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In recent years, distributed photovoltaics (DPV) has ushered in a good development situation due to the advantages of pollution-free power generation, full utilization of the ground or roof of the installation site, and balancing a large number of loads nearby. However, under the background of a large-scale DPV grid-connected to the county distribution network, an effective analysis method is needed to analyze its impact on the voltage of the distribution network in the early development stage of DPV. Therefore, a DPV orderly grid-connected method based on photovoltaics grid-connected order degree (PGOD) is proposed. This method aims to orderly analyze the change of voltage in the distribution network when large-scale DPV will be connected. Firstly, based on the voltage magnitude sensitivity (VMS) index of the photovoltaics permitted grid-connected node and the acceptance of grid-connected node (AoGCN) index of other nodes in the network, the PGOD index is constructed to determine the photovoltaics permitted grid-connected node of the current photovoltaics grid-connected state network. Secondly, a photovoltaics orderly grid-connected model with a continuous updating state is constructed to obtain an orderly DPV grid-connected order. The simulation results illustrate that the photovoltaics grid-connected order determined by this method based on PGOD can effectively analyze the voltage impact of large-scale photovoltaics grid-connected, and explore the internal factors and characteristics of the impact. [ABSTRACT FROM AUTHOR]

After the integration of large-scale Distributed Generation (DG) into the distribution network, the randomness and volatility of its output result in a reduction of spatiotemporal alignment between power generation and demand in the distribution network, exacerbating the phenomenon of wind and solar power wastage. As a novel power system model, the fundamental concept of Regional Autonomous Power Grids (RAPGs) is to achieve localized management and energy autonomy, thereby facilitating the effective consumption of DGs. Therefore, this paper proposes a distributed resource planning strategy that enhances the autonomy capabilities of regional power grids by considering multiple evaluation indexes for autonomy. First, a regional Energy Storage (ES) configuration strategy is proposed. This strategy can select a suitable reference value for the upper limit of ES configuration based on the regional load and DG output to maximize the elimination of source load deviations in the region as the upper limit constraint of ES capacity. Then, a control strategy for regional ES is proposed, the charging and discharging reference line of ES is set, and multiple autonomy and economic indexes are used as objective functions to select different proportions of ES to control the distributed resources of the regional power grid and establish evaluation indexes of the internal regional generation and load power ratio, the proportion of power supply matching hours, new energy consumption rate and tie line power imbalance outside the region to evaluate changes in the regional autonomy capabilities. The final simulation results show that in the real regional grid example, the planning method in the planning year in the region of the overall power supply matching hour ratio and new energy consumption rate increased by 3.9% and 4.8% on average, and the power imbalance of the tie line decreased by 7.8% on average. The proposed planning approach enables the maximization of regional autonomy while effectively smoothing the fluctuation of power exchange between the regional grid and the higher-level grid. This presents a rational and effective planning solution for the regional grid, facilitating the coordinated development between the region and the distribution network. Graphic Abstract [ABSTRACT FROM AUTHOR]

A capillary zone electrophoresis (CZE) system was coupled to an Orbitrap mass spectrometer operating in a data-independent acquisition (DIA) mode for in-depth proteomics analysis. The performance of this CZE-DIA-MS system was systemically evaluated and optimized under different operating conditions. The performance of the fully optimized CZE-DIA-MS system was subsequently compared to the one by using the same CZE-MS system operating in a data-dependent acquisition (DDA) mode. The experimental results show that the numbers of identified peptides and proteins acquired in the DIA mode are much higher than the ones acquired in the DDA mode, especially with the small sample loading amount. Specifically, the numbers of identified peptides and proteins acquired in the DIA mode are 1.8-fold and 2-fold higher than the ones acquired in the DDA mode by using 12.5 ng Hela digests. The proteins identified in the DIA mode also cover almost all the proteins identified in the DDA mode. In addition, a potential cancer biomarker protein, carbohydrate antigen 125, undetected in the DDA mode, can be easily identified in the DIA mode even with 12.5 ng Hela digests. The performance of the CZE-DIA-MS system for in-depth proteomics analysis with a limited sample amount has been fully demonstrated for the first time through this study. [ABSTRACT FROM AUTHOR]

Irritable bowel syndrome (IBS) is a prevalent disorder of gut‐brain interaction without a reliable cure. Evidence suggests that an alteration of the gut microbiome may contribute to IBS pathogenesis, motivating the development of microbiome‐targeted therapies to alleviate IBS symptoms. However, IBS‐specific microbiome signatures are variable across cohorts. A total of 9204 datasets were meta‐analyzed, derived from fourteen IBS microbiome discovery cohorts, three validation cohorts for diet‐microbiome interactions, and five rifaximin therapy cohorts. The consistent bacterial species and functional signatures associated with IBS were identified. Network analysis revealed two distinct IBS‐enriched microbiota clusters; obligate anaerobes that are found commonly in the gut, and facultative anaerobes typically present in the mouth, implying a possible association between oral bacterial translocation to gut and IBS pathogenesis. By analyzing diet‐microbiome interactions, microbiota‐targeted diets that can potentially modulate the altered gut microbiota of IBS subjects toward a healthy status were identified. Furthermore, rifaximin treatment of IBS subjects was linked with a reduction in the abundance of facultatively anaerobic pathobionts. Gut microbiome signatures were identified across IBS cohorts that may inform the development of therapies for microbiome modulation in IBS. The microbiota‐targeted diet patterns described may enable nutritional intervention trials in IBS and for assisting dietary management. [ABSTRACT FROM AUTHOR]

The grid-forming virtual synchronous generator (GFVSG) not only employs a first-order low-pass filter for virtual inertia control but also introduces grid-connected active power (GCAP) dynamic oscillation issues, akin to those observed in traditional synchronous generators. In response to this, an improved strategy for lead-lag filter based GFVSG (LLF-GFVSG) is presented in this article. Firstly, the grid-connected circuit structure and control principle of typical GFVSG are described, and a closed-loop small-signal model for GCAP in GFVSG is established. The causes of GCAP dynamic oscillation of GFVSG under the disturbances of active power command as well as grid frequency are analyzed. On this basis, the LLF-GFVSG improvement strategy and its parameter design method are given. Finally, the efficiency of the proposed control strategy in damping GCAP dynamic oscillations under various disturbances is verified using MATLAB simulations and experimental comparison results. [ABSTRACT FROM AUTHOR]

Transition metal selenides (TMSes) are considered promising candidates for the anodes of sodium‐ion batteries (SIBs) due to their substantial theoretical capacity. However, TMSes still face with inferior cycling lifespan caused by sluggish Na+ diffusion kinetics and vigorous volume variations during dis/charge processes. Engineering heterostructure is an attractive solution for rapid Na+ transfer, and introducing carbonaceous materials also facilitates enhanced conductivity and structural stability. Herein, CoSe/MoSe2 heterostructure combined with homogeneous carbon composites are rational designed. The kinetic analysis and theoretical calculations verified that heterointerface engineering induced build‐in electric field effect can amplifies the Na+ diffusion kinetics, while carbon contributes to enhanced electrical conductivity and structural stability. Expectedly, the CoSe/MoSe2‐C exhibits high capacity and extremely ultra‐long lifespan (320.9 mAh g−1 at 2.0 A g−1 over 10,000 cycles with an average decay of only 0.01781 mAh g−1 per cycle). Furthermore, in situ X‐ray diffraction (XRD), ex situ X‐ray photoelectorn (XPS), and high‐resolution electron microscopy (HRTEM) are exploited to explore the Na+ storage mechanism. In addition, the Na3V2(PO4)3@rGO//CoSe/MoSe2‐C (NVP@rGO//CoSe/MoSe2‐C) pouch‐type full‐cells are successfully assembled and delivered satisfactory performance. This research presents a viable strategy for the targeted engineering of TMSes aimed at enhancing the efficiency of SIBs. [ABSTRACT FROM AUTHOR]

Surfactant‐assisted co‐precipitation for catalyst preparation is an excellent potential preparation method for enhanced metal‐support interactions and abundant oxygen vacancy. Cu/SiO2 catalysts were prepared containing either glycolic acid ethoxylate lauryl ether (BRIJ35, B5), polyethylene oxide‐polypropylene oxide‐polyethylene oxide (P123 P3), or no surfactant. After further treatment with surfactants, the 1,6‐hexanediol selectivity was significantly increased, especially for P123 surfactant. The Cu/SiO2‐P3 catalyst showed good performance, which may be related to more surface basic amount, the larger dispersion of the Cu and abundant oxygen vacancy. Abundant Ovac may facilitate the hydrogenation of DMA by weakening the C=O bond. After the Cu/SiO2 catalyst was treated with surfactant, the micropores disappeared and SBET of the catalyst increased. Among them, Cu/SiO2‐P3 had the largest specific surface area (312.6 m2 g−1). [ABSTRACT FROM AUTHOR]

In order to solve the issue of low efficiency in retired battery clustering, a method for quickly obtaining a charging curve and Incremental Capacity (IC) curve based on Convolutional Neural Networks (CNN) is proposed. By training a CNN model, the method enables accurate prediction of complete IC curves and V-Q curves from local charging curves starting at any beginning. The prediction accuracy was validated using the Oxford battery degradation dataset, and transfer learning was conducted by fine-tuning the model trained on LCO batteries for use with LFP batteries, which reduced the RMSE of the estimation and validated the generalizability of the model. Peak parameters were extracted from both the original and predicted IC curves for clustering, and the t-test was applied to eliminate outliers, which significantly reduced the time required to obtain clustering features and improved clustering efficiency. [ABSTRACT FROM AUTHOR]

After apple fruit maturation, the optimal harvest period is short, and the picking robot is expected to improve harvesting efficiency. While it is common for apples to be overlapped and occluded by branches and leaves, which pose challenges to the robot's apple harvesting. Therefore, precise and swift identification and localization of the target fruit is crucial. To this end, this paper proposes a lightweight apple detection method, YOLOv5s-ShuffleNetV2-DWconv-Add, or "YOLOv5s-SDA" for short. The red and green apple datasets in natural environment were collected by a mobile phone, which were divided into four categories: red and green apples that can be directly grasped and cannot be directly grasped, in order to avoid damage to the robotic arm. Different deep learning object detection models were compared, with the YOLOv5s algorithm providing superior recognition performance. To improve harvest efficiency and portability of hardware devices, modifications are made to the YOLOv5s algorithm, replacing the Focus, C3, and Conv structures within the backbone with 3 × 3 Conv structures and ShuffleNetV2, removing SPP and C3 structures; substituting the C3 in the Neck portion with DWConv modules; and replacing two Concat layers in the PANet structure with smaller computational Add layers. Results demonstrate that the model achieved a mAP of 94.6% on the test set, doubled the detection speed, and compressed the model weight to 11.8% of its original value, while maintaining model accuracy. This new method exhibits promising performance in fruit target recognition in natural scenes, providing an effective means of visual acquisition for fruit picking robots. [ABSTRACT FROM AUTHOR]

Magnesium metal batteries (MMBs), recognized as promising contenders for post‐lithium battery technologies, face challenges such as uneven magnesium (Mg) plating and stripping behaviors, leading to uncontrollable dendrite growth and irreversible structural damage. Herein, we have developed a Mg foil featuring prominently exposed (002) facets and an architecture of nanosheet arrays (termed (002)‐Mg), created through a one‐step acid etching method. Specifically, the prominent exposure of Mg (002) facets, known for their inherently low surface and adsorption energies with Mg atoms, not only facilitates smooth nucleation and dense deposition but also significantly mitigates side reactions on the Mg anode. Moreover, the nanosheet arrays on the surface evenly distribute the electric field and Mg ion flux, enhancing Mg ion transfer kinetics. As a result, the fabricated (002)‐Mg electrodes exhibit unprecedented long‐cycle performance, lasting over 6000 h (>8 months) at a current density of 3 mA cm−2 for a capacity of 3 mAh cm−2. Furthermore, the corresponding pouch cells equipped with various electrolytes and cathodes demonstrate remarkable capacity and cycling stability, highlighting the superior electrochemical compatibility of the (002)‐Mg electrode. This study provides new insights into the advancement of durable MMBs by modifying the crystal structure and morphology of Mg. [ABSTRACT FROM AUTHOR]

Magnesium metal batteries (MMBs), recognized as promising contenders for post‐lithium battery technologies, face challenges such as uneven magnesium (Mg) plating and stripping behaviors, leading to uncontrollable dendrite growth and irreversible structural damage. Herein, we have developed a Mg foil featuring prominently exposed (002) facets and an architecture of nanosheet arrays (termed (002)‐Mg), created through a one‐step acid etching method. Specifically, the prominent exposure of Mg (002) facets, known for their inherently low surface and adsorption energies with Mg atoms, not only facilitates smooth nucleation and dense deposition but also significantly mitigates side reactions on the Mg anode. Moreover, the nanosheet arrays on the surface evenly distribute the electric field and Mg ion flux, enhancing Mg ion transfer kinetics. As a result, the fabricated (002)‐Mg electrodes exhibit unprecedented long‐cycle performance, lasting over 6000 h (>8 months) at a current density of 3 mA cm−2 for a capacity of 3 mAh cm−2. Furthermore, the corresponding pouch cells equipped with various electrolytes and cathodes demonstrate remarkable capacity and cycling stability, highlighting the superior electrochemical compatibility of the (002)‐Mg electrode. This study provides new insights into the advancement of durable MMBs by modifying the crystal structure and morphology of Mg. [ABSTRACT FROM AUTHOR]

Aiming at the consumption problems caused by the high proportion of renewable energy being connected to the distribution network, it also aims to improve the power supply reliability of the power system and reduce the operating costs of the power system. This paper proposes a two-stage planning method for distributed generation and energy storage systems that considers the hierarchical partitioning of source-storage-load. Firstly, an electrical distance structural index that comprehensively considers active power output and reactive power output is proposed to divide the distributed generation voltage regulation domain and determine the access location and number of distributed power sources. Secondly, a two-stage planning is carried out based on the zoning results. In the phase 1 distribution network-zoning optimization layer, the network loss is minimized so that the node voltage in the area does not exceed the limit, and the distributed generation configuration results are initially determined; in phase 2, the partition-node optimization layer is planned with the goal of economic optimization, and the distance-based improved ant lion algorithm is used to solve the problem to obtain the optimal distributed generation and energy storage system configuration. Finally, the IEEE33 node system was used for simulation. The results showed that the voltage quality was significantly improved after optimization, and the overall revenue increased by about 20.6%, verifying the effectiveness of the two-stage planning. [ABSTRACT FROM AUTHOR]

To study the impact of faults on the enrichment and mobility of shale oil in the Gulong area, representative rock samples were selected in this paper. Based on geochemical data and chemical kinetics methods, coupled with shale oil enrichment and mobility analysis techniques, the shale oil generation quantity and in situ oil content were evaluated from the perspectives of shale oil generation and micro migration, and the mobility of shale oil was revealed. At the same time, the hydrocarbon expulsion efficiency (HEE) of shale was qualitatively and quantitatively characterized, combined with the development of faults. The research results indicate that the study area mainly develops organic-rich felsic (ORF)/organic-containing felsic (OCF) shale, their proportion in both wells exceeds 65%, and the resource amount is the largest in this type of lithofacies. The development of a fault controls the enrichment of shale oil, and the in situ oil content and oil saturation index (OSI) of the shale in well Y58, which is close to the fault, are significantly worse than those in well S2. Well Y58 has 9.52 mg/g and 424.83 mg/g TOC respectively, while well S2 has 11.34 mg/g and 488.73 mg/g TOC respectively. The fault enhanced the migration of shale oil, increasing the efficiency of oil expulsion. As a result, the components with weak polarity or small molecules, such as saturated hydrocarbons and low carbon number n-alkanes, are prone to migration, reducing the mobility of shale oil. [ABSTRACT FROM AUTHOR]

Salinity significantly affects shellfish metabolism and growth. In this study, we evaluated the characterization of metabolomic differences in the juvenile black-shelled pearl oyster, Pinctada fucata martensii, under 15‰ (LSG), 25‰ (CG), and 35‰ (HSG) salinity conditions. Non-targeted metabolomics analyses revealed that salinity stress altered the metabolism of pearl oyster. A total of 229 significant differential metabolites (SDMs) were identified between LSG and CG via an in-house MS2 database, 241 SDMs were identified between LSG and HSG, and 50 SDMs were identified between CG and HSG. The pathway analysis showed that 21 metabolic pathways were found between LSG and CG, such as arginine and proline metabolism, glycerophospholipid metabolism, and pentose and glucuronide interconversion. A total of 23 metabolic pathways were obtained between LSG and HSG, such as aspartate, alanine, and glutamate metabolism. Only aminoacyl-tRNA biosynthesis, cysteine and methionine metabolism, and biotin metabolism were enriched between CG and HSG. A further integrated analysis suggested that amino acid metabolism might participate in osmoregulation and energy metabolism to respond to salinity stress in P. f. martensii, and the metabolic pathways differed under varying salinity stress conditions. In addition, low salinity stress might promote apoptosis in pearl oysters. Altogether, these results clarify the salinity tolerance mechanism of pearl oysters. [ABSTRACT FROM AUTHOR]

Pancreatic ductal adenocarcinoma (PDAC) has quickly risen to become the third leading cause of cancer-related death in the United States. This is in part because of its fibrotic tumor microenvironment (TME) that contributes to poor vascularization and immune infiltration and subsequent chemo- and immunotherapy failure. Here, we investigated an immunotherapy approach combining delivery of stimulator of interferon genes (STING) and Toll-like receptor 4 (TLR4) innate immune agonists by lipid-based nanoparticle (NP) coencapsulation with senescence-inducing RAS-targeted therapies, which can remodel the immune suppressive PDAC TME through the senescence-associated secretory phenotype. Treatment of transplanted and autochthonous PDAC mouse models with these regimens led to enhanced uptake of NPs by multiple cell types in the PDAC TME, induction of type I interferon and other proinflammatory signaling pathways, increased antigen presentation by tumor cells and antigen-presenting cells, and subsequent activation of both innate and adaptive immune responses. This two-pronged approach produced potent T cell–driven and type I interferon–mediated tumor regression and long-term survival in preclinical PDAC models dependent on both tumor and host STING activation. STING and TLR4-mediated type I interferon signaling was also associated with enhanced natural killer and CD8+ T cell immunity in human PDAC samples. Thus, combining localized immune agonist delivery with systemic tumor-targeted therapy can orchestrate a coordinated type I interferon–driven innate and adaptive immune response with durable antitumor efficacy against PDAC. Editor's summary: Unlike other tumor types, existing immunotherapies have not proven very successful for pancreatic ductal adenocarcinoma (PDAC), necessitating the development of additional approaches. Here, Chibaya et al. developed a combination therapy approach that included nanoparticle delivery of STING and TLR4 agonists, which stimulate the immune response, in combination with the MEK inhibitor trametinib and CDK4/6 inhibitor palbociclib, which promote senescence of tumor cells. The combination therapy induced robust antitumor immune responses that controlled tumor burden in implanted and autochthonous PDAC mouse models. These data support further development of this potent combination therapy for PDAC. —Courtney Malo [ABSTRACT FROM AUTHOR]

Background: Schistosomiasis is still one of the most serious parasitic diseases. Evidence showed that the metabolite profile in serum can potentially act as a marker for parasitic disease diagnosis and evaluate disease progression and prognosis. However, the serum metabolome in patients with Schistosoma japonicum infection is not well defined. In this study, we investigated the metabolite profiles of patients with chronic and with advanced S. japonicum infection. Methods: The sera of 33 chronic S. japonicum patients, 15 patients with advanced schistosomiasis and 17 healthy volunteers were collected. Samples were extracted for metabolites and analyzed with ultra-performance liquid chromatography-mass spectrometry (UPLC-MS). Results: We observed significant differences in metabolite profiles in positive and negative ion modes between patients with advanced and chronic S. japonicum infection. In patients with chronic S. japonicum infection, 199 metabolites were significantly upregulated while 207 metabolites were downregulated in advanced infection. These differential metabolites were mainly concentrated in steroid hormone biosynthesis, cholesterol metabolism and bile secretion pathways. We also found that certain bile acid levels were significantly upregulated in the progression from chronic to advanced S. japonicum infection. In receiver operator characteristic (ROC) analysis, we identified three metabolites with area under the curve (AUC) > 0.8, including glycocholic (GCA), glycochenodeoxycholate (GCDCA) and taurochenodeoxycholic acid (TCDCA) concentrated in cholesterol metabolism, biliary secretion and primary bile acid biosynthesis. Conclusions: This study provides evidence that GCA, GCDCA and TCDCA can potentially act as novel metabolite biomarkers to distinguish patients in different stages of S. japonicum infection. This study will contribute to the understanding of the metabolite mechanisms of the transition from chronic to advanced S. japonicum infection, although more studies are needed to validate this potential role and explore the underlying mechanisms. [ABSTRACT FROM AUTHOR]

Truncated singular value decomposition (TSVD) is a popular recovery algorithm for multimode fiber single-pixel imaging (MMF-SPI), and it uses truncation thresholds to suppress noise influences. However, due to the sensitivity of MMF relative to stochastic disturbances, the threshold requires frequent re-determination as noise levels dynamically fluctuate. In response, we design an adaptive truncation threshold determination (ATTD) method for TSVD-based MMF-SPI in disturbed environments. Simulations and experiments reveal that ATTD approaches the performance of ideal clairvoyant benchmarks, and it corresponds to the best possible image recovery under certain noise levels and surpasses both traditional truncation threshold determination methods with less computation—fixed threshold and Stein's unbiased risk estimator (SURE)—specifically under high noise levels. Moreover, target insensitivity is demonstrated via numerical simulations, and the robustness of the self-contained parameters is explored. Finally, we also compare and discuss the performance of TSVD-based MMF-SPI, which uses ATTD, and machine learning-based MMF-SPI, which uses diffusion models, to provide a comprehensive understanding of ATTD. [ABSTRACT FROM AUTHOR]

Hydrothermal activity is widespread in sedimentary basins, but its dual effects (chemistry and temperature) on shale reservoirs are rarely discussed. In this research, we systematically collected 33 well core samples from Q1 to Q9 units in Gulong Sag, Songliao Basin, and analyzed them using a variety of analytical techniques, including a field emission scanning electron microscopy (FE-SEM), an energy-dispersive spectrometer (EDS), X-ray diffraction (XRD), and stable C-O isotopes. Combined with the collected vitrinite reflectance (Ro), total organic carbon (TOC), and soluble hydrocarbon content data, which is the sum of free oil (pyrolysis S1) and sorb oil content (pyrolysis S2), the results show that (1) Q4 and Q8 units have large amounts of hydrothermal minerals, and its C-O isotope obviously shifts to negative, which implied those units are the main hydrothermal fluid influence area; (2) the hydrothermal activity occurred in the late depositional period of Q1–Q9 units such that its geochemistry has little effect on the proliferation of algae blooms, but its high temperature calculated by δ18O temperature formulas (around 208 °C) promoted the organic matter maturation process around Q4 and Q8; and (3) the overpressure caused by hydrothermal activity protected the shale reservoir and minimized the decrease in mineral reservoir brittleness index caused by hydrothermal fluid influence. We suggest that the shale reservoir affected by hydrothermal fluid will become a good geology "dessert", and its upper and/or lower bounds can form an engineering "dessert" due to the precipitation of large amounts of brittle carbonate minerals. [ABSTRACT FROM AUTHOR]

Near-infrared spectroscopy models for gross calorific value (GCV) were developed for different subsets of woody and herbaceous samples to screen subsets based on the biomass type for optimal models. A woody subset model had an R2 between 0.88 and 0.90, an RPD between 2.90 and 3.10, and an RMSE between 0.35 and 0.43 MJ/kg, which had a better fit and higher accuracy than the full set. The herbaceous subset model had the highest R2 (0.91–0.95), RPD (3.31–4.53), and the lowest RMSE (0.31–0.39 MJ/kg). Of six subsets, the woody subset was selected for diverse woody samples to build a model, and the herbaceous subset could be used to model most herbaceous samples with the exception of maize and wheat straw. The sample subsets of maize straw, and maize + wheat straw had the highest R2 (0.97–0.98), RPD (5.49–7.35), and the lowest RMSE values (0.18–0.21 MJ/kg) of the nine sample subsets. In conclusion, subsets of biomass samples proved useful for building optimized models for GCV. [ABSTRACT FROM AUTHOR]

Hepatic ischaemia–reperfusion (I/R) injury is a frequent and nearly inevitable pathophysiological process without widely accepted effective therapy. Soluble egg antigen (SEA) of Schistosoma japonicum (S. japonicum) is the main mediators capable of regulating immunological activities and has received increased attention in immune‐mediated diseases. But its role in hepatic I/R injury has not been well defined. This study aimed to elucidate whether SEA protects liver against hepatic I/R injury and explore underlying mechanism. After intraperitoneal injecting SEA three times a week for 4 weeks, mice underwent 70% hepatic I/R injury. Serum alanine aminotransferase (ALT), aspartate aminotransferase (AST), haematoxylin–eosin (HE) and TdT‐mediated dUTP nick‐end labelling (TUNEL) staining were used to evaluate liver injury. The severity related to the inflammatory response was also investigated. Furthermore, immunofluorescence was used to detect macrophage polarisation. Compared with the hepatic I/R injury group, SEA pretreatment significantly alleviated hepatic I/R injury induced liver damage, apoptosis and inflammatory. Interestingly, SEA enhanced the polarisation of macrophages towards M2 macrophages in vivo. We are the first to investigate the therapeutic efficacy of S. japonicum SEA in a hepatic I/R injury model in mice. We provided the first direct evidence that SEA attenuated hepatic I/R injury by promoting M2 macrophage polarisation. [ABSTRACT FROM AUTHOR]

Thermal regulators are devices that can adopt either the role of a thermal insulator or a thermal conductor, depending on the thermal input conditions, and play an increasingly important role in thermal management systems. In this study, we developed and tested a new passive thermal regulator design that operates around room temperature and achieves high switching ratios. Our regulator is structurally integer, scalable, orientation-independent, resistant to vibration, and can be easily integrated into existing thermal management solutions. The working principle of the passive regulator is simple yet effective, whereby an aluminum plug attached to a bimetallic strip enters and exists a wedge-shaped gap between two conductors. We demonstrate a switching ratio of ≈ 50 (- 8 + 34) :1 for a fully packaged prototype (≈ 320 (± 200):1 for a non-packaged regulator) operated in the open laboratory environment. Through geometric optimization using numerical simulations, we show that a switching ratio of ≈ 100 (- 15 + 18) :1 can be easily obtained, which can be further increased by increasing the cross-sectional area of the input conductor, hence increasing the ON-state heat transfer rate. The OFF-state thermal performance is much less sensitive to the size of the conductor, making the device highly scalable. [ABSTRACT FROM AUTHOR]

Rationale: Gas chromatography–mass spectrometry (GC–MS) combines chromatography and MS, providing full play to the advantages of high separation efficiency of GC, strong qualitative ability of MS, and high sensitivity of detector. In GC–MS data processing, determining the experimental compounds is one of the most important analytical steps, which is usually realized by one‐to‐one similarity calculations between the experimental mass spectrum and the standard mass spectrum library. Although the accuracy of the algorithm has been improved in recent years, it is still difficult to distinguish structurally similar mass spectra, especially isomers. At the same time, the library capacity is very large and increasing every year, and the algorithm needs to perform large numbers of calculations with irrelevant compounds in the library to recognize unknown compounds, which leads to a significant reduction in efficiency. Methods: This work proposed to exclude a large number of irrelevant mass spectra by presearching, perform preliminary similarity calculations using similarity algorithms, and finally improve the accuracy of similarity calculations using deep classification models. The replica library of NIST17 is used as the query data, and the master library is used as the reference database. Results: Compared with the traditional recognition algorithm, the preprocessing algorithm has reduced the time by 4.2 h, and by adding the deep learning models 1 and 2 as the final determination, the recognition accuracy has been improved by 1.9% and 6.5%, respectively, based on the original algorithm. Conclusions: This method improves the recognition efficiency compared to conventional algorithms and at the same time has better recognition accuracy for structurally similar mass spectra and isomers. [ABSTRACT FROM AUTHOR]

Liver fibrosis is an important pathological process in chronic liver disease and cirrhosis. Recent studies have found a close association between intestinal microbiota and the development of liver fibrosis. To determine whether there are differences in the intestinal microbiota between rhesus macaques with liver fibrosis (MG) and normal rhesus macaques (MN), fecal samples were collected from 8 male MG and 12 male MN. The biological composition of the intestinal microbiota was then detected using 16S rRNA gene sequencing. The results revealed statistically significant differences in ASVs and Chao1 in the alpha-diversity and the beta-diversity of intestinal microbiota between MG and MN. Both groups shared Prevotella and Lactobacillus as common dominant microbiota. However, beneficial bacteria such as Lactobacillus were significantly less abundant in MG (P = 0.02). Predictive functional analysis using PICRUSt2 gene prediction revealed that MG exhibited a higher relative abundance of functions related to substance transport and metabolic pathways. This study may provide insight into further exploration of the mechanisms by which intestinal microbiota affect liver fibrosis and its potential future use in treating liver fibrosis. [ABSTRACT FROM AUTHOR]

Peak detection is an important step in mass spectrometry as accurately identifying characteristic peaks is key to data analysis. In order to address the issue of false peak detection, while simultaneously ensuring accurate detection of weak and overlapped peaks, this paper introduces an improved algorithm for mass spectrometry integrating weighted continuous wavelet transform with particle swarm optimization-based Otsu (WWTPO). The algorithm applies the weighted continuous wavelet transform (WCWT) to compress the frequency spectrum signal into a smaller scale range, which allows for the acquisition of more distinct and informative peak information. Moreover, the algorithm employs the particle swarm optimization (PSO) algorithm to iteratively evaluate the optimal image segmentation threshold, which addresses the challenge of inaccurate Otsu image segmentation. The method was applied to detect simulated peaks as well as matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) datasets. The performance evaluation was conducted using receiver operating characteristic (ROC) curves, F1 measure and F-scores. Through comparison with continuous wavelet transform (CWT) and genetic algorithm-based threshold segmentation (WSTGA), multi-scale peak detection (MSPD) and CWT and image segmentation (CWT-IS), the results demonstrate that WWTPO exhibits excellent performance in peak detection. The determination of 4-isopropyltoluene also demonstrates that WWTPO has excellent practical application. This method not only maintains a low false peak identification rate but also detects more weak peaks and overlapping peaks, further improving the accuracy and efficiency of peak detection in mass spectrometry. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to investigate the prognosis of unrelated umbilical cord blood transplantation (UCBT) using low-dose anti-thymocyte globulin (ATG) in children diagnosed with severe aplastic anemia (SAA). Methods: This retrospective case series study was conducted involving pediatric SAA patients treated at the Capital Institute of Pediatrics from January 2020 to February 2023. All patients underwent a reduced-intensity conditioning (RIC) regimen alongside low-dose ATG. Results: The study comprised nine patients (five males) with a median age of 5 years (range: 1.7 to 7 years). The median follow-up duration was 799 days (range: 367 to 1481 days), during which all patients survived. The median time interval from diagnosis to transplantation was 3 months (range: 1 to 9 months). The median dosage of ATG administered was 5 mg/kg (range: 2.5 to 7.5 mg/kg). The median durations for granulocyte and platelet engraftment were 15 days (range: 12 to 23 days) and 26 days (range: 12 to 41 days), respectively. Three patients experienced grade 2–4 acute graft-versus-host disease (aGVHD). Epstein-Barr virus (EBV) reactivation was observed in three patients, while cytomegalovirus (CMV) reactivation occurred in seven patients, with no cases of CMV disease or post-transplant lymphoproliferative disorder (PTLD). One patient experienced recurrence 15 months after transplantation due to influenza A infection. Conclusion: These findings indicate that SAA patients may attain a favorable prognosis following UCBT with a RIC regimen combined with low-dose ATG. [ABSTRACT FROM AUTHOR]

Kirkovirus (kirV), a seemingly novel virus family, has been found in horses and donkeys. The study's objectives are to investigate the presence of the virus in swine. In this study, donkey-like kirV was detected in rectal swabs of piglets with diarrhea, and the positive rate was found to be 100% (149/149). However, this virus was detected in only one of 261 clinically healthy piglets, which suggested a strong relationship between the kirV and the diarrheic disease. We obtained the whole-genome sequences of three kirVs (Cj-D5, Cj-D32, and Cj-D43), with a length of 3750 nucleotides (nt) and sharing 99.9% nt identity with donkey kirVs. Furthermore, the three viruses shared 88.5–100% and 23–51% of the Rep protein sequence, similar to available reference strains of Kirkoviridae and Circoviridae, respectively. Moreover, like horse and donkey kirVs, the RCR domain and P-loop NTPase domains of Rep protein and nonanucleotide motif (CAATATTAC) of the three viruses were similar to those of Circoviruses and Cycloviruses. Phylogenetic analysis showed that these viruses could be grouped with members in the proposed family Kirkoviridae. This is the first report to describe that kirV can circulate in piglets with diarrhea, and future studies are needed to determine the pathogenesis of this virus. [ABSTRACT FROM AUTHOR]

Traditionally, 3D segmentation tasks have operated in silos, focusing separately on semantic and instance segmentation. However, this disjointed approach lacks interoperability and fails to fully unleash the potential of a more integrated, multitask solution. To overcome this limitation, we introduce TUS-Net, an innovative transformer-based architecture meticulously crafted for both semantic and instance segmentation of point clouds. Our model introduces two pivotal advancements: First, it employs a superpoint-based pre-processing step that minimizes computational overhead without compromising on precision. Second, we leverage a dual-branch design within the transformer architecture, allowing it to adapt to the nuances of both segmentation tasks dynamically. Through extensive experimentation on the ScanNet dataset, our findings demonstrate that TUS-Net surpasses prevailing specialized models by a substantial margin and maintains remarkable computational efficiency. Notably, we achieve a 5.7% enhancement in mean Average Precision (mAP), for instance, segmentation, while striking an optimal balance between accuracy and runtime for semantic segmentation. These outcomes underscore the versatility, efficiency and high-performance attributes of TUS-Net, positioning it as an indispensable framework for robust 3D point cloud segmentation. [ABSTRACT FROM AUTHOR]

This study intends to use the basic information and blood routine of schistosomiasis patients to establish a machine learning model for predicting liver fibrosis. We collected medical records of Schistosoma japonicum patients admitted to a hospital in China from June 2019 to June 2022. The method was to screen out the key variables and six different machine learning algorithms were used to establish prediction models. Finally, the optimal model was compared based on AUC, specificity, sensitivity and other indicators for further modeling. The interpretation of the model was shown by using the SHAP package. A total of 1049 patients’ medical records were collected, and 10 key variables were screened for modeling using lasso method, including red cell distribution width-standard deviation (RDW-SD), Mean corpuscular hemoglobin concentration (MCHC), Mean corpuscular volume (MCV), hematocrit (HCT), Red blood cells, Eosinophils, Monocytes, Lymphocytes, Neutrophils, Age. Among the 6 different machine learning algorithms, LightGBM performed the best, and its AUCs in the training set and validation set were 1 and 0.818, respectively. This study established a machine learning model for predicting liver fibrosis in patients with Schistosoma japonicum. The model could help improve the early diagnosis and provide early intervention for schistosomiasis patients with liver fibrosis. [ABSTRACT FROM AUTHOR]

Background: The prevalence of neoplastic polyps in gallbladder polyps (GPs) increases sharply with age and is associated with gallbladder carcinoma (GBC). This study aims to predict neoplastic polyps and provide appropriate treatment strategies based on preoperative ultrasound features in patients with different age level. Methods: According to the age classification of WHO, 1523 patients with GPs who underwent cholecystectomy from January 2015 to December 2019 at 11 tertiary hospitals in China were divided into young adults group (n=622), middle-aged group (n=665) and elderly group (n=236). Linear scoring models were established based on independent risk variables screened by the Logistic regression model in different age groups. The area under ROC (AUC) to evaluate the predictive ability of linear scoring models, long- and short- diameter of GPs. Results: Independent risk factors for neoplastic polyps included the number of polyps, polyp size (long diameter), and fundus in the young adults and elderly groups, while the number of polyps, polyp size (long diameter), and polyp size (short diameter) in the middle-aged groups. In different age groups, the AUCs of its linear scoring model were higher than the AUCs of the long- and short- diameter of GPs for differentiating neoplastic and non-neoplastic polyps (all P<0.05), and Hosmer-Lemeshow goodness of fit test showed that the prediction accuracy of the linear scoring models was higher than the long- and short- diameter of GPs (all P>0.05). Conclusion: The linear scoring models of the young adults, middle-aged and elderly groups can effectively distinguish neoplastic polyps from non-neoplastic polyps based on preoperative ultrasound features. [ABSTRACT FROM AUTHOR]

In the process of multi-energy system optimal scheduling, due to the high data processing requirements of the multi-energy devices and loads and the complexity of the operating states of the multi-energy devices, the scheduling optimization of the system is to some extent more difficult. To address this problem, this paper proposes a regional multi-energy system optimal scheduling model based on the theory of cloud-edge collaboration. First, based on intelligent data sensors, a cloud-edge cooperative scheduling framework of the regional multi-energy system is constructed. Then, the physical model of operating state data of multi-energy system equipment and the allocation mechanism of system scheduling tasks are studied. With the cloud service application layer and the edge computing layer as the upper and lower optimization scheduling layers, the double-layer optimization scheduling model of the regional multi-energy system is established. The objectives of the model are optimal scheduling cost and minimum delay of scheduling data transmission. The multi-objective whale optimization algorithm is used to solve the model. Finally, a simulation model is built for verification. The simulation results show that the scheduling model established in this paper can effectively improve the scheduling data processing capability and improve the economy of regional multi-energy system scheduling. [ABSTRACT FROM AUTHOR]

Rationale: Ion mobility spectrometry (IMS), as a promising analytical tool, has been widely employed in the structural characterization of biomolecules. Nevertheless, the inherent limitation in the structural resolution of IMS frequently results in peak overlap during the analysis of isomers exhibiting comparable structures. Methods: The radial basis function (RBF) neural network optimization algorithm based on dynamic inertial weight particle swarm optimization (DIWPSO) was proposed for separating overlapping peaks in IMS. The RBF network structure and parameters were optimized using the DIWPSO algorithm. By extensively training using a large dataset, an adaptive model was developed to effectively separate overlapping peaks in IMS data. This approach successfully overcomes issues related to local optima, ensuring efficient and precise separation of overlapping peaks. Results: The method's performance was evaluated using experimental validation and analysis of overlapping peaks in the IMS spectra of two sets of isomers: 3′/6′‐sialyllactose; fructose‐6‐phosphate, glucose‐1‐phosphate, and glucose‐6‐phosphate. A comparative analysis was conducted using other algorithms, including the sparrow search algorithm, DIWPSO algorithm, and multi‐objective dynamic teaching‐learning‐based optimization algorithm. The comparison results show that the DIWPSO‐RBF algorithm achieved remarkably low maximum relative errors of only 0.42%, 0.092%, and 0.41% for ion height, mobility, and half peak width, respectively. These error rates are significantly lower than those obtained using the other three algorithms. Conclusions: The experimental results convincingly demonstrate that this method can adaptively, rapidly, and accurately separate overlapping peaks of multiple components, improving the structural resolution of IMS. [ABSTRACT FROM AUTHOR]

Background: Schistosomiasis, a neglected tropical disease, remains an important public health problem. Although there are various methods for diagnosing schistosomiasis, many limitations still exist. Early diagnosis and treatment of schistosomiasis can significantly improve survival and prognosis of patients. Methodology: Circulating cell-free (cf)DNA has been widely used in the diagnosis of various diseases. In our study, we evaluated the diagnostic value of circulating cfDNA for schistosomiasis caused by Schistosoma japonicum. We focused on the tandem sequences and mitochondrial genes of S. japonicum to identify highly sensitive and specific targets for diagnosis of Schistosomiasis japonica. Results: Through data screening and analysis, we ultimately identified four specific tandem sequences (TD-1, TD-2, TD-3. and TD-4) and six mitochondrial genes (COX1(1), COX1(2), CYTB, ATP6, COX3, and ND5). We designed specific primers to detect the amount of circulating cfDNA in S. japonicum-infected mouse and chronic schistosomiasis patients. Our results showed that the number of tandem sequences was significantly higher than that of the mitochondrial genes. A S. japonicum infection model in mice suggested that infection of S. japonicum can be diagnosed by detecting circulating cfDNA as early as the first week. We measured the expression levels of circulating cfDNA (TD-1, TD-2, and TD-3) at different time points and found that TD-3 expression was significantly higher than that of TD-1 or TD-2. We also infected mice with different quantities of cercariae (20 s and 80 s). The level of cfDNA (TD-3) in the 80 s infection group was significantly higher than in the 20 s infection group. Additionally, cfDNA (TD-3) levels increased after egg deposition. Meanwhile, we tested 42 patients with chronic Schistosomiasis japonica and circulating cfDNA (TD-3) was detected in nine patients. Conclusions: We have screened highly sensitive targets for the diagnosis of Schistosomiasis japonica, and the detection of circulating cfDNA is a rapid and effective method for the diagnosis of Schistosomiasis japonica. The levels of cfDNA is correlated with cercariae infection severity. Early detection and diagnosis of schistosomiasis is crucial for patient treatment and improving prognosis. [ABSTRACT FROM AUTHOR]

Accurate and rapid identification and location of apples contributes to speeding up automation harvesting. However, in unstructured orchard environments, it is common for apples to be overlapped and occluded by branches and leaves, which interferes with apple identification and localization. In order to quickly reconstruct the fruits under overlapping and occlusion conditions, an adaptive radius selection strategy based on random sample consensus algorithm (ARSS-RANSAC) was proposed. Firstly, the edge of apple in the image was obtained by using image preprocessing method. Secondly, an adaptive radius selection strategy was proposed, which is based on fruit shape characteristics. The fruit initial radius was obtained through horizontal or vertical scanning. Then, combined with RANSAC algorithm to select effective contour points by the determined radius, and the circle center coordinates were obtained. Finally, fitting the circle according to the selected valid contour and achieving the recognition and localization of overlapped and occluded apples. 175 apple images with different overlaps and branches and leaves occlusion were applied to verify the effectiveness of algorithm. The evaluation indicators of overlap rate, average false-positive rate, average false-negative rate, and average segmentation error of ARSS-RANSAC were improved compared with the classical Hough transform method. The detection time of a single image was less than 50 ms, which can meet requirements of real-time target detection. The experimental results show that the ARSS-RANSAC algorithm can quickly and accurately identify and locate occluded and overlapped apples and is expected to be applied to harvesting robots of apple and other round fruits. [ABSTRACT FROM AUTHOR]

Purpose: The performance of oil-filled pressure cores is very much affected by the corrugated diaphragm and the oil filling volume. The purpose of this paper is to show the effects of different corrugated diaphragms, different oil filling volumes and different treatments of the corrugated diaphragms on the performance of pressure sensors. Design/methodology/approach: Pressure-sensitive cores with different diaphragm diameters, different diaphragm ripple numbers and different oil filling volumes are produced, and thermal cycling is introduced to improve the diaphragm performance, and finally the performance of each pressure-sensitive core is tested and the test data are analyzed and compared. Findings: The experimental results show that the larger the diameter of the corrugated diaphragm used for encapsulation, the better the performance. For pressure-sensitive cores using smaller diameter corrugated diaphragms, the performance of one corrugation is better than that of two corrugations. When the number of corrugations and the diameter are the same size, the performance of the outer ring of the diaphragm with concave corrugations is better than that with convex corrugations. At the same time, the diaphragm after thermal cycling treatment and appropriate reduction of encapsulated oil filling can improve the performance of the pressure-sensitive core. Originality/value: By exploring the effects of corrugated diaphragm and oil filling volume on the performance of oil-filled pressure cores, the design of oil-filled pressure sensors can be guided to improve sensor performance. [ABSTRACT FROM AUTHOR]

Aerobic glycolysis plays an important role in multidrug resistance of cancer cells. Here, we screened different expressed lncRNAs associated with sorafenib resistance of liver cancer cells, by intersecting the bioinformatics analyses of TCGA and GEO (the GSE62813 dataset) databases. Our results revealed that the 18 upregulated lncRNAs in the intersection are associated with and enriched in metabolism of small molecule organic acids, suggesting their potential in glycolysis. The lncRNA small nucleolar RNA host gene 1 (Snhg1) was chosen as a potential regulator of aerobic glycolysis in liver cancer cells, for its significant promotion on lactate production. Gain‐ and loss‐of‐function experiments mediated by Crispr‐Cas9 technique in HepG2 cells indicated that Snhg1 promoted cell proliferation, invasion, sorafenib resistance, and aerobic glycolysis. In the mechanism exploration, we found that Snhg1 can interact with SND1 protein, a famous RNA binding protein and recently identified "Reader" of N6‐methyladenosine (m6A). SND1 was demonstrated to be positively regulated by Snhg1 and had similar promoting effects on proliferation, invasion, sorafenib resistance, and aerobic glycolysis of HepG2 cells. SND1 bound with and promoted the expression of SLC7A11, an aerobic glycolysis regulator. Furthermore, either silencing SLC7A11 or blocking aerobic glycolysis with 2‐deoxy‐d‐glucose (2‐DG) was able to reverse the promotion of Snhg1 overexpression on malignancy, sorafenib resistance, and aerobic glycolysis of HepG2 cells. Finally, in a liver cancer xenograft mouse model, we found that formed tumors with Snhg1‐knocked‐down HepG2 cells were more sensitive to sorafenib administration. Altogether, SNHG1 contributes to sorafenib resistance of liver cancer cells by promoting SND1‐m6A‐SLC7A11‐mediated aerobic glycolysis. [ABSTRACT FROM AUTHOR]

Polar format algorithm (PFA) is a widely used high-resolution SAR imaging algorithm that can be implemented in advanced widefield staring synthetic aperture radar (WFS-SAR). However, existing algorithms have limited analysis in wavefront curvature error (WCE) and are challenging to apply to WFS-SAR with high-resolution and large-swath scenes. This paper proposes an extended polar format algorithm for joint envelope and phase error correction in WFS-SAR imaging with maneuvering trajectory. The impact of the WCE and residual acceleration error (RAE) are analyzed in detail by deriving the specific wavenumber domain signal based on the mapping relationship between the geometry space and wavenumber space. Subsequently, this paper improves the traditional WCE compensation function and introduces a new range cell migration (RCM) recalibration function for joint envelope and phase error correction. The 2D precisely focused SAR image is acquired based on the spatially variant inverse filtering in the final. Simulation experiments validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

High‐performance bio‐based liquid crystal copolyesters derived from 6‐hydroxy‐2‐naphthalenoic acid (HNA), 4,4′‐dihydroxybiphenyl (BP), bio‐based 2,5‐furandicarboxylic acid (FDCA) and suberic acid (Sa) were successfully designed and prepared by "one‐pot" melt polycondensation method. The influence of small amount (1–5 mol%) of FDCA on structure and properties of the copolyesters were fully investigated. Aromatic heterocyclic FDCA units had unique effect on molecular structure and properties of the copolyesters ascribed to its high stiffness and nonlinearity. The glass transition temperature, melting temperature and initial decomposition temperature of the copolyester was improved. In addition, a small amount of FDCA addition was conducive to crystallization, which can be attributed to FDCA addition increasing the molecular chain interaction. The copolyesters exhibited a broad liquid crystal phase temperature range and viscous behavior, which provided great convenience for its melt processing. The tensile strength and modulus of injected splines were in range of 82.38 ~ 121.30 MPa and 2.68 ~ 3.95 GPa, respectively, which were much higher than those of PET, PLA and other biobased polyesters. The obtained copolyester has good thermal stability, melt processing ability, mechanical properties and hydrophilicity, which indicates that they have great potential for medical and packaging applications. [ABSTRACT FROM AUTHOR]

The primary methods of assessing the reliability of distribution networks comprise analytic and simulation methods. However, both approaches require the identification and computation of network topology, which precludes their expression in explicit, continuous functions, consequently impeding the incorporation of reliability constraints into planning and operational optimization models. To tackle this restriction, the present work puts forth a novel linear‐programming‐based reliability assessment method that is mathematically formulated, considering distribution automation (DA) and distributed generations (DGs), consisting of both conventional and renewable energy sources. In this paper, the clustering method and the scenario‐based method are used to model DGs. Next, a mixed integer linear programming (MILP) model, considering the DA and DGs with the System Average Interruption Duration Index (SAIDI) as the optimization objective, is proposed. Finally, the feasibility and effectiveness of the proposed method are verified in a 37‐node distribution network system. [ABSTRACT FROM AUTHOR]

N- and O-glycosylation modifications of proteins are closely linked to the onset and development of many diseases and have gained widespread attention as potential targets for therapy and diagnosis. However, the low abundance and low ionization efficiency of glycopeptides as well as the high heterogeneity make glycosylation analysis challenging. Here, an enrichment strategy, using Knoevenagel copolymers modified with polydopamine–adenosine (denoted as PDA–ADE@KCP), was firstly proposed for simultaneous enrichment of N- and O-glycopeptides through the synergistic effects of hydrophilic and electrostatic interactions. The adjustable charged surface and hydrophilic properties endow the material with the capability to achieve effective enrichment of intact N- and O-glycopeptides. The experimental results exhibited excellent selectivity (1 : 5000) and sensitivity (0.1 fmol μL−1) of the prepared material for N-glycopeptides from standard protein digest samples. Moreover, it was further applied to simultaneous capturing of N- and O-glycopeptides from mouse liver protein digests. Compared to the commercially available zwitterionic hydrophilic interaction liquid chromatography (ZIC-HILIC) material, the number of glycoproteins corresponding to all N- and O-glycopeptides enriched with PDA–ADE@KCP was much more than that with ZIC-HILIC. Furthermore, PDA–ADE@KCP captured more O-glycopeptides than ZIC-HILIC, revealing its superior performance in O-glycopeptide enrichment. All these results indicated that the strategy holds immense potential in characterizing N- and O-intact glycopeptides in the field of proteomics. [ABSTRACT FROM AUTHOR]

In this paper, the reliability of copper pillar micro-bump under random vibration is investigated. Three kinds of copper pillar solder joints with different morphologies were obtained by changing the hot press bonding process, and the random vibration fatigue simulation of flip-flop interconnect solder joints was carried out by using Ansys to obtain the stress–strain distribution law of the solder joints and predict the vibration fatigue life of the solder joints. It is found that the outermost solder joints of flip-flop bonding are most likely to fail; the fatigue life of copper pillar solder joints is predicted based on the three interval method, the Miner linear cumulative damage criterion and the high cycle fatigue formula; reliability experiments are conducted, and it is found that the drum solder joints are most prone to cracking, followed by hourglass and columnar shapes and that all cracks are caused by the substrate side of copper pillar. Cracks are sprouted from the contact surface between the copper pillar and solder on the substrate side and expand internally in the intermetallic compound (IMC) layer, while the presence of voids aggravates the generation of cracks. [ABSTRACT FROM AUTHOR]

Apples are highly susceptible to bruising during picking, packing, and transportation, and early detection of apple bruises allows timely screening and treatment to reduce food safety risks. However, early bruises on apples, especially bruises of 30 min or less, with similar appearance to sound tissue, making manual sorting difficult. To address this problem, a near‐infrared (NIR) camera imaging technology combined with adaptive threshold segmentation algorithm for early apple bruise detection is proposed in this article. First, a NIR camera imaging system is built to acquire full‐band images of fruit face, fruit stem face and calyx face of sound apples and apples with single bruise and multiple bruises at 30 min of occurrence. Then, an adaptive threshold segmentation algorithm is proposed and an image processing algorithm, which includes high‐pass filtering, feature extraction, adaptive threshold segmentation and Hough circle detection, is developed and applied to full‐band images in response to the interference of apple stem and calyx on early bruising detection in this article. The results show that the proposed algorithm has an accuracy of 95.56% for the detection of sound apple samples and an F1‐score of 94.70% for the detection of early bruised samples, which is better than the commonly used image segmentation algorithms such as Otsu and fixed thresholding. This study shows that the combination of NIR camera imaging and the adaptive threshold segmentation algorithm proposed in this article can effectively detect early bruises on apples, providing a new idea for rapid and nondestructive detection of agricultural product quality. Practical applications: Detecting early bruises on apples can achieve effective monitoring and control of apple supply chain, and guarantee the quality and safety of agricultural products. Most studies are based on conventional machine vision with RGB camera or hyperspectral imaging technology, while conventional machine vision based on RGB camera is difficult to detect apple early bruises that are not visible to the naked eye, and hyperspectral imaging has a large amount of data and redundant image data among wavelengths. Compared with previous studies, apple early bruise detection method proposed in this article combining NIR camera imaging technology and adaptive threshold segmentation algorithm can avoid the interference of bright spots, fruit stem and calyx on bruise segmentation while capturing a single channel image, providing a new method for accurate and rapid detection of early bruises on apples. [ABSTRACT FROM AUTHOR]

Hepatic ischemia‐reperfusion (IR) injury is a complex systemic process causing a series clinical problem. C/EBPα is a key transcription factor for hepatocyte function, but its role and mechanism in regulating hepatic IR injury are largely unknown. Occluding portal vein and hepatic artery was used to establish a mouse model of hepatic IR injury. C/EBPα expression was decreased in IR‐injured liver compared with the sham, accompanied by increased contents of serum alanine transaminase (ALT), aspartate transaminase (AST), high mobility group box‐1, and proportion of hepatic cells. Oxygen and glucose deprivation/recovery (OGD/R) was used to establish a cellular hepatic IR model in WRL‐68 hepatocytes in vitro, and C/EBPα was overexpressed in the hepatocytes to evaluate its effect on hepatic IR injury. OGD/R promoted oxidative stress, cell apoptosis and endoplasmic reticulum (ER) stress in hepatocytes, which was reversed by C/EBPα overexpression. Then, we found that C/EBPα promoted histone deacetylase 1 (HDAC1) transcription through binding to HDAC1 promoter. Moreover, HDAC1 deacetylated the activating transcription factor 4 (ATF4), a key positive regulator of ER stress. Trichostatin‐A (an HDAC inhibitor) or ATF4 overexpression reversed the improvement of C/EBPα on OGD/R‐induced ER stress and hepatocyte dysfunction. 4‐Phenylbutyric acid (an endoplasmic reticulum stress inhibitor) also reversed the hepatic IR injury induced by ATF4 overexpression. Finally, lentivirus‐mediated C/EBPα overexpression vector was applied to administrate hepatic IR mice, and the results showed that C/EBPα overexpression ameliorated IR‐induced hepatic injury, manifesting with reduced ALT/AST, oxidative stress and ER stress. Altogether, our findings suggested that C/EBPα ameliorated hepatic IR injury by inhibiting ER stress via HDAC1‐mediated deacetylation of ATF4 promoter. [ABSTRACT FROM AUTHOR]

With the continuous development of new energy generation technology and the increasingly complex power grid environment, the traditional black start scheme cannot meet the requirements of today's power grid in order to ensure the stable operation of the power system can be restored quickly in the face of large power outages, so a more complete black start scheme needs to be developed to cope with the new power system. With the development of energy storage technology, the limitations of the traditional black-start scheme can be solved by new energy farms with energy storage configuration. Therefore, this paper investigates the problems faced by black-start, the key technologies of energy storage assisted new energy black-start, and introduces the research related to new energy black-start technology to provide reference for future research and application of new energy black-start. [ABSTRACT FROM AUTHOR]

To prevent products with missing character information from reaching the market, manufacturers need an automatic character recognition method. One of the key problems of this recognition method is to recognise text under complex package patterns. In addition, some products use dot matrix characters to reduce printing costs, which makes text extraction more difficult. We propose a character detection algorithm using DBNet as the base network, combined with the Convolutional Block Attention Module (CBAM) to improve its feature extraction of characters in complex contexts. After the character area has been located by the detection algorithm, it is intercepted and fed into a fully convolutional character recognition network to achieve print character recognition. We use ResNet as the backbone network and CTC loss for training. In addition, the CBAM module was added to the backbone network to enhance its recognition of dot matrix characters. The algorithm was finally deployed on the jetson nano. The experimental results show that the character detection accuracy reaches 97.9%, an improvement of 1.9% compared to the original network. As for the character recognition algorithm, the inference speed is doubled when deployed to the nano platform compared to the CRNN network, with an accuracy of 97.8%. [ABSTRACT FROM AUTHOR]

The road transportation of explosives is highly concerning due to its substantial impact on social safety. For the safety management of explosive transportation, e.g., transport route planning and emergency rescue, explosion consequence evaluation is of paramount importance. The consequence evaluation of explosion accidents is affected by many factors, especially spatial features, such as the location of transport vehicles, the distribution of buildings, and the presence of individuals around the road, etc. However, there is still a lack of quantification methods for building damage evaluation, human casualty evaluation that considers real-time population density, and efficient interactive damage evaluation methods. In this paper, we formalize three typical scenarios of damage evaluation for explosive road transportation accidents, i.e., explosion point-based, road segment-based, and route-based damage evaluation. For each scenario, we propose a Height-aware Hierarchical Building Damage (HHBD) model and a Shelter-aware Human Casualty (SHC) model for building damage evaluation and human casualty evaluation, respectively. We also develop a GIS-based interactive visualization platform that integrates multi-source geospatial data and that enables efficient geospatial computation. In addition, a case study of liquefied natural gas (LNG) transportation in Wuhan is demonstrated in order to verify the effectiveness and efficiency of the proposed system. The research results can support the decision-making process of explosive transportation safety warnings and emergency rescue. [ABSTRACT FROM AUTHOR]

A phase change material is an ideal energy storage material with huge latent heat and nearly constant phase change temperature, but there are serious problems in application such as leakage and low thermal conductivity. Porous ceramic matrixes are widely used in phase change material packaging because of their controllable porosity, large specific surface area, and high thermal conductivity. This paper introduces the preparation of porous ceramics and encapsulation of form‐stable phase change materials (FSPCMs) based on porous ceramics, thermal conductivity enhancement and other aspects of the comb, in addition, the applications of porous ceramic‐based FSPCMs in industrial waste heat recovery, temperature regulation of buildings, solar energy collection and storage, and battery thermal management system are summarized, in order to provide reference for the performance optimization of porous ceramic‐based FSPCMs and explore new preparation methods, finally, the challenges and future development of the FSPCMS are summarized. Porous ceramic‐based FSPCMs are expected to make major breakthroughs in the future to solve the increasingly serious energy problems. [ABSTRACT FROM AUTHOR]