Electrostatic-Field-Induced Collapse of Nanobubbles in Nanochannels.

The adsorbed nanobubbles inside the nanochannels can cause fluid transport blockages, which will obviously degrade the nanodevice performance and reduce the lifetime. However, due to small-scale effects, the removal of nanobubbles is a huge challenge at the nanoscale. Herein, molecular dynamics simulations are carried out to study the effect of the electrostatic field on underwater nitrogen nanobubbles confined in nanochannels. It is found that the nanobubbles will collapse under an appropriate electrostatic field, thereby unblocking the transport of water in the nanochannels. The formation of ordered water structures induced by electrostatic fields plays an important role in the removal of nanobubbles from the nanochannels. Our findings provide a convenient, controllable, and remote way to address the blockage problem of nanobubbles in nanochannels, which may have potential applications in improving the performance of fuel cells.

Cushing's syndrome caused by giant Ewing's sarcoma of the kidney: A case report and review of literature.

BACKGROUND: Primary renal Ewing's sarcoma (ES) is extremely rare, and only two cases causing Cushing's syndrome (CS) have been reported to date. We report that the case of an 18-year-old patient is diagnosed primary renal ES with typical CS characterized by purple stripes, weight gain, and hypertension. CASE SUMMARY: CS was first diagnosed by laboratory testing. A huge tumor was revealed in the kidney following an imaging examination. Moreover, brain and bone metastases were observed. After comprehensive treatment, primarily based on surgery, primary renal ES was pathologically diagnosed with a typical EWSR1-FLI1 genetic mutation through genetic testing. Furthermore, the glucocorticoid level returned to normal. By the ninth postoperative month of follow-up, the patient was recovering well. Cushing-related symptoms had improved, and a satisfactory curative effect was achieved. CONCLUSION: Primary renal ES, a rare adult malignant tumor, can cause CS and a poor prognosis.

FXR, MRP-1 and SLC7A5: New Targets for the Treatment of Hepatocellular Carcinoma.

Detect the expression of Farnesoid X Receptor(FXR), Multiple Drug Resistance Associated Protein-1(MRP-1) and Solute Carrier Family 7, Member 5 (SLC7A5) in hepatocellular carcinoma(HCC) of rat model, so as to provide new therapeutic targets for gene therapy of HCC. Sixty male Wistar rats were randomly divided into three groups. The rats in experimental group were given 0.2% diethylnitrosamine (DEN) by gavage with a dose of 10 mg/kg, 3 times a week, and it stopped at 12 weeks. The control group rats were given physiological saline by gavage, while the sham operation group did not receive anything by gavage. At 10 weeks, one rat in the experimental group was euthanized, and the changes of livers were recorded. The procedure was repeated at 12 weeks. After 12 weeks, HCC only occurred in the experimental group. After confirming the formation of the tumor through pathological examination, liver tissues and tumor tissues were taken from the three groups. FXR, MRP-1 and SLC7A5 expression in liver tissues and tumor tissues was detected. After 7 weeks the rats in experimental group ate less, and their weight was significantly reduced. Three months later, HCC was detected in 15 rats in the experimental group. The ratio of FXR/GAPDH mRNA, MRP-1/GAPDH mRNA, SLC7A5/GAPDH mRNA were significantly different among the three groups. Under the light microscope the FXR protein, MRP-1 protein, and SLC7A5 protein react with their respective antibodies, and they showed granular expression. Every pathological section included different numbers of positive cells in each group. FXR expression in HCC of rats was significantly lower than that in normal liver tissues, but MRP-1 and SLC7A5 expression in HCC were significantly higher than that in normal liver tissues, suggesting that drugs targeting FXR, MRP-1 and SLC7A5 may be new strategies for the treatment of HCC.

Identification Analysis of Angelicae sinensis radix and Angelicae pubescentis radix Based on Quantized "Digital Identity" and UHPLC-QTOF-MSE Analysis.

Angelicae sinensis radix (ASR) and Angelicae pubescentis radix (APR), as traditional herbal medicines, are often confused and doped in the material market. However, the traditional identification method is to characterize the whole herb with a single or a few components, which do not have representation and cannot realize the effective utilization of unknown components. Consequently, the result is not convincing. In addition, the whole process is time-consuming and labor-intensive. To avoid the confusion and adulteration of ASR and APR as well as to strengthen quality control and improve identification efficiency, in this study, a UHPLC-QTOF-MSE method was used to analyze ASR and APR. Based on digital representation, the shared data with high ionic strength were extracted from different batches of the same herbal medicine as their "digital identity". Further, the above "digital identity" was used as the benchmark for matching and identifying unknown samples to feedback on matching credibility (MC). The results showed that based on the "digital identities" of ASR and APR, the digital identification of two herbal samples can be realized efficiently and accurately at the individual level. And the matching credibility (MC) was higher than 94.00%, even if only 1% of APR or ASR in the mixed samples can still be identified efficiently and accurately. The study is of great practical significance for improving the efficiency of the identification of ASR and APR, cracking down on adulterated and counterfeit drugs, and strengthening the quality control of ASR and APR. In addition, it has important reference significance for developing nontargeted digital identification of herbal medicines at the individual level based on UHPLC-QTOF-MSE and "digital identity", which is beneficial to the construction of digital Chinese medicine and digital quality control.

Mechanisms for carbon stock driving and scenario modeling in typical mountainous watersheds of northeastern China.

Watershed ecosystems play a pivotal role in maintaining the global carbon cycle and reducing global warming by serving as vital carbon reservoirs for sustainable ecosystem management. In this study, we based on the "quantity-mechanism-scenario" frameworks, integrate the MCE-CA-Markov and InVEST models to evaluate the spatiotemporal variations of carbon stocks in mid- to high-latitude alpine watersheds in China under historical and future climate scenarios. Additionally, the study employs the Geographic Detector model to explore the driving mechanisms influencing the carbon storage capacity of watershed ecosystems. The results showed that the carbon stock of the watershed increased by about 15.9 Tg from 1980 to 2020. Fractional Vegetation Cover (FVC), Digital Elevation Model (DEM), and Mean Annual Temperature (MAT) had the strongest explanatory power for carbon stocks. Under different climate scenarios, it was found that the SSP2-4.5 scenario had a significant rise in carbon stock from 2020 to 2050, roughly 24.1 Tg. This increase was primarily observed in the southeastern region of the watersheds, with forest and grassland effectively protected. Conversely, according to the SSP5-8.5 scenario, the carbon stock would decrease by about 50.53 Tg with the expansion of cultivated and construction land in the watershed's southwest part. Therefore, given the vulnerability of mid- to high-latitude mountain watersheds, global warming trends continue to pose a greater threat to carbon sequestration in watersheds. Our findings carry important implications for tackling potential ecological threats in mid- to high-latitude watersheds in the Northern Hemisphere and assisting policymakers in creating carbon sequestration plans, as well as for reducing climate change.

HTFSMMA: Higher-Order Topological Guided Small Molecule-MicroRNA Associations Prediction.

Small molecules (SMs) play a pivotal role in regulating microRNAs (miRNAs). Existing prediction methods for associations between SM-miRNA have overlooked crucial aspects: the incorporation of local topological features between nodes, which represent either SMs or miRNAs, and the effective fusion of node features with topological features. This study introduces a novel approach, termed high-order topological features for SM-miRNA association prediction (HTFSMMA), which specifically addresses these limitations. Initially, an association graph is formed by integrating SM-miRNA association data, SM similarity, and miRNA similarity. Subsequently, we focus on the local information of links and propose target neighborhood graph convolutional network for extracting local topological features. Then, HTFSMMA employs graph attention networks to amalgamate these local features, thereby establishing a platform for the acquisition of high-order features through random walks. Finally, the extracted features are integrated into the multilayer perceptron to derive the association prediction scores. To demonstrate the performance of HTFSMMA, we conducted comprehensive evaluations including five-fold cross-validation, leave-one-out cross-validation (LOOCV), SM-fixed local LOOCV, and miRNA-fixed local LOOCV. The area under receiver operating characteristic curve values were 0.9958 ± 0.0024 (0.8722 ± 0.0021), 0.9986 (0.9504), 0.9974 (0.9111), and 0.9977 (0.9074), respectively. Our findings demonstrate the superior performance of HTFSMMA over existing approaches. In addition, three case studies and the DeLong test have confirmed the effectiveness of the proposed method. These results collectively underscore the significance of HTFSMMA in facilitating the inference of associations between SMs and miRNAs.

The first high-altitude autotetraploid haplotype-resolved genome assembled (Rhododendron nivale subsp. boreale) provides new insights into mountaintop adaptation.

BACKGROUND: Rhododendron nivale subsp. boreale Philipson et M. N. Philipson is an alpine woody species with ornamental qualities that serve as the predominant species in mountainous scrub habitats found at an altitude of ∼4,200 m. As a high-altitude woody polyploid, this species may serve as a model to understand how plants adapt to alpine environments. Despite its ecological significance, the lack of genomic resources has hindered a comprehensive understanding of its evolutionary and adaptive characteristics in high-altitude mountainous environments. FINDINGS: We sequenced and assembled the genome of R. nivale subsp. boreale, an assembly of the first subgenus Rhododendron and the first high-altitude woody flowering tetraploid, contributing an important genomic resource for alpine woody flora. The assembly included 52 pseudochromosomes (scaffold N50 = 42.93 Mb; BUSCO = 98.8%; QV = 45.51; S-AQI = 98.69), which belonged to 4 haplotypes, harboring 127,810 predicted protein-coding genes. Conjoint k-mer analysis, collinearity assessment, and phylogenetic investigation corroborated autotetraploid identity. Comparative genomic analysis revealed that R. nivale subsp. boreale originated as a neopolyploid of R. nivale and underwent 2 rounds of ancient polyploidy events. Transcriptional expression analysis showed that differences in expression between alleles were common and randomly distributed in the genome. We identified extended gene families and signatures of positive selection that are involved not only in adaptation to the mountaintop ecosystem (response to stress and developmental regulation) but also in autotetraploid reproduction (meiotic stabilization). Additionally, the expression levels of the (group VII ethylene response factor transcription factors) ERF VIIs were significantly higher than the mean global gene expression. We suspect that these changes have enabled the success of this species at high altitudes. CONCLUSIONS: We assembled the first high-altitude autopolyploid genome and achieved chromosome-level assembly within the subgenus Rhododendron. In addition, a high-altitude adaptation strategy of R. nivale subsp. boreale was reasonably speculated. This study provides valuable data for the exploration of alpine mountaintop adaptations and the correlation between extreme environments and species polyploidization.

First-principles calculation of structural, electronic, optical, and mechanical properties of SrVO3.

CONTEXT AND RESULTS : In this paper, the crystal structure, electronic, optical, and mechanical properties of SrVO3 have been systematically studied by first-principles calculation. The results show that the calculated lattice parameters are in good agreement with the experimental values of X-ray diffraction. The density of states is described in detail in this paper. By analyzing the crystal structure and electronic properties of SrVO3, the magnetic properties of SrVO3 are obtained from the one unpaired electrons of V and the exchange interaction between two V ions. At the same time, a detailed analysis of the optical properties of SrVO3 was conducted, and it was found that it is transparent in the visible light range. Finally, the mechanical properties of SrVO3 are calculated, which can provide some references for future research. COMPUTATIONAL METHOD: In this paper, a first-principles method based on density functional theory (DFT) is reported for PBE-GGA analysis using the plane wave-pseudo potential method in a quantum concentrate packet, U value of 7 eV to V-d and a U value of 2 eV to O-p, Grimme correction by DFT-D method. The k points in the Brillouin region are set to 4 × 4 × 4. The energy convergence criterion for self-consistent field calculation is set at 5.0 × 10-6 eV/atom, and the cutoff energy is 1170 eV. In this paper, the force acting on each atom is not more than 0.01 eV/Å, the maximum stress is not more than 0.02GPa, and the maximum atomic displacement is 5 × 10-4 Å.

γ-Tocotrienol enhances autophagy of gastric cancer cells by the regulation of GSK3ß/ß-Catenin pathway.

γ-Tocotrienol (γ-T3) is a major subtype of vitamin E, mainly extracted from palm trees, barley, walnuts, and other plants. γ-T3 has effects on anti-inflammation, anti-oxidation, and potential chemoprevention against malignancies. It is still uncompleted to understand the effect of γ-T3 on the inhibitory mechanism of cancer. This study aimed to investigate whether γ-T3 enhanced autophagy in gastric cancer and the underlying molecular mechanism. The results showed that γ-T3 (0-90 µmol/L) inhibited the proliferation of gastric cancer MKN45 cells and AGS cells, and arrested the cell cycle at the G0/G1 phase in a dose-dependent manner. Autophagy was increased in MKN45 cells treated with γ-T3 (0-45 µmol/L), especially at a dose of 30 µmol/L for 24 h. These effects were reversed by 3-methyladenine pretreatment. Furthermore, γ-T3 (30 µmol/L) also significantly downregulated the expression of pGSK-3ß (ser9) and ß-catenin protein in MKN45 cells, and γ-T3 (20 mg/kg b.w.) effectively decreased the growth of MKN45 cell xenografts in BABL/c mice. GSK-3ß inhibitor-CHIR-99021 reversed the negative regulation of GSK-3ß/ß-Catenin signaling and autophagy. Our findings indicated that γ-T3 enhances autophagy in gastric cancer cells mediated by GSK-3ß/ß-Catenin signaling, which provides new insights into the role of γ-T3 enhancing autophagy in gastric cancer.

Digital identification of Aucklandiae radix, Vladimiriae radix, and Inulae radix based on multivariate algorithms and UHPLC-QTOF-MS analysis.

INTRODUCTION: The identification of Aucklandiae Radix (AR), Vladimiriae Radix (VR), and Inulae Radix (IR) based on traits and microscopic features is susceptible to the state of samples and the subjective awareness of personnel, and the identification based on a few or single chemical compositions is a cumbersome and time-consuming procedure and fails to rationally and effectively utilize the information of unknown components and is not specificity enough. OBJECTIVES: This study aimed to improve the identification efficiency, strengthen supervision, and realize digital identification of three Chinese medicines. Ultra-high-performance liquid chromatography with quadrupole time-of-flight mass spectrometry (UHPLC-QTOF-MS) combined with multivariate algorithms was used to explore the digital identification of AR, VR, and IR. MATERIALS AND METHODS: UHPLC-QTOF-MS was used to analyze AR, VR, and IR. The MS data combined with multivariate algorithms such as partial least squares discrimination analysis (PLS-DA) and artificial neural networks (ANNs) was used to filter important variables and data modeling. Finally, the optimal model was selected for the digital identification of three herbs. RESULTS: The results showed that three herbs can be distinguished on the whole level, and through feature screening, 591 characteristic variables combined with multivariate algorithms to construct data models. The ANN model was the best with accuracy = 0.983, precision = 0.984, and external verification showed the reliability and practicability of ANN model. CONCLUSION: ANN model combined with MS data is of great significance for tdigital identification of AR, VR, and IR. It is an important reference for developing the digital identification of traditional Chinese medicines at the individual level based on UHPLC-QTOF-MS and multivariate algorithms.

Progress on CRISPR/Cas9 system in the genetic improvement of livestock and poultry.

CRISPR/Cas9 gene editing technology, as a highly efficient genome editing method, has been extensively employed in the realm of animal husbandry for genetic improvement. With its remarkable efficiency and precision, this technology has revolutionized the field of animal husbandry. Currently, CRISPR/Cas9-based gene knockout, gene knock-in and gene modification techniques are widely employed to achieve precise enhancements in crucial production traits of livestock and poultry species. In this review, we summarize the operational principle and development history of CRISPR/Cas9 technology. Additionally, we highlight the research advancements utilizing this technology in muscle growth and development, fiber growth, milk quality composition, disease resistance breeding, and animal welfare within the livestock and poultry sectors. Our aim is to provide a more comprehensive understanding of the application of CRISPR/Cas9 technology in gene editing for livestock and poultry.

A Method for Predicting the Melting Temperature of Ionic Compounds.

Predicting the melting temperature of materials has always been a topic of great concern. This article proposes an alternative model for determining the melting temperature of materials based on the main idea of the Lindemann melting criterion combined with the first-principles calculations of density functional theory. To verify the accuracy of the melting model, this article selected typical ionic crystals of MgO and 10 alkali metal halides as the validation objects. The calculation results indicate that the melting temperature of the MgO crystals and I-VII compounds is in good agreement with the experimental results.

Deep learning assists detection of esophageal cancer and precursor lesions in a prospective, randomized controlled study.

Endoscopy is the primary modality for detecting asymptomatic esophageal squamous cell carcinoma (ESCC) and precancerous lesions. Improving detection rate remains challenging. We developed a system based on deep convolutional neural networks (CNNs) for detecting esophageal cancer and precancerous lesions [high-risk esophageal lesions (HrELs)] and validated its efficacy in improving HrEL detection rate in clinical practice (trial registration ChiCTR2100044126 at www.chictr.org.cn). Between April 2021 and March 2022, 3117 patients ≥50 years old were consecutively recruited from Taizhou Hospital, Zhejiang Province, and randomly assigned 1:1 to an experimental group (CNN-assisted endoscopy) or a control group (unassisted endoscopy) based on block randomization. The primary endpoint was the HrEL detection rate. In the intention-to-treat population, the HrEL detection rate [28 of 1556 (1.8%)] was significantly higher in the experimental group than in the control group [14 of 1561 (0.9%), P = 0.029], and the experimental group detection rate was twice that of the control group. Similar findings were observed between the experimental and control groups [28 of 1524 (1.9%) versus 13 of 1534 (0.9%), respectively; P = 0.021]. The system's sensitivity, specificity, and accuracy for detecting HrELs were 89.7, 98.5, and 98.2%, respectively. No adverse events occurred. The proposed system thus improved HrEL detection rate during endoscopy and was safe. Deep learning assistance may enhance early diagnosis and treatment of esophageal cancer and may become a useful tool for esophageal cancer screening.

Hydrogen sulfide mitigates memory impairments via the restoration of glutamatergic neurons in a mouse model of hemorrhage shock and resuscitation.

Impaired long-term memory, a complication of traumatic stress including hemorrhage shock and resuscitation (HSR), has been reported to be associated with multiple neurodegenerations. The ventral tegmental area (VTA) participates in both learned appetitive and aversive behaviors. In addition to being prospective targets for the therapy of addiction, depression, and other stress-related diseases, VTA glutamatergic neurons are becoming more widely acknowledged as powerful regulators of reward and aversion. This study revealed that HSR exposure induces memory impairments and decreases the activation in glutamatergic neurons, and decreased ß power in the VTA. We also found that optogenetic activation of glutamatergic neurons in the VTA mitigated HSR-induced memory impairments, and restored ß power. Moreover, hydrogen sulfide (H2S), a gasotransmitter with pleiotropic roles, has neuroprotective functions at physiological concentrations. In vivo, H2S administration improved HSR-induced memory deficits, elevated c-fos-positive vesicular glutamate transporters (Vglut2) neurons, increased ß power, and restored the balance of γ-aminobutyric acid (GABA) and glutamate in the VTA. This work suggests that glutamatergic neuron stimulation via optogenetic assay and exogenous H2S may be useful therapeutic approaches for improving memory deficits following HSR.

An Exploratory Pilot Study on the Application of Radiofrequency Ablation for Atrial Fibrillation Guided by Computed Tomography-Based 3D Printing Technology.

Radiofrequency ablation (RFA) is the treatment of choice for atrial fibrillation (AF). Additionally, the utilization of 3D printing for cardiac models offers an in-depth insight into cardiac anatomy and cardiovascular diseases. The study aims to evaluate the clinical utility and outcomes of RFA following in vitro visualization of the left atrium (LA) and pulmonary vein (PV) structures via 3D printing (3DP). Between November 2017 and April 2021, patients who underwent RFA at the First Affiliated Hospital of Xinxiang Medical University were consecutively enrolled and randomly allocated into two groups: the 3DP group and the control group, in a 1:1 ratio. Computed tomography angiography (CTA) was employed to capture the morphology and diameter of the LA and PV, which facilitated the construction of a 3D entity model. Additionally, surgical procedures were simulated using the 3D model. Parameters such as the duration of the procedure, complications, and rates of RFA recurrence were meticulously documented. Statistical analysis was performed using the t-test or Mann-Whitney U test to evaluate the differences between the groups, with a P-value of less than 0.05 considered statistically significant. In this study, a total of 122 patients were included, with 53 allocated to the 3DP group and 69 to the control group. The analysis of the morphological measurements of the LA and PV taken from the workstation or direct entity measurement showed no significant difference between the two groups (P > 0.05). However, patients in the 3DP group experienced significantly shorter RFA times (97.03 ± 28.39 compared to 120.51 ± 44.76 min, t = 3.05, P = 0.003), reduced duration of radiation exposure (2.55 [interquartile range 2.01, 3.24] versus 3.20 [2.28, 3.91] min, Z = 3.23, P < 0.001), and shorter modeling times (7.68 ± 1.03 compared to 8.89 ± 1.45 min, t = 5.38, P < 0.001). 3DP technology has the potential to enhance standard RFA practices by reducing the time required for intraoperative interventions and exposure to radiation.

Data mining-based identification of epigenetic signatures with discrimination potential of lung adenocarcinoma and squamous cell carcinoma.

BACKGROUND: Mounting evidence suggests that lung adenocarcinoma (LAC) and lung squamous cell carcinoma (LSC) have different biological behaviors and therapeutic regimens in clinical practice. However, limited improvements in molecular differential diagnosis of the two entities have been achieved in recent decades. We aimed to find novel markers that could define non-small cell lung cancer (NSCLC) subtypes. METHODS: We first explored publically available databases to search for DNA methylation signatures that enable a precise discrimination of LAC and LSC. Next-generation sequencing (NGS) was then used to analyze the methylation status and sites of candidate genes in LAC/LSC tissue samples, and a quantitative methylation-sensitive PCR (qMS-PCR) assay was conducted to test the performance of the selected maker in tissue samples and bronchoalveolar lavage fluid (BALF) specimens. RESULTS: We screened 19 top-ranked methylation loci that are differentially methylated between LAC and LSC. Among these hits, 6 methylation sites are enriched within the PREX1 gene promoter, thus becoming our focus. NGS analysis confirmed markedly higher PREX1 methylation levels in LAC than in LSC and revealed the right sites for detection of PREX1 methylation. Furthermore, PREX1 methylation analysis in lung cancer tissue samples defined 9 of 11 pathologically proven LACs, as well as 12 of 14 LSCs. In addition, ~ 80% LAC BALF samples showed methylated PREX1 compared to substantially lower test positivity (0-9%) of it in LSC and other lung conditions (P < 0.01). CONCLUSION: Our pilot study identified a unique epigenetic signature that could effectively distinguish LAC from LSC in various lung samples. It may enhance our in-depth understanding of the biology of lung cancer and pave the way for better accurate diagnosis and treatment stratification in the future.

Prediction of MicroRNA-Disease Potential Association Based on Sparse Learning and Multilayer Random Walks.

More and more studies have shown that microRNAs (miRNAs) play an indispensable role in the study of complex diseases in humans. Traditional biological experiments to detect miRNA-disease associations are expensive and time-consuming. Therefore, it is necessary to propose efficient and meaningful computational models to predict miRNA-disease associations. In this study, we aim to propose a miRNA-disease association prediction model based on sparse learning and multilayer random walks (SLMRWMDA). The miRNA-disease association matrix is decomposed and reconstructed by the sparse learning method to obtain richer association information, and at the same time, the initial probability matrix for the random walk with restart algorithm is obtained. The disease similarity network, miRNA similarity network, and miRNA-disease association network are used to construct heterogeneous networks, and the stable probability is obtained based on the topological structure features of diseases and miRNAs through a multilayer random walk algorithm to predict miRNA-disease potential association. The experimental results show that the prediction accuracy of this model is significantly improved compared with the previous related models. We evaluated the model using global leave-one-out cross-validation (global LOOCV) and fivefold cross-validation (5-fold CV). The area under the curve (AUC) value for the LOOCV is 0.9368. The mean AUC value for 5-fold CV is 0.9335 and the variance is 0.0004. In the case study, the results show that SLMRWMDA is effective in inferring the potential association of miRNA-disease.

Core and variable antimicrobial resistance genes in the gut microbiomes of Chinese and European pigs.

Monitoring the prevalence of antimicrobial resistance genes (ARGs) is vital for addressing the global crisis of antibiotic-resistant bacterial infections. Despite its importance, the characterization of ARGs and microbiome structures, as well as the identification of indicators for routine ARG monitoring in pig farms, are still lacking, particularly concerning variations in antimicrobial exposure in different countries or regions. Here, metagenomics and random forest machine learning were used to elucidate the ARG profiles, microbiome structures, and ARG contamination indicators in pig manure under different antimicrobial pressures between China and Europe. Results showed that Chinese pigs exposed to high-level antimicrobials exhibited higher total and plasmid-mediated ARG abundances compared to those in European pigs ( P<0.05). ANT(6)-Ib, APH(3')-IIIa, and tet(40) were identified as shared core ARGs between the two pig populations. Furthermore, the core ARGs identified in pig populations were correlated with those found in human populations within the same geographical regions. Lactobacillus and Prevotella were identified as the dominant genera in the core microbiomes of Chinese and European pigs, respectively. Forty ARG markers and 43 biomarkers were able to differentiate between the Chinese and European pig manure samples with accuracies of 100% and 98.7%, respectively. Indicators for assessing ARG contamination in Chinese and European pigs also achieved high accuracy ( r=0.72-0.88). Escherichia flexneri in both Chinese and European pig populations carried between 21 and 37 ARGs. The results of this study emphasize the importance of global collaboration in reducing antimicrobial resistance risk and provide validated indicators for evaluating the risk of ARG contamination in pig farms.

The silk gland proteome of Stenopsyche angustata provides insights into the underwater silk secretion.

Caddisworms (Trichoptera) spin adhesive silks to construct a variety of underwater composite structures. Many studies have focused on the fibroin heavy chain of caddisworm silk and found that it contains heavy phosphorylation to maintain a stable secondary structure. Besides fibroins, recent studies have also identified some new silk proteins within caddisworm silk. To better understand the silk composition and its secretion process, this study reports the silk gland proteome of a retreat-building caddisworm, Stenopsyche angustata Martynov (Trichoptera, Stenopsychidae). Using liquid chromatography tandem mass spectrometry (LC-MS/MS), 2389 proteins were identified in the silk gland of S. angustata, among which 192 were predicted as secreted silk proteins. Twenty-nine proteins were found to be enriched in the front silk gland, whereas 109 proteins were enriched in the caudal silk gland. The fibroin heavy chain and nine uncharacterized silk proteins were identified as phosphorylated proteins. By analysing the sequence of the fibroin heavy chain, we found that it contains 13 Gly/Thr/Pro-rich regions, 12 Val/Ser/Arg-rich regions and a Gly/Arg/Thr-rich region. Three uncharacterized proteins were identified as sericin-like proteins due to their larger molecular weights, signal peptides and repetitive motifs rich in serine. This study provides valuable information for further clarifying the secretion and adhesion of underwater caddisworm silk.

Band Structure Optimized by Electron-Acceptor Cations for Sensitive Perovskite Single Crystal Self-Powered Photodetectors.

Low-dimensional perovskites afford improved stability against moisture, heat, and ionic migration. However, the low dimensionality typically results in a wide bandgap and strong electron-phonon coupling, which is undesirable for optoelectronic applications. Herein, semiconducting A-site organic cation engineering by electron-acceptor bipyridine (bpy) cations (2,2'-bpy2+ and 4,4'-bpy2+) is employed to optimize band structure in low-dimensional perovskites. Benefiting from the merits of lower lowest unoccupied molecular orbital (LUMO) energy for 4,4'-bpy2+ cation, the corresponding (4,4'-bpy)PbI4 is endowed with a smaller bandgap (1.44 eV) than the (CH3NH3)PbI3 (1.57 eV) benchmark. Encouragingly, an intramolecular type II band alignment formation between inorganic Pb-I octahedron anions and bpy2+ cations favors photogenerated electron-hole pairs separation. In addition, a shortening distance between inorganic Pb-I octahedral chains in (4,4'-bpy)PbI4 single crystal (SC) can effectively promote carrier transfer. As a result, a self-powered photodetector based on (4,4'-bpy)PbI4 SC exhibits 131 folds higher on/off ratio (3807) than the counterpart of (2,2'-bpy)2Pb3I10 SC (29). The presented result provides an effective strategy for exporting novel organic cation-based low-dimensional perovskite SC for high-performance optoelectronic devices.