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Abstract Background Despite the improved survival observed in PD-1/PD-L1 blockade therapy, a substantial proportion of cancer patients, including those with non-small cell lung cancer (NSCLC), still lack a response. Methods Transcriptomic profiling was conducted on a discovery cohort comprising 100 whole blood samples, as collected multiple times from 48 healthy controls (including 43 published data) and 31 NSCLC patients that under treatment with a combination of anti-PD-1 Tislelizumab and chemotherapy. Differentially expressed genes (DEGs), simulated immune cell subsets, and germline DNA mutational markers were identified from patients achieved a pathological complete response during the early treatment cycles. The predictive values of mutational markers were further validated in an independent immunotherapy cohort of 1661 subjects, and then confirmed in genetically matched lung cancer cell lines by a co-culturing model. Results The gene expression of hundreds of DEGs (FDR p 2) distinguished responders from healthy controls, indicating the potential to stratify patients utilizing early on-treatment features from blood. PD-1-mediated cell abundance changes in memory CD4 + and regulatory T cell subset were more significant or exclusively observed in responders. A panel of top-ranked genetic alterations showed significant associations with improved survival (p

Abstract Background Urinary incontinence symptoms severely affect older people with different body mass index (BMI).To compare the efficacy of the pelvic floor muscle training (PFMT) in patients with post-prostatectomy incontinence with different BMI. Methods Thirty-seven patients with post-prostatectomy incontinence were included. They were divided into group A (BMI ≤ 25,12), group B (26 ≤ BMI ≤ 30,14), and group C (BMI ≥ 31,11) based on difference BMI. Three groups of patients underwent the same Pilates combined with kegel training. Participants were assessed with 1-hour pad test, the number of incontinence episodes, International Consultation on Incontinence Questionnaire and Oxford Grading Scale. Results In the 1-hour pad test, the differences before and after training were statistically significant in all three groups of participants. Group A decreased from 81.83 ± 8.79 to 31.08 ± 5.64 g (P

Hepatocellular carcinoma (HCC), one of the most common malignant tumors in China, severely threatens the life and health of patients. In recent years, precision medicine, clinical diagnoses, treatments, and innovative research have led to important breakthroughs in HCC care. The discovery of new biomarkers and the promotion of liquid biopsy technologies have greatly facilitated the early diagnosis and treatment of HCC. Progress in targeted therapy and immunotherapy has provided more choices for precise HCC treatment. Multiomics technologies, such as genomics, transcriptomics, and metabolomics, have enabled deeper understanding of the occurrence and development mechanisms, heterogeneity, and genetic mutation characteristics of HCC. The continued promotion and accurate typing of HCC, accurate guidance of treatment, and accurate prognostication have provided more treatment opportunities and prolonged survival timelines for patients with HCC. Innovative HCC research providing an in-depth understanding of the biological characteristics of HCC will be translated into accurate clinical practices for the diagnosis and treatment of HCC.

Socioeconomic development often leads to environmental pollution and degradation initially while, beyond a certain point, there is the potential for improvements in environmental quality. In this study, we conducted a comprehensive review of published literature and national data to investigate changes in nutrient concentrations and limitations in Poyang Lake from 1978 to 2021. Our objective was to examine the relationships between these changes and the process of socioeconomic development in the watershed. The findings revealed a rapid socioeconomic development of the Poyang Lake Watershed, showing significant changes in various indexes. For example, population, Gross Domestic Product (GDP), urbanization, grain and meat productions, sewage amount and treatment rate, and forest coverage in the watershed showed increasing trends with different fitting curves, each following distinct fitting curves such as exponential, binary, and linear models. Concurrently, the concentrations of total nitrogen (TN) and total phosphorus (TP) in Poyang Lake exhibited a linear increase over the years, surpassing eutrophication thresholds since the early 1980s. However, TN and TP have shown a decreasing trend in recent years. Notably, the lake displayed co-limitation by N and P, with TN primarily driving the N:P ratio. TN and TP showed a significant “∩” shape with the increase in GDP and urbanization, while they increased with the population. TN:TP showed an increasing pattern with GDP and urbanization but a “U” shape with the population. This research contributes significant insights into the long-term changes in nutrient concentrations, shifts in nutrient limitations, and their associations with socioeconomic development. The findings highlight the need for a balanced and strategic approach to appropriately manage both nutrients for effective eutrophication mitigation.

The field of cancer neoantigen investigation has developed swiftly in the past decade. Predicting novel and true neoantigens derived from large multi-omics data became difficult but critical challenges. The rise of Artificial Intelligence (AI) or Machine Learning (ML) in biomedicine application has brought benefits to strengthen the current computational pipeline for neoantigen prediction. ML algorithms offer powerful tools to recognize the multidimensional nature of the omics data and therefore extract the key neoantigen features enabling a successful discovery of new neoantigens. The present review aims to outline the significant technology progress of machine learning approaches, especially the newly deep learning tools and pipelines, that were recently applied in neoantigen prediction. In this review article, we summarize the current state-of-the-art tools developed to predict neoantigens. The standard workflow includes calling genetic variants in paired tumor and blood samples, and rating the binding affinity between mutated peptide, MHC (I and II) and T cell receptor (TCR), followed by characterizing the immunogenicity of tumor epitopes. More specifically, we highlight the outstanding feature extraction tools and multi-layer neural network architectures in typical ML models. It is noted that more integrated neoantigen-predicting pipelines are constructed with hybrid or combined ML algorithms instead of conventional machine learning models. In addition, the trends and challenges in further optimizing and integrating the existing pipelines are discussed.

BackgroundWith the increasing incidence and prevalence of alcoholic liver disease, alcohol-related hepatocellular carcinoma has become a serious public health problem worthy of attention in China. However, there is currently no prognostic prediction model for alcohol-related hepatocellular carcinoma.MethodsThe retrospective analysis research of alcohol related hepatocellular carcinoma patients was conducted from January 2010 to December 2014. Independent prognostic factors of alcohol related hepatocellular carcinoma were identified by Lasso regression and multivariate COX proportional model analysis, and the nomogram model was constructed. The reliability and accuracy of the model were assessed using the concordance index(C-Index), receiver operating characteristic (ROC) curve and calibration curve. Evaluate the clinical benefit and application value of the model through clinical decision curve analysis (DCA). The prognosis was assessed by the Kaplan-Meier (KM) survival curve.ResultsIn sum, 383 patients were included in our study. Patients were stochastically assigned to training cohort (n=271) and validation cohort (n=112) according to 7:3 ratio. The predictors included in the nomogram were splenectomy, platelet count (PLT), creatinine (CRE), Prealbumin (PA), mean erythrocyte hemoglobin concentration (MCHC), red blood cell distribution width (RDW) and TNM. Our nomogram demonstrated excellent discriminatory power (C-index) and good calibration at 1-year, 3-year and 5- year overall survival (OS). Compared to TNM and Child-Pugh model, the nomogram had better discriminative ability and higher accuracy. DCA showed high clinical benefit and application value of the model.ConclusionThe nomogram model we established can precisely forcasting the prognosis of alcohol related hepatocellular carcinoma patients, which would be helpful for the early warning of alcohol related hepatocellular carcinoma and predict prognosis in patients with alcoholic hepatocellular carcinoma.

Microstrip filters are widely used in high-frequency circuit design for signal frequency selection. However, designing these filters often requires extensive trial and error to achieve the desired performance metrics, leading to significant time costs. In this work, we propose an automated design flow for hairpin filters, a specific type of microstrip filter. We employ artificial neural network (ANN) modeling techniques to predict the circuit performance of hairpin filters, and leverage the efficiency of low-cost models to deploy reinforcement learning agents. Specifically, we use the proximal policy optimization (PPO) reinforcement learning algorithm to learn abstract design actions for the filters, allowing us to achieve automated optimization design. Through simulation results, we demonstrate the effectiveness of the proposed approach. By optimizing the geometric dimensions, we significantly improve the performance metrics of hairpin filters, and the trained agent successfully meets our specified design goals within 5 to 15 design steps. This work serves as a conceptual validation attempt to apply reinforcement learning techniques and pre-trained ANN models to automate MMIC filter design. It exhibits clear advantages in terms of time-saving and performance efficiency when compared to other optimization algorithms.

Layout stitching is a repetitive and tedious task of the radio frequency integrated circuit (RFIC) design process. While academic research on layout splicing algorithms mainly focuses on analog and digital circuits, there is still a lack of well-developed algorithms for RFICs. An RFIC system usually has a symmetrical layout, such as transmitter and receiver components, low-noise amplifier (LNA), an SPDT switch, etc. This paper aims to address this gap by proposing an automated procedure for the layout of RFICs by relying on the basic device/PCell structure based on the interconnection among circuit topologies. This approach makes the in-series generation of layouts and automatic splicing based on circuit logic possible, resulting in superior stitching performance compared with related modules in Advanced Design System. To demonstrate the physical application possibilities, we implemented our algorithm on an LNA and a switch circuit.

Abstract Ferroptosis, a recently identified and iron-dependent cell death, differs from other cell death such as apoptosis, necroptosis, pyroptosis, and autophagy-dependent cell death. This form of cell death does not exhibit typical morphological and biochemical characteristics, including cell shrinkage, mitochondrial fragmentation, nuclear condensation. The dysfunction of lipid peroxide clearance, the presence of redox-active iron as well as oxidation of polyunsaturated fatty acid (PUFA)-containing phospholipids are three essential features of ferroptosis. Iron metabolism and lipid peroxidation signaling are increasingly recognized as central mediators of ferroptosis. Ferroptosis plays an important role in the regulation of oxidative stress and inflammatory responses. Accumulating evidence suggests that ferroptosis is implicated in a variety of cardiovascular diseases such as atherosclerosis, stroke, ischemia-reperfusion injury, and heart failure, indicating that targeting ferroptosis will present a novel therapeutic approach against cardiovascular diseases. Here, we provide an overview of the features, process, function, and mechanisms of ferroptosis, and its increasingly connected relevance to oxidative stress, inflammation, and cardiovascular diseases.

In this article, the response characteristics of multicomponent induction logging (MCIL) tool in anisotropic formation are analyzed. To solve the 1-D MCIL problems, we use a planar layered anisotropic medium (PLAM) Green's function (PLAMGF) method which is derived from the layered-medium Green's function (LMGF) and combined with MCIL excitation source and anisotropic formation environment, and then use it to investigate the equivalence of the MCIL tool responses in two models, i.e. the microscopic thin interbed formation and macroscopic anisotropic formation, and also analyze the effects of shoulder bed and formation dip on MCIL tools. In addition, in order to solve the complex 3-D problems, such as the effects of borehole, tool eccentricity and invasion on the tool response, we use the finite element method (FEM) based on a proposed new meshing scheme to simulate and analyze. This new meshing scheme includes two aspects: the use of cylindrical hexahedron element and the flexible meshing strategy combined with domain decomposition. Finally, some key detection performances of coaxial coils and coplanar coils of MCIL tool are studied. Numerical experiments are conducted to prove the effectiveness of our proposed method and provide the detailed results of the aforementioned aspects.

A scientific review is a type of article that summarizes the current state of a specific field, which is crucial for promoting the advancement of our science community. Authors need to read hundreds of research articles to prepare the data and insights for a comprehensive review, which is time-consuming and labor-intensive. In this work, we present an algorithm that can automatically extract keywords from the meta-information of each article and generate the basic data for review articles. Two different fields—communication engineering, and lab on a chip technology—were analyzed as examples. We first built an article library by downloading all the articles from the target journal using a python-based crawler. Second, the rapid automatic keyword extraction algorithm was implemented on the title and abstract of each article. Finally, we classified all extracted keywords into class by calculating the Levenshtein distance between each of them. The results demonstrated its capability of not only finding out how communication engineering and lab on a chip were evolved in the past decades but also summarizing the analytical outcomes after data mining of the extracted keywords. Our algorithm is more than a useful tool for researchers during the preparation of a review article, it can also be applied to quantitatively analyze the past, present and help authors predict the future trend of a specific research field.

Abstract The vertebral foramen, lamina, and vertebral body are three critical components of the spine structure, essential for maintaining spinal connectivity and stability. Accurately segmenting lumbar structures such as the vertebral body, vertebral foramen, and lamina in MRI cross-sections helps doctors better understand and diagnose the pathological causes of spine-related diseases. This study presents a multi-structure semantic segmentation method for vertebral transverse section MRI slices using WGAN with a residual U-Net and clustered Transformer. The generator network was replaced with a combination of a residual U-Net and a clustered Transformer-based segmentation network. The enhanced U-Net encoder, utilizing dilated convolutions and residual structures, improved multi-scale feature extraction capabilities. Meanwhile, the clustered Transformer structure, with reduced progressive linear complexity, ensured the extraction of global positional information. The results of multiple experiments show that the Dice coefficient for vertebral body segmentation increased by 3.1%, the Hausdorff distance decreased by 0.6 mm, mIOU improved by 4.1–96.2%, and PPV increased by 2.0–98.8% compared to mainstream segmentation models. These improvements are statistically significant (p

Abstract Spinal cord injury (SCI) often accompanies impairment of motor function, yet there is currently no highly effective treatment method specifically for this condition. Oxidative stress and inflammation are pivotal factors contributing to severe neurological deficits after SCI. In this study, a type of curcumin (Cur) nanoparticle (HA-CurNPs) was developed to address this challenge by alleviating oxidative stress and inflammation. Through non-covalent interactions, curcumin (Cur) and poly (-)-epigallocatechin-3-gallate (pEGCG) are co-encapsulated within hyaluronic acid (HA), resulting in nanoparticles termed HA-CurNPs. These nanoparticles gradually release curcumin and pEGCG at the SCI site. The released pEGCG and curcumin not only scavenge reactive oxygen species (ROS) and prevents apoptosis, thereby improving the neuronal microenvironment, but also regulate CD74 to promote microglial polarization toward an M2 phenotype, and inhibits M1 polarization, thereby suppressing the inflammatory response and fostering neuronal regeneration. Moreover, in vivo experiments on SCI mice demonstrate that HA-CurNPs effectively protect neuronal cells and myelin, reduce glial scar formation, thereby facilitating the repair of damaged spinal cord tissues, restoring electrical signaling at the injury site, and improving motor functions. Overall, this study demonstrates that HA-CurNPs significantly reduce oxidative stress and inflammation following SCI, markedly improving motor function in SCI mice. This provides a promising therapeutic approach for the treatment of SCI.

Abstract Background Progressive oxidative stress easily occurs as a result of a gradual increase in the intensity of maternal metabolism due to rapid foetal development and increased intensity of lactation. However, studies on the effects of processive oxidative stress on nutrient transport in the placenta have received little attention. The present study was conducted on sows at 85 days of gestation to study the effects of pterostilbene (PTE) on maternal oxidative stress status and placental nutrient transport. Results PTE increased the antioxidant capacity and immunoglobulin content in mothers’ blood and milk, reduced the level of inflammatory factors, and improved the nutrient content of milk. PTE also reduced sow backfat loss and the number of weak sons, and increased piglet weaning weight and total weaning litter weight. We subsequently found that PTE enhanced placental glucose and fatty acid transport and further affected glycolipid metabolism by increasing the expression of LAL, PYGM, and Gbe-1, which activated the PI3K phosphorylation pathway. Moreover, PTE addition altered the relative abundance of the Firmicutes, Proteobacteria, Parabacillus, and Bacteroidetes-like RF16 groups in sow faeces. PTE increased the levels of acetate, propionate, butyrate and isovalerate in the faeces. Conclusions These findings reveal that the addition of PTE during pregnancy and lactation mitigates the effects of processive oxidative stress on offspring development by altering maternal microbial and placental nutrient transport capacity. Graphical Abstract

Abstract Intermetallic nanoparticles (NPs) possess significant potentials for catalytic applications, yet their production presents challenges as achieving the disorder-to-order transition during the atom ordering process involves overcoming a kinetic energy barrier. Here, we demonstrate a robust approach utilizing atomic gas-migration for the in-situ synthesis of stable and homogeneous intermetallic alloys for propane dehydrogenation (PDH). This approach relies on the physical mixture of two separately supported metal species in one reactor. The synthesized platinum-zinc intermetallic catalysts demonstrate exceptional stability for 1300 h in continuous propane dehydrogenation under industrially relevant industrial conditions, with extending 95% propylene selectivity and propane conversions approaching thermodynamic equilibrium values at 550–600 oC. In situ characterizations and density functional theory/molecular dynamics simulation reveal Zn atoms adsorb on the particle surface and then diffuse inward, aiding in the formation of ultrasmall and highly ordered intermetallic alloys. This in-situ gas-migration strategy is applicable to a wide range of intermetallic systems.

ObjectivesPolycrystalline diamond (PCD), as a superhard material with high hardness, high wear resistance, and good impact toughness, has a wide range of applications in fields such as cutting tools for superhard materials. However, due to its high hardness, the precision surface processing of PCD has always been a technical challenge. This paper aims to study the influence of lapping pressure on the surface quality of PCD, particularly focusing on the material removal rate, surface roughness, and changes in surface morphology under high-speed grinding conditions.MethodsThe experiment adopts a single-factor test method, keeping the abrasive particle size W5 of the diamond flat grinding disc and the lapping speed of 43.3 m/s constant, while varying the lapping pressure (from 0.10 MPa to 0.18 MPa) to study its impact on the material removal rate, the surface roughness, and the surface morphology of polycrystalline diamond material. The experimental material is a ϕ13 mm × 2 mm polycrystalline diamond composite sheet with a diamond layer thickness of approximately 1 mm. The experimental apparatus is a high-speed grinding testing machine, which uses an electric spindle to drive the diamond flat grinding disc, with a lapping time of 20 minutes. The lapping process is conducted without the use of coolant to avoid its influence on the experimental results.ResultsThe experimental results indicate that the lapping pressure has a significant impact on the material removal rate of PCD. As the lapping pressure increases from 0.10 MPa to 0.18 MPa, the lapping removal rate increases from 0.005 mg/min to 0.030 mg/min, indicating that an increase in lapping pressure significantly improves material removal efficiency. The influence of lapping pressure on the surface roughness of PCD is also significant. With an increase in lapping pressure, the surface roughness gradually decreases, reducing from a higher value at 0.10 MPa to a lower value at 0.18 MPa, with a roughness Ra reduction of approximately 0.020 μm. The impact of lapping pressure on the surface morphology of PCD is manifested by a reduction in surface defects and an expansion of smooth areas. At lower lapping pressures, the surface exhibits numerous pits, interstitial cracks, and mechanical scratches. As the lapping pressure increases, the number of these defects gradually decrease, and smooth areas gradually expand. Scanning electron microscope observations reveal that an increase in grinding pressure helps reduce defects such as intergranular fractures, minute pits, and mechanical scratches, resulting in a smoother surface.ConclusionsThis paper experimentally studies the influence of lapping pressure on the material removal rate, surface roughness, and surface morphology of PCD, and draws the following conclusions. The lapping removal rate of PCD material significantly increases with an increase in lapping pressure, primarily attributed to the high-frequency collisions and frictional heat between diamond abrasive particles and PCD during the lapping process. An increase in lapping pressure significantly reduces the surface roughness of PCD, primarily benefiting from the decreased cutting depth of abrasive particles and increased actual contact area caused by the increased lapping pressure, and the promotion of surface thermochemical reactions and mechanical thermal removal effects by frictional heat. An increase in lapping pressure leads to a reduction in surface defects, an expansion of smooth areas, smaller mechanical scratches, and a significant improvement in the surface quality of PCD.

Abstract Forest soils store substantial amounts of carbon in various soil organic matter (SOM) components due to high plant litter inputs and active microbial turnover. However, the variations in plant- and microbial-derived SOM components in surface and subsurface forest soils across a wide geographic scale remain poorly understood. This study investigated the SOM components from aboveground and belowground plant inputs and fungal and bacterial necromass in surface (soil0–5 cm) and subsurface (soil5–10 cm) soils across 32 forest sites in China and analyzed their relationships with climate and edaphic factors. Compared to soil0–5 cm, soil5–10 cm exhibited lower soil organic carbon content and cutin biomarker concentration but higher concentrations of fungal necromass carbon and lignin phenols. Higher mean annual precipitation led to higher concentrations of cutin and suberin biomarkers in soil0–5 cm and soil5–10 cm, respectively. Higher soil organic carbon content was associated with lower plant-derived lignin biomarkers, higher lignin oxidation degrees, and increased microbial necromass-derived amino sugars across sites, highlighting the pivotal role of microbial necromass in SOM stabilization. Additionally, both fungal and bacterial necromass decreased with increasing mineral weathering across sites. These insights improve the understanding of environmental drivers of source-specific carbon storage in forest soils. Graphical Abstract

Abstract The paper combines experimental and numerical simulation methods to investigate the tip leakage vortex (TLV) in an oil–gas multiphase pump. The study aims to understand the evolution and dynamic characteristics of the TLV. The vorticity decomposition theory is used to analyze the spatial–temporal evolution, transient behavior, and rigid vorticity of the TLV. The findings reveal that the TLV is formed near the pressure surface when the tip clearance jet passes through the shear layer. In one impeller rotation period, transient fluctuations of the TLV are attributed to pressure differences and velocity changes across the tip clearance, and the TLV undergoes two split–dissipation–recurrence processes within one impeller rotation cycle. The presence of the gas phase enhances the scale and strength of the TLV while prolonging its existence in the flow channel. Analysis based on the rigid vorticity transport equation shows that the growth of the TLV and collapse are primarily governed by the rigid vortex generation term. The Coriolis force term contributes to stabilizing the TLV, while the rigid vorticity stretching term affects its shape. Furthermore, the gas phase significantly increases the value of the Rigid vorticity Curl of pseudo‐Lamb vector Term (RCT), which plays a crucial role in the prolonged existence of the TLV under gas–liquid two‐phase conditions. From an inlet gas void fraction of 0%–20%, the RCT value increases by 4.8 × 106/s2, the entropy production value increases by 75.55%, and the hydraulic efficiency decreases by 16.29%.

Abstract IL-17+ γδ T cells (γδ T17) are kick-starters of inflammation due to their strict immunosurveillance of xenobiotics or cellular damages and rapid response to pro-inflammatory stimulators. IL-27 is a well-recognized pleiotropic immune regulator with potent inhibitory effects on type 17 immune responses. However, its actions on γδ T17 mediated inflammation and the underlying mechanisms are less well understood. Here we find that IL-27 inhibits the production of IL-17 from γδ T cells. Mechanistically, IL-27 promotes lipolysis while inhibits lipogenesis, thus reduces the accumulation of lipids and subsequent membrane phospholipids, which leads to mitochondrial deactivation and ensuing reduction of IL-17. More importantly, Il27ra deficient γδ T cells are more pathogenic in an imiquimod-induced murine psoriasis model, while intracutaneous injection of rmIL-27 ameliorates psoriatic inflammation. In summary, this work uncovered the metabolic basis for the immune regulatory activity of IL-27 in restraining γδ T17 mediated inflammation, which provides novel insights into IL-27/IL-27Ra signaling, γδ T17 biology and the pathogenesis of psoriasis.

Abstract Natural killer (NK) cells play a crucial role in immune response against viral infections and tumors. However, further investigation is needed to better understand the key molecules responsible for determining the fate and function of NK cells. In this study, we made an important discovery regarding the involvement of the Hippo kinases Mst1 and Mst2 as novel regulators in maintaining mouse NK cell homeostasis. The presence of high Mst1 and Mst2 (Mst1/2) activity in NK cells is essential for their proper development, survival and function in a canonical Hippo signaling independent mode. Mechanistically, Mst1/2 induce cellular quiescence by regulating the processes of proliferation and mitochondrial metabolism, thereby ensuring the development and survival of NK cells. Furthermore, Mst1/2 effectively sense IL-15 signaling and facilitate the activation of pSTAT3-TCF1, which contributes to NK cell homeostasis. Overall, our investigation highlights the crucial role of Mst1/2 as key regulators in metabolic reprogramming and transcriptional regulation for mouse NK cell survival and function, emphasizing the significance of cellular quiescence during NK cell development and functional maturation.

Abstract Black carbon (BC) is a highly persistent yet poorly understood component of forest soil carbon reservoirs, while its inventory, distribution, and determining factors in forest soils on a large geographic scale remain unclear. Here, we characterized soil BC across 68 Chinese forest sites using benzene polycarboxylic acid method and developed machine learning (ML) models to predict and interpret potential impacts of soil organic matter (SOM) properties, soil physiochemical properties, meteorological conditions, wildfire history, and microbial diversity on BC. Results revealed that SOM properties were the most critical in predicting BC, complemented by the negative impact of mean annual temperature and alkaline mineral composition. The superior prediction accuracy for BC with higher condensed aromaticity (more benzene hexa‐ and penta‐carboxylic acid monomers) likely results from its simpler sources and greater resistance to transformation. This study introduces an effective ML model for predicting and interpreting soil BC inventory to better understand BC cycling.

Achieving high strength and ductility for Ti alloys and their composites has been a key objective and also a great challenge for their engineering applications. Herein, we designed TiC/(TC18 + TC4) composites with bionic and heterogeneously lamella structures, achieving an exceptional combination of high strength and ductility. This is attributed to the formation of alternative layers of TC18-coarse grains and TC4-fine grains with TiC precipitated at their interfaces. Plastic incompatibility of these three-phases produces significant hetero-deformation-induced hardening, induced by mutual interactions of heterogeneous grains and TiC precipitates, as well as activation of extensive slip groups. An optimal yield strength of ∼ 1168 MPa and a uniform elongation of ∼ 6% have been achieved, which are much higher than those of the TC4 matrix (i.e. ∼919 MPa and ∼2%). This study provides a new strategy for composite designs to overcome the trade-off dilemma for strength-ductility.

The presence of atmospheric particulates, including bioaerosols such as pollen, significantly impacts air quality and poses potential health risks. Therefore, accurate detection and categorization of these particulates are crucial for evaluating their health implications. This study introduces an innovative, automated system for analyzing atmospheric particulates, combining microfluidic technology with a smartphone-based photonic detection platform. The system integrates a miniaturized optical microscopy module and a consumer-grade high-frame-rate camera, creating an affordable, accessible solution priced at only 50 USD. Such cost-effectiveness promotes the widespread adoption of the technology and facilitates community-driven environmental monitoring. The study developed a compact, rapid, real-time, and continuous air micro-particle sampling and detection platform. The integration of an automatic collection device with a microfluidic detection chip enables continuous sampling and detection. A lightweight object detection network, designated as “PollenDet,” was trained, achieving a mean average precision (mAP) of 94.6%. This technology provides essential insights into the dynamics of airborne particulates, thereby enhancing our understanding of their overall impact.

Summary: Inflammatory bowel disease (IBD) has high prevalence in Western counties. The high fat content in Western diets is one of the leading causes for this prevalence; however, the underlying mechanisms have not been fully defined. Here, we find that high-fat diet (HFD) induces ferroptosis of intestinal regulatory T (Treg) cells, which might be the key initiating step for the disruption of immunotolerance and the development of colitis. Compared with effector T cells, Treg cells favor lipid metabolism and prefer polyunsaturated fatty acids (PUFAs) for the synthesis of membrane phospholipids. Therefore, consumption of HFD, which has high content of PUFAs such as arachidonic acid, cultivates vulnerable Tregs that are fragile to lipid peroxidation and ferroptosis. Treg-cell-specific deficiency of GPX4, the key enzyme in maintaining cellular redox homeostasis and preventing ferroptosis, dramatically aggravates the pathogenesis of HFD-induced IBD. Taken together, these studies expand our understanding of IBD etiology.

In this project, a threshold-free recursive analysis approach was investigated to reveal the recursive properties of dynamic systems. Specifically, the occurrence of recurrent patterns in phase trajectory was identified by a threshold-free recursive matrix whose elements would possess continuous values within the interval of [0, 1] and are inversely proportional to the distance between phase points. Subsequently, mapping and feature extraction were carried out to visualize and quantify the threshold-free recursive matrix. Theoretical time-series, along with observed time-series in the fields of engineering, finance, and hydrology, have been utilized to validate the efficacy of this approach. The results demonstrate the applicability of this method in analyzing the dynamic characteristics of scalar time-series. Furthermore, the rebuilt grayscale recurrence plot morphology and extracted measures through grayscale recurrence quantification analysis measures can effectively unveil the dynamical complexity of nonlinear systems by offering more precise analysis outcomes for investigating the evolution of recursive characteristics.

Due to the high incidence of lumbar vertebral lesions causing spondylolisthesis and intervertebral disc protrusion, lumbar spine (vertebrae and intervertebral discs) MRI image segmentation can provide effective clinical information for the initial diagnosis and early treatment of current lumbar spine-related diseases. However, in MRI images, there is a significant overlap and similarity in features between the vertebral bones and intervertebral discs within the lumbar spine. Therefore, the effective identification and segmentation of each vertebra and intervertebral disc in the lumbar spine pose a significant challenge. We propose a lumbar spine MRI segmentation model based on the 3D Residual U-Net, incorporating boundary segmentation structures and a hybrid attention mechanism. The model achieves boundary-constrained segmentation of individual vertebrae and intervertebral discs by integrating the boundary segmentation module. Additionally, it utilizes a hybrid attention module based on convolutional attention and self-attention mechanisms for multiscale feature extraction in the lumbar spine. The proposed model is trained and validated using the publicly available datasets MRSpineSeg2021 and SpineSagT2Wdataset3. Experimental results demonstrate a significant improvement in segmentation performance, as measured by metrics such as the Dice similarity coefficient (DSC) and Hausdorff distance (HD). This validates the superiority and generalization performance of our proposed lumbar spine MRI.

Abstract The state-of-the-art alkaline hydrogen evolution catalyst of united ruthenium single atoms and small ruthenium nanoparticles has sparked considerable research interest. However, it remains a serious problem that hydrogen evolution primarily proceeds on the less active ruthenium single atoms instead of the more efficient small ruthenium nanoparticles in the catalyst, hence largely falling short of its full activity potential. Here, we report that by combining highly oxophilic cerium single atoms and fully-exposed ruthenium nanoclusters on a nitrogen functionalized carbon support, the alkaline hydrogen evolution centers are facilely reversed to the more active ruthenium nanoclusters driven by the strong oxophilicity of cerium, which significantly improves the hydrogen evolution activity of the catalyst with its mass activity up to −10.1 A mg−1 at −0.05 V. This finding is expected to shed new light on developing more efficient alkaline hydrogen evolution catalyst by rational regulation of the active centers for hydrogen evolution.

High-temperature furnaces and coal-fired boilers are widely employed in the petrochemical and metal-smelting sectors. Over time, the deterioration, corrosion, and wear of pipelines can lead to equipment malfunctions and safety incidents. Nevertheless, effective real-time monitoring of equipment conditions remains insufficient, primarily due to the interference caused by flames generated from fuel combustion. To address this issue, in this study, a through-flame infrared imager is developed based on the mid-wave infrared (MWIR) radiation characteristics of the flame. The imager incorporates a narrowband filter that operates within the wavelength range of 3.80 μm to 4.05 μm, which is integrated into conventional thermal imagers to perform flame filtering. This configuration enables the radiation from the background to pass through the flame and reach the detector, thereby allowing the infrared imager to visualize objects obscured by the flame and measure their temperatures directly. Our experimental findings indicate that the imager is capable of through-flame imaging; specifically, when the temperature of the target exceeds 50 °C, the imager can effectively penetrate the outer flame of an alcohol lamp and distinctly capture the target’s outline. Importantly, as the temperature of the target increases, the clarity of the target’s contour in the images improves. The MWIR through-flame imager presents considerable potential for the real-time monitoring and preventive maintenance of high-temperature furnaces and similar equipment, such as detecting the degradation of refractory materials and damage to pipelines.