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Abstract The direct utilization of solar energy for the artificial photosynthesis of hydrogen peroxide (H2O2) provides a reliable approach for producing this high-value green oxidant. Here we report on the utility of high-entropy oxide (HEO) semiconductor as an all-in-one photocatalyst for visible light-driven H2O2 production directly from H2O and atmospheric O2 without the need of any additional cocatalysts or sacrificial agents. This high-entropy photocatalyst contains eight earth-abundant metal elements (Ti/V/Cr/Nb/Mo/W/Al/Cu) homogeneously arranged within a single rutile phase, and the intrinsic chemical complexity along with the presence of a high density of oxygen vacancies endow high-entropy photocatalyst with distinct broadband light harvesting capability. An efficient H2O2 production rate with an apparent quantum yield of 38.8% at 550 nm can be achieved. The high-entropy photocatalyst can be readily assembled into floating artificial leaves for sustained on-site production of H2O2 from open water resources under natural sunlight irradiation.

Abstract Color centers, arising from zero-dimensional defects, exploit quantum confinement to access internal electron quantum degrees of freedom, holding potential for quantum technologies. Despite intensive research, the structural origin of many color centers remains elusive. In this study, we employ in-situ cathodoluminescence scanning transmission electron microscopy combined with integrated differential phase contrast imaging to examine how defect configuration in tungsten sulfide determines color-center emission. Using an 80-kV accelerated electron beam, defects were deliberately produced, visualized, excited in situ and characterized in real time in monolayer WS2 within hBN|WS2 | hBN heterostructures at 100 K. These color centers were simultaneously measured by cathodoluminescence microscopy and differentiated by machine learning. Supported by DFT calculations, our results identified a crucial sulfur vacancy configuration organized into featured vacancy pairs, generating stable and bright luminescence at 660 nm. These findings elucidate the atomic-level structure-exciton relationship of color centers, advancing our understanding and quantum applications of defects in 2D materials.

Abstract The quest to eradicate poverty, central to the United Nations Sustainable Development Goals (SDGs), poses a significant global challenge. Advancement in sustainable rural development is critical to this effort, requiring the seamless integration of environmental, economic, and governmental elements. Previous research often omits the complex interactions among these factors. Addressing this gap, this study evaluates sustainable rural development in China by examining the interconnection between agricultural production and government-led poverty reduction, with annual rainfall considered an influential factor of climate change impacts on these sectors and overall sustainability. Utilizing a Meta-frontier entropy network dynamic Directional Distance Function (DDF) within an exogenous Data Envelopment Analysis (DEA) model, we categorize China’s 27 provinces into southern and northern regions according to the Qinling-Huaihe line for a comparative study of environmental, economic, and governmental efficiency. This innovative approach overcomes the limitations of previous static analyses. The findings reveal: (1) Rainfall, as an exogenous variable, significantly affects agricultural production efficiency. (2) The overall efficiency in both southern and northern regions increases when accounting for rainfall. (3) Government effectiveness in poverty reduction is comparatively lower in the northern region than in the southern region when rainfall is considered. These insights underscore the importance of including climatic variables in sustainable development policies and emphasize the need for region-specific strategies to bolster resilience against climatic challenges.

Abstract N6-methyladenosine (m6A) and 5-methylcytosine (m5C) RNA modifications have garnered significant attention in the field of epigenetic research due to their close association with human cancers. This study we focus on elucidating the expression patterns of m6A/m5C-related long non-coding RNAs (lncRNAs) in esophageal squamous cell carcinoma (ESCC) and assessing their prognostic significance and therapeutic potential. Transcriptomic profiles of ESCC were derived from public resources. m6A/m5C-related lncRNAs were obtained from TCGA using Spearman’s correlations analysis. The m6A/m5C-lncRNAs prognostic signature was selected to construct a RiskScore model for survival prediction, and their correlation with the immune microenvironment and immunotherapy response was analyzed. A total of 606 m6A/m5C-lncRNAs were screened, and ESCC cases in the TCGA cohort were stratified into three clusters, which showed significantly distinct in various clinical features and immune landscapes. A RiskScore model comprising ten m6A/m5C-lncRNAs prognostic signature were constructed and displayed good independent prediction ability in validation datasets. Patients in the low-RiskScore group had a better prognosis, a higher abundance of immune cells (CD4 + T cell, CD4 + naive T cell, class-switched memory B cell, and Treg), and enhanced expression of most immune checkpoint genes. Importantly, patients with low-RiskScore were more cline benefit from immune checkpoint inhibitor treatment (P

Abstract The aim of this study is to explore the potential and economic benefits of utilising Supervisory Control and Data Acquisition (SCADA) data to improve wind turbine operation and maintenance activities. The review identifies a gap in the current understanding of how to effectively use SCADA data in wind turbine applications. It emphasises the need for pre‐processing SCADA data to ensure data integrity by addressing outliers and employing interpolation techniques. Additionally, it highlights the challenges associated with early fault detection methods using SCADA data, including the development of physical models, data‐driven machine learning models, and statistical regression models. The review also recognises the limitations caused by the lack of public data from wind turbine developers and the imbalance between normal operation data samples and abnormal data samples, negatively impacting model accuracy. The key findings of the review demonstrate that SCADA data‐driven techniques can lead to significant improvements in wind turbine operations and maintenance. The application of data‐driven technologies based on SCADA data has proven effective in reducing operation and maintenance costs and enhancing wind power generation. Moreover, the development of robust decision support systems using SCADA data minimises the need for frequent maintenance interventions in offshore wind farms. To bridge the gap and further enhance wind turbine applications using SCADA data, several recommendations are provided. These include encouraging greater openness in sharing SCADA data to improve the robustness and accuracy of AI models, adopting transfer learning techniques to overcome the scarcity of quality datasets, establishing unified standards and taxonomies, and providing specialised resources such as software applications with interactive graphical user interfaces for easier storage, annotation, and analysis of SCADA data. The authors’ review paper identifies a gap in the current understanding of how to effectively utilise SCADA data in wind turbine applications. It emphasises the importance of pre‐processing SCADA data to ensure data integrity by addressing outliers and employing interpolation techniques. Furthermore, the authors highlight the challenges associated with early fault detection methods using SCADA data, including the development of physical models, data‐driven machine learning models, and statistical regression models.

ABSTRACT: A novel strain of duck picornavirus was isolated from duck tissue in Taian, Shandong Province, in 2017 in our laboratory. The virus was amplified in specific-pathogen-free (SPF) chicken embryos, purified and then analyzed by whole genome sequencing, which revealed a new duck-derived small RNA virus that was designated as Duck/FC22/China/2017 (FC22, GenBank accession no. MN102111) based on its genome structure and phylogenetic relationship. An in-depth study revealed that the virus grew well on the Leghorn male hepatoma (LMH) cell line. After propagation of the virus, SPF ducks were inoculated for pathogenicity tests, and their mental state, growth and development were observed after inoculation; the ducks were dissected to observe the organs and histopathological changes. A TaqMan fluorescence quantitative PCR method was utilized to detect the proliferation and shedding patterns of the virus within the ducks, while the SYBR Green I fluorescence quantitative PCR method was used to assess cytokine expression levels in the organs. The results showed that following inoculation with the FC22 strain, the mental status of the SPF ducks remained unchanged. Mild oedema was observed in some tissue organs during dissection; however, no pathological changes, such as congestion or degeneration, were noted. Histopathological analysis revealed cellular necrosis in organs, including the heart, liver, and bursa of Fabricius, as well as a reduced volume and deep staining of certain neurons in the cerebrum. Following infection, the virus titres in various organs and in cloacal swabs of the SPF ducks peaked on the first day before gradually decreasing daily. By the 10th d, the virus titres in all organs had decreased to less than 101 copies/μL. Additionally, notable alterations were observed in the expression levels of the cytokines IFNα, IL6, IL10, and TNFα. This indicates that the FC22 strain does not cause significant disease in ducks. This report presents the initial study on a recently discovered picornavirus, offering a thorough and methodical examination of its pathogenic characteristics and provides a reference for the clinical evaluation and scientific strategies for the prevention and treatment of this particular picornavirus.

Introduction: Developing effective breast cancer survival prediction models is critical to breast cancer prognosis. With the widespread use of next-generation sequencing technologies, numerous studies have focused on survival prediction. However, previous methods predominantly relied on single-omics data, and survival prediction using multi-omics data remains a significant challenge.Methods: In this study, considering the similarity of patients and the relevance of multi-omics data, we propose a novel multi-omics stacked fusion network (MSFN) based on a stacking strategy to predict the survival of breast cancer patients. MSFN first constructs a patient similarity network (PSN) and employs a residual graph neural network (ResGCN) to obtain correlative prognostic information from PSN. Simultaneously, it employs convolutional neural networks (CNNs) to obtain specificity prognostic information from multi-omics data. Finally, MSFN stacks the prognostic information from these networks and feeds into AdaboostRF for survival prediction.Results: Experiments results demonstrated that our method outperformed several state-of-the-art methods, and biologically validated by Kaplan-Meier and t-SNE.

Invasive lobular carcinoma (ILC) and invasive ductal carcinoma (IDC) account for most cases of breast cancer. However, there is ongoing debate about any potential variations in overall survival (OS) between ILC and IDC. This study aimed to compare survival between IDC and ILC, identify prognostic factors for ILC patients, and construct a nomogram for predicting OS rates. This retrospective cohort analysis utilized data from the Surveillance, Epidemiology, and End Results (SEER) Cancer Database. Patients diagnosed with ILC and IDC between 2000 and 2019 were enrolled. To minimize baseline differences in clinicopathological characteristics and survival outcomes, a propensity score matching (PSM) method was used. Data from the multivariate Cox regression analyses were used to construct a predictive nomogram for OS at 1, 3, and 5 years, incorporating all independent prognostic factors. Following the PSM procedure, patients with ILC exhibited a better prognosis compared to those with IDC. TNM stage, age >70, radiotherapy, surgery, estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HR-/HER2+) subtype were identified as independent factors for OS in ILC patients. Surgery and radiotherapy effectively reduced the risk of death, while chemotherapy did not demonstrate the same benefit. This model could support clinicians in evaluating the prognosis of ILC for decision-making and patient counseling.

Abstract All-solid-state lithium batteries have attracted widespread attention for next-generation energy storage, potentially providing enhanced safety and cycling stability. The performance of such batteries relies on solid electrolyte materials; hence many structures/phases are being investigated with increasing compositional complexity. Among the various solid electrolytes, lithium halides show promising ionic conductivity and cathode compatibility, however, there are no effective guidelines when moving toward complex compositions that go beyond ab-initio modeling. Here, we show that ionic potential, the ratio of charge number and ion radius, can effectively capture the key interactions within halide materials, making it possible to guide the design of the representative crystal structures. This is demonstrated by the preparation of a family of complex layered halides that combine an enhanced conductivity with a favorable isometric morphology, induced by the high configurational entropy. This work provides insights into the characteristics of complex halide phases and presents a methodology for designing solid materials.

As a common agricultural intensification, the winter wheat and summer maize rotation cropping pattern (wheat–maize) plays a crucial role in achieving sustainable food security in China. Reliable regional wheat–maize maps are of great importance to ensure the sustainability of agro-ecosystems. However, conventional previous studies typically depended on vegetation index time-series for detecting wheat–maize, which was challenging for rapid wheat–maize mapping. This study proposed a simpler phenology index-based method for mapping wheat–maize from multitemporal Sentinel-2 data. To better explore the mapping performance, two indices [i.e., normalized difference vegetation index (NDVI) and two-band enhanced vegetation index (EVI2)] and two mathematical combinations (i.e., multiplication and addition) were introduced to generate four uncorrelated indices. The wheat–maize maps obtained using phenology indices were evaluated using samples and high-precision maps derived from random forest. The results showed that the resulting maps achieved high overall accuracy of above 94% and F1-score of over 0.95, as well as agreed well with random forest derived maps (overall accuracy ≥ 91%, F1-score ≥ 0.88). In addition, this study found that EVI2 was better suited for designing phenology difference-based index than NDVI; concerning combination approaches, multiplication performed better than addition in enhancing spectral differences. Our results demonstrated the advantages of index-based method in mapping wheat–maize and its potential to be applied over larger regions. We hope that this study will advance our understanding of phenology-based methods in agriculture mapping.

Synthetic lethality (SL) is widely used to discover the anti-cancer drug targets. However, the identification of SL interactions through wet experiments is costly and inefficient. Hence, the development of efficient and high-accuracy computational methods for SL interactions prediction is of great significance. In this study, we propose MPASL, a multi-perspective learning knowledge graph attention network to enhance synthetic lethality prediction. MPASL utilizes knowledge graph hierarchy propagation to explore multi-source neighbor nodes related to genes. The knowledge graph ripple propagation expands gene representations through existing gene SL preference sets. MPASL can learn the gene representations from both gene-entity perspective and entity-entity perspective. Specifically, based on the aggregation method, we learn to obtain gene-oriented entity embeddings. Then, the gene representations are refined by comparing the various layer-wise neighborhood features of entities using the discrepancy contrastive technique. Finally, the learned gene representation is applied in SL prediction. Experimental results demonstrated that MPASL outperforms several state-of-the-art methods. Additionally, case studies have validated the effectiveness of MPASL in identifying SL interactions between genes.

Abstract Background Leeches are an integral component of aquatic biocenosis and can be found in a wide range of ecosystems such as freshwater, saltwater, flowing, and still-water ecosystems. It especially plays an important role in the freshwater benthic community and is an important part of the food web. In this study, a leech species was found in the mantle cavity of wild freshwater mussels in Zigong City, Sichuan Province, China, and its identity was determined through morphological analysis and molecular biological analysis. Results The leech is Hemiclepsis khankiana, a new species of Hemiclepsis that has been discovered in Russia in recent years. Through morphological analysis, the current survey observed that the morphological characteristics of Hemiclepsis khankiana eyespots were significantly different from the first reported description. The first pair of eyespots on the leech were separated and clear, while it had been reduced to unclear shadows in the previous report. The phylogenetic tree based on the COI gene showed that the COI gene sequence obtained in this study was in the same evolutionary branch as Hemiclepsis khankiana (MN295420, MN295421). Genetically, it was most closely related to Hemiclepsis kasmiana (mean COI p-distance = 3.98%). Conclusions The current study reported on the new distribution range of Hemiclepsis khankiana, which was initially discovered in China. This study indicates that the distribution range of the leech species has expanded, laying a foundation for further studies in China.

Abstract Spintronic terahertz (THz) emitters (STEs) based on magnetic heterostructures have emerged as promising THz sources. However, it is still a challenge to achieve a higher intensity STE to satisfy all kinds of practical applications. Herein, we report a STE based on Pt0.93(MgO)0.07/CoFeB nanofilm by introducing dispersed MgO impurities into Pt, which reaches a 200% intensity compared to Pt/CoFeB and approaches the signal of 500 μm ZnTe crystal under the same pump power. We obtain a smaller spin diffusion length of Pt0.93(MgO)0.07 and an increased thickness-dependent spin Hall angle relative to the undoped Pt. We also find that the thickness of a Pt layer leads to a drastic change in the interface role in the spintronic THz emission, suggesting that the underlying mechanism of THz emission enhancement is a combined effect of enhanced bulk spin hall angle and the interfacial skew scattering by MgO impurities. Our findings demonstrate a simple way to realize high-efficiency, stable, advanced spintronic THz devices.

Zearalenone (ZEN) is a common fungal toxin with reproductive toxicity in various grains. It poses a serious threat to ovine and other animal husbandry industries, as well as human reproductive health. Therefore, investigating the mechanism of toxicity and screening antagonistic drugs are of great importance. In this study, based on the natural compound library and previous Smart-seq2 results, antioxidant and anti-apoptotic drugs were selected for screening as potential antagonistic drugs. Three natural plant compounds (oxysophoridine, rutin, and phellodendrine) were screened for their ability to counteract the reproductive toxicity of ZEN on ovine oocytes in vitro using quantitative polymerase chain reaction (qPCR) and reactive oxygen species detection. The compounds exhibited varying pharmacological effects, notably impacting the expression of antioxidant (GPX, SOD1, and SOD2), autophagic (ATG3, ULK2, and LC3), and apoptotic (CAS3, CAS8, and CAS9) genes. Oxysophoridine promoted GPX, SOD1, ULK2, and LC3 expression, while inhibiting CAS3 and CAS8 expression. Rutin promoted SOD2 and ATG3 expression, and inhibited CAS3 and CAS9 expression. Phellodendrine promoted SOD2 and ATG3 expression, and inhibited CAS9 expression. However, all compounds promoted the expression of genes related to cell cycle, spindle checkpoint, oocyte maturation, and cumulus expansion factors. Although the three drugs had different regulatory mechanisms in enhancing antioxidant capacity, enhancing autophagy, and inhibiting cell apoptosis, they all maintained a stable intracellular environment and a normal cell cycle, promoted oocyte maturation and release of cumulus expansion factors, and, ultimately, counteracted ZEN reproductive toxicity to promote the in vitro maturation of ovine oocytes. This study identified three drugs that antagonize the reproductive toxicity of ZEN on ovine oocytes, and compared their mechanisms of action, providing data support and a theoretical basis for their subsequent application in the ovine breeding industry, reducing losses in the breeding industry, screening of ZEN reproductive toxicity antagonists and various toxin antagonists, improving the study of ZEN reproductive toxicity mechanisms, and even protection of human reproductive health.

Abstract Calcium ions (Ca2+) are indispensable and versatile metal ions that play a pivotal role in regulating cell metabolism, encompassing cell survival, proliferation, migration, and gene expression. Aberrant Ca2+ levels are frequently linked to cell dysfunction and a variety of pathological conditions. Therefore, it is essential to maintain Ca2+ homeostasis to coordinate body function. Disrupting the balance of Ca2+ levels has emerged as a potential therapeutic strategy for various diseases, and there has been extensive research on integrating this approach into nanoplatforms. In this review, the current nanoplatforms that regulate Ca2+ homeostasis for cancer therapy are first discussed, including both direct and indirect approaches to manage Ca2+ overload or inhibit Ca2+ signalling. Then, the applications of these nanoplatforms in targeting different cells to regulate their Ca2+ homeostasis for achieving therapeutic effects in cancer treatment are systematically introduced, including tumour cells and immune cells. Finally, perspectives on the further development of nanoplatforms for regulating Ca2+ homeostasis, identifying scientific limitations and future directions for exploitation are offered.

Accurately identifying novel indications for drugs is crucial in drug research and discovery. Traditional drug discovery is costly and time-consuming. Computational drug repositioning can provide an effective strategy for discovering potential drug-disease associations. However, the known experimentally verified drug-disease associations is relatively sparse, which may affect the prediction performance of the computational drug repositioning methods. Moreover, while the existing drug-disease prediction method based on metric learning algorithm has achieved better performance, it simply learns features of drugs and diseases only from the drug-centered perspective, and cannot comprehensively model the latent features of drugs and diseases. In this study, we propose a novel drug repositioning method named RSML-GCN, which applies graph convolutional network and reinforcement symmetric metric learning to predict potential drug-disease associations. RSML-GCN first constructs a drug–disease heterogeneous network by integrating the association and feature information of drugs and diseases. Then, the graph convolutional network (GCN) is applied to complement the drug–disease association information. Finally, reinforcement symmetric metric learning with adaptive margin is designed to learn the latent vector representation of drugs and diseases. Based on the learned latent vector representation, the novel drug–disease associations can be identified by the metric function. Comprehensive experiments on benchmark datasets demonstrated the superior prediction performance of RSML-GCN for drug repositioning.

Abstract With the CoO2 slabs consisting of Co4O4 cubane structure, layered NaxCoO2 are considered promising candidates for oxygen evolution reaction (OER) in alkaline media given their earth‐abundant and structural advantages. However, due to the strong adsorption of intermediates on the large basal planes, NaxCoO2 cannot meet the activity demands. Here, a novel one‐pot synthesis strategy is proposed to realize the high solubility of iron in NaxCoO2 in an air atmosphere. The optimist Na0.6Co0.9Fe0.1O2 exhibits enhanced OER activity compared to their pristine and other reported Fe‐doped NaxCoO2 counterparts. Such an enhancement is mainly ascribed to the abundant active sites on the activated basal planes and the participation of oxidized oxygen as active sites independently, which breaks the scaling relationship limit in the OER process. This work is expected to contribute to the understanding of the modification mechanism of Fe‐doped cobalt‐based oxides and the exploitation of layer‐structured oxides for energy application.

Hylurgus ligniperda is an imported quarantine plant pest in China. Its identification is usually based on morphological characteristics; therefore, species identification needs high professional requirements of staff and professionals with high experience accumulated through long-term training. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) is a rapid identification technology, which is based on protein profiles of species. It has been widely used for the identification of pathogenic microorganisms. Many studies have reported the identification of mosquitoes, ticks, and other arthropods. The application of MALDI-TOF MS in the identification of H. ligniperda can improve the identification efficiency of H. ligniperda, preventing and control its harm and further spread. To construct a spectra database for H. ligniperda, we analyzed the effect of different factors, such as different body parts, developmental stages, populations, and preservation conditions, on its protein spectrum. We collected protein spectrum profiles from 19 specimens of H. ligniperda and its related species, obtaining 211 protein spectra to construct a reference database and validate identification. The protein spectrum from the chest specimens of H. ligniperda showed many peaks, high intensity, and a stable signal, indicating a successful data establishment. The difference in protein spectra between different regions of the same species was less, but did not affect the identification results. Clear differences were observed in the protein spectrum across many developmental stages. The database established by the adult specimens protein spectrum can accurately identify Dendroctonus valens, Tomicus piniperda, and H. ligniperda. MALDI-TOF MS technology can be used for the rapid identification of H. ligniperda. This method is rapid and direct, and the identification results are robust. It does not require specialized entomological expertise and can be used for customs interception and field investigations.

Electrolytes, function as an ion conducting membrane between battery electrodes, are essential for rechargeable batteries. Here, the authors report high-entropy liquid electrolytes and reveal substantial impact of the increasing entropy on lithium-ion solvation structures for highly reversible lithium batteries.

Abstract Background Peste des petits ruminants (PPR), foot-and-mouth disease (FMD) and sheep pox and goat pox are three important infectious diseases that infect goats, sheep and other small ruminants. It is well-known that the prevention of three diseases rely mainly on their individual vaccines. However, the vaccines have a variety of different disadvantages, such as short duration of immunity, increasing the number of vaccinations, and poor thermal stability. The purpose of this study is to construct a recombinant goat pox virus (rGPV) capable of expressing the F gene of PPRV and the P12A3C gene of FMDV as a live vector vaccine. Results The IRES, FMDV P12A3C and PPRV F genes into the multi-cloning site of the universal transfer plasmid pTKfpgigp to construct a recombinant transfer plasmid pTKfpgigpFiP12A3C, and transfected GPV-infected lamb testis (LT) cells with liposomes and produced by homologous recombination Recombinant GPV (rGPV/PPRVF-FMDVP12A3C, rGPV). The rGPV was screened and purified by green florescence protein (GFP) and xanthine-guanine-phosphoribosyltransferase gene (gpt) of Escherichia coli as selective markers, and the expression of rGPV in LT cells was detected by RT-PCR and immunofluorescence techniques. The results showed that the virus strain rGPV/PPRVF-FMDVP12A3C containing FMDV P12A3C and PPRV F genes was obtained. The exogenous genes FMDV P12A3C and PPRV F contained in rGPV were normally transcribed and translated in LT cells, and the expression products could specifically react with PPRV and FMDV antiserum. Then, the rGPV was intradermally inoculated with goats, the animal experiments showed that rGPV/PPRVF-FMDVP12A3C could induce high levels of specific antibodies against GPV, PPRV and FMDV. Conclusions The constructed rGPV induced high levels of specific antibodies against GPV, PPRV and FMDV. The study provides a reference for “ one vaccine with multiple uses “ of GPV live vector vaccine.

Context Veratramine may have a potential therapeutic effect for diabetic peripheral neuropathy (DPN).Objective To evaluate whether veratramine ameliorates neuropathic pain in a rat diabetic model.Materials and methods Sprague–Dawley rats were used for a diabetic model induced by a streptozotocin + high-fat diet. Two months after the induction of the diabetic model, the rats with DPN were screened according to the mechanical pain threshold. The rats with DPN were divided into a model group (n = 12) and a treated group (n = 12). Rats with diabetes, but without peripheral neuropathy, were used in the vehicle group (n = 9). The treatment group received 50 μg/kg veratramine via the tail vein once a day for 4 weeks. During modelling and treatment, rats in all three groups were fed a high-fat diet.Results The mechanical withdrawal threshold increased from 7.5 ± 1.9 N to 17.9 ± 2.6 N in DPN rats treated with veratramine. The tolerance time of the treated group to hot and cold ectopic pain increased from 11.8 ± 4.2 s and 3.4 ± 0.8 s to 20.4 ± 4.1 s and 5.9 ± 1.7 s, respectively. Veratramine effectively alleviated L4-L5 spinal cord and sciatic nerve pathological injury. Veratramine inhibited the expression of SIGMAR1 and the phosphorylation of the N-methyl-d-aspartate receptor (NMDAR) Ser896 site in spinal cord tissue, as well as inhibited the formation of SIGMAR1-NMDAR and NMDAR-CaMKII complexes.Discussion and conclusions Veratramine may alleviate the occurrence of pain symptoms in rats with DPN by inhibiting activation of the SIGMAR1-NMDAR pathway.

Abstract Liver cancer is the main malignancy in terms of mortality rate, accurate diagnosis can help the treatment outcome of liver cancer. Patient similarity network is an important information which helps in cancer diagnosis. However, recent works rarely take patient similarity into consideration. To address this issue, we constructed patient similarity network using three liver cancer omics data, and proposed a novel liver cancer diagnosis method consisted of similarity network fusion, denoising autoencoder and dense graph convolutional neural network to capitalize on patient similarity network and multi omics data. We compared our proposed method with other state-of-the-art methods and machine learning methods on TCGA-LIHC dataset to evaluate its performance. The results confirmed that our proposed method surpasses these comparison methods in terms of all the metrics. Especially, our proposed method has attained an accuracy up to 0.9857.

Saline water irrigation contributes significantly to forage yield. However, the acceptable salinity levels for saline water irrigation of tall wheatgrass remains unclear. In this study, field supplemental irrigations of transplanted-tall wheatgrass with saline drainage waters having salinities of electrical conductivity (ECw) = 2.45, 4.36, 4.42, and 5.42 dS m−1 were conducted to evaluate the effects of saline water irrigation on forage yield and soil salinization. In addition, the effects of plastic film mulching, fertilization, and saline water irrigation on sward establishment of seed-propagated tall wheatgrass were determined. Finally, a pot experiment was carried out to confirm the above field results. The results showed that two irrigations with ECw = 2.45 and 4.36 dS m−1 saline waters produced the highest dry matter yield, followed by one irrigation with ECw = 4.42 or 5.42 dS m−1. After rainfall leaching, the soil EC1:5 was reduced by 41.7–79.3% for the saline water irrigation treatments. In combination with saline water irrigation, plastic film mulching promoted sward establishment and enhanced the plant height and dry matter yield of seed-propagated tall wheatgrass, while fertilization played a marginal role. However, two irrigations with ECw = 7.13 and 4.36 dS m−1 saline waters resulted in rates of 3.2% and 16.0% of dead plants under the mulching and no mulching conditions, respectively. Furthermore, a pot experiment demonstrated that irrigation with ECw = 5.79 dS m−1 saline water led to the lowest reduction in forage yield and the highest crude protein content in leaves. However, the plants irrigated with ECw ≥ 6.31 dS m−1 saline water enhanced soil salinity and reduced the plant height, leaf size, and gas exchange rate. Conclusively, one irrigation with ECw ≤ 5.42 dS m−1 and SAR ≤ 36.31 saline water at the end of April or early May could be acceptable for tall wheatgrass production and minimize the soil salinization risk in the coastal saline–alkaline land around the Bohai Sea.

Zearalenone (ZEN), a non-steroidal estrogenic fungal toxin widely present in forage, food, and their ingredients, poses a serious threat to animal and human reproductive health. ZEN also threatens ovine, a major source of human food and breeding stock. However, the mechanisms underlying the impact of ZEN on the in vitro maturation (IVM) of ovine oocytes remain unclear. This study aimed to elucidate these mechanisms using the Smart-seq2 technology. A total of 146 differentially expressed genes were obtained, using Smart-seq2, from sheep oocytes cultured in vitro after ZEN treatment. ZEN treatment inhibited RUNX2 and SPP1 expression in the PI3K signaling pathway, leading to the downregulation of THBS1 and ultimately the downregulation of TNFAIP6; ZEN can also decrease TNFAIP6 by reducing PTPRC and ITGAM. Both inhibit in vitro maturation of ovine oocytes and proliferation of cumulus cells by downregulating TNFAIP6. These findings provide data and a theoretical basis for elucidating ZEN’s toxicity mechanisms, screening therapeutic drugs, and reducing ZEN-related losses in the ovine industry.

Brachial plexus block is a common regional anesthesia method widely used in upper limb surgery. Nowadays, ultrasound-guided brachial plexus block has been extensively used in clinical anesthesia. However, accurate brachial plexus block is highly dependent on the physician’s experience, and a physician without extensive clinical experience may cause nerve injury when performing a nerve block. With the development of artificial intelligence technology, the deep learning method can automatically identify the brachial plexus in ultrasound images and assist doctors in completing the brachial plexus block accurately and quickly. In this paper, we aim to evaluate the performance of different deep learning models in identifying brachial plexus (i.e., segmentation of brachial plexus) from ultrasonic images to explore the best models and training strategies for this task. To this end, we use a new dataset containing 340 brachial plexus ultrasound images annotated by three experienced clinicians. Among the 12 deep learning models we evaluated, U-Net achieves the best segmentation accuracy, with an intersection over union (IoU) of 68.50%. However, the number of U-Net parameters is very large, and it can only process 15 images per second. Compared to U-Net, LinkNet can process 142 images per second and achieve the second-best segmentation accuracy with an IoU of 66.27%. It achieves the balance between segmentation accuracy and processing efficiency, which has a good potential for the brachial plexus’s real-time segmentation task.

Abstract Photoimmunotherapy, with spatiotemporal precision and noninvasive property, has provided a novel targeted therapeutic strategy for highly malignant triple‐negative breast cancer (TNBC). However, their therapeutic effect is severely restricted by the insufficient generation of tumor antigens and the weak activation of immune response, which is caused by the limited tissue penetration of light and complex immunosuppressive microenvironment. To improve the outcomes, herein, mace‐like plasmonic AuPd heterostructures (Au Pd HSs) have been fabricated to boost near‐infrared (NIR) photoimmunotherapy. The plasmonic Au Pd HSs exhibit strong photothermal and photodynamic effects under NIR light irradiation, effectively triggering immunogenic cell death (ICD) to activate the immune response. Meanwhile, the spiky surface of Au Pd HSs can also stimulate the maturation of DCs to present these antigens, amplifying the immune response. Ultimately, combining with anti‐programmed death‐ligand 1 (α‐PD‐L1) will further reverse the immunosuppressive microenvironment and enhance the infiltration of cytotoxic T lymphocytes (CTLs), not only eradicating primary TNBC but also completely inhibiting mimetic metastatic TNBC. Overall, the current study opens a new path for the treatment of TNBC through immunotherapy by integrating nanotopology and plasmonic performance.

Since more and more large-scale farms appear in China and changes in fecal sewage source disposal, the production of high-concentration solid manure waste is also increasing, and its conversion and utilization are gaining attention. This study investigated the effect of heat pre-treatment (HPT) on the thermophilic anaerobic digestion (AD) of high-solid manure (HSM). Pig manure (PM) feed with a total solids of 13% was used for the HPT and subsequent anaerobic digestion (AD) test. The HPT was carried out at 60°C, 80°C, and 100°C, respectively, for 15 min after the heating reached the set temperature. The results show that HPT led to PM feed COD solubilization, observing a maximum increase of 24.57% after pretreated at 100°C, and the treated PM feed under this condition received the maximum methane production potential of 264.64 mL·g−1 VS in batch AD test, which was 28.76% higher than that of the untreated group. Another semi-continuous AD test explored the maximum volume biogas production rate (VBPR). It involves two organic loading rates (OLR) of 13.4 and 17.8 g VSadded·L−1·d−1. The continuous test exhibited that all the HPT groups could produce biogas normally when the OLR increased to the high level, while the digester fed with untreated PM showed failure. The maximum VBPR of 4.71 L L−1·d−1 was observed from PM feed after pre-treated at 100°C and running at the high OLR. This reveals that thermal treatment can weaken the impact of a larger volume of feed on the AD system. Energy balance analysis demonstrates that it is necessary to use a heat exchanger to reuse energy in the HPT process to reduce the amount of energy input. In this case, the energy input to energy output (Ei/Eo) ranged from 0.34 to 0.55, which was much less than one, suggesting that biogas increment due to heat treatment can reasonably cover the energy consumption of the pre-treatment itself. Thus combining HPT and high-load anaerobic digestion of PM was suitable.

ABSTRACT: This study aimed to analyze the growth performance, antioxidant activity, serum lipid profile, meat quality, and lipid metabolism of broiler chickens fed mixtures containing Enteromorpha polysaccharide (EP) and yeast glycoprotein (YG). A total of 400 one-day-old broiler chickens were randomly divided into 4 treatment groups of 10 replicates with 10 birds each replicate. The dietary treatments consisted of the control group (fed basal diet), and diets supplemented with Enteromorpha polysaccharide (EP; 400 mg/kg), yeast glycoprotein (YG;400 mg/kg), and EP+YG (200 mg/kg EP + 200 mg/kg YG). Compared with the control group, EP+YG supplementation enhanced growth performance and significantly reduced (P < 0.05) serum total triglyceride (TG), cholesterol (CHOL), and low-density lipoprotein LDL levels, and increased high-density lipoprotein (HDL) levels. Besides, birds fed EP+YG supplemented diet exhibited higher (P < 0.05) serum catalase (CAT), total antioxidant capacity, superoxide dismutase (SOD), and lower malonaldehyde (MDA) activities, and upregulated expressions of related genes, such as nuclear factor-erythroid factor 2-related factor 2 (NRF2), SOD1, and glutathione peroxidase 4 (GPX4) in the liver and intestinal tissues than the control group. Interestingly, higher (P < 0.05) serum SOD and lower MDA contents were observed in the EP+YG group than in either EP or YG group, suggesting a synergetic effect. Breast meat from EP+YG supplemented group had significantly higher redness value (a*), and lower pH24, total saturated fatty acid profiles, C14:0, C16:0, C18:0 fatty acid, atherogenic index, and thrombogenicity index than meat from the control group (P < 0.05). Furthermore, the mRNA expressions of fatty acid synthesis genes were downregulated (P < 0.05), whereas lipid β-oxidation-related genes were upregulated (P < 0.05) in the liver of the EP+YG supplemented group than in the control group. Overall, our data suggest that dietary EP+YG inclusion may have a synergistic effect, and therefore improve growth performance, regulate serum biochemical indexes, enhance antioxidant activity, and modulate lipid metabolism in broilers, indicating that it is a potential feed additive for chickens.

Background: Esophageal cancer is a tumor type with high invasiveness and low prognosis. As immunotherapy has been shown to improve the prognosis of esophageal cancer patients, we were interested in the establishment of an immune-associated gene prognostic index to effectively predict the prognosis of patients. Methods: To establish the immune-related gene prognostic index of esophageal cancer (EC), we screened 363 upregulated and 83 downregulated immune-related genes that were differentially expressed in EC compared to normal tissues. By multivariate Cox regression and weighted gene coexpression network analysis (WGCNA), we built a prognostic model based on eight immune-related genes (IRGs). We confirmed the prognostic model in both TCGA and GEO cohorts and found that the low-risk group had better overall survival than the high-risk group. Results: In this study, we identified 363 upregulated IRGs and 83 downregulated IRGs. Next, we found a prognostic model that was constructed with eight IRGs (OSM, CEACAM8, HSPA6, HSP90AB1, PCSK2, PLXNA1, TRIB2, and HMGB3) by multivariate Cox regression analysis and WGCNA. According to the Kaplan–Meier survival analysis results, the model we constructed can predict the prognosis of patients with esophageal cancer. This result can be verified by the Gene Expression Omnibus (GEO). Patients were divided into two groups with different outcomes. IRGPI-low patients had better overall survival than IRGPI-high patients.Conclusion: Our findings indicated the potential value of the IRGPI risk model for predicting the prognosis of EC patients.

N6-methyladenosine (m6A) is an epigenetic modification that widely exists in long noncoding RNAs (lncRNAs) and is involved in the regulation of oncogenes or tumor suppressor genes that form complex enzymes to affect the occurrence of tumors. The abnormal modification of m6A methylation can alter the overall m6A level and thus contribute to the malignant biological behaviors of hepatocellular carcinoma (HCC). LncRNAs related to m6A methylation are involved in lipogenesis, the proliferation, migration and invasion of HCC cells, the stemness of tumor cells and sorafenib resistance. In this review, we systematically elaborated the occurrence mechanism of lncRNA and m6A methylation modification in HCC and the effect of m6A methylation modification of lncRNA on the occurrence of HCC, suggesting that the combination of m6A methylation modification and lncRNA will be more meaningful as molecular markers or prognostic markers. It is helpful to provide further ideas for exploring the pathogenesis of HCC and identifying new targets for HCC treatment and diagnosis and achieve precise individual treatment of liver cancer.

The Anatomical Therapeutic Chemical (ATC) classification system is a drug classification scheme proposed by the World Health Organization, which is widely used for drug screening, repositioning, and similarity research. The ATC system assigns different ATC codes to drugs based on their anatomy, pharmacological, therapeutics and chemical properties. Predicting the ATC code of a given drug helps to understand the indication and potential toxicity of the drug, thus promoting its use in the therapeutic phase and accelerating its development. In this article, we propose an end-to-end model DACPGTN to predict the ATC code for the given drug. DACPGTN constructs composite features of drugs, diseases and targets by applying diverse biomedical information. Inspired by the application of Graph Transformer Network, we learn potential novel interactions among drugs diseases and targets from the known interactions to construct drug-target-disease heterogeneous networks containing comprehensive interaction information. Based on the constructed composite features and learned heterogeneous networks, we employ graph convolution network to generate the embedding of drug nodes, which are further used for the multi-label learning tasks in drug discovery. Experiments on the benchmark datasets demonstrate that the proposed DACPGTN model can achieve better prediction performance than the existing methods. The source codes of our method are available at https://github.com/Szhgege/DACPGTN.

Global warming is one of the most common environmental challenges faced by cold-water fish farming. Intestinal barrier function, gut microbiota, and gut microbial metabolites are significantly altered under heat stress, posing serious obstacles to the healthy artificial culture of rainbow trout. However, the molecular mechanisms underlying intestinal injury in rainbow trout under heat stress remain unclear. In the present study, the optimal growth temperature for rainbow trout (16 °C) was used for the control group, and the maximum temperature tolerated by rainbow trout (24 °C) was used for the heat stress group, which was subjected to heat stress for 21 days. The mechanism of intestinal injury in rainbow trout under heat stress was explored by combining animal histology, 16S rRNA gene amplicon sequencing, ultra-high performance liquid chromatography-mass spectrometry, and transcriptome sequencing. The results showed that the antioxidant capacity of rainbow trout was enhanced under heat stress, the levels of stress-related hormones were significantly increased, and the relative expression of genes related to heat stress proteins was significantly increased, indicating that the heat stress model of rainbow trout was successfully established. Secondly, the intestinal tract of rainbow trout showed inflammatory pathological characteristics under heat stress, with increased permeability, activation of the inflammatory factor signaling pathway, and increased relative expression of inflammatory factor genes, suggesting that the intestinal barrier function was impaired. Thirdly, heat stress caused an imbalance of intestinal commensal microbiota and changes in intestinal metabolites in rainbow trout, which participated in the stress response mainly by affecting lipid metabolism and amino acid metabolism. Finally, heat stress promoted intestinal injury in rainbow trout by activating the peroxisome proliferator-activated receptor-α signaling pathway. These results not only expand the understanding of fish stress physiology and regulation mechanisms, but also provide a scientific basis for healthy artificial culture and the reduction of rainbow trout production costs.

Background: Triple-negative breast cancer (TNBC) was closely related to high metastatic risk and mortality and has not yet found a targeted receptor for targeted therapy. Cancer immunotherapy, especially photoimmunotherapy, shows promising potential in TNBC treatment because of great spatiotemporal controllability and non-trauma. However, the therapeutic effectiveness was limited by insufficient tumor antigen generation and the immunosuppressive microenvironment. Methods: We report on the design of cerium oxide (CeO2) end-deposited gold nanorods (CEG) to achieve excellent near-infrared photoimmunotherapy. CEG was synthesized through hydrolyzing of ceria precursor (cerium acetate, Ce(AC)3) on the surface of Au nanorods (NRs) for cancer therapy. The therapeutic response was first verified in murine mammary carcinoma (4T1) cells and then monitored by analysis of the anti-tumor effect in xenograft mouse models. Results: Under near-infrared (NIR) light irradiation, CEG can efficiently generate hot electrons and avoid hot-electron recombination to release heat and form reactive oxygen species (ROS), triggering immunogenic cell death (ICD) and activating part of the immune response. Simultaneously, combining with PD-1 antibody could further enhance cytotoxic T lymphocyte infiltration. Conclusions: Compared with CBG NRs, CEG NRs showed strong photothermal and photodynamic effects to destroy tumors and activate a part of the immune response. Combining with PD-1 antibody could reverse the immunosuppressive microenvironment and thoroughly activate the immune response. This platform demonstrates the superiority of combination therapy of photoimmunotherapy and PD-1 blockade in TNBC therapy.

Cancer is one of the leading causes of death worldwide, which brings an urgent need for its effective treatment. However, cancer is highly heterogeneous, meaning that one cancer can be divided into several subtypes with distinct pathogenesis and outcomes. This is considered as the main problem which limits the precision treatment of cancer. Thus, cancer subtypes identification is of great importance for cancer diagnosis and treatment. In this work, we propose a deep learning method which is based on multi-omics and attention mechanism to effectively identify cancer subtypes. We first used similarity network fusion to integrate multi-omics data to construct a similarity graph. Then, the similarity graph and the feature matrix of the patient are input into a graph autoencoder composed of a graph attention network and omics-level attention mechanism to learn embedding representation. The K-means clustering method is applied to the embedding representation to identify cancer subtypes. The experiment on eight TCGA datasets confirmed that our proposed method performs better for cancer subtypes identification when compared with the other state-of-the-art methods. The source codes of our method are available at https://github.com/kataomoi7/multiGATAE.

Global warming is one of the most common environmental challenges faced by cold-water fish farming. Heat stress seriously affects the feeding, growth, immunity, and disease resistance of fish. These changes are closely related to the destruction of intestinal barrier function, the change of intestinal microbiota, and metabolic dysfunction. However, the causal relationship between the phenotypic effects of heat stress as well as intestinal and metabolic functions of fish is unknown. In the current study, the optimal growth temperature (16°C) of rainbow trout was used as the control group, while the fish treated at 22.5°C, 23.5°C, and 24.5°C for 24 h, respectively, were the treatment groups. The 16S rRNA gene sequencing analysis showed that with the increase in temperature, the relative abundance and diversity of intestinal microbiota decreased significantly, while the number of Mycoplasma, Firmicutes, and Tenericutes increased significantly. Non-targeted metabolomics analysis by liquid chromatography-mass spectrometry analysis and correlation analysis showed that the changes of metabolites related to amino acids, vitamins, and short-chain fatty acids in serum of rainbow trout under acute heat stress were strongly correlated with the decrease of relative abundance of various intestinal microbiota, especially Morganella, Enterobacter, Lactobacillus, Lawsonia, and Cloacibacterium. In addition, we also found that acute heat stress seriously affected the intestinal structure and barrier function, and also caused the pathological damage of epithelial cells. These results indicate that the gut microbiome of acute heat-stressed rainbow trout could mediate metabolite transfer through the gut barrier by affecting its integrity. Significant changes in gut morphology, permeability, antioxidant capacity, and pro-inflammatory cytokine levels were observed. Therefore, it is necessary to explore the changes of intestinal microbiota under heat stress to help understand the regulatory mechanism of heat stress and protect the intestinal health of rainbow trout from the negative effects of rising water temperature.

Abstract Backgrounds Knowledge discovery from breast cancer treatment records has promoted downstream clinical studies such as careflow mining and therapy analysis. However, the clinical treatment text from electronic health data might be recorded by different doctors under their hospital guidelines, making the final data rich in author- and domain-specific idiosyncrasies. Therefore, breast cancer treatment entity normalization becomes an essential task for the above downstream clinical studies. The latest studies have demonstrated the superiority of deep learning methods in named entity normalization tasks. Fundamentally, most existing approaches adopt pipeline implementations that treat it as an independent process after named entity recognition, which can propagate errors to later tasks. In addition, despite its importance in clinical and translational research, few studies directly deal with the normalization task in Chinese clinical text due to the complexity of composition forms. Methods To address these issues, we propose PASCAL, an end-to-end and accurate framework for breast cancer treatment entity normalization (TEN). PASCAL leverages a gated convolutional neural network to obtain a representation vector that can capture contextual features and long-term dependencies. Additionally, it treats treatment entity recognition (TER) as an auxiliary task that can provide meaningful information to the primary TEN task and as a particular regularization to further optimize the shared parameters. Finally, by concatenating the context-aware vector and probabilistic distribution vector from TEN, we utilize the conditional random field layer (CRF) to model the normalization sequence and predict the TEN sequential results. Results To evaluate the effectiveness of the proposed framework, we employ the three latest sequential models as baselines and build the model in single- and multitask on a real-world database. Experimental results show that our method achieves better accuracy and efficiency than state-of-the-art approaches. Conclusions The effectiveness and efficiency of the presented pseudo cascade learning framework were validated for breast cancer treatment normalization in clinical text. We believe the predominant performance lies in its ability to extract valuable information from unstructured text data, which will significantly contribute to downstream tasks, such as treatment recommendations, breast cancer staging and careflow mining.

Hepatocellular carcinoma (HCC) is one of the most lethal malignant diseases worldwide. Despite advances in the diagnosis and treatment of HCC, its overall prognosis remains poor. Recent studies have shown that long noncoding RNAs (lncRNAs) play crucial roles in various pathophysiological processes, including liver cancer. In the current study, we report that lncRNA SLC2A1‐AS1 is frequently downregulated in HCC samples, as shown by quantitative real‐time polymerase chain reaction analysis. SLC2A1‐AS1 deletion is significantly associated with recurrence‐free survival in HCC. By performing glucose uptake, lactate production and ATP detection assays, we found that SLC2A1‐AS1‐mediated glucose transporter 1 (GLUT1) downregulation significantly suppressed glycolysis of HCC. In vitro Cell Counting Kit‐8, colony formation, transwell assays as well as in vivo tumorigenesis and metastasis assays showed that SLC2A1‐AS1 overexpression significantly suppressed proliferation and metastasis in HCC through the transcriptional inhibition of GLUT1. Results from fluorescence in situ hybridization, ChIP and luciferase reporter assays demonstrated that SLC2A1‐AS1 exerts its regulatory role on GLUT1 by competitively binding to transketolase and signal transducer and activator of transcription 3 (STAT3) and inhibits the transactivation of Forkhead box M1 (FOXM1) via STAT3, thus resulting in inactivation of the FOXM1/GLUT1 axis in HCC cells. Our findings will be helpful for understanding the function and mechanism of lncRNA in HCC. These data also highlight the crucial role of SLC2A1‐AS1 in HCC aerobic glycolysis and progression and pave the way for further research regarding the potential of SLC2A1‐AS1 as a valuable predictive biomarker for HCC recurrence.

The pose of texture-less objects is very important for intelligent manufacturing and intelligent assembly. Existing methods cannot accurately estimate pose when partial features are missing or cluttered due to shading, reflection, and occlusion. We propose a hybrid framework based on the warped hierarchical tree for pose estimation which integrates template matching and sparse representation classification in this paper. Firstly, the template is formed by the prospectively cumulated orientation feature (PCOF), which is a probabilistic representation of orientations extracted from template images. And the warped hierarchical tree can be built offline according to the parameters for projecting the 3D object and the similarity between templates. Then the online searching can be repeated through the warped hierarchical tree until the template candidates have been found. Finally, the pose corresponding to the best-fitting template can be obtained by sparse representation classification based on the dictionaries consisted of the spreading orientations of template candidate images. The experiment results show the effectiveness of our method when partial features are missing or cluttered.

Tall wheatgrass (Elytrigia elongata) has the potential to be utilized on marginal land, such as coastal saline-alkaline soils, to meet rising ruminant feed demand. However, the salinity threshold for cultivation of tall wheatgrass remains unclear, which restricts its extensive application. Here, a tall wheatgrass line, Zhongyan 1, was grown in saline-alkaline soils in the Yellow River Delta region to determine its salinity threshold. The results showed that the soil salinity of AM = 1.23, measured with a PNT3000 activity meter, led to only 5% dead plants of tall wheatgrass. Four grades of seedling plants were classified according to the morphological response of Zhongyan 1 to saline soils. The soil salinity declined while the survival rate and forage yield increased from grade 1 to grade 4 plants. Plant height and dry matter yield were negatively related to soil salinity. When the salinity in the soil depth of 0–10 cm was over 1%, the survival rate of tall wheatgrass declined dramatically with the increase in soil salinity. Under saline-alkaline stress, the plant height during 12–31 May was positively related to forage yield, which can be used as an indicator of productivity. The tall type (70–120 cm) produced 5627.2 kg ha−1 of dry matter, which was 3.32 times that of the dwarf type (20–69 cm). The forage yield of tall wheatgrass in saline-alkaline land was largely affected by the proportion of highly saline soil. Collectively, the soil salinity of 1% at a depth of 0–10 cm and the AM values of 1.23 measured with a PNT3000 activity meter can be used as the salinity threshold for cultivation of tall wheatgrass in coastal saline-alkaline land.

GLT-1, the major glutamate transporter in the mammalian central nervous system, is expressed in presynaptic terminals that use glutamate as a neurotransmitter, in addition to astrocytes. It is widely assumed that glutamate homeostasis is regulated primarily by glutamate transporters expressed in astrocytes, leaving the function of GLT-1 in neurons relatively unexplored. We generated conditional GLT-1 knockout (KO) mouse lines to understand the cell-specific functions of GLT-1. We found that stimulus-evoked field extracellular postsynaptic potentials (fEPSPs) recorded in the CA1 region of the hippocampus were normal in the astrocytic GLT-1 KO but were reduced and often absent in the neuronal GLT-1 KO at 40 weeks. The failure of fEPSP generation in the neuronal GLT-1 KO was also observed in slices from 20 weeks old mice but not consistently from 10 weeks old mice. Using an extracellular FRET-based glutamate sensor, we found no difference in stimulus-evoked glutamate accumulation in the neuronal GLT-1 KO, suggesting a postsynaptic cause of the transmission failure. We hypothesized that excitotoxicity underlies the failure of functional recovery of slices from the neuronal GLT-1 KO. Consistent with this hypothesis, the non-competitive NMDA receptor antagonist MK801, when present in the ACSF during the recovery period following cutting of slices, promoted full restoration of fEPSP generation. The inclusion of an enzymatic glutamate scavenging system in the ACSF conferred partial protection. Excitotoxicity might be due to excess release or accumulation of excitatory amino acids, or to metabolic perturbation resulting in increased vulnerability to NMDA receptor activation. Previous studies have demonstrated a defect in the utilization of glutamate by synaptic mitochondria and aspartate production in the synGLT-1 KO in vivo, and we found evidence for similar metabolic perturbations in the slice preparation. In addition, mitochondrial cristae density was higher in synaptic mitochondria in the CA1 region in 20–25 weeks old synGLT-1 KO mice in the CA1 region, suggesting compensation for loss of axon terminal GLT-1 by increased mitochondrial efficiency. These data suggest that GLT-1 expressed in presynaptic terminals serves an important role in the regulation of vulnerability to excitotoxicity, and this regulation may be related to the metabolic role of GLT-1 expressed in glutamatergic axon terminals.

The Topological Hall Effect (THE) has been considered a hallmark of magnetic skyrmions in SrRuO3 single films and heterostructures. Alternative interpretations suggest that the THE-like anomalies could also originate from multi-channel anomalous Hall effects (AHEs) induced by structural inhomogeneity. Here, we reveal another type of THE-like anomaly in SrRuO3 films that was rarely reported previously. By scanning minor loops of the Hall resistivity of ultrathin SrRuO3 films, we demonstrate that the increased saturation resistivity and reversed polarity with decreasing a scanning field cannot be explained by THE, while it shows consistence with spatially inhomogeneous AHE. Our research provides a simple but effective way to quantitatively distinguish between THE and inhomogeneous AHE in those magnetic systems even beyond SrRuO3.

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder with memory loss and cognitive impairment. The white matter (WM) BOLD signal has recently been shown to provide an important role in understanding the intrinsic cerebral activity. Although the altered homotopic functional connectivity within gray matter (GM-HFC) has been examined in AD, the abnormal HFC to WM remains unknown. The present study sought to identify changes in the WM-HFC and anatomic characteristics by combining functional magnetic resonance imaging with diffusion tensor imaging (DTI). Resting-state and DTI magnetic resonance images were collected from the OASIS-3 dataset and consisted of 53 mild cognitive impairment (MCI) patients, 90 very MCI (VMCI), and 100 normal cognitive (NC) subjects. Voxel-mirrored HFC was adopted to examine whether WM-HFC was disrupted in VMCI and MCI participants. Moreover, the DTI technique was used to investigate whether specific alterations of WM-HFC were associated with anatomic characteristics. Support vector machine analyses were used to identify the MCI and VMCI participants using the abnormal WM-HFC as the features. Compared with NC, MCI, and VMCI participants showed significantly decreased GM-HFC in the middle occipital gyrus and inferior parietal gyrus and decreased WM-HFC in the bilateral middle occipital and parietal lobe-WM. In addition, specific WM-functional network alteration for the bilateral sub-lobar-WM was found in MCI subjects. MCI subjects showed abnormal anatomic characteristics for bilateral sub-lobar and parietal lobe-WM. Results of GM-HFC mainly showed common neuroimaging features for VMCI and MCI subjects, whereas analysis of WM-HFC showed specific clinical neuromarkers and effectively compensated for the lack of GM-HFC to distinguish NC, VMCI, and MCI subjects.

Low-temperature pretreatment (LTPT, Temp. < 100 °C or 140 °C) has the advantages of low input, simplicity, and energy saving, which makes engineering easy to use for improving biogas production. However, compared with high-temperature pretreatment (>150 °C) that can destroy recalcitrant polymerized matter in biomass, the action mechanism of heat treatment of biomass is unclear. Improving LTPT on biogas yield is often influenced by feedstock type, treatment temperature, exposure time, and fermentation conditions. Such as, even when belonging to the same algal biomass, the response to LTPT varies between species. Therefore, forming a unified method for LTPT to be applied in practice is difficult. This review focuses on the LTPT used in different biomass materials to improve anaerobic digestion performance, including food waste, sludge, animal manure, algae, straw, etc. It also discusses the challenge and cost issues faced during LTPT application according to the energy balance and proposes some proposals for economically promoting the implementation of LTPT.

The Pan-Cancer Atlas consists of original sequencing data from various sources, provides the opportunity to perform systematic studies on the commonalities and differences between diverse cancers. The analysis for the pan-cancer dataset could help researchers to identify the key factors that could trigger cancer. In this paper, we present a novel pan-cancer classification method, referred to MI_DenseNetCAM, to identify a set of genes that can differentiate all tumor types accurately. First, the Mutual Information (MI) was utilized to eliminate noise and redundancy from the pan-cancer datasets. Then, the gene data was further converted to 2D images. Next, the DenseNet model was adopted as a classifier and the Guided Grad-CAM algorithm was applied to identify the key genes. Extensive experimental results on the public RNA-seq data sets with 33 different tumor types show that our method outperforms the other state-of-the-art classification methods. Moreover, gene analysis further demonstrated that the genes selected by our method were related to the corresponding tumor types.

Supercapacitors are an important part of the hybrid energy storage system for electric vehicles (EVs.) They not only protect the battery, but also improve the starting performance, acceleration performance, and regenerative braking performance of the EVs. Accurate modeling and state of charge (SOC) estimation of supercapacitors are essential for reliability, resilience, and safety in hybrid energy storage system operations. In this paper, a fractional order dynamic equivalent circuit model of the supercapacitor is proposed. Through the parameter identification and the experimental analysis, it is proved that the fractional equivalent circuit has better precision. Furthermore, the SOC estimation method of particle filter and Kalman filter is investigated, and a method of estimating supercapacitor SOC is proposed by combining the particle filter and the fractional Kalman filter. The validation results prove that the proposed method has better accuracy and real-time performance based on the fractional order.

We demonstrate a broadband and polarization selective terahertz modulator with ultrafast modulation speed, based on the Si grating metasurface on the Sapphire substrate. The Si layer is implanted with B+ and subsequently annealed, which significantly reduces the lifetime of carriers and increases the carrier density, and thus realizes ultrafast and efficient modulation. Our modulator achieves modulation depth up to 60% for the x-polarized THz wave and less than 1% for the y-polarized THz wave in the frequency ranging from 0.5 to 2.5 THz, with switch-on and switch-off time of modulation in 20 ps and around 300 ps, respectively. The carrier dynamics of the fabricated Si grating is also studied to illustrate the ultrafast modulated mechanism. Our results pave the way for ultrafast manipulation of terahertz spectrum and shows great potential in terahertz communication.