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In this study, five hydrochlorides are used as additives to slow the crystallization rate between SnI2 and AI (A+=MA+, FA+, Cs+) to prepare high-quality perovskite films. This study on film morphology, crystallization and non-radiative recombination demonstrates the effects of organic amine cations with different chain lengths on the quality of thin films. A flexible solar cell device with a lead-free perovskite film doped with PACl is also obtained achieving an efficiency of 5.52 %.

In this study, we prepared a novel three-dimensional boron-doped diamond (BDD) electrode. Subsequently, we systematically conducted an electrochemical oxidation reaction on sulfamethoxazole (SMX) using a BDD anode and a platinum plate (Pt) cathode. The effects of initial concentration, current density, initial pH, and carrier electrolyte on SMX degradation were investigated. The SMX was completely removed after 4 h of electrolysis at a current density of 30 mA/cm2, 0.1 mol sodium sulphate as the supporting electrolyte, and a pH of 7. Additionally, the COD removal rate was 65.6 %, while the energy consumption was 40.1 %. Compared with two-dimensional BDD electrode degradation, the energy saving was 54 %. Density functional theory (DFT) and high-performance liquid chromatography (HPLC) were used to analyse the SMX degradation mechanism. Three possible degradation pathways were proposed: ·OH substitution of the amino group in the aromatic ring, oxidation of the amino group to nitrogen dioxide, and addition of ·OH to the isoxazole ring. The active sites detected in the reaction corresponded closely with the calculated results using DFT. Moreover, ECOSAR toxicity analysis was applied to evaluate the toxicity of the intermediates produced during electrolysis. We discovered that the toxicity of SMX and its intermediates decreased significantly during electrolysis.

Environmental assessments are critical for ensuring the sustainable development of human civilization. The integration of artificial intelligence (AI) in these assessments has shown great promise, yet the ''black box'' nature of AI models often undermines trust due to the lack of transparency in their decision-making processes, even when these models demonstrate high accuracy. To address this challenge, we evaluated the performance of a transformer model against other AI approaches, utilizing extensive multivariate and spatiotemporal environmental datasets encompassing both natural and anthropogenic indicators. We further explored the application of saliency maps as a novel explainability tool in multi-source AI-driven environmental assessments, enabling the identification of individual indicators' contributions to the model's predictions. We find that the transformer model outperforms others, achieving an accuracy of about 98% and an area under the receiver operating characteristic curve (AUC) of 0.891. Regionally, the environmental assessment values are predominantly classified as level II or III in the central and southwestern study areas, level IV in the northern region, and level V in the western region. Through explainability analysis, we identify that water hardness, total dissolved solids, and arsenic concentrations are the most influential indicators in the model. Our AI-driven environmental assessment model is accurate and explainable, offering actionable insights for targeted environmental management. Furthermore, this study advances the application of AI in environmental science by presenting a robust, explainable model that bridges the gap between machine learning and environmental governance, enhancing both understanding and trust in AI-assisted environmental assessments.

In the present paper, quantum coherence of fermionic Gaussian states is investigated from the point of quantum resource theory. Some characterizations of m-mode (m≥1) incoherent fermionic Gaussian states and incoherent fermionic Gaussian channels are obtained, two quantification measures of fermionic Gaussian quantum coherence based on respectively the relative entropy and the Hellinger distance are proposed, and some formulas to calculate the value of these measures are established. It is shown that the m-mode fermionic Gaussian coherence is a quantum resource.

Fe–20Mn–9Al-1.5C–2Ni–3Cr low-density steel is subjected to multi-stage heat treatment (MHT) to improve the toughness while maintaining high strength and ductility. The mechanical properties and fracture behavior of MHT steel are compared with those resulting from single-stage heat treatment (S1000 and S1050) usnig SEM, EBSD, and TEM microstructural characterization. The grain sizes of S1000, S1050 and MHT are 3.17, 3.84, and 7.31 μm, respectively. The proportion of “special” grain boundaries changes from 71.7 % to 68.3 % and finally to 71.2 % (by length). Simultaneously, dissolution of intergranular Cr7C3 precipitates in S1000. The half-size impact energy of MHT is 88.6 % higher than that of S1000, reaching 55.1 J, and the ultimate tensile strength and total elongation are maintained at approximately 1100 MPa and 50 %, respectively. As demonstrated by quantitative analysis, the yield strength is maintained at 910 MPa due to solution, grain boundary, and precipitation strengthening, whereas the increase in toughness is due to the rise in the crack propagation energy. The increased toughness is derived from the combined effect of reducing the number of intergranular precipitates and a high percentage of “special grain boundaries''.

Researching infrared adversarial attacks is crucial for ensuring the safe deployment of security-sensitive systems reliant on infrared object detectors. However, current research on infrared adversarial attacks mainly focuses on pedestrian detection tasks. Due to the complex shape and structure of vehicles and the changing working conditions, adversarial attack in infrared vehicle detection pose challenges like difficult multi-angle attack, poor physical transferability, and weak environmental adaptation. This paper proposed Leaf-like Mask Bar Code (LMBC), a novel adversarial attack method for multi-angle physical black-box attack on infrared detectors. Inspired by natural leaf structures, a mask was designed to restrict the adversarial patch contour. Then, adversarial parameters of the patches (angle, sparsity, and position) were optimized using the Genetic Algorithm with Multi-segment (GAM). Moreover, leaf-like structures in physical adversarial patches were constructed using suitable infrared coating materials. deploying them at multiple angles. Experimental results demonstrated LMBC’s efficacy, paralyzing the infrared vehicle detector with an Average Precision (AP) as low as 33.7% and an average Attack Success Rate (ASR) as high as 92.9% across a distance of 2.4m 4.2 m and angles of 0° 360°. Moreover, LMBC’s adversarial patches transferred to mainstream detectors (e.g., Faster RCNN, Yolov3, etc.) and pedestrian detection tasks.

Objectives: Fungal bloodstream infection (fBSI) following pediatric liver transplantation presents a significant challenge; however, there remains a paucity of guidance regarding antifungal prophylaxis in this population. This study aimed to evaluate the effectiveness of universal antifungal prophylaxis and propose a desirable strategy. Methods: We enrolled 604 pediatric patients who underwent liver transplantation between 2020 and 2023, including 242 patients with empirical prophylaxis and 362 patients with universal prophylaxis. Univariate and multivariate logistic regression analyses were performed to identify independent factors for fBSI. Results: Eight (2.2%) pediatric recipients in the universal prophylaxis group and 13 (5.4%) in the empirical group developed fBSI (P = 0.038). Universal prophylaxis was a protective factor (P = 0.044), while high-volume intraoperative plasma transfusion and deceased donor liver transplantation were independent risk factors for fBSI (P = 0.035 and 0.008, respectively). Universal antifungal strategy showed an increased overall survival trend after liver transplantation although without significant statistical difference (P = 0.217). Patients with fBSI had poorer survival than those without fBSI (P

Against the background of China’s strengthening of finance and accounting supervision, this study examines the practice among listed companies of changing signing auditors at the last minute and explores whether Chinese investors can capture this information in a timely manner. We find that China’s capital market responds significantly negatively to these last-minute changes, implying that investors perceive a potential negative impact of this behavior. Cross-sectional analyses suggest that the characteristics of the change event, recent corporate events, and accounting firm capability significantly affect the stock price response. Furthermore, in terms of the individual characteristics of signing auditors, external investors appear to comprehensively consider busyness level, industry experience, and the timing of the change to determine the causes and effects of the auditor change and make different market reactions accordingly. In addition, consistent with investor perceptions, we find that last-minute changes significantly impair the quality of financial statements, indicating that external investors’ judgments based on information about changes in signing auditors are rational and effective.

Context: Diabetes mellitus (DM) is one of the fastest-growing diseases worldwide; however, its pathogenesis remains unclear. Complications seriously affect the quality of life of patients in the later stages of diabetes, ultimately leading to suffering. Natural small molecules are an important source of antidiabetic agents. Objective: Astragaloside IV (AS-IV) is an active ingredient of Astragalus mongholicus (Fisch.) Bunge. We reviewed the efficacy and mechanism of action of AS-IV in animal and cellular models of diabetes and the mechanism of action of AS-IV on diabetic complications in animal and cellular models. We also summarized the safety of AS-IV and provided ideas and rationales for its future clinical application. Methods: Articles on the intervention in DM and its complications using AS-IV, such as those published in SCIENCE, PubMed, Springer, ACS, SCOPUS, and CNKI from the establishment of the database to February 2022, were reviewed. The following points were systematically summarized: dose/concentration, route of administration, potential mechanisms, and efficacy of AS-IV in animal models of DM and its complications. Results: AS-IV has shown therapeutic effects in animal models of DM, such as alleviating gestational diabetes, delaying diabetic nephropathy, preventing myocardial cell apoptosis, and inhibiting vascular endothelial dysfunction; however, the potential effects of AS-IV on DM should be investigated. Conclusion: AS-IV is a potential drug for the treatment of diabetes and its complications, including diabetic vascular disease, cardiomyopathy, retinopathy, peripheral neuropathy, and nephropathy. In addition, preclinical toxicity studies indicate that it appears to be safe, but the safe human dose limit is yet to be determined, and formal assessments of adverse drug reactions among humans need to be further investigated. However, additional formulations or structural modifications are required to improve the pharmacokinetic parameters and facilitate the clinical use of AS-IV.

Colorectal cancer (CRC), also known as colon cancer, is the third most common cancer and the fourth most cause of cancer-related death in the world. CRC can be classified into two major subtypes, including microsatellite instability (MSI) and microsatellite stability (MSS), which showed different characteristics in immunotherapy. Low sensitivity of diagnostic biomarkers and metastasis are still the principal cause of mortality, especially in MSI. Here, applying computational programs, we identified recurring expression programs based on single cell RNA sequencing (scRNA-Seq) data of CRC cell lines. Notably, three MSI specific recurring modules were identified by non-negative matrix factorization (NMF). High NMF score genes enriched in the function of metabolism and inflammatory response. Focusing on top specific active transcription factor (TF), RUNX3 (Runt-related transcription factor 3), our results suggest that T cell infiltration was increased in RUNX3 high MSI CRC samples. Unbiased Gene Set Enrichment Analysis (GSEA) showed that RUNX3 was strongly associated with immune and metastasis related functions, such as Interferon Gamma (IFN-γ) and EPITHELIAL MESENCHYMAL TRANSITION (EMT). In addition, RUNX3 shows specific highly activated at epigenetic level in MSI compared with other gastrointestinal carcinomas. Positive correlation between RUNX3 and most immune checkpoints further confirmed RUNX3 might have crucial roles in MSI cancer progression and immunotherapy. Taken together, these results indicate significant tumor heterogeneity of two CRC subtypes at single-cell level and epigenetic modification level. These results also linked transcriptional dysregulation with immune infiltration at single-cell level in MSI, which may advance the application of scRNA-Seq technology in immunotherapy and contribute to developing novel biomarkers of this malignancy.

Protein oxidation leads to changes in shrimp texture, which affects sensory profile and consumer acceptability. This study aimed to evaluate the impact of electron beam irradiation (EBI) on protein oxidation and textural properties of Litopenaeus vannamei during refrigerated storage. Results revealed that EBI treatment and storage increased the protein oxidation level of shrimps. Shrimps irradiated with ≥ 7 kGy exhibited remarkably higher (P

ABSTRACT: The purpose of this experiment was to explore the effects of zinc supplementation in breeding pigeons diet on carcass traits, meat quality, antioxidant capacity and mRNA expressions of myogenic regulatory factors of squabs. A total of 120 healthy White King pigeons were randomly assigned to 5 treatments, each involving 8 replicates. The experiment lasted for 46 d (18-d incubation period of eggs and 28-d growth period of squabs). The 5 groups were 0, 30, 60, 90, and 120 mg/kg zinc addition. Results showed that the 28-d body weight, breast muscle yield, zinc content in crop milk and myogenic factor 6 (MyF6) abundance of breast muscle were linearly increased (P < 0.050), but the abdominal fat yield linearly decreased (P = 0.040) with increasing dietary zinc supplementation. Both the linear (P < 0.050) and quadratic responses (P < 0.001) were observed in copper zinc superoxide dismutase (Cu-Zn SOD), total antioxidant capacity (T-AOC) and malondialdehyde (MDA) contents in liver and breast muscle. The 28-d body weight was increased by 90 mg/kg zinc supplementation (P < 0.05), and there is no significant difference between 90 and 120 mg/kg zinc addition. The breast muscle yield, Cu-Zn SOD and T-AOC contents in breast muscle and liver, zinc contents in crop milk and breast muscle, MyF6 mRNA expression in breast muscle were higher (P < 0.05) in the group supplemented with 120 mg/kg zinc than the control. The abdominal fat yield was numerically lowest, and MDA contents in breast muscle and liver were significantly lowest in the group fed 120 mg/kg zinc (P < 0.05). However, the meat quality traits were not affected (P > 0.05) by zinc supplementation, except for shear force. It should be stated dietary zinc supplementation at the level of 120 mg/kg for breeding pigeons increased body weight and breast muscle yield of squabs, which may be associated with the up-regulating MyF6 mRNA expression and antioxidant capacity in liver and breast muscle.

Maintaining the stability of natural capital stock is an important basis for realizing regional sustainable development goals. Based on the improved three-dimensional (3D) footprint family model, this study analyzes the spatial and temporal evolution of the natural capital utilization of water-carbon-ecological footprints in the Central Plains Urban Agglomeration (CPUA) from 2010 to 2020. It considers water resources, carbon emissions and ecological environment comprehensively, and establishes an evaluation system to assess the regional resource and environmental pressure. The results showed that: (1) During the period of 2010–2020, each component of the per capita water-carbon-ecological footprint of the CPUA were in deficit. Footprint depths were all greater than 1 and in a state of depleting capital stock. The water footprint depth (WFdepth) showed a large fluctuation between 1.721 and 4.302, the carbon footprint depth (CFdepth) increased from 6.025 to 7.440, and the ecological footprint depth (EFdepth) fluctuated between 4.801 and 5.779. (2) There were spatial differences in the depth of each type of footprint, with the WFdepth showing a south-high and north-low distribution, the CFdepth showing a northwest-high and southeast-low distribution, and the EFdepth showing an east-high and west-low distribution. (3) Exploratory spatial data analysis (ESDA) showed that there were spatial correlations between the various types of footprint size and footprint depth in the CPUA, and that cities showed local spatial characteristics of High-High cluster and Low-Low cluster. (4) All types of resource and environmental pressure measured were at high levels (>0.8), and the resource-environment pressure index (REPI) in the northwest showed a gradual increasing trend.

The incidence of diabetes mellitus (DM) is gradually increasing, making it a widespread global health concern. Cadmium (Cd) is a common toxic heavy metal in the environment, and cadmium exposure may be associated with diabetic nephropathy (DN). However, the mechanism of Cd-induced DN remains unclear. In this study, we aimed to determine the effect of cadmium on diabetic kidney injury and the underlying mechanism in diabetic rats and a renal tubular epithelial cell line (NRK-52E cells). Our results could provide novel insights on the nephrotoxic mechanism of cadmium. HE, PAS, and Masson staining were used to observe pathological renal injury. COL-I, COL-IV, CTSB, and CTSD protein levels were detected by immunohistochemistry and western blotting. Immunofluorescence was used to detect the fluorescence intensity of p62 and LC3 proteins in kidney tissue. TEM was used to observe the ultrastructure of mitochondria and number of autophagosomes. After cadmium exposure, DM rats showed a dramatic decrease in body weight compared to the unexposed DM group. Relative kidney weight showed a contrasting trend after cadmium exposure. Urinary microalbumin/creatinine significantly increased in normal and DM rats after cadmium exposure. However, the trend was clearer in the DM groups than in the control groups. Endogenous creatinine clearance exhibited a contrasting trend. After cadmium exposure in DM rats, MDA content significantly increased and GSH, CAT, SOD, and GSH-PX activation reduced compared to normal controls. Pathological damage was more pronounced, and the expression of autophagy related proteins and apoptosis and fibrosis proteins was significantly higher in vivo and vitro in the cadmium-exposed groups than in unexposed controls. Further, lysosomal protein levels were lower, and ROS content and autophagosome count significantly higher in the cadmium exposed groups compared to the unexposed controls. Therefore, Cadmium exposure aggravates diabetic kidney injury via autophagy inhibition.

This study aims to investigate the evolution patterns of fluidity and rheological properties of AASCM under varying dosages of foaming agent and particle sizes of filling aggregate. The flow characteristics of AASCM are significantly affected by the filling aggregate's size and the foaming agent's dosage. Specifically, an increase in filling aggregate size (D(4,3) ϵ [26 μm, 69 μm]) enhances the fluidity of foamed AASCM, while an increase in foaming agent dosage reduces fluidity. These observed variations can be attributed to the presence of particle voids, the specific surface area of the aggregate, as well as the quantity and spatial distribution of bubbles within the slurry. A bubble-particle packing model is established, and by calibrating the simulation error coefficient to 1.1, the study investigates the evolution of water film thickness (WFT) in foamed AASCM with slurry expansion degree. It is observed that bubbles in the slurry affect the fluidity by altering the overall compactness and specific surface area of the foamed slurry, subsequently modifying the WFT.

The environmental concerns resulting from coal-fired power generation that produces large amounts of CO2 and fly ash are of great interest. To mitigate, this study aims to develop a novel carbonated CO2-fly ash-based backfill (CFBF) material under ambient conditions. The performance of CFBF was investigated for different fly ash–cement ratios and compared with non-CO2 reacted samples. The fresh CFBF slurry conformed to the Herschel-Bulkley model with shear thinning characteristics. After carbonation, the yield stress of the fresh slurry increased significantly by lowering fly ash ratio due to gel formation. The setting times were accelerated, resulting in approximately 40.6% of increased early strength. The final strength decreased when incorporating a lower fly ash ratio (50% and 60%), which was related to the existing heterogeneous pores caused by rapid fluid loss. The strength increased with fly ash content above 70% because additional C–S(A)–H and silica gels were characterized to precipitate on the grain surface, so the binding between particles increased. The C–S(A)–H gel was developed through the pozzolanic reaction, where CaCO3 was the prerequisite calcium source obtained in the CO2-fly ash reaction. Furthermore, the maximum CO2 uptake efficiency was 1.39 mg-CO2/g-CFBF. The CFBF material is feasible to co-dispose CO2 and fly ash in the mine goaf as negative carbon backfill materials, and simultaneously mitigates the strata movement and water lost in post-subsurface mining.

Mulching is widely recognized as an effective soil and water conservation measure all over the world. Nevertheless, a comprehensive evaluation of mulching's effectiveness in controlling soil erosion and the influencing factors is still lacking. A global meta-analysis based on 421 runoff and 512 soil loss observations from 90 publications was conducted to evaluate the effectiveness of mulching in reducing runoff and soil loss across a variety of variables (i.e. mulch type, mulch coverage and application rates, rainfall intensity, land use, soil texture, slope gradient, and slope length). Our meta-analysis shows that overall, mulching significantly reduced runoff and soil loss by 47.4% and 76.2%, respectively. Straw mulch and wood-based mulch are generally more effective in reducing runoff and soil loss than rock fragments, highlighting the effectiveness of organic materials in controlling water erosion. In general, increasing mulch coverage and application rates resulted in a significant reduction in runoff and soil loss. It is recommended, however, that mulch coverage should be at least 60% based on the trade-off between economic costs and ecological benefits, which can reduce runoff and soil loss by approximately 50% and 80%, respectively. An application rate of 0.3–0.4 kg m−2 for straw and 0.6–0.8 kg m−2 for wood-based mulch should be sufficient to effectively control soil erosion. The study also found that mulching is more effective in post-fire forests on medium-textured soils and less effective on steep slopes (>60%) and under both natural and simulated extremely heavy rainfall conditions (>90 mm/h). Overall, this study provides further insights into the impact of mulching on water erosion globally and proposes an overall framework for a precision mulching strategy (P-M-S) to guide the implementation of mulching in soil erosion control.

Edge intelligence is an emerging technology that enables artificial intelligence on connected systems and devices in close proximity to the data sources. decentralized collaborative learning (DCL) is a novel edge intelligence technique that allows distributed clients to cooperatively train a global learning model without revealing their data. DCL has a wide range of applications in various domains, such as smart city and autonomous driving. However, DCL faces significant challenges in ensuring its trustworthiness, as data isolation and privacy issues make DCL systems vulnerable to adversarial attacks that aim to breach system confidentiality, undermine learning reliability or violate data privacy. Therefore, it is crucial to design DCL in a trustworthy manner, with a focus on security, robustness, and privacy. In this survey, we present a comprehensive review of existing efforts for designing trustworthy DCL systems from the three key aformentioned aspects: security, robustness, and privacy. We analyze the threats that affect the trustworthiness of DCL across different scenarios and assess specific technical solutions for achieving each aspect of trustworthy DCL (TDCL). Finally, we highlight open challenges and future directions for advancing TDCL research and practice.

Summary: Hippocampal CA1 neurons generate single spikes and stereotyped bursts of spikes. However, it is unclear how individual neurons dynamically switch between these output modes and whether these two spiking outputs relay distinct information. We performed extracellular recordings in spatially navigating rats and cellular voltage imaging and optogenetics in awake mice. We found that spike bursts are preferentially linked to cellular and network theta rhythms (3–12 Hz) and encode an animal’s position via theta phase precession, particularly as animals are entering a place field. In contrast, single spikes exhibit additional coupling to gamma rhythms (30–100 Hz), particularly as animals leave a place field. Biophysical modeling suggests that intracellular properties alone are sufficient to explain the observed input frequency-dependent spike coding. Thus, hippocampal neurons regulate the generation of bursts and single spikes according to frequency-specific network and intracellular dynamics, suggesting that these spiking modes perform distinct computations to support spatial behavior.

This study aimed to achieve the rapid evaluation of moisture, ash and protein of sweet potato simultaneously by near-infrared (NIR) hyperspectral imaging (900–1700 nm). Hyperspectral images of 300 samples for each parameter were acquired and the spectra within images were extracted, averaged and preprocessed to relate to the three measured parameters, using partial least squares (PLS) algorithm, respectively, resulting in good performances. Nine, eleven and eleven informative wavelengths were selected to accelerate the prediction of the three parameters, generating a correlation coefficient of prediction (rP) of 0.984, 0.905, 0.935 and root mean square error of prediction (RMSEP) of 0.907%, 0.138%, 0.0941% for moisture, ash and protein, respectively. By transferring the best optimized PLS models to generate color chemical maps, the distributions and variations of the three parameters were visualized. NIR hyperspectral imaging is promising and can be applied to simultaneously evaluate multiple quality parameters of sweet potato.

Heavy metal pollution caused by a large number of abandoned industrial sites cannot be underestimated, but its human health risks have not been accurately assessed. This study investigated the pollution of heavy metals in soils of the typical abandoned industrial sites in southeastern China. Based on the bioaccessibility of different heavy metals (Pb, Ni, Cu, Zn, Cd, Cr) in the industrial soils, the human health risks were accurately evaluated, and the controlling factors were quantitatively assessed. The results showed that the heavy metals in each typical abandoned industrial sites had a high degree of spatial heterogeneity. Among them, Cd was the most susceptible to relevant discrete input from external factors such as human activities, followed by Zn, Pb, Cr, Ni and Cu. The bioaccessible concentration of heavy metals by the physiological-based extraction test (PBET) had a good correlation (R2 = 0.58 ∼ 0.86) with its bioavailable concentration by diethylenetriaminepentaacetic acid (DTPA) extraction. The regression model based on soil parameters had great potential to predict the bioaccessibility of heavy metals in abandoned industrial sites (R2 = 0.49 ∼ 0.95). The total concentration of heavy metals, Fe, soil texture and pH were the controlling factors of the metal bioaccessibility. Compared with the total concentration, the hazard index (HI) and carcinogenic risk (CR) values calculated based on gastrointestinal bioaccessibility were decreased by 39.0∼77.9% and 68.2∼79.9% in adults, and 45.3∼88.0% and 73.9∼83.5% in children, respectively. This work provides a feasible theoretical basis for reliable assessment of the human health risks of heavy metals in the abandoned industrial sites in the future.

Background: Difficult airway conditions represent a substantial challenge for clinicians. Predicting such conditions is essential for subsequent treatment planning, but the reported diagnostic accuracies are still quite low. To overcome these challenges, we developed a rapid, non-invasive, cost-effective, and highly-accurate deep-learning approach to identify difficult airway conditions through photographic image analysis. Methods: For each of 1000 patients scheduled for elective surgery under general anesthesia, images were captured from 9 specific and different viewpoints. The collected image set was divided into training and testing subsets in the ratio of 8:2. We used a semi-supervised deep-learning method to train and test an AI model for difficult airway prediction. Results: We trained our semi-supervised deep-learning model using only 30% of the labeled training samples (with the remaining 70% used without labels). We evaluated the model performance using metrics of accuracy, sensitivity, specificity, F1-score, and the area under the ROC curve (AUC). The numerical values of these four metrics were found to be 90.00%, 89.58%, 90.13%, 81.13%, and 0.9435, respectively. For a fully-supervised learning scheme (with 100% of the labeled training samples used for model training), the corresponding values were 90.50%, 91.67%, 90.13%, 82.25%, and 0.9457, respectively. When three professional anesthesiologists conducted comprehensive evaluation, the corresponding results were 91.00%, 91.67%, 90.79%, 83.26%, and 0.9497, respectively. It can be seen that the semi-supervised deep learning model trained by us with only 30% labeled samples can achieve a comparable effect with the fully supervised learning model, but the sample labeling cost is smaller. Our method can achieve a good balance between performance and cost. At the same time, the results of the semi-supervised model trained with only 30% labeled samples were very close to the performance of human experts. Conclusions: To the best of our knowledge, our study is the first one to apply a semi-supervised deep-learning method in order to identify the difficulties of both mask ventilation and intubation. Our AI-based image analysis system can be used as an effective tool to identify patients with difficult airway conditions. Clinical trial registration: ChiCTR2100049879 (URL: http://www.chictr.org.cn).

Summary: The cellular and molecular actions of general anesthetics to induce anesthesia state and also cellular signaling changes for subsequent potential “long term” effects remain largely elusive. General anesthetics were reported to act on voltage-gated ion channels and ligand-gated ion channels. Here we used single-cell RNA-sequencing complemented with whole-cell patch clamp and calcium transient techniques to examine the gene transcriptome and ion channels profiling of sevoflurane and propofol, both commonly used clinically, on the human fetal prefrontal cortex (PFC) mixed cell cultures. Both propofol and sevoflurane at clinically relevant dose/concentration promoted “microgliosis” but only sevoflurane decreased microglia transcriptional similarity. Propofol and sevoflurane each extensively but transiently (

We experimentally studied the dynamic process of dissipative soliton build-up by dispersive Fourier transform in net normal dispersion cavity. The oscillation of spectral bandwidth and pulse energy before the stabilization of dissipative soliton was observed, the build-up and termination of the double dissipative solitons accompanied with soliton explosion and energy interaction were observed with higher pump power. Simulation of dissipative soliton build-up process was demonstrated through the extended nonlinear Schrödinger equation, negative feedback mechanism was summarized as the reason of the pulse oscillation during the build-up process.

Glycoprotein nonmetastatic melanoma protein B (GPNMB) is a vital secreted factor that promotes the occurrence of obesity in mammals. However, the effects of GPNMB on abdominal fat deposition is still unknown in chickens. In this study, we looked into the genetic and expression association of GPNMB gene with abdominal fat traits in chicken, and found that a genetic variation rs31126482 in GPNMB promoter was significantly associated with abdominal fat weight (AFW, P < 0.05) and abdominal fat percentage (AFP, P < 0.01). Express profile analysis of the GPNMB indicated that the gene was mainly expressed in abdominal fat tissue, and its expression level was strongly positively correlated with AFW (R2 = 0.6356, P = 4.10E−05) and AFP (R2 = 0.6450, P = 2.90E−05). We then investigated biological function of GPNMB on adipogenesis in chicken, and found that GPNMB could inhibit abdominal preadipocyte proliferation, but promote abdominal preadipocyte differentiation and lipid deposition. Furthermore, we explored regulatory mechanism of GPNMB gene in chicken, and detected one nonclassical estrogen regulatory element (AP1) and one peroxisome proliferator-activated receptor α (PPARα) responsive element in the 2 kb promoter region of GPNMB gene, and demonstrated that estrogen could up-regulate GPNMB mRNA expression in adipose tissue and primary abdominal preadipocytes, while PPARα could down-regulate GPNMB expression in primary preadipocytes. Taken together, this study brings new insights into understanding the function and transcriptional control of GPNMB gene, and provides genetic markers for breeding selection to improve abdominal fat traits in chicken.

Dissemination of antibiotic resistance gene (ARG) is a huge challenge around the world. Natural organic matter (NOM) is one of the most commonly components in aquatic systems. Information regarding ARG transfer induced by NOM is still lacking. In this study, experimental exploration and model prediction on RP4 plasmid conjugative transfer between bacteria under NOM exposure was conducted. Compared with no exposure, the conjugative transfer frequency of RP4 plasmid increased 7.1-fold and 3.2-fold under exposure to 10 kDa and 100 kDa NOM exposure, respectively. NOM exposure with a lower molecular weight and higher concentration promoted gene expressions related to reactive oxygen species generation, cell membrane permeability, intercellular contact, quorum sensing, and energy driving force. Concurrently, the expressions of conjugation genes in RP4 plasmid were also upregulated. Moreover, model prediction demonstrated that the maintenance of the acquired plasmid was shortened to 133 h under 10 kDa NOM exposure compared with the control (200 h). Long-term NOM exposure enhanced transfer frequency and transfer rate of ARG. This study firstly theoretically and experimentally revealed the underlying mechanisms for promoting ARG transfer by NOM.

Background: Sleep is critical to human beings in a surprisingly diverse set of ways, and there is, thus, continual investigation into the mechanisms of sleep. Although current studies have confirmed that multiple brain regions are involved in the regulation of both sleep and wakefulness, the association between certain important brain regions such as the insula and sleep is still unclear. Objective: The purpose of this study was to systematically review studies on the insula and sleep and to discuss the relationship between the insula and sleep. Methods: We searched the PubMed and Web of Science Core Collection (WoSCC) for articles on sleep and the insula. The time span was from inception to June 30, 2022. The search results were then narratively summarized. Results: A total of 939 studies were identified in the PubMed and WoSCC of which 115 studies were finally included in the narrative synthesis. These 115 studies can be roughly divided into 41 studies on insomnia, 39 on sleep deprivation, 33 on sleep-related experiments examining the insula, and 2 studies using basic experiments. Conclusion: The combined findings of many sleep-related studies have confirmed a close link between the insula and sleep loss, including insomnia, sleep deprivation, sleep-related disorders, and more. Although these results do not directly confirm that the insula is involved in sleep, a overall analysis of the results indicates that the insula may be a potential key brain region involved in sleep.

γ-Glutamyl peptides are small molecular peptides that greatly influence the perception of kokumi taste. Multiple natural peptides, including γ-glutamyl di- and tripeptides and their derivatives, have been isolated and identified from various foodstuffs. Furthermore, some peptides have been effectively synthesized by γ-glutamyl transpeptidase and L-glutaminases. This review summarizes several aspects related to kokumi γ-glutamyl peptides, including their evaluation and quantification methods, biosynthetic pathways, contributions to foodstuffs, and changes during food processing. A better and more accurate understanding of these peptides is essential for further improving food processing and enhancing the desirable characteristics of food.

Summary: Neutrophils played a key role in the innate immune responses. Less is known about whether and how the neutrophils recruited in the immunization sites affecting the vaccine-induced antibody responses. In the process of evaluating the efficacy of an oil-in-water emulsion-formulated vaccine in mice, we found that neutrophils were rapidly and massively recruited to immunization sites but were barely detected in the draining lymph nodes. Interestingly, B cell-activating factor (BAFF) was abundantly expressed in the recruiting neutrophils at a very early stage. The initial neutrophil-derived BAFF firstly brought about the B cell responses in the local part, then subsequently in lymphoid organs. Activated B cells produced more BAFF through TLR9-IRF5 signaling pathway, thereby amplifying the vaccine-induced antibody responses. Suppressing BAFF in the neutrophils could weaken the B cell activation and reduce the antibody production. The data indicate that vaccines endow neutrophils with the potential to orchestrate antibody responses at immunization sites.

The aim of the present study was to screen novel dipeptidyl peptidase IV (DPP-IV) inhibitory peptides from Macadamia integrifolia antimicrobial protein 2 (MiAMP2) and evaluate the potential antidiabetic targets and involved signaling pathways using in silico approaches. In silico digestion of MiAMP2 with pepsin, trypsin and chymotrypsin was performed with ExPASy PeptideCutter and the generated peptides were subjected to BIOPEP-UWM, iDrug, INNOVAGEN and Autodock Vina for further analyses. Six novel peptides EQVR, EQVK, AESE, EEDNK, EECK, and EVEE were predicted to possess good DPP-IV inhibitory potentials, water solubility, and absorption, distribution, metabolism, excretion, and toxicity properties. Molecular dynamic simulation and molecular docking displayed that AESE was the most potent DPP-IV inhibitory peptide and can bind with the active sites of DPP-IV through hydrogen bonding and van der Waals forces. The potential antidiabetic targets of AESE were retrieved from SwissTargetPrediction and GeneCards databases. Protein-protein interaction analysis identified BIRC2, CASP3, MMP7 and BIRC3 to be the hub targets. Moreover, the KEGG pathway enrichment analysis showed that AESE prevented diabetes through the apoptosis and TNF signaling pathways. These results will provide new insights into utilization of MiAMP2 as functional food ingredients for the prevention and treatment of diabetes.

Summary: Recent improvements in genetically encoded voltage indicators enabled optical imaging of action potentials and subthreshold transmembrane voltage in vivo. To perform high-speed voltage imaging of many neurons simultaneously over a large anatomical area, widefield microscopy remains an essential tool. However, the lack of optical sectioning makes widefield microscopy prone to background cross-contamination. We implemented a digital-micromirror-device-based targeted illumination strategy to restrict illumination to the cells of interest and quantified the resulting improvement both theoretically and experimentally with SomArchon expressing neurons. We found that targeted illumination increased SomArchon signal contrast, decreased photobleaching, and reduced background cross-contamination. With the use of a high-speed, large-area sCMOS camera, we routinely imaged tens of spiking neurons simultaneously over minutes in behaving mice. Thus, the targeted illumination strategy described here offers a simple solution for widefield voltage imaging of many neurons over a large field of view in behaving animals.

A 6 weeks pot culture experiment was carried out to investigate the stabilization effects of a modified biochar (BCM) on metals in contaminated soil and the uptake of these metals by wheat seedlings. The results showed that the application of BCM significantly increased the soil fertility, the biomass of wheat seedling roots increased by more than 50%, and soil dehydrogenase (DHA) and catalase (CAT) activities increased by 369.23% and 12.61%, respectively. In addition, with the application of BCM, the diethylenetriaminepentaacetic acid extractable (DTPA-extractable) Cd, Pb, Cu and Zn in soil were reduced from 2.34 to 0.38 mg/kg, from 49.27 to 25.65 mg/kg, from 3.55 mg/kg to below the detection limit and from 4.05 to 3.55 mg/kg, respectively. Correspondingly, the uptake of these metals in wheat roots and shoots decreased by 62.43% and 79.83% for Cd, 73.21% and 66.32% for Pb, 57.98% and 68.92% for Cu, and 40.42% and 43.66% for Zn. Furthermore, BCM application decreased the abundance and alpha diversity of soil bacteria and changed the soil bacterial community structure dramatically. Overall, BCM has great potential for the remediation of metal-contaminated soils, but its long-term impact on soil metals and biota need further research.

Ecosystem service (ES) flow reveals the transregional benefits transferred from service supply areas (SSAs) to service benefiting areas (SBAs), which correspond to the sellers and buyers of interregional ecological compensation, respectively. However, current ecological compensation policies usually ignore this close connection. This study took the water supply services (WSSs) with the most obvious flow characteristics as an example and established a universal framework for interregional ecological compensation by combining WSSs flow simulation and regional disparity. The simulation process was programmed with Interactive Data Language (IDL) and analyzed with ArcGIS. Most regions serve as a dual role in the WSSs flow process, the water suppliers and users are relative and scale-dependent. Taking Ningxia as an example, As water benefiting areas (WBAs)/buyers, the total material inflow to Ningxia was 135.86 × 108 ~ 294.22 × 108 m3 from 2000 to 2015 and the value inflow ranged from 1077.39 × 108 ~ 2333.16 × 108 CNY, requiring 101.64 × 108 ~ 293.51 × 108 CNY ecological compensation paid by Ningxia. As water supply areas (WSAs)/sellers, the total material outflow from Ningxia was 72.83 × 108 ~ 200.46 × 108 m3 from 2000 to 2015, and the value outflow was between 577.54 × 108 CNY and 1589.65 × 108 CNY, requiring 63.80 × 108 ~ 112.34 × 108 CNY of ecological compensation to be paid by the downstream basins, especially the Shizuishan – Hekou Town subbasin. Overall, Ningxia was a beneficiary area of WSSs flow and the payers of interregional ecological compensation, with a net payment amount of 37.84 × 108 ~ 181.16 × 108 CNY. This study provides a direct spatial-visualized reference to water resource management for policy-makers and promotes the integration of ES flow and interregional ecological compensation. Furthermore, it can improve the public recognition of interregional ecological compensation with the spatial mapping of the levy and allocation and conducive to the sustainable provisioning of ESs ultimately.

Summary: Lipotoxicity plays an important role in the development of diabetic heart failure (HF). Canagliflozin (CAN), a marketed sodium-glucose co-transporter 2 inhibitor, has significantly beneficial effects on HF. In this study, we evaluated the protective effects and mechanism of CAN in the hearts of C57BL/6J mice induced by high-fat diet/streptozotocin (HFD/STZ) for 12 weeks in vivo and in HL-1 cells (a type of mouse cardiomyocyte line) induced by palmitic acid (PA) in vitro. The results showed that CAN significantly ameliorated heart functions and inflammatory responses in the hearts of the HFD/STZ-induced diabetic mice. CAN significantly attenuated the inflammatory injury induced by PA in the HL-1 cells. Furthermore, CAN seemed to bind to the mammalian target of rapamycin (mTOR) and then inhibit mTOR phosphorylation and hypoxia-inducible factor-1α (HIF-1α) expression. These results indicated that CAN might attenuate lipotoxicity in cardiomyocytes by inhibiting the mTOR/HIF-1α pathway and then show protective effects on diabetic hearts.

Heavy metal contamination in soil has attracted great attention worldwide. In situ stabilization has been considered an effective way to remediate soils contaminated by heavy metals. In the present research, a multiple-modified biochar (BCM) was prepared to stabilize Cd and Cu contamination in two different soils: a farmland soil (JYS) and a vegetable soil (ZZS). The results showed that BCM was a porous-like flake material and that modification increased its specific surface area and surface functional groups. The incubation experiment indicated that BCM decreased diethylenetriaminepentaacetic (DTPA)-extractable Cd and Cu by 92.02% and 100.00% for JYS and 90.27% and 100.00% for ZZS, respectively. The toxicity characteristic leaching procedure (TCLP)-extractable Cd and Cu decreased 66.46% and 100.00% for JYS and 46.33% and 100.00% for ZZS, respectively. BCM also reduced the mobility of Cd and Cu in soil and transformed them to more stable fractions. In addition, the application of BCM significantly increased the soil dehydrogenase, organic matter content and available K (p

Objectives: To develop and evaluate a machine learning radiomics model based on biparametric magnetic resonance imaging MRI (bpMRI) to predict bone metastasis (BM) status in newly diagnosed prostate cancer (PCa) patients., Methods: We retrospectively analyzed bpMRI scans of PCa patients from multiple centers between January 2016 and October 2021. 348 PCa patients were recruited from two institutions for this study. The first institution contributed 284 patients, stratified and randomly divided into training and internal validation cohorts at a 7:3 ratio. The remaining 64 patients were sourced from the second institution and comprised the external validation cohort. Radiomics features were extracted from axial T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) tumor regions. We developed the radiomics prediction model for BM in the training cohort and validated it in the internal and external validation cohorts. As a benchmark, we trained the logistic regression model with lasso feature reduction (LFR-LRM) in the training cohort and further compared it with Naive Bayes, eXtreme Gradient Boosting (XGboost), Random Forest (RF), GBDT, SVM, Adaboost, and KNN algorithms and validated in both the internal and external cohorts. The performance of several predictive models was assessed by receiver operating characteristic (ROC)., Results: The LFR-LRM model achieved an area under the receiver operating characteristic curve (AUC) of 0.89 (95% CI: 0.822-0.974) and an accuracy of 0.828 (95% CI: 0.713-0.911). The AUC and accuracy in external validation were 0.866 (95% CI: 0.784-0.948) and 0.769 (95% CI: 0.648-0.864), respectively. The RF and XGBoost models outperformed the LFR-LRM, with AUCs of 0.907 (95% CI: 0.863-0.949) and 0.928 (95% CI: 0.882-0.974) and accuracies of 0.831 (95% CI: 0.727-0.907) and 0.884 (95% CI: 0.792-0.946). External validation for these models yielded AUCs and accuracies of 0.911 (95% CI: 0.861-0.966), 0.921 (95% CI: 0.889-0.953), and 0.846 (95% CI: 0.735-0.923) and 0.876 (95% CI: 0.771-0.945), respectively., Conclusions: The XGboost machine learning model is more accurate than LFR-LRM for predicting BM in patients with newly confirmed PCa., (Copyright © 2023. Published by Elsevier Inc.)