Melamine Polymerization Promotes Compact Phosphorus/Carbon Composite for High-Performance and Safe Lithium Storage.

Phosphorus is regarded as a promising material for high-performance lithium-ion batteries (LIBs) due to its high theoretical capacity, appropriate lithiation potential, and low lithium-ion diffusion barrier. Phosphorus/carbon composites (PC) are engineered to serve as high-capacity high-rate anodes; the interaction between phosphorus and carbon, long-term capacity retention, and safety problems are important issues that must be well addressed simultaneously. Herein, an in situ polymerization approach to fabricate a poly-melamine-hybridized (pMA) phosphorus/carbon composite (pMA-PC) is employed. The pMA hybridization enhances the density and electrical conductivity of the PC, improves the structural integrity, and facilitates stable electron transfer within the pMA-PC composite. Moreover, the pMA-PC composite exhibits efficient adsorption of lithium polysulfides, enabling stable transport of Li+ ions. Therefore, the pMA-PC anode demonstrates a high specific charging capacity of 1,381 mAh g-1 at 10 A g-1, and a great capacity retention of 86.7% at 1 A g-1 over 500 cycles. The synergistic effect of phosphorus and nitrogen further confers excellent flame retardant properties to the pMA-PC anode, including self-extinguishing in 2.5 s, and a much lower combustion temperature than PC. The enhanced capacity and safety performance of pMA-PC show potential in future high-capacity and high-rate LIBs.

Prickly ash seeds can promote healthy production of sheep by regulating the rumen microbial community.

This study aimed to investigate the effect of prickly ash seeds (PAS) on the microbial community found in rumen microbes of Hu sheep by adding different percentages of prickly ash seeds and to carry out research on the relation between rumen flora and production performance. Twenty-seven male lambs of Hu sheep were classified into three groups based on the content of prickly ash seeds (PAS) fed for 90 days, i.e., 0%, 3%, and 6%. At the end of the feeding trial, rumen fluid samples were collected from six sheep in each group for 16S amplicon sequencing. The results showed that the addition of prickly ash seeds significantly increased both Chao1 and ACE indices (P < 0.05), and the differences between groups were greater than those within groups. The relative content of Bacteriodota decreased, and the relative content of Fusobacteriota, Proteobacteria, Acidobacteriota, and Euryarchaeota increased. The relative content of Papillibacter and Saccharofermentans was increased at the genus level, and the relative content of Bacteroides and Ruminococcus was decreased. The test group given 3% of prickly ash seeds was superior to the test group given 6% of prickly ash seeds. In addition, the addition of 3% of prickly ash seeds improved the metabolism or immunity of sheep. Fusobacteriota and Acidobacteriota were positively correlated with total weight, dressing percentage, and average daily gain (ADG) and negatively correlated with average daily feed intake (ADFI), feed-to-gain ratio (F/G), and lightness (L*). Methanobrevibacter and Saccharofermentans were positively correlated with ADG and negatively correlated with ADFI and L*. In conclusion, under the present experimental conditions, the addition of prickly ash seeds increased the abundance and diversity of rumen microorganisms in Hu sheep and changed the relative abundance of some genera. However, the addition of 6% prickly ash seeds may negatively affect the digestive and immune functions in sheep rumen.

AuNi bimetallic aerogel with ultra-high stability applied in smart and portable biosensing.

Glucose detection is of significant importance in providing information to the human health management. However, conventional enzymatic glucose sensors suffer from a limited long-term stability due to the losing activity of the enzymes. In this work, the AuNi bimetallic aerogel with a well-defined nanowire network is synthesized and applied as the sensing nanomaterial in the non-enzymatic glucose detection. The three-dimensional (3D) hierarchical porous structure of the AuNi bimetallic aerogel ensures the high sensitivity of the sensor (40.34 µA mM-1 cm-2). Theoretical investigation unveiled the mechanism of the boosting electrocatalytic activity of the AuNi bimetallic aerogel toward glucose. A better adhesion between the sensing nanomaterial and the screen-printing electrodes (SPEs) is obtained after the introduction of Ni. On the basis of a wide linearity in the range of 0.1-5 mM, an excellent selectivity, an outstanding long-term stability (90 days) as well as the help of the signal processing circuit and an M5stack development board, the as-prepared glucose sensor successfully realizes remote monitoring of the glucose concentration. We speculate that this work is favorable to motivating the technological innovations of the non-enzymatic glucose sensors and intelligent sensing devices.

Exploration of identifying individual tumor tissue based on probabilistic model.

Variations in the tumor genome can result in allelic changes compared to the reference profile of its homogenous body source on genetic markers. This brings a challenge to source identification of tumor samples, such as clinically collected pathological paraffin-embedded tissue and sections. In this study, a probabilistic model was developed for calculating likelihood ratio (LR) to tackle this issue, which utilizes short tandem repeat (STR) genotyping data. The core of the model is to consider tumor tissue as a mixture of normal and tumor cells and introduce the incidence of STR variants (φ) and the percentage of normal cells (Mxn) as a priori parameters when performing calculations. The relationship between LR values and φ or Mxn was also investigated. Analysis of tumor samples and reference blood samples from 17 colorectal cancer patients showed that all samples had Log 10(LR) values greater than 1014. In the non-contributor test, 99.9% of the quartiles had Log 10(LR) values less than 0. When the defense's hypothesis took into account the possibility that the tumor samples came from the patient's relatives, LR greater than 0 was still obtained. Furthermore, this study revealed that LR values increased with decreasing φ and increasing Mxn. Finally, LR interval value was provided for each tumor sample by considering the confidence interval of Mxn. The probabilistic model proposed in this paper could deal with the possibility of tumor allele variability and offers an evaluation of the strength of evidence for determining tumor origin in clinical practice and forensic identification.

The Role of Gut Microbiota in Male Erectile Dysfunction of Rats.

PURPOSE: Erectile dysfunction (ED) is a common male sexual dysfunction. Gut microbiota plays an important role in various diseases. To investigate the effects and mechanisms of intestinal flora dysregulation induced by high-fat diet (HFD) on erectile function. MATERIALS AND METHODS: Male Sprague-Dawley rats aged 8 weeks were randomly divided into the normal diet (ND) and HFD groups. After 24 weeks, a measurement of erectile function was performed. We performed 16S rRNA sequencing of stool samples. Then, we established fecal microbiota transplantation (FMT) rat models by transplanting fecal microbiota from rats of ND group and HFD group to two new groups of rats respectively. After 24 weeks, erectile function of the rats was evaluated and 16S rRNA sequencing was performed, and serum samples were collected for the untargeted metabolomics detection. RESULTS: The erectile function of rats and the species diversity of intestinal microbiota in the HFD group was significantly lower, and the characteristics of the intestinal microbiota community structure were also significantly different between the two groups. The erectile function of rats in the HFD-FMT group was significantly lower than that of rats in the ND-FMT group. The characteristics of the intestinal microbiota community structure were significantly different. In the HFD-FMT group, 27 metabolites were significantly different and they were mainly involved in the several inflammation-related pathways. CONCLUSIONS: Intestinal microbiota disorders induced by HFD can damage the intestinal barrier of rats, change the serum metabolic profile, induce low-grade inflammation and apoptosis in the corpus cavernosum of the penis, and lead to ED.

Association between the triglyceride-glucose index and thyroid disorders: a cross-sectional survey and Mendelian randomization analysis.

BACKGROUND: Metabolic diseases are associated with thyroid disorders. Insulin resistance is the common pathological basis of metabolic diseases. We explored the relationship between the triglyceride-glucose (TyG) index, a simple insulin-resistance marker, and thyroid disorders. METHODS: Eligible TIDE (Thyroid Diseases, Iodine Status and Diabetes Epidemiology) subjects (n = 47,710) were screened with inclusion/exclusion criteria. Thyroid disorder prevalence among different TyG index groups was stratified by sex. Logistic regression evaluated the correlation between the TyG index and thyroid disorders. Multiple linear regression evaluated the association between the TyG index and TSH. Additionally, two-sample Mendelian randomization (MR) using published genome-wide association study data evaluated causality in the association between the TyG index and TSH. RESULTS: Men and women with greater TyG indices had a significantly greater prevalence of thyroid disorders than individuals with the lowest quartile (Q1) of TyG index (p < 0.05). Following adjustment for confounding factors, we observed that a greater TyG index significantly increased the risk of subclinical hypothyroidism in men and women (men: Q2: odds ratio (OR) [95% confidence interval (CI)] = 1.22 [1.07-1.38], p = 0.002; Q3: OR [95% CI] = 1.28 [1.12-1.45], p < 0.001; Q4: OR [95% CI] = 1.29 [1.12-1.50], p = 0.001; women: Q2: OR [95% CI] = 1.25 [1.12-1.39], p < 0.001; Q3: OR [95% CI] = 1.47 [1.31-1.64], p < 0.001; Q4: OR [95% CI] = 1.61 [1.43-1.82], p < 0.001). Only among women was the highest TyG index quartile associated with hypothyroidism (OR [95% CI] = 1.70 [1.15-2.50], p = 0.007). Additionally, in men, the association exists only in the more than adequate iodine intake population. In women, the relationship between the TyG index and thyroid disorders disappears after menopause. Furthermore, the TyG index exhibited a linear positive correlation with TSH levels. The MR analysis results revealed a causal relationship between a genetically determined greater TyG index and increased TSH (inverse-variance weighting (IVW): OR [95% CI] = 1.14 [1.02-1.28], p = 0.020); however, this causal relationship disappeared after adjusting for BMI in multivariable MR (MVMR) analysis (MVMR-IVW: OR 1.03, 95% CI 0.87-1.22, p = 0.739). CONCLUSIONS: A greater TyG index is associated with hypothyroidism and subclinical hypothyroidism and varies by sex and menopausal status. MR analysis demonstrated that the causal relationship between a genetically determined greater TyG index and elevated TSH levels is confounded or mediated by BMI.

MLKL promotes hepatocarcinogenesis through inhibition of AMPK-mediated autophagy.

The pseudokinase mixed lineage kinase domain-like (MLKL) is an essential component of the activation of the necroptotic pathway. Emerging evidence suggests that MLKL plays a key role in liver disease. However, how MLKL contributes to hepatocarcinogenesis has not been fully elucidated. Herein, we report that MLKL is upregulated in a diethylnitrosamine (DEN)-induced murine HCC model and is associated with human hepatocellular carcinomas. Hepatocyte-specific MLKL knockout suppresses the progression of hepatocarcinogenesis. Conversely, MLKL overexpression aggravates the initiation and progression of DEN-induced HCC. Mechanistic study reveals that deletion of MLKL significantly increases the activation of autophagy, thereby protecting against hepatocarcinogenesis. MLKL directly interacts with AMPKα1 and inhibits its activity independent of its necroptotic function. Mechanistically, MLKL serves as a bridging molecule between AMPKα1 and protein phosphatase 1B (PPM1B), thus enhancing the dephosphorylation of AMPKα1. Consistently, MLKL expression correlates negatively with AMPKα1 phosphorylation in HCC patients. Taken together, our findings highlight MLKL as a novel AMPK gatekeeper that plays key roles in inhibiting autophagy and driving hepatocarcinogenesis, suggesting that the MLKL-AMPKα1 axis is a potential therapeutic target for HCC.

Metallofullerenol alleviates alcoholic liver damage via ROS clearance under static magnetic and electric fields.

Alcoholic liver disease (ALD) is a common chronic redox disease caused by increased alcohol consumption. Abstinence is a major challenge for people with alcohol dependence, and approved drugs have limited efficacy. Therefore, this study aimed to explore a new treatment strategy for ALD using ferroferric oxide endohedral fullerenol (Fe3O4@C60(OH)n) in combination with static magnetic and electric fields (sBE). The primary hepatocytes of 8-9-week-old female BALB/c mice were used to evaluate the efficacy of the proposed combination treatment. A mouse chronic binge ethanol feeding model was established to determine the alleviatory effect of Fe3O4@C60(OH)n on liver injury under sBE exposure. Furthermore, the ability of Fe3O4@C60(OH)n to eliminate •OH was evaluated. Alcohol-induced hepatocyte and mitochondrial damage were reversed in vitro. Additionally, the combination therapy reduced liver damage, alleviated oxidative stress by improving antioxidant levels, and effectively inhibited liver lipid accumulation in animal experiments. Here, we used a combination of magnetic derivatives of fullerenol and sBE to further improve the ROS clearance rate, thereby alleviating ALD. The developed combination treatment may effectively improve alcohol-induced liver damage and maintain redox balance without apparent toxicity, thereby enhancing therapy aimed at ALD and other redox diseases.

Regulating Schwann Cell Growth by Nanosecond Pulsed Electric Field for Peripheral Nerve Regeneration In Vitro.

Schwann cells (SCs) are myelinating cells of the peripheral nervous system, playing a crucial role in peripheral nerve regeneration. Nanosecond Pulse Electric Field (nsPEF) is an emerging method applicable in nerve electrical stimulation that has been demonstrated to be effective in stimulating cell proliferation and other biological processes. Aiming to assess whether SCs undergo significant changes under nsPEF and help explore the potential for new peripheral nerve regeneration methods, cultured RSC96 cells were subjected to nsPEF stimulation at 5 kV and 10 kV, followed by continued cultivation for 3-4 days. Subsequently, some relevant factors expressed by SCs were assessed to demonstrate the successful stimulation, including the specific marker protein, neurotrophic factor, transcription factor, and myelination regulator. The representative results showed that nsPEF significantly enhanced the proliferation and migration of SCs and the ability to synthesize relevant factors that contribute positively to the regeneration of peripheral nerves. Simultaneously, lower expression of GFAP indicated the benign prognosis of peripheral nerve injuries. All these outcomes show that nsPEF has great potential as an efficient treatment method for peripheral nerve injuries by stimulating SCs.

Research on autonomous navigation system of greenhouse electric crawler tractor based on LiDAR.

The application of autonomous navigation technology of electric crawler tractors is an important link in the development of intelligent greenhouses. Aiming at the characteristics of enclosed and narrow space and uneven ground potholes in greenhouse planting, to improve the intelligence level of greenhouse electric crawler tractors, this paper develops a navigation system of electric crawler tractors for the greenhouse planting environment based on LiDAR technology. The navigation hardware system consists of five modules: the information perception module, the control module, the communication module, the motion module, and the power module. The software system is composed of three layers: the application layer, the data processing layer, and the execution layer. The developed navigation system uses LiDAR, Inertial Measurement Unit (IMU) and wheel speed sensor to sense the greenhouse environment and the crawler tractor's information, employs the Gmapping algorithm to build the greenhouse environment map, and utilizes the adaptive Monte Carlo positioning algorithm for positioning. The simulation test of different global path planning algorithms in Matlab shows that the A* algorithm obtains the optimal overall global path. In the scene of map 5, the path planned by the A* algorithm is the most significant, and the number of inflection points is reduced by 40.00% and 87.50%, respectively; meanwhile, the path length is the same as that of the Dijkstra algorithm, but the runtime is reduced by 68.87% and 81.49%, respectively; compared with the RRT algorithm, the path length is reduced by 7.27%. Therefore, the A* algorithm and the Dynamic Window Approach (DWA) method are used for tractor navigation and obstacle avoidance, which ensures global path optimality while also achieving effective local path planning for obstacle avoidance. The test results suggest that the maximum lateral deviation of the built map is 6 cm, and the maximum longitudinal deviation is 16 cm, which meets the requirement of map accuracy. Additionally, the results of the navigation accuracy test indicate that the maximum lateral deviation of navigation is less than 13 cm, the average lateral deviation is less than 7 cm, and the standard lateral deviation is less than 8 cm. The maximum heading deviation is less than 14°, the average heading deviation is less than 7°, and the standard deviation is less than 8°. These results show that the developed navigation system meets the navigation accuracy requirements of electric crawler tractors in the greenhouse environment.

Micro-Scale Particle Tracking: From Conventional to Data-Driven Methods.

Micro-scale positioning techniques have become essential in numerous engineering systems. In the field of fluid mechanics, particle tracking velocimetry (PTV) stands out as a key method for tracking individual particles and reconstructing flow fields. Here, we present an overview of the micro-scale particle tracking methodologies that are predominantly employed for particle detection and flow field reconstruction. It covers various methods, including conventional and data-driven techniques. The advanced techniques, which combine developments in microscopy, photography, image processing, computer vision, and artificial intelligence, are making significant strides and will greatly benefit a wide range of scientific and engineering fields.

Mechanism of Microwave Radiation-Induced Learning and Memory Impairment Based on Hippocampal Metabolomics.

The brain is complex and metabolically active, and the detection of metabolites plays an important role in brain development and diseases. Currently, there is a lack of research on the metabolic spectrum changes in learning and memory impairment, and hippocampal damage induced by microwave radiation from the metabolic perspective. Aiming to provide sensitive indicators for microwave radiation-induced brain damage and establish a foundation for understanding its injury mechanisms, this study employed non-targeted metabolomics to investigate metabolic fluctuations and key metabolic pathway alterations in rats' hippocampal tissue after microwave radiation. The memory and spatial exploration abilities of rats decreased after radiation. The postsynaptic densities were thickened in the MW group. The cholesterol sulfate, SM(d16:1/24:1(15Z)), and linoelaidylcarnitine were significantly increased after radiation, whereas etrahydrocorticosterone, L-phenylalanine, and histamine were significantly decreased after radiation. These metabolites were enriched in signaling pathways related to the inflammatory mediator regulation of transient receptor potential (TRP) channels, neuroactive ligand-receptor interaction, steroid hormone biosynthesis, and phenylalanine, tyrosine, and tryptophan biosynthesis. These findings indicate that microwave radiation causes spatial learning and memory dysfunction in rats and structural damage to hippocampal tissue.

Bioinformatics analysis and experimental verification of the cancer-promoting effect of DHODH in clear cell renal cell carcinoma.

Clear cell renal cell carcinoma (ccRCC) is a malignant tumor of the urinary system. To explore the potential mechanisms of DHODH in ccRCC, we analyzed its molecular characteristics using public databases. TCGA pan-cancer dataset was used to analyze DHODH expression in different cancer types and TCGA ccRCC dataset was used to assess differential expression, prognosis correlation, immune infiltration, single-gene, and functional enrichment due to DHODH. The GSCALite and CellMiner databases were employed to explore drugs and perform molecular docking analysis with DHODH. Protein-protein interaction networks and ceRNA regulatory networks of DHODH were constructed using multiple databases. The effect of DHODH on ccRCC was confirmed in vitro. DHODH was highly expressed in ccRCC. Immune infiltration analysis revealed that DHODH may be involved in regulating the infiltration of immunosuppressive cells such as Tregs. Notably, DHODH influenced ccRCC progression by forming regulatory networks with molecules, such as hsa-miR-26b-5p and UMPS and significantly enhanced the malignant characteristics of ccRCC cells. Several drugs, such as lapatinib, silmitasertib, itraconazole, and dasatinib, were sensitive to DHODH expression and exhibited strong molecular binding with it. Thus, DHODH may promote ccRCC progression and is a candidate effective therapeutic target for ccRCC.

Carbon-based molecular properties efficiently predicted by deep learning-based quantum chemical simulation with large language models.

The prediction of thermodynamic properties of carbon-based molecules based on their geometrical conformation using fluctuation and density functional theories has achieved great success in the field of energy chemistry, while the excessive computational cost provides both opportunities and challenges for the integration of machine learning. In this work, a deep learning-based quantum chemical prediction model was constructed for efficient prediction of thermodynamic properties of carbon-based molecules. We constructed a novel framework - encoding the 3D information into a large language model (LLM), which in turn generates a 2D SMILES string, while embedding a learnable encoding designed to preserve the integrity of the original 3D information, providing better structural information for the model. Additionally, we have designed an equivariant learning module to encompass representations of conformations and feature learning for conformational sampling. This framework aims to predict thermodynamic properties more accurately than learning from 2D topology alone, while providing faster computational speeds than conventional simulations. By combining machine learning and quantum chemistry, we pioneer efficient practical applications in the field of energy chemistry. Our model advances the integration of data-driven and physics-based modeling to unlock novel insights into carbon-based molecules.

Early detection of nicosulfuron toxicity and physiological prediction in maize using multi-branch deep learning models and hyperspectral imaging.

The misuse of herbicides in fields can cause severe toxicity in maize, resulting in significant reductions in both yield and quality. Therefore, it is crucial to develop early and efficient methods for assessing herbicide toxicity, protecting maize production, and maintaining the field environment. In this study, we utilized maize crops treated with the widely used nicosulfuron herbicide and their hyperspectral images to develop the HerbiNet model. After 4 d of nicosulfuron treatment, the model achieved an accuracy of 91.37 % in predicting toxicity levels, with correlation coefficient R² values of 0.82 and 0.73 for soil plant analysis development (SPAD) and water content, respectively. Additionally, the model exhibited higher generalizability across datasets from different years and seasons, which significantly surpassed support vector machines, AlexNet, and partial least squares regression models. A lightweight model, HerbiNet-Lite, exhibited significantly low complexity using 18 spectral wavelengths. After 4 d of nicosulfuron treatment, the HerbiNet-Lite model achieved an accuracy of 87.93 % for toxicity prediction and R² values of 0.80 and 0.71 for SPAD and water content, respectively, while significantly reducing overfitting. Overall, this study provides an innovative approach for the early and accurate detection of nicosulfuron toxicity within maize fields.

Transparent grating-based metamaterials for dynamic infrared radiative regulation smart windows.

Dynamic infrared radiation regulation has been widely explored for smart windows because of its vital importance for comfortable and energy-efficient buildings. However, it remains a great challenge to synchronously achieve high visible transmittance and pronounced infrared tunability. Here, we propose a dynamic infrared tunable metamaterial composed of indium tin oxide (ITO) gratings, an air insulator, and an ITO reflector. The ITO grating-based infrared radiation regulator exhibits a high emissivity tunability of 0.73 at 8-13 µm while maintaining a high visible transmittance of 0.65 and 0.72 before and after actuation, respectively. By adjusting the geometric parameters, the tunable bandwidth can be further extended to 3-30 µm and the ultra-broadband tunability reaches 0.62. The excellent infrared tunable performance arises from the insulator thickness-dependent effect of Fabry-Pérot and propagating surface plasmon resonance coupling and decoupling, which lead to perfect and low absorption, respectively. This work provides potential for the advancement of smart window technology and makes a significant contribution to sustainable buildings.

A Compact-Sized Fully Self-Powered Wireless Flowmeter Based on Triboelectric Discharge.

Flow sensing exhibits significant potential for monitoring, controlling, and optimizing processes in industries, resource management, and environmental protection. However, achieving wireless real-time and omnidirectional sensing of gas/liquid flow on a simple, self-contained device without external power support has remained a formidable challenge. In this study, a compact-sized, fully self-powered wireless sensing flowmeter (CSWF) is introduced with a small size diameter of down to less than 50 mm, which can transmit real-time and omnidirectional wireless signals, as driven by a rotating triboelectric nanogenerator (R-TENG). The R-TENG triggers the breakdown discharge of a gas discharge tube (GDT), which enables flow rate wireless sensing through emitted electromagnetic waves. Importantly, the performance of the CSWF is not affected by the R-TENG's varied output, while the transmission distance is greater than 10 m. Real-time wireless remote monitoring of wind speed and water flow rate is successfully demonstrated. This research introduces an approach to achieve a wireless, self-powered environmental monitoring system with a diverse range of potential applications, including prolonged meteorological observations, marine environment monitoring, early warning systems for natural disasters, and remote ecosystem monitoring.

Magnetic-driven hydrogel microrobots for promoting osteosarcoma chemo-therapy with synthetic lethality strategy.

The advancements in the field of micro-robots for drug delivery systems have garnered considerable attention. In contrast to traditional drug delivery systems, which are dependent on blood circulation to reach their target, these engineered micro/nano robots possess the unique ability to navigate autonomously, thereby enabling the delivery of drugs to otherwise inaccessible regions. Precise drug delivery systems can improve the effectiveness and safety of synthetic lethality strategies, which are used for targeted therapy of solid tumors. MYC-overexpressing tumors show sensitivity to CDK1 inhibition. This study delves into the potential of Ro-3306 loaded magnetic-driven hydrogel micro-robots in the treatment of MYC-dependent osteosarcoma. Ro-3306, a specific inhibitor of CDK1, has been demonstrated to suppress tumor growth across various types of cancer. We have designed and fabricated this micro-robot, capable of delivering Ro-3306 precisely to tumor cells under the influence of a magnetic field, and evaluated its chemosensitizing effects, thereby augmenting the therapeutic efficacy and introducing a novel possibility for osteosarcoma treatment. The clinical translation of this method necessitates further investigation and validation. In summary, the Ro-3306-loaded magnetic-driven hydrogel micro-robots present a novel strategy for enhancing the chemosensitivity of MYC-dependent osteosarcoma, paving the way for new possibilities in future clinical applications.