Search

This paper describes a new modeling framework for microscopic to reservoir-scale simulations of hydraulic fracturing and production. The approach builds upon a fusion of two existing high-performance simulators for reservoir-scale behavior: the GEOS code for hydromechanical evolution during stimulation and the TOUGH+ code for multi-phase flow during production. The reservoir-scale simulations are informed by experimental and modeling studies at the laboratory scale to incorporate important micro-scale mechanical processes and chemical reactions occurring within the fractures, the shale matrix, and at the fracture-fluid interfaces. These processes include, among others, changes in stimulated fracture permeability as a result of proppant behavior rearrangement or embedment, or mineral scale precipitation within pores and microfractures, at µm to cm scales. In our new modeling framework, such micro-scale testing and modeling provides upscaled hydromechanical parameters for the reservoir scale models. We are currently testing the new modeling framework using field data and core samples from the Hydraulic Fracturing Field Test (HFTS), a recent field-based joint research experiment with intense monitoring of hydraulic fracturing and shale production in the Wolfcamp Formation in the Permian Basin (USA). Below, we present our approach coupling the reservoir simulators GEOS and TOUGH+ informed by upscaled parameters from micro-scale experiments and modeling. We provide a brief overview of the HFTS and the available field data, and then discuss the ongoing application of our new workflow to the HFTS data set. [ABSTRACT FROM AUTHOR]

Sandwich composites are widely used in engineering due to their excellent mechanical properties. Accordingly, the problem of interface bonding between their panels and core layers has always been a hot research topic. The emergence of biomimetic technology has enabled the integration of the structure and function of biological materials from living organisms or nature into the design of sandwich composites, greatly improving the interface bonding and overall performance of heterogeneous materials. In this paper, we review the most commonly used biomimetic structures and the fusion design of multi-biomimetic structures in the engineering field. They are analyzed with respect to their mechanical properties, and several biomimetic structures derived from abstraction in plants and animals are highlighted. Their structural advantages are further discussed specifically. Regarding the optimization of different interface combinations of multilayer composites, this paper explores the optimization of simulations and the contributions of molecular dynamics, machine learning, and other techniques used for optimization. Additionally, the latest molding methods for sandwich composites based on biomimetic structural design are introduced, and the materials applicable to different processes, as well as their advantages and disadvantages, are briefly analyzed. Our research results can help improve the mechanical properties of sandwich composites and promote the application of biomimetic structures in engineering.

American Oyster Defensin (AOD) is a marine peptide that is derived from North American mussels. It has been demonstrated to exhibit potent antimicrobial activity and high safety in both in vitro and in vivo models. In this study, to facilitate synthesis, mutants of AOD with fewer disulfide bonds were designed and subjected to structural, antimicrobial, and anti-biofilm analysis. The antimicrobial activity of AOD-derived peptides decreased after reduction in the disulfide bond, and among its three derivatives, only AOD-1 inhibited very few bacteria with a MIC value of 64 μg/mL, whereas the others had no inhibitory effect on pathogenic bacteria. The findings demonstrated that full disulfide bonds are indispensable for bactericidal activity, with the α-helix playing a pivotal role in inhibiting bacterial membranes. Furthermore, the results of the ATP, ROS, membrane potential, and membrane fluidity assays demonstrated that intracellular ATP, reactive oxygen species, and membrane fluidity were all increased, while membrane potential was reduced. This indicated that AOD resulted in the impairment of membrane fluidity and induced metabolic disorders, ultimately leading to bacterial death. The inhibitory effect of AOD on the biofilm of S. epidermidis G-81 was determined through the crystal violet and confocal microscopy. The results demonstrated that AOD exhibited a notable inhibitory impact on the biofilm of S. epidermidis G-81. The minimum biofilm inhibitory concentration of AOD on S. epidermidis G-81 was 16 μg/mL, and the minimum biofilm scavenging concentration was 32 μg/mL, which exhibited superior efficacy compared to that of lincomycin. The inhibitory effect on the primary biofilm was 90.3%, and that on the mature biofilm was 82.85%, with a dose-dependent inhibition effect. Concurrently, AOD cleared intra-biofilm organisms and reduced the number of biofilm-holding bacteria by six orders of magnitude. These data indicate that disulfide bonds are essential to the structure and activity of AOD, and AOD may potentially become an effective dual-action antimicrobial and anti-biofilm agent.

The autophagy-lysosome axis is an evolutionarily conserved intracellular degradation pathway which constitutes an important component of host innate immunity against microbial infections. Here, we show that African swine fever virus (ASFV), one of most devastating pathogens to the worldwide swine industry, can reshape the autophagy-lysosome axis by recruiting the critical lysosome membrane proteins (LAMP1 and LAMP2) to viral factories while inhibiting autophagic induction in macrophages. The screening of viral membrane proteins led to the identification of several ASFV membrane proteins, exemplified by viral protein pEP153R, that could significantly alter the subcellular localization of LAMP1/2 when expressed alone in transfected cells. Further analysis showed that pEP153R was also a component of viral factories and could induce endoplasmic reticulum (ER) retention of LAMP1/2, leading to the inhibition of the fusion of autophagosomes with lysosomes. Interestingly, the ASFV mutant lacking EP153R could still actively recruit LAMP into viral factories (VFs) and inhibit autophagic flux, indicating the existence of a functional redundancy of other viral proteins in the absence of pEP153R and highlighting the complexity of ASFV replication biology. Taken together, our results reveal novel information about the interplay of ASFV with the autophagy-lysosome axis and a previously unrecognized function of ASFV protein pEP153R in regulating the cellular autophagic process.

In order to improve the off-state and breakdown characteristics of double-channel GaN HEMTs, an ultra-thin barrier layer was chosen as the second barrier layer. The strongly polarized and ultra-thin AlN sub-barrier and the InAlN sub-barrier are great candidates. In this article, the two epitaxial structures, AlGaN/GaN/AlN/GaN (sub-AlN) HEMTs and AlGaN/GaN/InAlN/GaN (sub-InAlN) HEMTs, were compared to select a more suitable sub-barrier layer. Through TEM images of the InAlN barrier layer, the segregation of In components can be seen, which decreases the mobility of the second channel. Thus, the sub-AlN HEMTs have a higher output current density and transconductance than those of the sub-InAlN HEMTs. Because the high-quality AlN barrier layer shields the gate leakage current, a 294 V breakdown voltage was achieved by the sub-AlN HEMTs, which is higher than the 121 V of the sub-InAlN HEMTs. The current gain cut-off frequency ( f T ) and maximum oscillation frequency ( f max ) of the sub-AlN HEMTs are higher than that of the sub-InAlN HEMTs from low to high bias voltage. The power-added efficiency (PAE) and output power density ( P out ) of the sub-AlN HEMTs are 57% and 11.3 W/mm at 3.6 GHz and 50 V of drain voltage ( V d ), respectively. For the sub-InAlN HEMTs, the PAE and P out are 41.4% and 8.69 W/mm, because of the worse drain lag ratio. Thus, the P out of the sub-AlN HEMTs is higher than that of the sub-InAlN HEMTs.

Background: Arterial thrombosis, a condition in which thrombi form in arteries, can lead to various acute cardiovascular diseases and impact the quality of life and survival of patients. Berberine (BBR), a quaternary ammonium alkaloid, has been shown to treat these diseases. However, further exploration is needed to understand underlying mechanisms of BBR., Methods and Results: Rats were administered BBR via intramuscular injection. Then, an FeCl 3 -coated filter paper was applied to a carotid artery to induce thrombosis. The size of the thrombus and the blood flow velocity were evaluated by carotid ultrasound. The shape of the thrombus was observed using staining and microscopy. The expression levels of mRNA and proteins were verified. Additionally, mass spectrometry and single-cell RNA sequencing analysis were conducted. The administration of BBR resulted in a significant reduction in the thrombus area and an extension of the thrombus-clogging time. Furthermore, BBR administration effectively reversed the decreasing tissue-plasminogen activator (t-PA) expression and alterations in fibrinolysis system of model group. Additionally, the expression of PKM2 was suppressed following BBR administration, and the overexpression of PKM2 inhibited t-PA expression., Conclusions: BBR ameliorates thrombosis by modulating expression of PKM2, subsequently impacting the expression of t-PA within fibrinolytic system. These preliminary findings suggest that BBR could be a potential preventive and therapeutic strategy for arterial thromboembolic diseases.

Black pepper ( Piper nigrum L.), a prominent spice crop, known as the "king of spices", originated from India. The growth and development of black pepper are influenced by various environmental conditions. MYB transcription factors play a crucial role in controlling metabolic processes, abiotic stress management, and plant growth and development. In this study, we identified 160 PnMYB transcription factors in the black pepper genome. Phylogenetic analysis was performed using 125 R2R3-MYB proteins from black pepper and Arabidopsis thaliana , resulting in the mapping of 20 groups on the phylogenetic tree, each containing members from both species. Most members of the PnMYB family possess two introns, and motif 3 and motif 4 are conserved in all members. The number of genes on each chromosome ranges from 1 to 10. Collinear analysis indicated the creation of new members through gene fragments and tandem replication. The Ka/Ks ratio indicated that purifying selection and positive selection acted on PnMYB of pepper. The majority of pepper PnMYB family members were in the nucleus. Significant differences in gene expression levels were observed between different species and infection periods when Piper nigrum L. and Piper flaviflorum were infected with Phytophthora capsici . These findings are valuable for future studies on the biological role and molecular mechanism of the PnMYB gene.

Tomato plants favor warmth, making them particularly susceptible to cold conditions, especially their reproductive development. Therefore, understanding how pollen reacts to cold stress is vital for selecting and improving cold-resistant tomato varieties. The programmed cell death (PCD) in the tapetum is particularly susceptible to cold temperatures which could hinder the degradation of the tapetal layer in the anthers, thus affecting pollen development. However, it is not clear yet how genes integral to tapetal degradation respond to cold stress. Here, we report that SlHB8 , working upstream of the conserved genetic module DYT1-TDF1-AMS-MYB80, is crucial for regulating cold tolerance in tomato anthers. SlHB8 expression increases in the tapetum when exposed to low temperatures. CRISPR/Cas9-generated SlHB8-knockout mutants exhibit improved pollen cold tolerance due to the reduced temperature sensitivity of the tapetum. SlHB8 directly upregulates SlDYT1 and SlMYB80 by binding to their promoters. In normal anthers, cold treatment boosts SlHB8 levels, which then elevates the expression of genes like SlDYT1 , SlTDF1 , SlAMS , and SlMYB80 ; however, slhb8 mutants do not show this gene activation during cold stress, leading to a complete blockage of delayed tapetal programmed cell death (PCD). Furthermore, we found that SlHB8 can interact with both SlTDF1 and SlMYB80, suggesting the possibility that SlHB8 might regulate tapetal PCD at the protein level. This study sheds light on molecular mechanisms of anther adaptation to temperature fluctuations.

The success of near-infrared spectroscopy (NIRS) analysis hinges on the precision and robustness of the calibration model. Shallow learning (SL) algorithms like partial least squares discriminant analysis (PLS-DA) often fall short in capturing the interrelationships between adjacent spectral variables, and the analysis results are easily affected by spectral noise, which dramatically limits the breadth and depth of applications of NIRS. Deep learning (DL) methods, with their capacity to discern intricate features from limited samples, have been progressively integrated into NIRS. In this paper, two discriminant analysis problems, including wheat kernels and Yali pears as examples, and several representative calibration models were used to research the robustness and effectiveness of the model. Additionally, this article proposed a near-infrared calibration model, which was based on the Gramian angular difference field method and coordinate attention convolutional neural networks (G-CACNNs). The research results show that, compared with SL, spectral preprocessing has a smaller impact on the analysis accuracy of consensus learning (CL) and DL, and the latter has the highest analysis accuracy in the modeling results using the original spectrum. The accuracy of G-CACNNs in two discrimination tasks was 98.48% and 99.39%. Finally, this research compared the performance of various models under noise to evaluate the robustness and noise resistance of the proposed method.

As the consumption of antibiotics rises, they have generated some negative impacts on organisms and the environment because they are often unable to be effectively degraded, and seeking effective detection methods is currently a challenge. Covalent-organic frameworks (COFs) are new types of crystalline porous crystals created based on the strong covalent interactions between blocked monomers, and COFs demonstrate great potential in the detection of antibiotics from aqueous solutions because of their large surface area, adjustable porosity, recyclability, and predictable structure. This review aims to present state-of-the-art insights into COFs (properties, classification, synthesis methods, and functionalization). The key mechanisms for the detection of antibiotics and the application performance of COFs in the detection of antibiotics from water are also discussed, followed by the challenges and opportunities for COFs in future research.

Background: Renal angiomyolipoma (AML) without local invasion is generally considered benign. However, it may extend to the renal sinus, even the renal vein, or the inferior vena cava (IVC). In patients with non-tuberous sclerosis complex, coexistence of renal cell carcinoma (RCC) and renal AML is uncommon. Case presentation: A 72-year-old woman was incidentally found to have a solitary right renal mass with an IVC thrombus extending into the right atrium during a routine health checkup. Robot-assisted laparoscopic radical nephrectomy and thrombectomy were successfully performed through adequate preoperative examination and preparation. Two tumor lesions were found and pathologically confirmed as renal AML and RCC, and the tumor thrombus was derived from the renal AML. During the one-year follow-up period, no signs of recurrence or metastatic disease were observed. Conclusions: Renal AML with a tumor thrombus in the IVC and right atrium accompanied by RCC may occur, although rarely. In clinical practice, if preoperative manifestations differ from those of common diseases, rare diseases must be considered to avoid missed diagnoses. In addition, adequate examination and multidisciplinary discussions before making a diagnosis are necessary. For a level 4 tumor thrombus with no infringement of the venous wall, adoption of robot-assisted minimally invasive surgery, without extracorporeal circulation technology, is feasible.

The application of GaN HEMTs on silicon substrates in high-voltage environments is significantly limited due to their complex buffer layer structure and the difficulty in controlling wafer warpage. In this work, we successfully fabricated GaN power HEMTs on 6-inch sapphire substrates using a CMOS-compatible process. A 1.5 µm thin GaN buffer layer with excellent uniformity and a 20 nm in situ SiN gate dielectric ensured uniformly distributed V TH and R ON across the entire 6-inch wafer. The fabricated devices with an L GD of 30 µm and W G of 36 mm exhibited an R ON of 18.06 Ω·mm and an off-state breakdown voltage of over 3 kV. The electrical mapping visualizes the high uniformity of R ON and V TH distributed across the whole 6-inch wafer, which is of great significance in promoting the applications of GaN power HEMTs for medium-voltage power electronics in the future.

Multifactorial diseases demand therapeutics that can modulate multiple targets for enhanced safety and efficacy, yet the clinical approval of multitarget drugs remains rare. The integration of machine learning (ML) and deep learning (DL) in drug discovery has revolutionized virtual screening. This study investigates the synergy between ML/DL methodologies, molecular representations, and data augmentation strategies. Notably, we found that SVM can match or even surpass the performance of state-of-the-art DL methods. However, conventional data augmentation often involves a trade-off between the true positive rate and false positive rate. To address this, we introduce Negative-Augmented PU-bagging (NAPU-bagging) SVM, a novel semi-supervised learning framework. By leveraging ensemble SVM classifiers trained on resampled bags containing positive, negative, and unlabeled data, our approach is capable of managing false positive rates while maintaining high recall rates. We applied this method to the identification of multitarget-directed ligands (MTDLs), where high recall rates are critical for compiling a list of interaction candidate compounds. Case studies demonstrate that NAPU-bagging SVM can identify structurally novel MTDL hits for ALK-EGFR with favorable docking scores and binding modes, as well as pan-agonists for dopamine receptors. The NAPU-bagging SVM methodology should serve as a promising avenue to virtual screening, especially for the discovery of MTDLs.

Mutations in human CILK1 (ciliogenesis associated kinase 1) are linked to ciliopathies and epilepsy. Homozygous point and nonsense mutations that extinguish kinase activity impair primary cilia function, whereas mutations outside the kinase domain are not well understood. Here, we produced a knock-in mouse equivalent to the human CILK1 A615T variant identified in juvenile myoclonic epilepsy (JME). This residue is in the intrinsically disordered C-terminal region of CILK1 separate from the kinase domain. Mouse embryo fibroblasts (MEFs) with either heterozygous or homozygous A612T mutant alleles exhibited a higher ciliation rate, shorter individual cilia, and upregulation of ciliary Hedgehog signaling. Thus, a single A612T mutant allele was sufficient to impair primary cilia and ciliary signaling in MEFs. Gene expression profiles of wild-type versus mutant MEFs revealed profound changes in cilia-related molecular functions and biological processes. The CILK1 A615T mutant protein was not increased to the same level as the wild-type protein when co-expressed with scaffold protein KATNIP (katanin-interacting protein). Our data show that KATNIP regulation of a JME-associated single-residue variant of CILK1 is compromised, and this impairs the maintenance of primary cilia and Hedgehog signaling.

GaN HEMT devices are sensitive to the single event effect (SEE) caused by heavy ions, and their reliability affects the safe use of space equipment. In this work, a Ge ion (LET = 37 MeV·cm 2 /mg) and Bi ion (LET = 98 MeV·cm 2 /mg) were used to irradiate Cascode GaN power devices by heavy ion accelerator experimental device. The differences of SEE under three conditions: pre-applied electrical stress, different LET values, and gate voltages are studied, and the related damage mechanism is discussed. The experimental results show that the pre-application of electrical stress before radiation leads to an electron de-trapping effect, generating defects within the GaN device. These defects will assist in charge collection so that the drain leakage current of the device will be enhanced. The higher the LET value, the more electron-hole pairs are ionized. Therefore, the charge collected by the drain increases, and the burn-out voltage advances. In the off state, the more negative the gate voltage, the higher the drain voltage of the GaN HEMT device, and the more serious the back-channel effect. This study provides an important theoretical basis for the reliability of GaN power devices in radiation environments.

In order to reduce the risk of early freezing damage to cement-based materials in winter construction, lime powder was used to improve the properties of the Portland cement-sulphoaluminate cement (PC-CSA) composite system at low temperatures. In this study, the effects of lime powder dosage on the properties of a PC-CSA blended system with two proportions (PC:CSA = 9:1 and 7:3) at -10 °C were investigated, and the mechanisms of improvement were revealed. The results showed that the compressive strength of the PC-CSA composite system was effectively improved, and the setting time was shortened by the addition of lime powder. Lime powder could effectively act as an early heating source in the PC-CSA composite system, as the maximum temperature of samples exposed to sub-zero temperatures was increased and the time before dropping to 0 °C was prolonged by the addition of lime powder. The extra CH generated by the hydration of lime powder provided an added hydration path for C 4 A 3 S¯, which accelerated the formation of AFt at each stage. Frozen water as well as the early frost damage were effectively decreased by lime powder because of the faster consumption of free water at an early stage. The modification of the hydration products also contributed to the denseness of the microstructure.

The hot spot temperature of transformer windings is an important indicator for measuring insulation performance, and its accurate inversion is crucial to ensure the timely and accurate fault prediction of transformers. However, existing studies mostly directly input obtained experimental or operational data into networks to construct data-driven models, without considering the lag between temperatures, which may lead to the insufficient accuracy of the inversion model. In this paper, a method for inverting the hot spot temperature of transformer windings based on the SA-GRU model is proposed. Firstly, temperature rise experiments are designed to collect the temperatures of the entire side and top of the transformer tank, top oil temperature, ambient temperature, the cooling inlet and outlet temperatures, and winding hot spot temperature. Secondly, experimental data are integrated, considering the lag of the data, to obtain candidate input feature parameters. Then, a feature selection algorithm based on mutual information (MI) is used to analyze the correlation of the data and construct the optimal feature subset to ensure the maximum information gain. Finally, Self-Attention (SA) is applied to optimize the Gate Recurrent Unit (GRU) network, establishing the GRU-SA model to perceive the potential patterns between output feature parameters and input feature parameters, achieving the precise inversion of the hot spot temperature of the transformer windings. The experimental results show that considering the lag of the data can more accurately invert the hot spot temperature of the windings. The inversion method proposed in this paper can reduce redundant input features, lower the complexity of the model, accurately invert the changing trend of the hot spot temperature, and achieve higher inversion accuracy than other classical models, thereby obtaining better inversion results.

Hyperspectral image (HSI) classification is a vital part of the HSI application field. Since HSIs contain rich spectral information, it is a major challenge to effectively extract deep representation features. In existing methods, although edge data augmentation is used to strengthen the edge representation, a large amount of high-frequency noise is also introduced at the edges. In addition, the importance of different spectra for classification decisions has not been emphasized. Responding to the above challenges, we propose an edge-aware and spectral-spatial feature learning network (ESSN). ESSN contains an edge feature augment block and a spectral-spatial feature extraction block. Firstly, in the edge feature augment block, the edges of the image are sensed, and the edge features of different spectral bands are adaptively strengthened. Then, in the spectral-spatial feature extraction block, the weights of different spectra are adaptively adjusted, and more comprehensive depth representation features are extracted on this basis. Extensive experiments on three publicly available hyperspectral datasets have been conducted, and the experimental results indicate that the proposed method has higher accuracy and immunity to interference compared to state-of-the-art (SOTA) method.

In this paper, we investigate the impact of classical optical communications in quantum key distribution (QKD) over hollow-core fiber (HCF), multi-core fiber (MCF) and single-core fiber (SCF) and propose wavelength allocation schemes to enhance QKD performance. Firstly, we theoretically analyze noise interference in QKD over HCF, MCF and SCF, such as spontaneous Raman scattering (SpRS) and four-wave mixing (FWM). To mitigate these noise types and optimize QKD performance, we propose a joint noise suppression wavelength allocation (JSWA) scheme. FWM noise suppression wavelength allocation and Raman noise suppression wavelength allocation are also proposed for comparison. The JSWA scheme indicates a significant enhancement in extending the simultaneous transmission distance of classical signals and QKD, reaching approximately 100 km in HCF and 165 km in MCF under a classical power per channel of 10 dBm. Therefore, MCF offers a longer secure transmission distance compared with HCF when classical signals and QKD coexist in the C-band. However, when classical signals are in the C-band and QKD operates in the O-band, the performance of QKD in HCF surpasses that in MCF. This research establishes technical foundations for the design and deployment of QKD optical networks.

H. perforatum , as one of the Traditional Chinese Medicinal materials, possesses a variety of pharmacological activities and high medicinal value. However, in recent years, the wild resources of H. perforatum have been severely depleted due to global climate change and human activities, and artificial cultivation faces problems such as unstable yield and active ingredient content. This poses a serious obstacle to the development and utilization of its resources. Therefore, this experiment took H. perforatum as the research object and used 894 distribution records of H. perforatum and 36 climatic environmental factors, using the MaxEnt model and GIS technology to explore the main climatic factors affecting the distribution of H. perforatum . Additionally, by utilizing the principles of ecological niche theory, the potential suitable distribution regions of H. perforatum across past, present, and future timelines were predicted, which can ascertain the dynamics of its spatial distribution patterns and the trend of centroid migration. The results indicate that the main environmental factors affecting the geographical distribution of H. perforatum are solar radiation in April (Srad4), solar radiation in September (Srad9), mean temperature of driest quarter (Bio9), solar radiation in November (Srad11), annual mean temperature (Bio1), and annual precipitation (Bio12). Under future climate scenarios, there is a remarkable trend of expansion in the suitable distribution areas of H. perforatum . The centroid migration indicates a trend of migration towards the northwest direction and high-altitude areas. These results can provide a scientific basis for formulating conservation and sustainable use management strategies for H. perforatum resources.

Phytochemicals and tryptophan (Trp) metabolites have been found to modulate gut function and health. However, whether these metabolites modulate gut ion transport and serotonin (5-HT) metabolism and signaling requires further investigation. The aim of this study was to investigate the effects of selected phytochemicals and Trp metabolites on the ion transport and 5-HT metabolism and signaling in the ileum of mice in vitro using the Ussing chamber technique. During the in vitro incubation, vanillylmandelic acid (VMA) reduced ( p < 0.05) the short-circuit current, and 100 μM chlorogenic acid (CGA) ( p = 0.12) and perillic acid (PA) ( p = 0.14) had a tendency to reduce the short-circuit current of the ileum. Compared with the control, PA and N -acetylserotonin treatment upregulated the expression of tryptophan hydroxylase 1 ( Tph1 ), while 100 μM cinnamic acid, indolelactic acid (ILA), and 10 μM CGA or indoleacetaldehyde (IAld) treatments downregulated ( p < 0.05) the mRNA levels of Tph1 . In addition, 10 μM IAld or 100 μM ILA upregulated ( p < 0.05) the expression of monoamine oxidase A ( Maoa ). However, 10 μM CGA or 100 μM PA downregulated ( p < 0.05) Maoa expression. All selected phytochemicals and Trp metabolites upregulated ( p < 0.05) the expression of Htr4 and Htr7 compared to that of the control group. VMA and CGA reduced ( p < 0.05) the ratios of Htr1a / Htr7 and Htr4 / Htr7 . These findings may help to elucidate the effects of phytochemicals and Trp metabolites on the regulation of gut ion transport and 5-HT signaling-related gut homeostasis in health and disease.

The precision of short-term photovoltaic power forecasts is of utmost importance for the planning and operation of the electrical grid system. To enhance the precision of short-term output power prediction in photovoltaic systems, this paper proposes a method integrating K-means clustering: an improved snake optimization algorithm with a convolutional neural network-bidirectional long short-term memory network to predict short-term photovoltaic power. Firstly, K-means clustering is utilized to categorize weather scenarios into three categories: sunny, cloudy, and rainy. The Pearson correlation coefficient method is then utilized to determine the inputs of the model. Secondly, the snake optimization algorithm is improved by introducing Tent chaotic mapping, lens imaging backward learning, and an optimal individual adaptive perturbation strategy to enhance its optimization ability. Then, the multi-strategy improved snake optimization algorithm is employed to optimize the parameters of the convolutional neural network-bidirectional long short-term memory network model, thereby augmenting the predictive precision of the model. Finally, the model established in this paper is utilized to forecast photovoltaic power in diverse weather scenarios. The simulation findings indicate that the regression coefficients of this method can reach 0.99216, 0.95772, and 0.93163 on sunny, cloudy, and rainy days, which has better prediction precision and adaptability under various weather conditions.

Magnesium-based hydrogen storage alloys have attracted significant attention as promising materials for solid-state hydrogen storage due to their high hydrogen storage capacity, abundant reserves, low cost, and reversibility. However, the widespread application of these alloys is hindered by several challenges, including slow hydrogen absorption/desorption kinetics, high thermodynamic stability of magnesium hydride, and limited cycle life. This comprehensive review provides an in-depth overview of the recent advances in magnesium-based hydrogen storage alloys, covering their fundamental properties, synthesis methods, modification strategies, hydrogen storage performance, and potential applications. The review discusses the thermodynamic and kinetic properties of magnesium-based alloys, as well as the effects of alloying, nanostructuring, and surface modification on their hydrogen storage performance. The hydrogen absorption/desorption properties of different magnesium-based alloy systems are compared, and the influence of various modification strategies on these properties is examined. The review also explores the potential applications of magnesium-based hydrogen storage alloys, including mobile and stationary hydrogen storage, rechargeable batteries, and thermal energy storage. Finally, the current challenges and future research directions in this field are discussed, highlighting the need for fundamental understanding of hydrogen storage mechanisms, development of novel alloy compositions, optimization of modification strategies, integration of magnesium-based alloys into hydrogen storage systems, and collaboration between academia and industry.

In recent years, significant advancements have been made in the research of photoswitchable probes. These probes undergo reversible structural and electronic changes upon light exposure, thus exhibiting vast potential in molecular detection, biological imaging, material science, and information storage. Through precisely engineered molecular structures, the photoswitchable probes can toggle between "on" and "off" states at specific wavelengths, enabling highly sensitive and selective detection of targeted analytes. This review systematically presents photoswitchable fluorescent and colorimetric probes built on various molecular photoswitches, primarily focusing on the types involving photoswitching in their detection and/or signal response processes. It begins with an analysis of various molecular photoswitches, including their photophysical properties, photoisomerization and photochromic mechanisms, and fundamental design concepts for constructing photoswitchable probes. The article then elaborates on the applications of these probes in detecting diverse targets, including cations, anions, small molecules, and biomacromolecules. Finally, it offers perspectives on the current state and future development of photoswitchable probes. This review aims to provide a clear introduction for researchers in the field and guidance for the design and application of new, efficient fluorescent and colorimetric probes.

Mg-based materials have been widely studied as potential hydrogen storage media due to their high theoretical hydrogen capacity, low cost, and abundant reserves. However, the sluggish hydrogen absorption/desorption kinetics and high thermodynamic stability of Mg-based hydrides have hindered their practical application. Ball milling has emerged as a versatile and effective technique to synthesize and modify nanostructured Mg-based hydrides with enhanced hydrogen storage properties. This review provides a comprehensive summary of the state-of-the-art progress in the ball milling of Mg-based hydrogen storage materials. The synthesis mechanisms, microstructural evolution, and hydrogen storage properties of nanocrystalline and amorphous Mg-based hydrides prepared via ball milling are systematically reviewed. The effects of various catalytic additives, including transition metals, metal oxides, carbon materials, and metal halides, on the kinetics and thermodynamics of Mg-based hydrides are discussed in detail. Furthermore, the strategies for synthesizing nanocomposite Mg-based hydrides via ball milling with other hydrides, MOFs, and carbon scaffolds are highlighted, with an emphasis on the importance of nanoconfinement and interfacial effects. Finally, the challenges and future perspectives of ball-milled Mg-based hydrides for practical on-board hydrogen storage applications are outlined. This review aims to provide valuable insights and guidance for the development of advanced Mg-based hydrogen storage materials with superior performance.

Magnesium-based hydrogen storage materials have garnered significant attention due to their high hydrogen storage capacity, abundance, and low cost. However, the slow kinetics and high desorption temperature of magnesium hydride hinder its practical application. Various preparation methods have been developed to improve the hydrogen storage properties of magnesium-based materials. This review comprehensively summarizes the recent advances in the preparation methods of magnesium-based hydrogen storage materials, including mechanical ball milling, methanol-wrapped chemical vapor deposition, plasma-assisted ball milling, organic ligand-assisted synthesis, and other emerging methods. The principles, processes, key parameters, and modification strategies of each method are discussed in detail, along with representative research cases. Furthermore, the advantages and disadvantages of different preparation methods are compared and evaluated, and their influence on hydrogen storage properties is analyzed. The practical application potential of these methods is also assessed, considering factors such as hydrogen storage performance, scalability, and cost-effectiveness. Finally, the existing challenges and future research directions in this field are outlined, emphasizing the need for further development of high-performance and cost-effective magnesium-based hydrogen storage materials for clean energy applications. This review provides valuable insights and references for researchers working on the development of advanced magnesium-based hydrogen storage technologies.

Polarization imaging has achieved a wide range of applications in military and civilian fields such as camouflage detection and autonomous driving. However, when the imaging environment involves a low-light condition, the number of photons is low and the photon transmittance of the conventional Division-of-Focal-Plane (DoFP) structure is small. Therefore, the traditional demosaicing methods are often used to deal with the serious noise and distortion generated by polarization demosaicing in low-light environment. Based on the aforementioned issues, this paper proposes a model called Low-Light Sparse Polarization Demosaicing Network (LLSPD-Net) for simulating a sparse polarization sensor acquisition of polarization images in low-light environments. The model consists of two parts: an intensity image enhancement network and a Stokes vector complementation network. In this work, the intensity image enhancement network is used to enhance low-light images and obtain high-quality RGB images, while the Stokes vector is used to complement the network. We discard the traditional idea of polarization intensity image interpolation and instead design a polarization demosaicing method with Stokes vector complementation. By using the enhanced intensity image as a guide, the completion of the Stokes vector is achieved. In addition, to train our network, we collected a dataset of paired color polarization images that includes both low-light and regular-light conditions. A comparison with state-of-the-art methods on both self-constructed and publicly available datasets reveals that our model outperforms traditional low-light image enhancement demosaicing methods in both qualitative and quantitative experiments.

Pyrethroid pesticides (PYRs) have found widespread application in agriculture for the protection of fruit and vegetable crops. Nonetheless, excessive usage or improper application may allow the residues to exceed the safe limits and pose a threat to consumer safety. Thus, there is an urgent need to develop efficient technologies for the elimination or trace detection of PYRs from vegetables. Here, a simple and efficient magnetic solid-phase extraction (MSPE) strategy was developed for the simultaneous purification and enrichment of five PYRs in vegetables, employing the magnetic covalent organic framework nanomaterial COF-SiO 2 @Fe 3 O 4 as an adsorbent. COF-SiO 2 @Fe 3 O 4 was prepared by a straightforward solvothermal method, using Fe 3 O 4 as a magnetic core and benzidine and 3,3,5,5-tetraaldehyde biphenyl as the two building units. COF-SiO 2 @Fe 3 O 4 could effectively capture the targeted PYRs by virtue of its abundant π-electron system and hydroxyl groups. The impact of various experimental parameters on the extraction efficiency was investigated to optimize the MSPE conditions, including the adsorbent amount, extraction time, elution solvent type and elution time. Subsequently, method validation was conducted under the optimal conditions in conjunction with gas chromatography-mass spectrometry (GC-MS). Within the range of 5.00-100 μg·kg -1 (1.00-100 μg·kg -1 for bifenthrin and 2.5-100 μg·kg -1 for fenpropathrin), the five PYRs exhibited a strong linear relationship, with determination coefficients ranging from 0.9990 to 0.9997. The limits of detection (LODs) were 0.3-1.5 μg·kg -1 , and the limits of quantification (LOQs) were 0.9-4.5 μg·kg -1 . The recoveries were 80.2-116.7% with relative standard deviations (RSDs) below 7.0%. Finally, COF-SiO 2 @Fe 3 O 4 , NH 2 -SiO 2 @Fe 3 O 4 and Fe 3 O 4 were compared as MSPE adsorbents for PYRs. The results indicated that COF-SiO 2 @Fe 3 O 4 was an efficient and rapid selective adsorbent for PYRs. This method holds promise for the determination of PYRs in real samples.

This study aimed to evaluate the effects of physical measures and the applications of phosphorus fertilizer and soil conditioner on the growth of lettuce (Lactuca sativa) and its uptake of cadmium (Cd). In a solar greenhouse that contained soil enriched with cadmium (Cd) (1.75 ± 0.41 mg/kg) with lettuce used as a test plant, field experimental methods were utilized to explore the influence of physical measures, such as deep plowing and soil covering, and the applications of phosphorus fertilizer, including diammonium phosphate (DAP), calcium magnesium phosphate (CMP), and calcium superphosphate (SSP), and soil conditioners, such as biochar, attapulgite clay, and nano-hydroxyapatite, on the uptake of Cd in lettuce. The results indicated that the concentrations of Cd in the aboveground parts of lettuce were 1.49 ± 0.45, 1.26 ± 0.02, 1.00 ± 0.21, and 0.24 ± 0.13 mg/kg when the soil was plowed 30, 40, and 50 cm deep, respectively, and when the soil was covered with 15 cm, this resulted in reductions of 27.5%, 38.3%, 51.4%, and 88.4%, respectively, compared with the control treatment that entailed plowing to 15 cm. When 75, 150, and 225 kg/ha of phosphorus pentoxide (P 2 O 5 ) were applied compared with the lack of application, the contents of Cd in the aboveground parts of lettuce increased by 2.0%, 54.5%, and 73.7%, respectively, when DAP was applied; by 52.5%, 48.5%, and 8.1%, respectively, when CMP was applied; and by 13.1%, 61.6%, and 90.9%, respectively, when SSP was applied. When the amounts of biochar applied were 0, 2, 4, 6, 8, 10, and 12 t/ha, the contents of Cd in the aboveground parts of lettuce were 1.36 ± 0.27, 1.47 ± 0.56, 1.80 ± 0.73, 1.96 ± 0.12, 1.89 ± 0.52, 1.44 ± 0.30, and 1.10 ± 0.27 mg/kg, respectively. Under concentrations of 0, 40, 80, 120, 160, and 200 kg/ha, the application of nano-hydroxyapatite resulted in Cd contents of 1.34 ± 0.56, 1.47 ± 0.10, 1.60 ± 0.44, 1.70 ± 0.21, 1.31 ± 0.09, and 1.51 ± 0.34 mg/kg, respectively. The concentrations of Cd in the aboveground parts of lettuce treated with attapulgite clay were 1.44 ± 0.48, 1.88 ± 0.67, 2.10 ± 0.80, 2.24 ± 0.75, 1.78 ± 0.41, and 1.88 ± 0.48 mg/kg, respectively. In summary, under the conditions in this study, deep plowing and soil covering measures can reduce the concentration of Cd in the aboveground parts of lettuce. The application of phosphorus fertilizer increased the concentration of Cd in the aboveground parts of lettuce. The application of higher amounts of DAP and SSP led to greater concentrations of Cd in the aboveground parts of lettuce. The application of higher amounts of CMP caused a lower concentration of Cd in the aboveground parts of lettuce. When biochar, attapulgite clay, and nano-hydroxyapatite were applied, the concentration of Cd in the aboveground parts of lettuce increased in parallel with the increase in the concentration of application when low amounts were applied. In contrast, when high amounts were applied, the concentration of Cd in the aboveground parts of lettuce began to decrease.

In our previous studies, a novel cryothermal therapy (CTT) was developed to induce systemic long-term anti-tumor immunity. Natural killer (NK) cells were found to play an important role in CTT-induced long-term immune-mediated tumor control at the late stage after CTT, but the underlying mechanism is unclear. Myeloid-derived suppressor cells (MDSCs) are immature myeloid cells that have potent immunosuppressive effects on T cells and weaken the long-term benefits of immunotherapy. Consequently, overcoming MDSC immunosuppression is essential for maintaining the long-term efficacy of immunotherapy. In this study, we revealed that NK cells considerably diminish MDSC accumulation at the late stage after CTT, boost T cell production, increase T cell activation, and promote MDSC maturation, culminating in Th1-dominant CD4 + T cell differentiation and enhancing NK and CD8 + T cell cytotoxicity. Additionally, NK cells activate ERK signaling in MDSCs through NKG2D-ligand interaction to increase the activity of tumor necrosis factor (TNF)-α converting enzyme (TACE)-cleaved membrane TNF-α. Furthermore, Increased TACE activity releases more soluble TNF-α from MDSCs to promote MDSC maturation. In our studies, we propose a novel mechanism by which NK cells can overcome MDSC-induced immunosuppression and maintain CTT-induced persistent anti-tumor immunity, providing a prospective therapeutic option to improve the performance of cancer immunotherapy.

People with blindness and low vision (pBLV) encounter substantial challenges when it comes to comprehensive scene recognition and precise object identification in unfamiliar environments. Additionally, due to the vision loss, pBLV have difficulty in accessing and identifying potential tripping hazards independently. Previous assistive technologies for the visually impaired often struggle in real-world scenarios due to the need for constant training and lack of robustness, which limits their effectiveness, especially in dynamic and unfamiliar environments, where accurate and efficient perception is crucial. Therefore, we frame our research question in this paper as: How can we assist pBLV in recognizing scenes, identifying objects, and detecting potential tripping hazards in unfamiliar environments, where existing assistive technologies often falter due to their lack of robustness? We hypothesize that by leveraging large pretrained foundation models and prompt engineering, we can create a system that effectively addresses the challenges faced by pBLV in unfamiliar environments. Motivated by the prevalence of large pretrained foundation models, particularly in assistive robotics applications, due to their accurate perception and robust contextual understanding in real-world scenarios induced by extensive pretraining, we present a pioneering approach that leverages foundation models to enhance visual perception for pBLV, offering detailed and comprehensive descriptions of the surrounding environment and providing warnings about potential risks. Specifically, our method begins by leveraging a large-image tagging model (i.e., Recognize Anything Model (RAM)) to identify all common objects present in the captured images. The recognition results and user query are then integrated into a prompt, tailored specifically for pBLV, using prompt engineering. By combining the prompt and input image, a vision-language foundation model (i.e., InstructBLIP) generates detailed and comprehensive descriptions of the environment and identifies potential risks in the environment by analyzing environmental objects and scenic landmarks, relevant to the prompt. We evaluate our approach through experiments conducted on both indoor and outdoor datasets. Our results demonstrate that our method can recognize objects accurately and provide insightful descriptions and analysis of the environment for pBLV.

Physically unclonable functions (PUFs) are crucial for enhancing cybersecurity by providing unique, intrinsic identifiers for electronic devices, thus ensuring their authenticity and preventing unauthorized cloning. The SRAM-PUF, characterized by its simple structure and ease of implementation in various scenarios, has gained widespread usage. The soft-decision Reed-Muller (RM) code, an error correction code, is commonly employed in these designs. This paper introduces the design of an RM code soft-decision attack algorithm to reveal its potential security risks. To address this problem, we propose a soft-decision SRAM-PUF structure based on the elliptic curve digital signature algorithm (ECDSA). To improve the processing speed of the proposed secure SRAM-PUF, we propose a custom ECDSA scheme. Further, we also propose a universal architecture for the critical operations in ECDSA, elliptic curve scalar multiplication (ECSM), and elliptic curve double scalar multiplication (ECDSM) based on the differential addition chain (DAC). For ECSMs, iterations can be performed directly; for ECDSMs, a two-dimensional DAC is constructed through precomputation, followed by iterations. Moreover, due to the high similarity of ECSM and ECDSM data paths, this universal architecture saves hardware resources. Our design is implemented on a field-programmable gate array (FPGA) and an application-specific integrated circuit (ASIC) using a Xilinx Virtex-7 and an TSMC 40 nm process. Compared to existing research, our design exhibits a lower bit error rate (2.7×10-10) and better area-time performance (3902 slices, 6.615 μs ECDSM latency).

The Hippo pathway plays crucial roles in governing various biological processes during tumorigenesis and metastasis. Within this pathway, upstream signaling stimuli activate a core kinase cascade, involving MST1/2 and LATS1/2, that subsequently phosphorylates and inhibits the transcriptional co-activators YAP and its paralog TAZ. This inhibition modulates the transcriptional regulation of downstream target genes, impacting cell proliferation, migration, and death. Despite the acknowledged significance of protein kinases in the Hippo pathway, the regulatory influence of protein phosphatases remains largely unexplored. In this study, we conducted the first gain-of-functional screen for protein tyrosine phosphatases (PTPs) regulating the Hippo pathway. Utilizing a LATS kinase biosensor (LATS-BS), a YAP/TAZ activity reporter (STBS-Luc), and a comprehensive PTP library, we identified numerous novel PTPs that play regulatory roles in the Hippo pathway. Subsequent experiments validated PTPN12, a master regulator of oncogenic receptor tyrosine kinases (RTKs), as a previously unrecognized negative regulator of the Hippo pathway effectors, oncogenic YAP/TAZ, influencing breast cancer cell proliferation and migration. In summary, our findings offer valuable insights into the roles of PTPs in the Hippo signaling pathway, significantly contributing to our understanding of breast cancer biology and potential therapeutic strategies.

The discovery of new genes with novel functions is a major driver of adaptive evolutionary innovation in plants. Especially in woody plants, due to genome expansion, new genes evolve to regulate the processes of growth and development. In this study, we characterized the unique VeA transcription factor family in Populus alba × Populus glandulosa , which is associated with secondary metabolism. Twenty VeA genes were characterized systematically on their phylogeny, genomic distribution, gene structure and conserved motif, promoter binding site, and expression profiling. Furthermore, through ChIP-qPCR, Y1H, and effector-reporter assays, it was demonstrated that PagMYB128 directly regulated PagVeA3 to influence the biosynthesis of secondary metabolites. These results provide a basis for further elucidating the function of VeAs gene in poplar and its genetic regulation mechanism.

Erythritol has shown excellent insecticidal performance against a wide range of insect species, but the molecular mechanism by which it causes insect mortality and sterility is not fully understood. The mortality and sterility of Drosophila melanogaster were assessed after feeding with 1M erythritol for 72 h and 96 h, and gene expression profiles were further compared through RNA sequencing. Enrichment analysis of GO and KEGG revealed that expressions of the adipokinetic hormone gene ( Akh ), amylase gene ( Amyrel ), α-glucosidase gene ( Mal-B1/2 , Mal-A1-4 , Mal-A7/8 ), and triglyceride lipase gene ( Bmm ) were significantly up-regulated, while insulin-like peptide genes ( Dilp2 , Dilp3 and Dilp5 ) were dramatically down-regulated. Seventeen genes associated with eggshell assembly, including Dec-1 (down 315-fold), Vm26Ab (down 2014-fold) and Vm34Ca (down 6034-fold), were significantly down-regulated or even showed no expression. However, there were no significant differences in the expression of three diuretic hormone genes ( DH44 , DH31 , CAPA ) and eight aquaporin genes ( Drip , Big brain , AQP , Eglp1 , Eglp2 , Eglp3 , Eglp4 and Prip ) involved in osmolality regulation (all p value > 0.05). We concluded that erythritol, a competitive inhibitor of α-glucosidase, severely reduced substrates and enzyme binding, inhibiting effective carbohydrate hydrolysis in the midgut and eventually causing death due to energy deprivation. It was clear that Drosophila melanogaster did not die from the osmolality of the hemolymph. Our findings elucidate the molecular mechanism underlying the mortality and sterility in Drosophila melanogaster induced by erythritol feeding. It also provides an important theoretical basis for the application of erythritol as an environmentally friendly pesticide.

Emotion is one of the most important higher cognitive functions of the human brain and plays an important role in transaction processing and decisions. In traditional emotion recognition studies, the frequency band features in EEG signals have been shown to have a high correlation with emotion production. However, traditional emotion recognition methods cannot satisfactorily solve the problem of individual differences in subjects and data heterogeneity in EEG, and subject-independent emotion recognition based on EEG signals has attracted extensive attention from researchers. In this paper, we propose a subject-independent emotion recognition model based on adaptive extraction of layer structure based on frequency bands (BFE-Net), which is adaptive in extracting EEG map features through the multi-graphic layer construction module to obtain a frequency band-based multi-graphic layer emotion representation. To evaluate the performance of the model in subject-independent emotion recognition studies, extensive experiments are conducted on two public datasets including SEED and SEED-IV. The experimental results show that in most experimental settings, our model has a more advanced performance than the existing studies of the same type. In addition, the visualization of brain connectivity patterns reveals that some of the findings are consistent with previous neuroscientific validations, further validating the model in subject-independent emotion recognition studies.

In this study, the performance of a zero-gap flow-through reactor with three-dimensional (3D) porous Ti/RuO 2 -TiO 2 @Pt anodes was systematically investigated for the electrocatalytic oxidation of phenolic wastewater, considering phenol and 4-nitrophenol (4-NP) as the target pollutants. The optimum parameters for the electrochemical oxidation of phenol and 4-NP were examined. For phenol degradation, at an initial concentration of 50 mg/L, initial pH of 7, NaCl concentration of 10.0 g/L, current density of 10 mA/cm 2 , and retention time of 30 min, the degradation efficiency achieved was 95.05%, with an energy consumption of 15.39 kWh/kg; meanwhile, for 4-NP, the degradation efficiency was 98.42% and energy consumption was 19.21 kWh/kg (at an initial concentration of 40 mg/L, initial pH of 3, NaCl concentration of 10.0 g/L, current density of 10 mA/cm 2 , and retention time of 30 min). The electrocatalytic oxidation of phenol and 4-NP conformed to the pseudo-first-order kinetics model, and the k values were 0.2562 min -1 and 0.1736 min -1 , respectively, which are 1.7 and 3.6-times higher than those of a conventional electrolyzer. Liquid chromatography-mass spectrometry (LC-MS) was used to verify the intermediates formed during the degradation of phenol or 4-NP and a possible degradation pathway was provided. The extremely narrow electrode distance and the flow-through configuration of the zero-gap flow-through reactor were thought to be essential for its lower energy consumption and higher mass transfer efficiency. The zero-gap flow-through reactor with a novel 3D porous Ti/RuO 2 -TiO 2 @Pt electrode is a superior alternative for the treatment of industrial wastewater.

Hollow silica spheres have been widely studied for drug delivery because of their excellent biosecurity and high porosity. However, difficulties with degradation in the tumor microenvironment (TME) and premature leaking during drug delivery limit their clinical applications. To alleviate these problems, herein, hollow organosilica spheres (HOS) were initially prepared using a "selective etching strategy" and loaded with a photothermal drug: new indocyanine green (IR820). Then, the Cu 2+ -tannic acid complex (Cu-TA) was deposited on the surface of the HOS, and a new nanoplatform named HOS@IR820@Cu-TA (HICT) was finally obtained. The deposition of Cu-TA can gate the pores of HOS completely to prevent the leakage of IR820 and significantly enhance the loading capacity of HOS. Once in the mildly acidic TME, the HOS and outer Cu-TA decompose quickly in response, resulting in the release of Cu 2+ and IR820. The released Cu 2+ can react with the endogenous glutathione (GSH) to consume it and produce Cu + , leading to the enhanced production of highly toxic ·OH through a Fenton-like reaction due to the overexpressed H 2 O 2 in the TME. Meanwhile, the ·OH generation was remarkably enhanced by the NIR light-responsive photothermal effect of IR820. These collective properties of HICT enable it to be a smart nanomedicine for dually enhanced chemodynamic therapy through GSH depletions and NIR light-triggered photothermal effects.

1,3,6-Trigalloylglucose is a natural compound that can be extracted from the aqueous extracts of ripe fruit of Terminalia chebula Retz, commonly known as " Haritaki ". The potential anti- Helicobacter pylori (HP) activity of this compound has not been extensively studied or confirmed in scientific research. This compound was isolated using a semi-preparative liquid chromatography (LC) system and identified through Ultra-high-performance liquid chromatography-MS/MS (UPLC-MS/MS) and Nuclear Magnetic Resonance (NMR). Its role was evaluated using Minimum inhibitory concentration (MIC) assay and minimum bactericidal concentration (MBC) assay, scanning electron microscope (SEM), inhibiting kinetics curves, urea fast test, Cell Counting Kit-8 (CCK-8) assay, Western blot, and Griess Reagent System. Results showed that this compound effectively inhibits the growth of HP strain ATCC 700392, damages the HP structure, and suppresses the Cytotoxin-associated gene A (Cag A) protein, a crucial factor in HP infection. Importantly, it exhibits selective antimicrobial activity without impacting normal epithelial cells GES-1. In vitro studies have revealed that 1,3,6-Trigalloylglucose acts as an anti-adhesive agent, disrupting the adhesion of HP to host cells, a critical step in HP infection. These findings underscore the potential of 1,3,6-Trigalloylglucose as a targeted therapeutic agent against HP infections.

Mutations of the FBN1 gene lead to Marfan syndrome (MFS), which is an autosomal dominant connective tissue disorder featured by thoracic aortic aneurysm risk. There is currently no effective treatment for MFS. Here, we studied the role of mitochondrial dysfunction in the phenotypic transformation of human smooth muscle cells (SMCs) and whether a mitochondrial boosting strategy can be a potential treatment. We knocked down FBN1 in SMCs to create an MFS cell model and used rotenone to induce mitochondrial dysfunction. Furthermore, we incubated the shFBN1 SMCs with Coenzyme Q10 (CoQ10) to assess whether restoring mitochondrial function can reverse the phenotypic transformation. The results showed that shFBN1 SMCs had decreased TFAM (mitochondrial transcription factor A), mtDNA levels and mitochondrial mass, lost their contractile capacity and had increased synthetic phenotype markers. Inhibiting the mitochondrial function of SMCs can decrease the expression of contractile markers and increase the expression of synthetic genes. Imposing mitochondrial stress causes a double-hit effect on the TFAM level, oxidative phosphorylation and phenotypic transformation of FBN1 -knockdown SMCs while restoring mitochondrial metabolism with CoQ10 can rapidly reverse the synthetic phenotype. Our results suggest that mitochondria function is a potential therapeutic target for the phenotypic transformation of SMCs in MFS.

Staphylococcus pseudintermedius ( S. pseudintermedius ) is the main pathogen causing pyoderma of canines. With the emergence of drug-resistant bacteria, traditional antibiotic treatments are limited. As a potential antibacterial agent, NZ2114 was effective against S. pseudintermedius , including drug-resistant strains. Its bactericidal efficacy was superior to mupiroxacin, ofloxacin and lincomycin. To facilitate the transcutaneous delivery of NZ2114 for the treatment of superficial pyoderma, chemical permeation enhancers were added since water-soluble NZ2114 does not easily penetrate the skin lipid layer. Two different NZ2114 sprays were prepared by combining 1% Azone + 10% propylene glycol (PG) or 5% N-methylpyrrolidone (NMP) + 10% PG with NZ2114 after screening. The cumulative permeability of NZ2114 sprays were 244.149 and 405.245 μg/cm 2 at 24 h with an in vitro percutaneous assay of mice skin, which showed a 244% and 405% increase in skin permeability than NZ2114, respectively. In addition, the efficacy of NZ2114 sprays in reducing skin bacteria colonisation was demonstrated in a mouse model of superficial pyoderma (24 mice, 3 mice/group) induced by S. pseudintermedius , and the 5% NMP + 10% PG + NZ2114 group had the best therapeutic effect compared to the other groups. This preparation did not cause any skin irritation, laying the foundation for the development of an effective and non-toxic topical product.

Cement-based material encapsulation is a method of encapsulating electronic devices in highly thermally conductive cement-based materials to improve the heat dissipation performance of electronic components. In the field of construction, a thermoelectric generator (TEG) encapsulated with cement-based materials used in the building envelope has significant potential for waste heat energy recovery. The purpose of this study was to investigate the effect of cement-based materials integrated with aluminum heatsinks on the heat dissipation of the TEG composite structure. In this work, three types of thermoelectric work units encapsulated with cement paste were proposed. Moreover, we explored the effect of encapsulated structure, heat dissipation area, the height of thermoelectric single leg, and heat input temperature on maintaining the temperature difference between the two sides of the thermoelectric single leg with COMSOL Multiphysics. The numerical simulation results showed that under the conditions of a heat source temperature of 313.15 K and ambient temperature of 298.15 K, the temperature difference between the two sides of the internal thermoelectric single leg of Type-III can maintain a stable temperature difference of 7.77 K, which is 32.14% higher than that of Type-I and Type-II (5.88 K), and increased by 26.82% in the actual experiment. This work provides a reference for the selection and application of TEG composite structures of cement-based materials combined with aluminum heatsinks.

By precisely controlling the distance between two train sets, virtual coupling (VC) enables flexible coupling and decoupling in urban rail transit. However, relying on train-to-train communication for obtaining the train distance can pose a safety risk in case of communication malfunctions. In this paper, a distance-estimation framework based on monocular vision is proposed. First, key structure features of the target train are extracted by an object-detection neural network, whose strategies include an additional detection head in the feature pyramid, labeling of object neighbor areas, and semantic filtering, which are utilized to improve the detection performance for small objects. Then, an optimization process based on multiple key structure features is implemented to estimate the distance between the two train sets in VC. For the validation and evaluation of the proposed framework, experiments were implemented on Beijing Subway Line 11. The results show that for train sets with distances between 20 m and 100 m, the proposed framework can achieve a distance estimation with an absolute error that is lower than 1 m and a relative error that is lower than 1.5%, which can be a reliable backup for communication-based VC operations.

Infrared image processing is an effective method for diagnosing faults in electrical equipment, in which target device segmentation and temperature feature extraction are key steps. Target device segmentation separates the device to be diagnosed from the image, while temperature feature extraction analyzes whether the device is overheating and has potential faults. However, the segmentation of infrared images of electrical equipment is slow due to issues such as high computational complexity, and the temperature information extracted lacks accuracy due to the insufficient consideration of the non-linear relationship between the image grayscale and temperature. Therefore, in this study, we propose an optimized maximum between-class variance thresholding method (OTSU) segmentation algorithm based on the Gray Wolf Optimization (GWO) algorithm, which accelerates the segmentation speed by optimizing the threshold determination process using OTSU. The experimental results show that compared to the non-optimized method, the optimized segmentation method increases the threshold calculation time by more than 83.99% while maintaining similar segmentation results. Based on this, to address the issue of insufficient accuracy in temperature feature extraction, we propose a temperature value extraction method for infrared images based on the K-nearest neighbor (KNN) algorithm. The experimental results demonstrate that compared to traditional linear methods, this method achieves a 73.68% improvement in the maximum residual absolute value of the extracted temperature values and a 78.95% improvement in the average residual absolute value.

Recently, 6DoF object pose estimation has become increasingly important for a broad range of applications in the fields of virtual reality, augmented reality, autonomous driving, and robotic operations. This task involves extracting the target area from the input data and subsequently determining the position and orientation of the objects. In recent years, many new advances have been made in pose estimation. However, existing reviews have the problem of only summarizing category-level or instance-level methods, and not comprehensively summarizing deep learning methods. This paper will provide a comprehensive review of the latest progress in 6D pose estimation to help researchers better understanding this area. In this study, the current methods about 6DoF object pose estimation are mainly categorized into two groups: instance-level and category-level groups, based on whether it is necessary to acquire the CAD model of the object. Recent advancements about learning-based 6DoF pose estimation methods are comprehensively reviewed. The study systematically explores the innovations and applicable scenarios of various methods. It provides an overview of widely used datasets, task metrics, and diverse application scenarios. Furthermore, state-of-the-art methods are compared across publicly accessible datasets, taking into account differences in input data types. Finally, we summarize the challenges of current tasks, methods for different applications, and future development directions.

PbSn solders are used in semiconductor devices for aerospace or military purposes with high levels of reliability requirements. Microalloying has been widely adopted to improve the reliability for Pb-free solders, but its application in PbSn solders is scarce. In this article, the optimization of PbSn solder reliability with Ge microalloying was investigated using both experimental and calculation methods. Intermetallic compounds (IMC) growth and morphologies evolution during reliability tests were considered to be the main factors of device failure. Through first-principle calculation, the growth mechanism of interfacial Ni 3 Sn 4 was discussed, including the formation of vacancies, the Ni-vacancies exchange diffusion and the dominant Ni diffusion along the [1 0 0] direction. The doping of Ge in the cell increased the exchange energy barrier and thus inhibited the IMC development and coarsening trend. In three reliability tests, only 0.013 wt% Ge microalloying in Pb60Sn40 was able to reduce IMC thickness by an increment of 22.6~38.7%. The proposed Ge microalloying method in traditional PbSn solder could yield a prospective candidate for highly reliable applications.

This paper characterizes the sensitivity of a time domain MEMS accelerometer. The sensitivity is defined by the increment in the measured time interval per gravitational acceleration. Two sensitivities exist, and they can be enhanced by decreasing the amplitude and frequency. The sensitivity with minor nonlinearity is chosen to evaluate the time domain sensor. The experimental results of the developed accelerometer demonstrate that the sensitivities span from -68.91 μs/g to -124.96 μs/g and the 1σ noises span from 8.59 mg to 6.2 mg (amplitude of 626 nm: -68.91 μs/g and 10.21 mg; amplitude of 455 nm: -94.51 μs/g and 7.76 mg; amplitude of 342 nm: -124.96 μs/g and 6.23 mg), which indicates the bigger the amplitude, the smaller the sensitivity and the bigger the 1σ noise. The adjustable sensitivity provides a theoretical foundation for range self-adaption, and all the results can be extended to other time domain inertial sensors, e.g., a gyroscope or an inclinometer.

The photoperiod is a major environmental factor in flowering control. Water spinach flowering under the inductive short-day condition decreases the yield of vegetative tissues and the eating quality. To obtain an insight into the molecular mechanism of the photoperiod-dependent regulation of the flowering time in water spinach, we performed transcriptome sequencing on water spinach under long- and short-day conditions with eight time points. Our results indicated that there were 6615 circadian-rhythm-related genes under the long-day condition and 8691 under the short-day condition. The three key circadian-rhythm genes, IaCCA1 , IaLHY , and IaTOC1 , still maintained single copies and similar IaCCA1 , IaLHY , and IaTOC1 feedback expression patterns, indicating the conservation of reverse feedback. In the photoperiod pathway, highly conserved GI genes were amplified into two copies ( IaGI1 and IaGI2 ) in water spinach. The significant difference in the expression of the two genes indicates functional diversity. Although the photoperiod core gene FT was duplicated to three copies in water spinach, only IaFT1 was highly expressed and strongly responsive to the photoperiod and circadian rhythms, and the almost complete inhibition of IaFT1 in water spinach may be the reason why water spinach does not bloom, no matter how long it lasts under the long-day condition. Differing from other species ( I. nil , I. triloba, I. trifida ) of the Ipomoea genus that have three CO members, water spinach lacks one of them, and the other two CO genes ( IaCO1 and IaCO2 ) encode only one CCT domain. In addition, through weighted correlation network analysis (WGCNA), some transcription factors closely related to the photoperiod pathway were obtained. This work provides valuable data for further in-depth analyses of the molecular regulation of the flowering time in water spinach and the Ipomoea genus.

This work presents highly responsive gate-controlled p-GaN/AlGaN/GaN ultraviolet photodetectors (UVPDs) on Si substrates with a high-transmittance ITO gate. The two-dimensional electron gas (2DEG) in the quantum well of the polarized AlGaN/GaN heterojunction was efficiently depleted by the p-GaN gate, leading to a high photo-to-dark current ratio (PDCR) of 3.2 × 10 5 . The quantum wells of the p-GaN/AlGaN and AlGaN/GaN heterojunctions can trap the holes and electrons excited by the UV illumination, thus efficiently triggering a photovoltaic effect and photoconductive effect, separately. Furthermore, the prepared photodetectors allow flexible adjustment of the static bias point, making it adaptable to different environments. Compared to traditional thin-film semi-transparent Ni/Au gates, indium tin oxide (ITO) exhibits higher transmittance. Under 355 nm illumination, the photodetector exhibited a super-high responsivity exceeding 3.5 × 10 4 A/W, and it could even exceed 10 6 A/W under 300 nm illumination. The well-designed UVPD combines both the advantages of the high-transmittance ITO gate and the structure of the commercialized p-GaN/AlGaN/GaN high-electron-mobility transistors (HEMTs), which opens a new possibility of fabricating large-scale, low-cost, and high-performance UVPDs in the future.

Existing formats based on the simplified molecular input line entry system (SMILES) encoding and molecular graph structure are designed to encode the complete semantic and structural information of molecules. However, the physicochemical properties of molecules are complex, and a single encoding of molecular features from SMILES sequences or molecular graph structures cannot adequately represent molecular information. Aiming to address this problem, this study proposes a sequence graph cross-attention (SG-ATT) representation architecture for a molecular property prediction model to efficiently use domain knowledge to enhance molecular graph feature encoding and combine the features of molecular SMILES sequences. The SG-ATT fuses the two-dimensional molecular features so that the current model input molecular information contains molecular structure information and semantic information. The SG-ATT was tested on nine molecular property prediction tasks. Among them, the biggest SG-ATT model performance improvement was 4.5% on the BACE dataset, and the average model performance improvement was 1.83% on the full dataset. Additionally, specific model interpretability studies were conducted to showcase the performance of the SG-ATT model on different datasets. In-depth analysis was provided through case studies of in vitro validation. Finally, network tools for molecular property prediction were developed for the use of researchers.