the rechargeable Li-S battery to realize the photo-assisted charging battery system. Specifically, the photo-charge contribution reduces the charge voltage of the sulfur cathode by 0.12 V and accelerates the charge process under light illumination, showing the great potential in saving electric energy and expediting fast-charge capacity of Li-S battery.

We synthesized a PbO2-coated electrode consisting of three layers, with Pb–0.6%Sb alloy as the substrate, α-PbO2 as the intermediate layer, and the outermost β-PbO2-Co3O4@C as the active layer for boosting OER activity.

Echinacea purpurea polysaccharide (EPP) is a functional compound in Echinacea purpurea.

It is easy to lead to poor generalization in machine learning tasks using real-world data directly, since such data is usually high-dimensional dimensionality and limited. Through learning the low dimensional representations of high-dimensional data, feature selection can retain useful features for machine learning tasks. Using these useful features effectively trains machine learning models. Hence, it is a challenge for feature selection from high-dimensional data. To address this issue, in this paper, a hybrid approach consisted of an autoencoder and Bayesian methods is proposed for a novel feature selection. Firstly, Bayesian methods are embedded in the proposed autoencoder as a special hidden layer. This of doing is to increase the precision during selecting non-redundant features. Then, the other hidden layers of the autoencoder are used for non-redundant feature selection. Finally, compared with the mainstream approaches for feature selection, the proposed method outperforms them. We find that the way consisted of autoencoders and probabilistic correction methods is more meaningful than that of stacking architectures or adding constraints to autoencoders as regards feature selection. We also demonstrate that stacked autoencoders are more suitable for large-scale feature selection, however, sparse autoencoders are beneficial for a smaller number of feature selection. We indicate that the value of the proposed method provides a theoretical reference to analyze the optimality of feature selection.

The fungus Sporidiobolus pararoseus not only produces carotenoids, but also produces bioactive extracellular polysaccharides (SPP). However, the relationship between SPP and the metabolism of gut microbiome is unclear. The aim of this study was to investigate the mechanism of SPP regulating intestinal health in vivo and in vitro. Results showed that SPP are nondigestible polysaccharides after the digestion with simulated stomach and small intestinal juice in vitro. After SPP was cultured in an in vitro intestinal simulation system for seven days, the concentration of short-chain fatty acids (SCFAs) increased; the microbial diversity changed; the relative abundance of Bifidobacterium and Streptococcus increased; and that of Escherichia Shigella and Lachnospiraceae NK4A136 decreased. In addition, metabolism of SPP by the mice colonic microbiome showed SPP decreased the relative abundance of Firmicutes and Bacteroidota, while the relative abundance of Verrucomicrobiota, Desulfobacterota, and Actinobacteriota increased. Finally, predicted Kyoto Encyclopedia of Genes and Genomes (KEGG) metabolism results also showed that SPP can enhance the metabolism of cofactors, vitamins, amino acids, starch, and sucrose. In conclusion, SPP can multiply the intestinal beneficial bacteria of humans and mice, promote the production of SCFAs and metabolism of amino acids, and promote intestinal health.

Isobaric labeling-based proteomics is widely applied in deep proteome quantification. Among the platforms for isobaric labeled proteomic data analysis, the commercial software Proteome Discoverer (PD) is widely used, incorporating the search engine CHIMERYS, while FragPipe (FP) is relatively new, free for noncommercial purposes, and integrates the engine MSFragger. Here, we compared PD and FP over three public proteomic data sets labeled using 6plex, 10plex, and 16plex tandem mass tags. Our results showed the protein abundances generated by the two software are highly correlated. PD quantified more proteins (10.02%, 15.44%, 8.19%) than FP with comparable NA ratios (0.00% vs. 0.00%, 0.85% vs. 0.38%, and 11.74% vs. 10.52%) in the three data sets. Using the 16plex data set, PD and FP outputs showed high consistency in quantifying technical replicates, batch effects, and functional enrichment in differentially expressed proteins. However, FP saved 93.93%, 96.65%, and 96.41% of processing time compared to PD for analyzing the three data sets, respectively. In conclusion, while PD is a well-maintained commercial software integrating various additional functions and can quantify more proteins, FP is freely available and achieves similar output with a shorter computational time. Our results will guide users in choosing the most suitable quantification software for their needs.

High oxygen evolution overpotential and low corrosion resistance are the main challenges for oxygen evolution materials in acidic media. In this study, a novel composite material, Ti/TiO2-NTs/PbO2–CNTs–MnO2, with high oxygen evolution electrocatalytic activity was successfully prepared. First, TiO2 nanotubes (TiO2-NTs) were synthesized in situ on a Ti sheet via anodization and used as an intermediate layer. Subsequently, the adhesion and conductivity of the TiO2-NTs layer were increased through additional anodization, annealing, and electrochemical reduction. Finally, PbO2 was electrodeposited with a constant current in a lead acetate medium and doped with carbon nanotubes (CNTs) and MnO2. The surface morphology, phase composition, and electrochemical performance of the composite materials were investigated. Notably, in an acidic electrolyte (150 g/L H2SO4), Ti/TiO2-NTs/PbO2–CNTs–MnO2 exhibited good stability (30 h) and a low oxygen evolution overpotential of 410 mV at 50 mA/cm2, which is almost equivalent to that of precious metals (RuO2 and IrO2) and 499 mV lower than that of the industrial Pb–0.76 wt% Ag alloy. The outstanding performance is mainly attributed to the high aspect ratio of the TiO2-NT structure, synergistic effects of the active particles, and inherently good electrochemical properties of the active particles. Therefore, this study provides a new synthetic route for oxygen evolution materials in acidic media.

Oxidative damage and intestinal dysbiosis are regarded as crucial culprits in alcoholic liver disease (ALD). This study aimed to examine the protective effects of Echinacea purpurea polysaccharides (EPPs) against ALD and explore the underlying mechanisms based on hepatic oxidative stress, inflammation, and intestinal barrier function. Three polysaccharide fractions, namely, EPP40, EPP60, and EPP80, were obtained by stepwise ethanol precipitation, and their antioxidant activity in vitro was investigated. The results showed that EPP80 with Mw 11.82 kDa had the strongest radical-scavenging capacity against DPPH, ABTS, and •OH radicals. Besides, EPP80 comprised arabinose, galactose, glucose, mannose, galacturonic acid, and glucuronic acid in molar ratios of 13.42:25.12:10.92:8.59:2.07:0.82. The in vivo results showed that EPP80 increased the activities of antioxidant enzymes and reduced the levels of inflammatory cytokines both in mouse serum and liver. Moreover, EPP80 upregulated the expression of Occludin and ZO-1, revealing its protective effect against intestinal barrier dysfunction. Furthermore, EPP80 inhibited alcohol-induced oxidative damage by promoting the expression of Nrf2, HO-1, and NQO1 in the liver. In summary, EPP80 markedly scavenged free radicals in vitro and ameliorated alcohol-induced liver injury via Nrf2/HO-1 pathways in vivo. These findings suggested that EPP80 could provide effective supplementary support in preventing and treating ALD.

Currently, specific metabolites and diagnostic biomarkers of panic disorder (PD) patients have not been identified in clinical practice. The aim of this study was to explore metabolites and metabolic pathways in serum through a metabolomics method.Fifty-five PD patients who completed 2 weeks of inpatient treatment and 55 healthy control subjects (HCs) matched for age, sex and BMI were recruited. Ultra-high performance liquid chromatography tandem mass spectrometry (UHPLC-MS/MS) was used to detect metabolites in serum. Multivariate Statistical Analysis was used to identify differential metabolites. The relevant biometabolic pathways were further identified by the online tool MetaboAnalyst 5.0.43 different metabolites in PD patients compared to HCs (P 0.05) were screened. Pathway analysis showed that these small molecules were mainly associated with amino acid metabolism. 14 metabolites were significantly changed after 2 weeks of drug treatment (P 0.05), which were mainly associated with tryptophan metabolism.In conclusion, our analysis of metabolomics of PD patients at baseline and two weeks after treatment screened for differential metabolites that could be potential diagnostic biomarkers involved in PD pathogenesis and influence some biometabolic pathways such as phenylalanine metabolism and tryptophan metabolism. In the future, we can summarize and observe the dynamic changes of differential metabolites that appear more frequently in similar studies to further explore the underlying mechanisms of PD evolution.

BackgroundStructural neuroimaging studies have identified similarities in the brains of individuals diagnosed with schizophrenia (SZ) and bipolar I disorder (BP), with overlap in regions of gray matter (GM) deficits between the two disorders. Recent studies have also shown that the symptom phenotypes associated with SZ and BP may allow for a more precise categorization than the current diagnostic criteria. In this study, we sought to identify GM alterations that were unique to each disorder and whether those alterations were also related to unique symptom profiles.Materials and methodsWe analyzed the GM patterns and clinical symptom presentations using independent component analysis (ICA), hierarchical clustering, and n-way biclustering in a large (N ∼ 3,000), merged dataset of neuroimaging data from healthy volunteers (HV), and individuals with either SZ or BP.ResultsComponent A showed a SZ and BP < HV GM pattern in the bilateral insula and cingulate gyrus. Component B showed a SZ and BP < HV GM pattern in the cerebellum and vermis. There were no significant differences between diagnostic groups in these components. Component C showed a SZ < HV and BP GM pattern bilaterally in the temporal poles. Hierarchical clustering of the PANSS scores and the ICA components did not yield new subgroups. N-way biclustering identified three unique subgroups of individuals within the sample that mapped onto different combinations of ICA components and symptom profiles categorized by the PANSS but no distinct diagnostic group differences.ConclusionThese multivariate results show that diagnostic boundaries are not clearly related to structural differences or distinct symptom profiles. Our findings add support that (1) BP tend to have less severe symptom profiles when compared to SZ on the PANSS without a clear distinction, and (2) all the gray matter alterations follow the pattern of SZ < BP < HV without a clear distinction between SZ and BP.

Background Although child maltreatment (CM) experiences are recognized risk factors for nonsuicidal self-injury (NSSI), the mechanisms underlying this relationship remain unclear. The purpose of this study was to examine whether difficulty in emotion regulation (DER) and depressive symptoms mediate the relationship between child maltreatment experiences and NSSI severity, adjusting for demographic variables. Methods The participants were 224 adolescent inpatients recruited from a hospital in China (mean age 15.30 years, SD = 1.83; 78.6% females). Study measures included the Clinician-Rated Severity of Nonsuicidal Self-Injury (CRSNSSI), Childhood Trauma Questionnaire (CTQ-SF), Difficulties in Emotion Regulation Scale (DERS), and Patient Health Questionnaire-9 (PHQ-9). The hypothesized chain mediation model was tested using the structural equation model. Results A total of 146 (65.18%) adolescents reported engaging in NSSI during the past 12 months, and 103 (45.98%) participants met the DSM-5 diagnostic criteria for NSSI. Emotional neglect (48.1%) and emotional abuse (46.1%) had the highest prevalence, followed by physical neglect (43.1%) and physical abuse (24.1%), whereas sexual abuse (12.5%) was the least prevalent form of CM. Separately, both DER and depressive symptoms significantly mediated the association between CM and NSSI, with DER being the strongest mediator, with an indirect effect of 49.40% (p = 0.014). At the same time, we also proved a potential chain-mediated pathway of DER and depression in the relationship between CM and NSSI. Conclusion Child maltreatment seems to play a role in the aetiology of NSSI. DER and depressive symptoms both have a mediating role in the relationship between CM and NSSI. Importantly, DER seems to be a mediator with a stronger indirect effect compared to depressive symptoms.

Mental health is one of the major causes of disability worldwide, and mental health problems such as depression and anxiety are ranked among the top 25 leading causes of disease burden in the world. This burden is considerable over the lifetime of both men and women and in various settings and ages. This study aims to compare the mental health status of people in China and Pakistan and to highlight the mental health laws and policies during COVID-19 and afterwards. According to the literature on mental health, before the COVID-19 pandemic, mental health problems increased gradually, but during and after the COVID-19 pandemic, an abrupt surge occurred in mental health problems. To overcome mental health disorders, most (but not all) countries have mental health laws, but some countries ignore mental health disorders. China is one such country that has mental health laws and policies and, during the COVID-19 pandemic, China made beneficial and robust policies and laws, thereby succeeding in defeating the COVID-19 pandemic. The mortality rate and financial loss were also lower than in other countries. While Pakistan has mental health laws and general health policies, the law is only limited to paperwork and books. When it came to COVID-19, Pakistan did not make any specific laws to overcome the virus. Mental health problems are greater in Pakistan than in China, and China’s mental health laws and policies are more robust and more widely implemented than those in Pakistan. We conclude that there are fewer mental health issues in China than in Pakistan both before and since the COVID-19 pandemic. China has strong mental health laws and these are robustly implemented, while the mental health law in Pakistan is not applied in practice.

Data fusion analyses of brain imaging and genomics enable the linking of genomic factors to brain patterns. Due to the small to modest effect sizes of common genetic variants, it is usually challenging to reliably identify relevant genetic factors from the rest of the genome with the typical sample size in neuroimaging studies. To alleviate this problem, we propose sparse parallel independent component analysis (spICA) to leverage the sparsity of individual genomic sources, building upon the existing parallel independent component analysis (pICA) algorithm. Sparsity is enforced by performing Hoyer projection on the estimated independent sources. Simulation results demonstrate that spICA yields improved recovery of imaging-genomic associations and sources compared to pICA. We applied spICA to whole-brain gray matter volume (GMV) and whole-genome single nucleotide polymorphisms (SNPs) data of five different sets of 24,985 discovery samples in the UK Biobank. We identified three GMV sources significantly and stably associated with one SNP source and replicated these associations. GMV sources highlighted frontal, parietal, and temporal regions. Their corresponding loadings on individuals were related to multiple cognitive measures, and the temporal region interacts with age influencing cognition. The SNP component underscored SNPs in chromosome 17 that were enriched in the inflammation response pathway and in regulation effect in the prefrontal cortex via gene expression, methylation, transcription expression, and isoform percentage.

Esophageal squamous cell carcinoma (ESCC) is one of the most common malignancies and cause of death from cancer in China. Previous studies showed that autophagy and apoptosis inhibition are critical for the survival of ESCC cells. However, the underlying mechanisms remain to be clarified. Recently, we found that PIWIL2, a novel cancer testis protein, is highly expressed in ESCC and associated with high T-stage and poor 5-year survival rate in patients. Our further study showed that PIWIL2 can directly bind to IKK and promote its phosphorylation, leading to phosphorylation of IκB and subsequently nuclear translocation of NF-κB for apoptosis inhibition. Meanwhile, PIWIL2 competitively inhibits binding of IKK to TSC1, and thus deactivate mTORC1 pathway which suppresses ULK1 phosphorylation and initiation of autophagy. The mouse xenograft model suggested that PIWIL2 can promote ESCC growth in an IKK-dependent manner. This present work firstly revealed that PIWIL2 can play a role in regulating autophagy and apoptosis, and is associated with poor prognosis in ESCC patients, providing novel insights into the roles of PIWIL2 in tumorigenesis.

The high oxygen evolution overpotential of the Pb–Ag anode is one of the main reasons for the high energy consumption in Zn electrowinning. PbO2, owing to its high conductivity, good corrosion resistance and low cost, is widely used as an excellent coating material. In present research, a novel composite Ti/TiO2-NTs/PbO2 material was synthesized through a facile anodization, annealing, electrochemical reduction and galvanostatic deposition. The surface morphology, internal structure and the mechanisms of TiO2-NTs enhancing electrochemical performance were discussed. The results show that the self-organized high aspect ratio TiO2-NTs with diameter of ∼120 nm and length of ∼8 μm were obtained on Ti substrate. The Ti/TiO2-NTs/PbO2 composite material exhibits excellent oxygen evolution performance and good stability in Zn electrowinning simulation solution (50 g L−1 Zn2+, 150 g L−1 H2SO4) at 35 °C. Its oxygen evolution overpotential is only 630 mV under current density 50 mA cm−2, which is 332 m lower than that of Pb-0.76 wt% Ag (η = 962 mV) and only increases 22 mV after 5000 cycles of CV scanning. Its outstanding electrochemical performance is mainly ascribed to the introduction of TiO2-NTs in Pb(CH3COO)2 media since it refines the crystal grains, increases the electrochemical surface area, greatly reduces the charge transfer resistance (25.4 Ω cm2 to 2.337 Ω cm2) and enhances corrosion resistance. Therefore, the Ti/TiO2-NTs/PbO2 material prepared in Pb(CH3COO)2 medium may be an ideal anode for Zn electrowinning.

In large smelting plants such as steel plant and coking plant the utilization rate of the byproduct gas is generally low, especially in developing countries. Usually, multiple low-parameter boilers are combined with multiple low-parameter steam turbines, which are built in phases, to drive the smelting accessory machineries. In terms of energy utilization efficiency, this model is definitely not economical. Based on the law of thermodynamics, the direction of efficiency improvement was proposed, and the solution of gas utilization system was put forward, according to the Rankine cycle principle. Finally, with a steel plant as the research object, the proposed scheme was applied in the transformation of this plant, and the economic benefit was estimated. The results show that the proposed scheme in this paper can generate more electricity with the same gas consumption.

Aimed at the limited engineering condition that only gas calorific value but not gas composition can be provided, a new theoretical combustion calculation method based on gas calorific value was proposed, which is suitable for calculating the theoretical dry flue gas volume of metallurgical gas. In the new method, three simplified models were proposed, including blast furnace gas (BFG) model, Linz–Donawitz converter gas (LDG) model and coke oven gas (COG) model. The simplified models were respectively derived from the original model of theoretical combustion calculation, according to the composition characteristics of each metallurgical gas. Finally, the proposed models were verified through large sample of BFG, LDG and COG. The verification results show that the new models for calculating the theoretical dry flue gas volume of metallurgical gas all have high accuracy. The average relative errors of BFG model, LDG model and COG model are only 0.35%, 0.12%, 0.64% respectively. Thus, the new method proposed in this paper is reasonable and feasible. It can meet the accuracy requirement of engineering and can provide a new way for theoretical combustion calculation of metallurgical gas.

In the control of air pollutant emissions, the measured emission concentration should be converted into the standard condition. The converted concentration is obtained in order to determine whether the emissions meet requirements. At present, there are several conversion methods available, and the EAR (excess air ratio) conversion method is one of them. Based on the conversion mechanism of air pollutant concentration, the origin of EAR conversion method was deduced and analyzed in this paper. The results show that the EAR method is a simplified method that is only suitable for the fuels with fuel composition factor close to zero, such as coal, biofuel, solid waste, oil, etc. It is not suitable for the fuels with big fuel composition factor, such as blast furnace gas, converter gas and producer gas, etc.