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Abstract Background The effectiveness of palliative gastrectomy for advanced GC remains a topic of debate. This study sought to establish whether palliative gastrectomy has an impact on prolonging survival. Methods We carried out systematic searches in PubMed, Cochrane Library, Web of Science, and the EMBASE databases from database inception to July 2023 to gather studies that examined the connection between palliative gastrectomy and the prognosis of advanced GC. The study employed overall survival as the primary outcome, with the hazard ratio serving as the selected parameter to gauge the association. Subgroup analyses were performed to delve into potential differences within the included studies, categorizing them by study region and sample size in order to examine possible sources of heterogeneity. The stability of individual studies was assessed through sensitivity analysis. The analysis included 20 articles, encompassing a total of 23,061 patients. Results According to the meta-analysis results, patients who underwent palliative gastrectomy exhibited a noteworthy enhancement in overall survival (HR: 1.49; 95% CI: 1.12–1.99; P = 0.006) in comparison to those who did not receive this procedure. There was no association between the type of surgery and the length of hospital stay, as revealed by the analysis (HR = -0.02; 95% CI: -0.84–0.81; P = 0.970). Conclusions Based on this meta-analysis, patients with advanced gastric cancer who underwent palliative gastrectomy may experience an extended survival duration without a significant prolongation of their hospitalization.

Abstract Feline mesenchymal stem cells (fMSCs) are well known for their robust differentiation capabilities and are commonly used in studying immune-related diseases in cats. Despite their importance, the susceptibility of fMSCs to viral infections remains uncertain. This study aimed to assess the susceptibility of feline adipose-derived mesenchymal stem cells (fAD-MSCs) and feline umbilical cord-derived mesenchymal stem cells (fUC-MSCs) to common feline viruses, including feline coronavirus (FCoV), feline herpesvirus type 1 (FHV-1), and feline panleukopenia virus (FPV). The results demonstrated that both FCoV and FHV-1 were able to infect both types of cells, while FPV did not exhibit cytopathic effects on fUC-MSCs. Furthermore, all three viruses were successfully isolated from fAD-MSCs. These findings suggest that certain feline viruses can replicate in fMSCs, indicating potential limitations in using fMSCs for treating viral diseases caused by these specific viruses. This study has important clinical implications for veterinarians, particularly in the management of viral diseases.

Abstract Spinal cord injury (SCI) leads to fibrotic scar formation at the lesion site, yet the heterogeneity of fibrotic scar remains elusive. Here we show the heterogeneity in distribution, origin, and function of fibroblasts within fibrotic scars after SCI in mice and female monkeys. Utilizing lineage tracing and single-cell RNA sequencing (scRNA-seq), we found that perivascular fibroblasts (PFs), and meningeal fibroblasts (MFs), rather than pericytes/vascular smooth cells (vSMCs), primarily contribute to fibrotic scar in both transection and crush SCI. Crabp2 + /Emb+ fibroblasts (CE-F) derived from meninges primarily localize in the central region of fibrotic scars, demonstrating enhanced cholesterol synthesis and secretion of type I collagen and fibronectin. In contrast, perivascular/pial Lama1 + /Lama2+ fibroblasts (LA-F) are predominantly found at the periphery of the lesion, expressing laminin and type IV collagen and functionally involved in angiogenesis and lipid transport. These findings may provide a comprehensive understanding for remodeling heterogeneous fibrotic scars after SCI.

Abstract Thermal conductivity is one of the most significant criterion of three-dimensional carbon fiber-reinforced SiC matrix composites (3D C/SiC). Represent volume element (RVE) models of microscale, void/matrix and mesoscale proposed in this work are used to simulate the thermal conductivity behaviors of the 3D C/SiC composites. An entirely new process is introduced to weave the preform with three-dimensional orthogonal architecture. The 3D steady-state analysis step is created for assessing the thermal conductivity behaviors of the composites by applying periodic temperature boundary conditions. Three RVE models of cuboid, hexagonal and fiber random distribution are respectively developed to comparatively study the influence of fiber package pattern on the thermal conductivities at the microscale. Besides, the effect of void morphology on the thermal conductivity of the matrix is analyzed by the void/matrix models. The prediction results at the mesoscale correspond closely to the experimental values. The effect of the porosities and fiber volume fractions on the thermal conductivities is also taken into consideration. The multi-scale models mentioned in this paper can be used to predict the thermal conductivity behaviors of other composites with complex structures.

Abstract Most of the adversarial attacks against speech recognition systems focus on specific adversarial perturbations, which are generated by adversaries for each normal example to achieve the attack. Universal adversarial perturbations (UAPs), which are independent of the examples, have recently received wide attention for their enhanced real-time applicability and expanded threat range. However, most of the UAP research concentrates on the image domain, and less on speech. In this paper, we propose a staged perturbation generation method that constructs CommanderUAP, which achieves a high success rate of universal adversarial attack against speech recognition models. Moreover, we apply some methods from model training to improve the generalization in attack and we control the imperceptibility of the perturbation in both time and frequency domains. In specific scenarios, CommanderUAP can also transfer attack some commercial speech recognition APIs.

Abstract Probabilistic computing is a computing scheme that offers a more efficient approach than conventional complementary metal-oxide–semiconductor (CMOS)-based logic in a variety of applications ranging from optimization to Bayesian inference, and invertible Boolean logic. The probabilistic bit (or p-bit, the base unit of probabilistic computing) is a naturally fluctuating entity that requires tunable stochasticity; by coupling low-barrier stochastic magnetic tunnel junctions (MTJs) with a transistor circuit, a compact implementation is achieved. In this work, by combining stochastic MTJs with 2D-MoS2 field-effect transistors (FETs), we demonstrate an on-chip realization of a p-bit building block displaying voltage-controllable stochasticity. Supported by circuit simulations, we analyze the three transistor-one magnetic tunnel junction (3T-1MTJ) p-bit design, evaluating how the characteristics of each component influence the overall p-bit output. While the current approach has not reached the level of maturity required to compete with CMOS-compatible MTJ technology, the design rules presented in this work are valuable for future experimental implementations of scaled on-chip p-bit networks with reduced footprint.

Abstract Two-dimensional (2D) materials have garnered significant attention in recent years due to their atomically thin structure and unique electronic and optoelectronic properties. To harness their full potential for applications in next-generation electronics and photonics, precise control over the dielectric environment surrounding the 2D material is critical. The lack of nucleation sites on 2D surfaces to form thin, uniform dielectric layers often leads to interfacial defects that degrade the device performance, posing a major roadblock in the realization of 2D-based devices. Here, we demonstrate a wafer-scale, low-temperature process (

Abstract General, catalytic and enantioselective construction of chiral α,α-dialkyl indoles represents an important yet challenging objective to be developed. Herein we describe a cobalt catalyzed enantioselective anti-Markovnikov alkene hydroalkylation via the remote stereocontrol for the synthesis of α,α-dialkyl indoles and other N-heterocycles. This asymmetric C(sp3)−C(sp3) coupling features high flexibility in introducing a diverse set of alkyl groups at the α-position of chiral N-heterocycles. The utility of this methodology has been demonstrated by late-stage functionalization of drug molecules, asymmetric synthesis of bioactive molecules, natural products and functional materials, and identification of a class of molecules exhibiting anti-apoptosis activities in UVB-irradiated HaCaT cells. Ligands play a vital role in controlling the reaction regioselectivity. Changing the ligand from bi-dentate L6 to tridentate L12 enables CoH-catalyzed Markovnikov hydroalkylation. Mechanistic studies disclose that the anti-Markovnikov hydroalkylation involves a migratory insertion process while the Markovnikov hydroalkylation involves a MHAT process.

China has abundant coalbed methane (CBM) resources, and most of them are low-permeability and tight reservoirs, with generally low production rate and small recovery factor. Existing technologies face great challenges to meet the demand on CBM in China. It is desirable to develop new methods to improve the production rate and enhance recovery factor. In addition to physical stimulation methods such as hydraulic fracturing and open-hole cave completion, the use of chemical methods to improve physical properties of coal reservoirs has also been a hot research topic in recent years. Coal reservoir acidification and oxidation technology can promote desorption of gas and enlarge permeability of reservoir. But for different coal rank coal reservoirs, the acidification and oxidation agents need to be optimized and their performance evaluated. Laboratory experiments are conducted to compare and analyze the physical properties coal samples from Baode, Mu’ai, and Xinjiang blocks, including coal rank, texture, macroscopic characteristics, quality, porosity, permeability, element, and mineral composition. The optimal concentration of hydrochloric acid is determined through pre-dissolution experiment of coal powder in acid solution. Then a five-factor and three-level orthogonal experiment for acid solution optimization is designed and performed by using Design-Expert software, which identifies the sensitive factors affecting the dissolution. For the coal samples in Baode, Mu’ai, and Xinjiang blocks, the oxidant types and the corresponding acidification and oxidation agent systems are optimized. Applying these acidification and oxidation agent systems to coal samples from Baode, Mu’ai, and Xinjiang blocks, the change of porosity, permeability, and wettability are compared and analyzed. Finally, through numerical simulation, the gas production is predicted for acidification and oxidation in typical well group in Block Mu’ai. Results show that the acid solution has the best dissolution at a concentration of hydrochloric acid of 3 mol/L to 4 mol/L; Top factors played in the experiment are soaking time, acid type, soaking temperature, coal sample type, and acid concentration, in descending order of importance; The optimal oxidant is a hydrogen peroxide solution with a concentration of 3%; the mixed acidification oxidant formula in Baode block is 10% HCl + 2% CH3COOH + 2% HF + 3% H2O2; The optimal mixed acidification oxidant formula in Mu’ai block is 8% HCl + 2% CH3COOH + 4% HF + 3% H2O2; the optimal mixed acidification oxidant formula in Xinjiang block is 12% HCl + 1% CH3COOH + 1% HF + 3% H2O2; The higher the coal rank, the greater the HF content in the optimal acidification oxidant system. Both acidification and oxidation improve the porosity and permeability of coal samples to some extent, and the improvement in low-rank coal is more significant than that in high-rank coal. Acidification and oxidation have different effects on the wettability of coal: Acidification increases the hydrophilicity of coal, whereas oxidation reduce the hydrophilicity of coal; and the hydrophilicity of coal samples treated by the optimized acidification and oxidation system is weakened. Reservoir simulation results show that acidification and oxidation lead to a recovery factor of 64.64% after 10 years of production, which is 19.72% higher than that without acidification and oxidation. The advantage of acidification and oxidation is 0.97% after 18 years of production. However, the acidification and oxidation saved 8 years of production time to achieve a close final recovery factor, which greatly reduces the operating costs. The optimized acidizing oxidation agent systems for CBM reservoirs with low, medium, and high ranks improved the desorption and permeability of the target reservoirs, and increase well production and recovery factor. This research provides technical support for stimulation practices of CBM reservoirs in the aforementioned blocks in China, as well as similar coal reservoirs in the world.

Background and purposePeak width of skeletonized mean diffusivity (PSMD) is an MRI-based biomarker that may reflect white matter lesions (WML). PSMD is based on skeletonization of MR DTI data and histogram analysis. Both chronic renal failure (CRF) and WML may be affected by multisystemic small-vessel disorder. We aimed to explore the relationship between PSMD and estimated glomerular filtration rate (eGFR).MethodsFifty followed-up CRF patients matched for age, sex, hypertension and smoking status were enrolled and classified into a progressive group (n = 16) and stable group (n = 34) based on eGFR levels. Longitudinal and horizontal differences of PSMD were compared between the progressive and stable groups at the initial and follow-up time points. Pearson’s correlation was used for correlation of eGFR with PSMD and WML (per Fazekas scale score). ROC was used to measure the sensitivity of PSMD and WML score to changes of eGFR.ResultsAt the follow-up time point, PSMD of the progressive group was significantly higher than at the initial time point (p

ABSTRACT: This study determined metabolizable energy (ME) and developed ME prediction equations for broilers based on chemical composition of soybean meal (SBM) and rapeseed meal (RSM) using a 2 × 10 factorial arrangement of age (11 to 14 or 25 to 28 d of age) and 10 sources of each ingredient. Each treatment contained 6 replicates of 8 broilers. The ME values were determined by total collection of feces and urine. Principal components analysis (PCA) of the chemical composition clearly revealed distinct differences in SBM and RSM based on a principal components (PC) score plot. The nitrogen-corrected apparent metabolizable energy (AMEn) of SBM was higher in broilers from 25 to 28 than 11 to 14 d of age (P = 0.013). Interactions between broiler age and ingredient source affected apparent metabolizable energy (AME) of SBM and ME of RSM (P < 0.05). The ME of SBM in 11 to 14 and 25 to 28-day-old broilers were estimated by crude protein (CP) content (R2≥ 0.782; SEP ≤ 83 kcal/kg DM; P < 0.001). The AME and AMEn of RSM in 11 to 14-day-old broilers were estimated by ether extract (EE), ash and acid detergent fiber (ADF) (R2 = 0.897, SEP = 106 kcal/kg DM; P = 0.002), and by EE and ash (R2 = 0.885, SEP = 98 kcal/kg DM; P = 0.001), respectively. The AME and AMEn of RSM in 25 to 28-day-old broilers were estimated by ash and ADF (R2 = 0.925, SEP = 104 kcal/kg DM; P < 0.001) and by ash and neutral detergent fiber (NDF) (R2 = 0.921, SEP = 91 kcal/kg DM; P < 0.001), respectively. These results indicate that ME of these 2 plant protein ingredients are affected interactively by chemical composition and age of broilers. This study developed robust, age-specific prediction equations of ME for broilers based on chemical composition for SBM and RSM. Overall, ME values can be predicted from CP content for SBM, or EE, ash, ADF, and NDF for RSM.

Huaier, derived from the fungus on Sophora japonica trees, is a fruiting body belonging to Polyporaceae family. Traditionally, it has been utilized for treating hemorrhoids and rectal bleeding. As a medicine food homology herb, its remarkable immune-regulating properties have led to widespread adoption in digestive system tumors in recent years. Currently, over 502 compounds, excluding polysaccharides, have been isolated from Huaier exhibiting diverse biological activities and medicinal value. Clinically, Huaier has demonstrated effectiveness against digestive system tumors. Mechanistic studies suggested that Huaier may exert its antitumor effects by bolstering immunity, inducing tumor cell death, inhibiting tumor proliferation, migration, invasion, metastasis, and dampening inflammatory cancer transformation. The abundance of active ingredients underscores Huaier's potential as an anticancer agent. Both its clinical application and mechanistic investigations highlight its significant promise in addressing digestive system tumors.

Abstract Lung adenocarcinoma (LUAD) is the leading cause of cancer-related death globally. Although the gut microbiota's role in the antitumor efficacy of many cancers has been revealed, its involvement in the response to gefitinib therapy for LUAD remains unclear. To fill this gap, we conducted a longitudinal study that profiled gut microbiota changes in PC-9 tumor-bearing mice under different treatments, including gefitinib monotherapy and combination therapies with probiotics, antibiotics, or Traditional Chinese Medicine (TCM). Our findings demonstrated that combining probiotics or TCM with gefitinib therapy outperformed gefitinib monotherapy, as evidenced by tumor volume, body weight, and tumor marker tests. By contrast, antibiotic intervention suppressed the antitumor efficacy of gefitinib. Notably, the temporal changes in gut microbiota were strongly correlated with the different treatments, prompting us to investigate whether there is a causal relationship between gut microbiota and the antitumor efficacy of gefitinib using Mediation Analysis (MA). Finally, our research revealed that thirteen mediators (Amplicon Sequence Variants, ASVs) regulate the antitumor effect of gefitinib, regardless of treatment. Our study provides robust evidence supporting the gut microbiota's significant and potentially causal role in mediating gefitinib treatment efficacy in mice. Our findings shed light on a novel strategy for antitumor drug development by targeting the gut microbiota.

Abstract The superfluorescence effect has received extensive attention due to the many-body physics of quantum correlation in dipole gas and the optical applications of ultrafast bright radiation field based on the cooperative quantum state. Here, we demonstrate not only to observe the superfluorescence effect but also to control the cooperative state of the excitons ensemble by externally applying a regulatory dimension of coupling light fields. A new quasi-particle called cooperative exciton-polariton is revealed in a light-matter hybrid structure of a perovskite quantum dot thin film spin-coated on a Distributed Bragg Reflector. Above the nonlinear threshold, polaritonic condensation occurs at a nonzero momentum state on the lower polariton branch owning to the vital role of the synchronized excitons. The phase transition from superfluorescence to polariton condensation exhibits typical signatures of a decrease of the linewidth, an increase of the macroscopic coherence as well as an accelerated radiation decay rate. These findings are promising for opening new potential applications for super-brightness and unconventional coherent light sources and could enable the exploitation of cooperative effects for quantum optics.

Histone deacetylases (HDAC) catalyze the removal of acetylation modifications on histones and non-histone proteins, which regulates gene expression and other cellular processes. HDAC inhibitors (HDACi), approved anti-cancer agents, emerge as a potential new therapy for heart diseases. Cardioprotective effects of HDACi are observed in many preclinical animal models of heart diseases. Genetic mouse models have been developed to understand the role of each HDAC in cardiac functions. Some of the findings are controversial. Here, we provide an overview of how HDACi and HDAC impact cardiac functions under physiological or pathological conditions. We focus on in vivo studies of zinc-dependent classical HDACs, emphasizing disease conditions involving cardiac hypertrophy, myocardial infarction (MI), ischemic reperfusion (I/R) injury, and heart failure. In particular, we review how non-biased omics studies can help our understanding of the mechanisms underlying the cardiac effects of HDACi and HDAC.

Biochar can serve as a soil amendment to immobilize soil nitrogen (N) and reduce N leaching from cropland without negative effect on crop yield. However, the interaction effect of biochar application and irrigation regimes on soil N status (N retention and N loss) and crop yield is rarely reported in the open perennial vegetable field. A two-years field trial (transplanting in first year and consecutive growth in second year) was conducted in citron daylily vegetable cropping system on a sandy brown alluvial soil. Two biochar application rates (0 and 30 t ha−1) and three irrigation regimes (CDI, conventional drip irrigation; WSDI, water-saving drip irrigation with 80% of full irrigation quota; APRDI, alternate partial root-zone drip irrigation with 80% of full irrigation quota) were included. The response of crop yield and soil N status to both biochar application and irrigation regimes varied across planting years for perennial citron daylily. After the first planting year's harvest, APRDI enhanced flower bud yield by 18–28% compared to CDI and WSDI, likely due to improved nitrate use efficiency evidenced by lower soil nitrate retention in the surface soil (0–20 cm) post-harvest. However, biochar application resulted in a reduction of yield by 27% under APRDI. Additionally, WSDI with reduced yields, decreased soil TN in the sub-surface layer (20–50 cm) with 9–19% by comparison with other two irrigation regimes, resulting in higher TN concentration in the soil solution (14–28%) in and thus an increased risk for N leaching. However, after the second harvest year, there were no variations in crop yield induced by biochar application and irrigation regimes. Irrigation regimes exhibited limited influence on soil N status, while biochar application mitigated soil nitrogen decline in the 0–50 cm layer by enhancing organic nitrogen retention capacity for nearly 16–85%. Furthermore, the lowest TN concentration in the soil solution in the sub-surface layer (20–50 cm) with biochar application under APRDI suggested a reduced risk for N leaching. We conclude that combining biochar application with the APRDI regime could help retain soil N, decrease the risk of N leaching, and enhance crop yield in total for two consecutive planting years. Therefore, this approach is recommended for sustainable N management in long-term planting of perennial crops.

BackgroundEngaging in anal sexual intercourse markedly increases the risk of developing HIV among men who have sex with men (MSM); oral sexual activities tend to uniquely introduce gut-derived microbes to salivary microbiota, which, combined with an individual’s positive HIV status, may greatly perturb oral microecology. However, till date, only a few published studies have addressed this aspect.MethodsBased on 16S rRNA sequencing data of bacterial taxa, MicroPITA picks representative samples for metagenomic analysis, effectively revealing how the development and progression of the HIV disease influences oral microbiota in MSM. Therefore, we collected samples from 11 HIV-negative and 44 HIV-positive MSM subjects (stage 0 was defined by HIV RNA positivity, but negative or indeterminate antibody status; stages 1, 2, and 3 were defined by CD4+ T lymphocyte counts ≥ 500, 200–499, and ≤ 200 or opportunistic infection) and selected 25 representative saliva samples (5 cases/stage) using MicroPITA. Metagenomic sequencing analysis were performed to explore whether positive HIV status changes salivary bacterial KEGG function and metabolic pathway in MSM.ResultsThe core functions of oral microbiota were maintained across each of the five groups, including metabolism, genetic and environmental information processing. All HIV-positive groups displayed KEGG functions of abnormal proliferation, most prominently at stage 0, and others related to metabolism. Clustering relationship analysis tentatively identified functional relationships between groups, with bacterial function being more similar between stage 0-control groups and stage 1-2 groups, whereas the stage 3 group exhibited large functional changes. Although we identified most metabolic pathways as being common to all five groups, several unique pathways formed clusters for certain groups; the stage 0 group had several, while the stage 2 and 3 groups had few, such clusters. The abundance of K03046 was positively correlated with CD4 counts.ConclusionAs HIV progresses, salivary bacterial function and metabolic pathways in MSM progressively changes, which may be related to HIV promoting abnormal energy metabolism and exacerbate pathogen virulence. Further, infection and drug resistance of acute stage and immune cell destruction of AIDS stage were abnormally increased, predicting an increased risk for MSM individuals to develop systemic and oral diseases.

Abstract In recent years, deep learning (DL) models have achieved significant progress in many domains, such as autonomous driving, facial recognition, and speech recognition. However, the vulnerability of deep learning models to adversarial attacks has raised serious concerns in the community because of their insufficient robustness and generalization. Also, transferable attacks have become a prominent method for black-box attacks. In this work, we explore the potential factors that impact adversarial examples (AEs) transferability in DL-based speech recognition. We also discuss the vulnerability of different DL systems and the irregular nature of decision boundaries. Our results show a remarkable difference in the transferability of AEs between speech and images, with the data relevance being low in images but opposite in speech recognition. Motivated by dropout-based ensemble approaches, we propose random gradient ensembles and dynamic gradient-weighted ensembles, and we evaluate the impact of ensembles on the transferability of AEs. The results show that the AEs created by both approaches are valid for transfer to the black box API.

Abstract A large current ripple may arise in the finite control set model‐free predictive current control (FCS‐MFPCC) strategy because only one voltage vector can be applied during the whole control period. Therefore, an improved three‐vector‐based model‐free predictive current control (MFPCC) strategy to improve the current tracking performance is proposed by increasing the number of the applied voltage vector. Based on the extreme existence theorem for the function of two variables, the optimal voltage vector can be synthesized. Compared with the one‐vector‐based FCS‐model‐based predicted current control (FCS‐MBPCC) and two‐vector‐based FCS‐MBPCC in the time and frequency domains, the maximum tracking error is decreased by 42.17% and 48.58%, and the total harmonic distortion is decreased by 40.97% and 37.56%, respectively. Besides, compared with the previous three‐vector‐based strategy, the computational time of the proposed strategy is reduced from 57.6 to 45.89 μs, which is reduced by 20.33%. Furthermore, the switching frequency of the proposed method can be decreased by 70.3% compared with the deadbeat MFPCC. The experimental results in the time and frequency domains demonstrate that the proposed three‐vector‐based MFPCC can not only improve the current tracking performance but also improve robustness against parameter mismatches.

Abstract A comprehensive atlas of cis-regulatory elements and their dynamic activity is necessary to understand the transcriptional basis of cellular structure maintenance, metabolism, and responses to the environment. Here we show, using matched single-nucleus chromatin accessibility and RNA-sequencing from juvenile male C57BL6 mice, an atlas of accessible chromatin regions in both normal and denervated skeletal muscles. We identified cell-type-specific cis-regulatory networks, highlighting the dynamic regulatory circuits mediating transitions between myonuclear types. Through comparison of normal and perturbed muscle, we delineated the reprogramming of cis-regulatory networks in response to denervation, described the interplay of promoters/enhancers and target genes. We further unveil a hierarchical structure of transcription factors that delineate a regulatory network in atrophic muscle, identifying ELK4 as a key atrophy-related transcription factor that instigates muscle atrophy through TGF-β1 regulation. This study furnishes a rich genomic resource, essential for decoding the regulatory dynamics of skeletal muscle in both physiological and pathological states.

Soil erodibility (K) refers to the inherent ability of soil to withstand erosion. Accurate estimation and spatial prediction of K values are vital for assessing soil erosion and managing land resources. However, as most K-value estimation models are empirical, they suffer from significant extrapolation uncertainty, and traditional studies on spatial prediction focusing on individual empirical K values have neglected to explore the spatial pattern differences between various empirical models. This work proposed a universal framework for selecting an optimal soil-erodibility map using empirical models enhanced by machine learning. Specifically, three empirical models, namely, the erosion-productivity impact calculator model (K_EPIC), the Shirazi model (K_Shirazi), and the Torri model (K_Torri) were used to estimate K values. Random Forest (RF) and Gradient-Boosting Decision Tree (GBDT) algorithms were employed to develop prediction models, which led to the creation of three K-value maps. The spatial distribution of K values and associated environmental covariates were also investigated across varying empirical models. Results showed that RF achieved the highest accuracy, with R2 of K_EPIC, K_Shirazi, and K_Torri increasing by 46%, 34%, and 22%, respectively, compared to GBDT. And distinctions among environmental variables that shape the spatial patterns of empirical models have been identified. The K_EPIC and K_Shirazi are influenced by soil porosity and soil moisture. The K_Torri is more sensitive to soil moisture conditions and terrain location. More importantly, our study has highlighted disparities in the spatial patterns across the three K-value maps. Considering the data distribution, spatial distribution, and measured K values, the K_Torri model outperformed others in estimating soil erodibility in the plateau lake watershed. This study proposed a framework that aimed to create optimal soil-erodibility maps and offered a scientific and accurate K-value estimation method for the assessment of soil erosion.

In scenarios where the global navigation satellite system is unavailable, unmanned aerial vehicles (UAVs) can employ visual algorithms to process aerial images. These images are integrated with satellite maps and digital elevation models (DEMs) to achieve global localization. To address the localization challenge in unfamiliar areas devoid of prior data, an iterative computation-based localization framework is commonly used. This framework iteratively refines its calculations using multiple observations from a downward-facing camera to determine an accurate global location. To improve the rate of convergence for localization, we introduced an innovative observation model. We derived a terrain descriptor from the images captured by a forward-facing camera and integrated it as supplementary observation into a point-mass filter (PMF) framework to enhance the confidence of the observation likelihood distribution. Furthermore, within this framework, the methods for the truncation of the convolution kernel and that of the probability distribution were developed, thereby enhancing the computational efficiency and convergence rate, respectively. The performance of the algorithm was evaluated using real UAV flight sequences, a satellite map, and a DEM in an area measuring 7.7 km × 8 km. The results demonstrate that this method significantly accelerates the localization convergence during both takeoff and ascent phases as well as during cruise flight. Additionally, it increases localization accuracy and robustness in complex environments, such as areas with uneven terrain and ambiguous scenes. The method is applicable to the localization of UAVs in large-scale unknown scenarios, thereby enhancing the flight safety and mission execution capabilities of UAVs.

Long-term high sugar accelerates skin ageing via glycosylation. In this study, microalgae-derived active compounds were investigated for their potentially beneficial effects on skin ageing. Two carotenoids, namely astaxanthin (in the form of either monoester or diester) and lutein, were separated from the green alga Haematococcus pluvialis. These carotenoid fractions significantly attenuated the high glucose (HG)-induced cell injury and oxidative stress. They also inhibited the formation of Nε-Carboxymethyllysine (CML), a typical product of glycosylation. The degradation of collagen I and III caused by HG was substantially recovered, in which astaxanthin monoester exhibited the most prominent effect. This was attributed to the down-regulation of matrix metalloproteinase (MMP)-2 mRNA expression. Through proteomics analyses, six hub proteins were identified, which work as highly connected nodes in the protective effects of astaxanthin monoester against HG stress. These findings strongly suggest the benefits of microalgae-derived carotenoids in alleviating HG-induced skin ageing.

Fan-spine magnetic structure, as a fundamental three-dimensional topology in magnetic reconnection theory, plays a crucial role in producing solar jets. However, how fan-spine configurations form in the solar atmosphere remains elusive. Using the Chinese H α Solar Explorer (CHASE) and the Solar Dynamics Observatory, we present a case study on the complete buildup of fan-spine topology driven by flux emergence and the subsequent jet production. Two fan-spine structures and the two associated null points are present. Variations in null-point heights and locations were tracked over time during flux emergence. The north fan-spine structure is found to be created through magnetic reconnection between the newly emerged flux and the background field. Gentle reconnection persistently occurs after formation of the north fan-spine structure, resulting in weak plasma outflows. Subsequently, as flux emergence and magnetic helicity injection continue, the formation and eruption of minifilaments after reconnection at the quasi-separatrix layer between the two nulls trigger three homologous jets. The CHASE observations reveal that the circular flare ribbon, inner bright patch, and remote brightening all exhibit redshifted signatures during these jet ejections. This work unveils the key role of flux emergence in the formation of fan-spine topology, and highlights the importance of minifilaments for subsequent jet production.

Motion artifacts are a major challenge in the in vivo application of catheter-based cardiac imaging modalities. Gating is a critical tool for suppressing motion artifacts. Electrocardiogram (ECG) gating requires a trigger device or synchronous ECG recordings for retrospective analysis. Existing retrospective software gating methods extract gating signals through separate steps based on changes in vessel morphology or image features, which require a high computational cost and are prone to error accumulation. In this paper, we report on an end-to-end unsupervised learning framework for retrospective image-based gating (IBG) of catheter-based intracoronary images, named IBG Network. It establishes a direct mapping from a continuously acquired image sequence to a gated subsequence. The network was trained on clinical data sets in an unsupervised manner, addressing the difficulty of obtaining the gold standard in deep learning-based motion suppression techniques. Experimental results of in vivo intravascular ultrasound and optical coherence tomography sequences show that the proposed method has better performance in terms of motion artifact suppression and processing efficiency compared with the state-of-the-art nonlearning signal-based and IBG methods.

Abstract Background Curdione is a sesquiterpene isolated from Curcumae Rhizoma that possesses high biological activity and extensive pharmacological effects. As a traditional Chinese medicine, Curcumae Rhizoma can inhibit the development of many types of cancer, especially colorectal cancer. However, the anti-colorectal mechanism of its monomer curdione remains unclear. Methods Colorectal cancer (CRC) cells were treated with curdione at doses of 12.5 μM, 25 μM, and 50 μM, and then the cells’ activity was measured with methyl thiazolyl tetrazolium (MTT). Nude mice were administered different doses of curdione subcutaneously and oxaliplatin by tail vein injection, and then hematoxylin–eosin (HE) staining was adopted to examine tumor histology. Moreover, flow cytometry was applied to detect reactive oxygen species in cells and tissues. Kits were employed to detect the levels of iron ions, malondialdehyde, lipid hydroperoxide, and glutathione. Polymerase chain reaction (PCR) and Western blotting were adopted to detect ferroptosis and m6A modification-related factors. A methylation spot hybridization assay was performed to measure changes in overall methylation. SLC7A11 and HOXA13 were measured by MeRIP-qPCR. The shRNA-METTL14 plasmid was constructed to verify the inhibitory effect of curdione on CRC. Results A dose-dependent decrease in activity was observed in curdione-treated cells. Curdione increased the accumulation of reactive oxygen species in CRC cells and tumor tissues, greatly enhanced the levels of malondialdehyde, lipid hydroperoxide and Fe2+, and lowered the activity of glutathione. According to the qPCR and Western blot results, curdione promoted the expression of METTL14 and YTHDF2 in CRC cells and tissues, respectively, and decreased the expression of SLC7A11, SLC3A2, HOXA13, and glutathione peroxidase 4. Additionally, in animal experiments, the curdione-treated group showed severe necrosis of tumor cells, as displayed by HE staining. Furthermore, compared with the control group, levels of m6A modifying factors (namely, SLC7A11 and HOXA13) were increased in the tissues after drug intervention. METTL14 knockdown was followed by an increase in CRC cell activity and glutathione levels. However, the levels of reactive oxygen species, malondialdehyde, and iron ions decreased. The expression levels of SLC7A11, SLC3A2, HOXA13, and GPX4 were all increased after METTL14 knockdown. Conclusion The results suggest that curdione induces ferroptosis in CRC by virtue of m6A methylation.

Abstract DNA base editors use deaminases fused to a programmable DNA-binding protein for targeted nucleotide conversion. However, the most widely used TadA deaminases lack post-translational control in living cells. Here, we present a split adenine base editor (sABE) that utilizes chemically induced dimerization (CID) to control the catalytic activity of the deoxyadenosine deaminase TadA-8e. sABE shows high on-target editing activity comparable to the original ABE with TadA-8e (ABE8e) upon rapamycin induction while maintaining low background activity without induction. Importantly, sABE exhibits a narrower activity window on DNA and higher precision than ABE8e, with an improved single-to-double ratio of adenine editing and reduced genomic and transcriptomic off-target effects. sABE can achieve gene knockout through multiplex splice donor disruption in human cells. Furthermore, when delivered via dual adeno-associated virus vectors, sABE can efficiently convert a single A•T base pair to a G•C base pair on the PCSK9 gene in mouse liver, demonstrating in vivo CID-controlled DNA base editing. Thus, sABE enables precise control of base editing, which will have broad implications for basic research and in vivo therapeutic applications.

Abstract Background Gene-environment interactions contribute to metabolic disorders such as diabetes and dyslipidemia. In addition to affecting metabolic homeostasis directly, drugs and environmental chemicals can cause persistent alterations in metabolic portfolios across generations in a sex-specific manner. Here, we use inorganic arsenic (iAs) as a prototype drug and chemical to dissect such sex differences. Methods After weaning, C57BL/6 WT male mice were treated with 250 ppb iAs in drinking water (iAsF0) or normal water (conF0) for 6 weeks and then bred with 15-week-old, non-exposed females for 3 days in cages with only normal water (without iAs), to generate iAsF1 or conF1 mice, respectively. F0 females and all F1 mice drank normal water without iAs all the time. Results We find that exposure of male mice to 250 ppb iAs leads to glucose intolerance and insulin resistance in F1 female offspring (iAsF1-F), with almost no change in blood lipid profiles. In contrast, F1 males (iAsF1-M) show lower liver and blood triglyceride levels than non-exposed control, with improved glucose tolerance and insulin sensitivity. The liver of F1 offspring shows sex-specific transcriptomic changes, with hepatocyte-autonomous alternations of metabolic fluxes in line with the sex-specific phenotypes. The iAsF1-F mice show altered levels of circulating estrogen and follicle-stimulating hormone. Ovariectomy or liver-specific knockout of estrogen receptor α/β made F1 females resemble F1 males in their metabolic responses to paternal iAs exposure. Conclusions These results demonstrate that disrupted reproductive hormone secretion in alliance with hepatic estrogen signaling accounts for the sex-specific intergenerational effects of paternal iAs exposure, which shed light on the sex disparities in long-term gene-environment interactions.

Abstract Accurate species identification and abundance estimation are critical for the interpretation of whole metagenome sequencing (WMS) data. Yet, existing metagenomic profilers suffer from false-positive identifications, which can account for more than 90% of total identified species. Here, by leveraging species-specific Type IIB restriction endonuclease digestion sites as reference instead of universal markers or whole microbial genomes, we present a metagenomic profiler, MAP2B (MetAgenomic Profiler based on type IIB restriction sites), to resolve those issues. We first illustrate the pitfalls of using relative abundance as the only feature in determining false positives. We then propose a feature set to distinguish false positives from true positives, and using simulated metagenomes from CAMI2, we establish a false-positive recognition model. By benchmarking the performance in metagenomic profiling using a simulation dataset with varying sequencing depth and species richness, we illustrate the superior performance of MAP2B over existing metagenomic profilers in species identification. We further test the performance of MAP2B using real WMS data from an ATCC mock community, confirming its superior precision against sequencing depth. Finally, by leveraging WMS data from an IBD cohort, we demonstrate the taxonomic features generated by MAP2B can better discriminate IBD and predict metabolomic profiles.

Abstract Spinal cord injury (SCI) leads to severe sensory and motor dysfunction below the lesion. However, the cellular dynamic responses and heterogeneity across different regions below the lesion remain to be elusive. Here, we used single-cell transcriptomics to investigate the region-related cellular responses in female rhesus monkeys with complete thoracic SCI from acute to chronic phases. We found that distal lumbar tissue cells were severely impacted, leading to degenerative microenvironments characterized by disease-associated microglia and oligodendrocytes activation alongside increased inhibitory interneurons proportion following SCI. By implanting scaffold into the injury sites, we could improve the injury microenvironment through glial cells and fibroblast regulation while remodeling spared lumbar tissues via reduced inhibitory neurons proportion and improved phagocytosis and myelination. Our findings offer crucial pathological insights into the spared distal tissues and proximal tissues after SCI, emphasizing the importance of scaffold-based treatment approaches targeting heterogeneous microenvironments.

Abstract Recently, studies show that deep learning-based automatic speech recognition (ASR) systems are vulnerable to adversarial examples (AEs), which add a small amount of noise to the original audio examples. These AE attacks pose new challenges to deep learning security and have raised significant concerns about deploying ASR systems and devices. The existing defense methods are either limited in application or only defend on results, but not on process. In this work, we propose a novel method to infer the adversary intent and discover audio adversarial examples based on the AEs generation process. The insight of this method is based on the observation: many existing audio AE attacks utilize query-based methods, which means the adversary must send continuous and similar queries to target ASR models during the audio AE generation process. Inspired by this observation, We propose a memory mechanism by adopting audio fingerprint technology to analyze the similarity of the current query with a certain length of memory query. Thus, we can identify when a sequence of queries appears to be suspectable to generate audio AEs. Through extensive evaluation on four state-of-the-art audio AE attacks, we demonstrate that on average our defense identify the adversary’s intent with over $$90\%$$ 90 % accuracy. With careful regard for robustness evaluations, we also analyze our proposed defense and its strength to withstand two adaptive attacks. Finally, our scheme is available out-of-the-box and directly compatible with any ensemble of ASR defense models to uncover audio AE attacks effectively without model retraining.

Conventional coalbed methane (CBM) reservoir models for injection fall-off testing often disregard the quadratic pressure gradient’s impact. This omission leads to discrepancies in simulating the transient behavior of formation fluids and extracting critical reservoir properties. Accurate determination of permeability, storability, and other properties is crucial for effective reservoir characterization and production forecasting. Inaccurate estimations can lead to suboptimal well placement, ineffective production strategies, and ultimately, missed economic opportunities. To address this shortcoming, we present a novel analytical model that explicitly incorporates the complexities of the quadratic pressure gradient and dual-permeability flow mechanisms, prevalent in many CBM formations where nanopores are rich, presenting a kind of natural nanomaterial. This model offers significant advantages over traditional approaches. By leveraging variable substitution, it facilitates the derivation of analytical solutions in the Laplace domain, subsequently converted to real-space solutions for practical application. These solutions empower reservoir engineers to generate novel type curves, a valuable tool for analyzing wellbore pressure responses during injection fall-off tests. By identifying distinct flow regimes within the reservoir based on these type curves, engineers gain valuable insights into the dynamic behavior of formation fluids. This model goes beyond traditional approaches by investigating the influence of the quadratic pressure gradient coefficient, inter-porosity flow coefficient, and storability ratio on the pressure response. A quantitative comparison with traditional models further elucidates the key discrepancies caused by neglecting the quadratic pressure gradient. The results demonstrate the proposed model’s ability to accurately depict the non-linear flow behavior observed in CBM wells. This translates to more reliable pressure and pressure derivative curves that account for the impact of the quadratic pressure gradient.

Since it was first reported in 2013, the NADC30-like PRRSV has been epidemic in China. Hubei Province is known as China’s key hog-exporting region. To understand the prevalence and genetic variation of PRRSV, herein, we detected and analyzed 317 lung tissue samples from pigs with respiratory disease in Hubei Province, and demonstrated that the NADC30-like strain was the second-most predominant strain during 2017–2018, following the highly pathogenic PRRSV (HP-PRRSV). Additionally, we isolated a new NADC30-like PRRSV strain, named CHN-HB-2018, which could be stably passaged in Marc-145 cells. Genetic characterization analysis showed that compared with the NADC30 strain, the CHN-HB-2018 strain had several amino acid variations in glycoprotein (GP) 3, GP5, and nonstructural protein 2 (NSP2). Moreover, the CHN-HB-2018 strain showed a unique 5-amino acid (aa) deletion in NSP2, which has not previously been reported. Gene recombination analysis identified the CHN-HB-2018 strain as a potentially recombinant PRRSV of the NADC30-like strain and HP-PRRSV. Animal experiments indicated that the CHN-HB-2018 strain has a mild pathogenicity, with no mortality and only mild fever observed in piglets. This study contributes to defining the evolutionary characteristics of PRRSV and its molecular epidemiology in Hubei Province, and provides a potential candidate strain for PRRSV vaccine development.

(1) Background: Illness perception (IP) is an important psychological construct for couples dealing with cancer, which impacts health outcomes and the psychological adjustment to cancer. More research is needed to explore the traits of IP and the efforts of couples coping with cancer. Thus, this study was designed to explore the coping experiences and features of the IPs of couples dealing with cancer. (2) Methods: A total of 24 patients with colorectal cancer (CRC) and 20 spousal caregivers (SCs) participated in semi-structured interviews. All interviews were recorded digitally, transcribed, and analyzed by using an inductive thematic analysis. (3) Results: Two themes (individualized and predominant IP; IP sharing and restructuring) were developed. A preliminary framework was formulated to illustrate the relations among subthemes and the relations between themes with an adjustment of a positive IP to CRC. In this framework, based on multiple sources and factors, the natural disparities formed the IPs of the partners of couples and determined the incongruence of IPs. The effects of IP incongruence on lives under the disease guided the three directions of coping approaches (i.e., information and available support, appropriate disclosure and reflection, and leaving the CRC diagnosis behind) which were adopted by couples dealing with CRC to share and restructure the IP with their spouses for effective dyadic coping. (4) Conclusions: This study provides insights to healthcare providers into the experiences of couples dealing with CRC and the development of couple-based IP intervention programs: (a) it initially provides adequate factual knowledge for enhancing beliefs in the ability to control illness, (b) encourages illness-centered conversations and disclosure regarding thoughts and emotions for promoting positive congruence of IP between the partners of couples dealing with a hard dilemma, and (c) guides couples to perceive positive changes and explore the illness’s meaning. Understanding each theme of personalized IP and adopting effective IP coping approaches can help guide couples dealing with CRC to efficiently promote constructive IP and better health outcomes.

Abstract Background Diabetes and obesity are associated with muscle atrophy that reduces life quality and lacks effective treatment. Mesenchymal stromal cell (MSC)‐based therapy can ameliorate high fat‐diet (HFD) and immobilization (IM)‐induced muscle atrophy in mice. However, the effect of MSCs on muscle atrophy in type 2 diabetes mellitus (T2DM) and the potential mechanism is unclear. Here, we evaluated the efficacy and explored molecular mechanisms of human umbilical cord MSCs (hucMSCs) and hucMSC‐derived exosomes (MSC‐EXO) on diabetes‐ and obesity‐induced muscle atrophy. Methods Diabetic db/db mice, mice fed with high‐fat diet (HFD), mice with hindlimb immobilization (IM), and C2C12 myotubes were used to explore the effect of hucMSCs or MSC‐EXO in alleviating muscle atrophy. Grip strength test and treadmill running were used to measure skeletal muscle strength and performance. Body composition, muscle weight, and muscle fibre cross‐sectional area (CSA) was used to evaluate muscle mass. RNA‐seq analysis of tibialis anterior (TA) muscle and Western blot analysis of muscle atrophy signalling, including MuRF1 and Atrogin 1, were performed to investigate the underlying mechanisms. Results hucMSCs increased grip strength (P = 0.0256 in db/db mice, P = 0.012 in HFD mice, P = 0.0097 in IM mice), running endurance (P = 0.0154 in HFD mice, P = 0.0006 in IM mice), and muscle mass (P = 0.0004 in db/db mice, P = 0.0076 in HFD mice, P = 0.0144 in IM mice) in all models tested, with elevated CSA of muscle fibres (P

ObjectivesCardiovascular magnetic resonance-feature tracking (CMR-FT) enables quantification of myocardial deformation and may be used as an objective measure of myocardial involvement in ST-elevation myocardial infarction (STEMI). We sought to investigate the associations between myocardial dyssynchrony parameters and myocardium damage for STEMI.MethodsWe analyzed 65 patients (45–80 years old) with anterior STEMI after primary percutaneous coronary intervention during 3–7 days [observational (STEMI) group] and 60 healthy volunteers [normal control (NC) group]. Myocardial dyssynchrony parameters were derived, including global and regional strain, radial rebound stretch and displacement, systolic septal time delay, and circumferential stretch.ResultsCMR characteristics, including morphologic parameters such as left ventricular ejection fraction (LVEF) (45.3% ± 8.2%) and myocardium damage in late gadolinium enhancement (LGE) (19.4% ± 4.7% LV), were assessed in the observation group. The global radial strain (GRS) and global longitudinal strain (GLS) substantially decreased in anterior STEMI compared with the NC group (GRS: 19.4% ± 5.1% vs. 24.8% ± 4.0%, P

Abstract Background Asthma is a heterogeneous disease with high morbidity. Advancement in high-throughput multi-omics approaches has enabled the collection of molecular assessments at different layers, providing a complementary perspective of complex diseases. Numerous computational methods have been developed for the omics-based patient classification or disease outcome prediction. Yet, a systematic benchmarking of those methods using various combinations of omics data for the prediction of asthma development is still lacking. Objective We aimed to investigate the computational methods in disease status prediction using multi-omics data. Method We systematically benchmarked 18 computational methods using all the 63 combinations of six omics data (GWAS, miRNA, mRNA, microbiome, metabolome, DNA methylation) collected in The Vitamin D Antenatal Asthma Reduction Trial (VDAART) cohort. We evaluated each method using standard performance metrics for each of the 63 omics combinations. Results Our results indicate that overall Logistic Regression, Multi-Layer Perceptron, and MOGONET display superior performance, and the combination of transcriptional, genomic and microbiome data achieves the best prediction. Moreover, we find that including the clinical data can further improve the prediction performance for some but not all the omics combinations. Conclusions Specific omics combinations can reach the optimal prediction of asthma development in children. And certain computational methods showed superior performance than other methods.

Bayesian network structure learning is one of the current research hotspots in fields such as statistics and machine learning. Although it has great potential and application prospects, when there are too many variables, this type of algorithm will not be able to accurately and efficiently provide the optimal solution. In response to this issue, this study improved the dragonfly swarm optimization algorithm and solved the problem of variable type conflicts through binary discretization, applying it to the Bayesian network structure learning algorithm. According to the algorithm testing results, when the sample size is 1000 and the missing rate is 30%, the Bayesian Information Criterion (BIC) of the proposed algorithm is -7896. Under the same missing rate, when the sample size is 2000, the proposed algorithm BIC is -15114. Their BIC scores are superior to the greedy search algorithm and the sine cosine algorithm used for comparison. Overall, the proposed algorithm has better convergence ability and BIC rating. But its disadvantage is that the running time has not been optimized, and it has no advantages compared to traditional algorithms. The proposed algorithm provides a promising development direction for the field of Bayesian network structure learning.

Unreasonable cultivation methods and management measures have led to widespread obstacles in tobacco continuous cropping in planting areas, resulting in reduced tobacco yield and soil degradation. Therefore, intercropping tobacco with other crops is an effective strategy to improve continuous cropping barriers. In this study, flue-cured tobacco NC102 and conventional planting varieties of Salvia miltiorrhiza were used as materials, and four treatments of flue-cured tobacco monoculture (CK), flue-cured tobacco, and Salvia miltiorrhiza at a ratio of 1:1 (TS11), 2:2 (TS22), and 2:3 (TS23), respectively, were set up to study their effects on soil microorganisms, physical and chemical properties, and yield and quality of flue-cured tobacco. The results showed that intercropping Salvia miltiorrhiza increased the number of soil bacteria and actinomycetes, decreased the number of fungi, and increased the activity of urease and sucrase. The content of available nitrogen and available phosphorus in intercropping Salvia miltiorrhiza soil was significantly higher than that of the flue-cured tobacco monoculture, while the content of available potassium was lower than that of the flue-cured tobacco monoculture. The soil environment was more conducive to the growth of flue-cured tobacco. Compared with the flue-cured tobacco monoculture, the proportion of superior tobacco in intercropping Salvia miltiorrhiza increased by 2.2–3.4%, and the ratio of potassium to chlorine in leaves of different parts of flue-cured tobacco increased by 12.3–75.0%. The content of total sugar and soluble sugar in middle and upper leaves of intercropping flue-cured tobacco was higher than that of the flue-cured tobacco monoculture, which improved the quality of flue-cured tobacco. From the analysis of the chemical composition of tobacco leaves, TS11 (flue-cured tobacco and Salvia miltiorrhiza intercropping row ratio of 1:1) had the best treatment effect, potassium content, total sugar, reducing sugar content, and potassium chloride ratio of flue-cured tobacco were the highest, the chlorine content was the lowest, and the quality was better than other treatments. From the analysis of total output value, the total output value of TS22 (flue-cured tobacco and Salvia miltiorrhiza intercropping row ratio of 2:2) was higher than that of other intercropping treatments. In 2018 and 2019, the total output value increased by 21.3% and 22.4%, respectively, compared with the flue-cured tobacco monoculture. The intercropping advantage was obvious, and the treatment effect was the best.

The North China Plain has a typical winter wheat–summer corn double-cropping pattern. The effects of nutrient deficiency conditions on the root characteristics and yield of summer corn in the double-cropping system were studied for four years. Long-term monotonous fertilization patterns undermine crop rotation systems and are detrimental to the sustainability of agricultural production. To complement the development of rational fertilization strategies by exploring the response of crop rotation systems to nutrient deficiencies, an experiment was conducted in a randomized complete block design consisting of five treatments with three replicates for each treatment: (1) an adequate supply of nitrogen and phosphate fertilizers and potash-deficient treatment (T1); (2) an adequate supply of nitrogen and potash fertilizers and phosphorus-deficient treatment (T2); (3) an adequate supply of phosphorus and potash fertilizers and nitrogen-deficient treatment (T3); (4) nutrient-sufficient treatment for crop growth (T4); and (5) no-fertilizer treatment (CK). The results showed that different nutrient treatments had significant effects on the root length density (RLD), root surface area density (RSAD), and root dry weight density (RDWD) in summer corn. At the physiological maturity stage (R6), the root indexes of RLD, RSAD, and RDWD were significantly higher in the 0–20 cm soil layer in T4 compared to CK, with an increase of 86.2%, 131.4%, and 100.0%, respectively. Similarly, in the 20–40 cm soil layer, the root indexes of T4 were 85.7%, 61.3%, and 50.0% higher than CK, with varied differences observed in the other nutrient-deficient treatments. However, there was no significant difference among the treatments in the 40–60 cm layer except for T4, whose root index showed a difference. The root fresh weight and root dry matter in T4, T3, T2, and T1 were increased to different degrees compared with CK. In addition, these differences in root indexes affected the annual yield of crops, which increased by 20.96%, 21.95%, and 8.14% in T4, T2, and T1, respectively, compared to CK. The spike number and the number of grains per spike of T4 were 10.8% and 8.3% higher than those of CK, which led to the differences in summer corn yields. The 1000-kernel weight of T4, T2, and T1 were 9.5%, 8.8%, and 7.4% higher than that of CK, whereas the determining nutrient was nitrogen fertilizer, and phosphorus fertilizer had a higher effect on yield than potassium fertilizer. This provides a theoretical basis for the effect of nutrient deficiency conditions on yield stability in a double-cropping system.

Stellar coronal mass ejections (CMEs) from host stars are an important factor that affects the habitability of exoplanets. Although their solar counterparts have been well observed for decades, it is still very difficult to find solid evidence of stellar CMEs. Using the spectral line profile asymmetry caused by the Doppler shift of erupting plasma, several stellar CME candidates have been identified from spectral lines formed at chromospheric or transition region temperatures of the stars. However, a successful detection of stellar CME signals based on the profile asymmetries of coronal lines is still lacking. It is unclear whether we can detect such signals. Here we construct an analytical model for CMEs on solar-type stars and derive an expression of stellar extreme-ultraviolet (EUV) line profiles during CME eruptions. For different instrumental parameters, exposure times, CME propagation directions, and stellar activity levels, we synthesized the corresponding line profiles of Fe ix λ 171.07 and Fe xv λ 284.16. Further investigations provide constraints on the instrumental requirements for successful detection and characterization of stellar CMEs. Our results show that it is possible to detect stellar CME signals and infer their velocities based on spectral profile asymmetries using an EUV spectrometer with a moderate spectral resolution and signal-to-noise ratio. Our work provides important references for the design of future EUV spectrometers for stellar CME detection and the development of observation strategies.

In the field of CO2 capture utilization and storage (CCUS), recent advancements in active-source monitoring have significantly enhanced the capabilities of time-lapse acoustical imaging, facilitating continuous capture of detailed physical parameter images from acoustic signals. Central to these advancements is time-lapse full waveform inversion (TLFWI), which is increasingly recognized for its ability to extract high-resolution images from active-source datasets. However, conventional TLFWI methodologies, which are reliant on gradient optimization, face a significant challenge due to the need for complex, explicit formulation of the physical model gradient relative to the misfit function between observed and predicted data over time. Addressing this limitation, our study introduces automatic differentiation (AD) into the TLFWI process, utilizing deep learning frameworks such as PyTorch to automate gradient calculation using the chain rule. This novel approach, AD-TLFWI, not only streamlines the inversion of time-lapse images for CO2 monitoring but also tackles the issue of local minima commonly encountered in deep learning optimizers. The effectiveness of AD-TLFWI was validated using a realistic model from the Frio-II CO2 injection site, where it successfully produced high-resolution images that demonstrate significant changes in velocity due to CO2 injection. This advancement in TLFWI methodology, underpinned by the integration of AD, represents a pivotal development in active-source monitoring systems, enhancing information extraction capabilities and providing potential solutions to complex multiphysics monitoring challenges.

Flexible strain sensors have a wide range of applications, such as human motion monitoring, wearable electronic devices, and human–computer interactions, due to their good conformability and sensitive deformation detection. To overcome the internal stress problem of solid sensing materials during deformation and prepare small-sized flexible strain sensors, it is necessary to choose a more suitable sensing material and preparation technology. We report a simple and high-performance flexible strain sensor based on liquid metal nanoparticles (LMNPs) on a polyimide substrate. The LMNPs were assembled using the femtosecond laser direct writing technology to form liquid metal microwires. A wearable strain sensor from the liquid metal microwire was fabricated with an excellent gauge factor of up to 76.18, a good linearity in a wide sensing range, and a fast response/recovery time of 159 ms/120 ms. Due to these extraordinary strain sensing performances, the strain sensor can monitor facial expressions in real time and detect vocal cord vibrations for speech recognition.

The fraction of absorbed photosynthetically active radiation (fPAR) is an important parameter reflecting the level of photosynthesis and growth status of vegetation, and is widely used in energy cycling, carbon cycling, and vegetation productivity estimation. In agricultural production, fPAR is often combined with the light use efficiency model to estimate crop yield. Therefore, accurate estimation of PAR is of great importance for improving the accuracy of crop yield estimation and ensuring national food security. Existing studies based on vegetation indices have not considered the effects of genetic variety, light, and water stress on fPAR estimation. This study uses ground-based reflectance data to simulate 21 common Sentinel-2 vegetation indices and compare their estimation ability for winter wheat fPAR. The stability of the vegetation index with the highest correlation in inverting fPAR under different cultivars, light, and water stress was tested, and then the model was validated at the satellite scale. Finally, a sensitivity analysis was performed. The results showed that the index model based on modified NDVI (MNDVI) had the highest correlation not only throughout the critical phenological period of winter wheat (R2 of 0.6649) but also under different varieties, observation dates, and water stress (R2 of 0.918, 0.881, and 0.830, respectively). It even performed the highest R2 of 0.8312 at the satellite scale. Moreover, through comparison, we found that considering water stress and variety differences can improve the estimation accuracy of fPAR. The study showed that using MNDVI for fPAR estimation is not only feasible but also has high accuracy and stability, providing a reference for rapid and accurate estimation of fPAR by Sentinel-2 and further exploring the potential of Sentinel-2 data for high-resolution fPAR mapping.

Permanent magnet synchronous linear motor (PMSLM) is widely used to meet the requirement of high dynamic accuracy positioning, such as in machine tools and devices of semiconductor manufacturing. A new 2-DOF control structure is proposed in this paper to improve the dynamic performance of the positioning servo system with PMSLM. Aiming at the position tracking performance, a control algorithm based on the model predictive control (MPC) is developed with position and speed as the feedback state variables. In addition, an extended state observer (ESO) is designed for the rejection of various disturbances, which are not involved in the control model and are regarded as the lumped disturbance to be estimated and compensated by the ESO. The experimental results show that, compared with the commonly used PPI controller (proportional position controller and proportional–integral speed controller), the proposed method enhances the position bandwidth and servo stiffness effectively.

The prompt and precise identification of corn and soybeans are essential for making informed decisions in agricultural production and ensuring food security. Nonetheless, conventional crop identification practices often occur after the completion of crop growth, lacking the timeliness required for effective agricultural management. To achieve in-season crop identification, a case study focused on corn and soybeans in the U.S. Corn Belt was conducted using a crop growth curve matching methodology. Initially, six vegetation indices datasets were derived from the publicly available HLS product, and then these datasets were integrated with known crop-type maps to extract the growth curves for both crops. Furthermore, crop-type information was acquired by assessing the similarity between time-series data and the respective growth curves. A total of 18 scenarios with varying input image numbers were arranged at approximately 10-day intervals to perform identical similarity recognition. The objective was to identify the scene that achieves an 80% recognition accuracy earliest, thereby establishing the optimal time for early crop identification. The results indicated the following: (1) The six vegetation index datasets demonstrate varying capabilities in identifying corn and soybean. Among those, the EVI index and two red-edge indices exhibit the best performance, all surpassing 90% accuracy when the entire time-series data are used as input. (2) EVI, NDPI, and REVI2 indices can achieve early identification, with an accuracy exceeding 80% around July 20, more than two months prior to the end of the crops’ growth periods. (3) Utilizing the same limited sample size, the early crop identification method based on crop growth curve matching outperforms the method based on random forest by approximately 20 days. These findings highlight the considerable potential and value of the crop growth curve matching method for early identification of corn and soybeans, especially when working with limited samples.

As a common central nervous system infection in newborns, neonatal bacterial meningitis (NBM) can seriously affect their health and growth. However, although metagenomic approaches are being applied in clinical diagnostic practice, there are some limitations for whole metagenome sequencing and amplicon sequencing in handling low microbial biomass samples. Through a newly developed ultra-sensitive metagenomic sequencing method named 2bRAD-M, we investigated the microbial signatures of central nervous system infections in neonates admitted to the neonatal intensive care unit. Particularly, we recruited a total of 23 neonates suspected of having NBM and collected their blood, cerebrospinal fluid, and skin samples for 2bRAD-M sequencing. Then we developed a novel decontamination method (Reads Level Decontamination, RLD) for 2bRAD-M by which we efficiently denoised the sequencing data and found some potential biomarkers that have significantly different relative abundance between 12 patients that were diagnosed as NBM and 11 Non-NBM based on their cerebrospinal fluid (CSF) examination results. Specifically, we discovered 11 and 8 potential biomarkers for NBM in blood and CSF separately and further identified 16 and 35 microbial species that highly correlated with the physiological indicators in blood and CSF. Our study not only provide microbiological evidence to aid in the diagnosis of NBM but also demonstrated the application of an ultra-sensitive metagenomic sequencing method in pathogenesis study.

Flavin adenine dinucleotide (FAD) interacts with flavoproteins to mediate oxidation-reduction reactions required for cellular energy demands. Not surprisingly, mutations that alter FAD binding to flavoproteins cause rare inborn errors of metabolism (IEMs) that disrupt liver function and render fasting intolerance, hepatic steatosis, and lipodystrophy. In our study, depleting FAD pools in mice with a vitamin B2-deficient diet (B2D) caused phenotypes associated with organic acidemias and other IEMs, including reduced body weight, hypoglycemia, and fatty liver disease. Integrated discovery approaches revealed B2D tempered fasting activation of target genes for the nuclear receptor PPARα, including those required for gluconeogenesis. We also found PPARα knockdown in the liver recapitulated B2D effects on glucose excursion and fatty liver disease in mice. Finally, treatment with the PPARα agonist fenofibrate activated the integrated stress response and refilled amino acid substrates to rescue fasting glucose availability and overcome B2D phenotypes. These findings identify metabolic responses to FAD availability and nominate strategies for the management of organic acidemias and other rare IEMs.