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Smart contracts play an essential role in the handling and management of digital assets, where vulnerabilities can lead to severe security issues and financial losses. Current detection techniques are largely limited to identifying single vulnerabilities and lack comprehensive identification capabilities for multiple vulnerabilities that may coexist in smart contracts. To address this challenge, we propose a novel multi-label vulnerability detection model that integrates extractive summarization methods with deep learning, referred to as Ext-ttg. The model begins by preprocessing the data using an extractive summarization approach, followed by the deployment of a custom-built deep learning model to detect vulnerabilities in smart contracts. Experimental results demonstrate that our method achieves commendable performance across various metrics, establishing the effectiveness of the proposed approach in the multi-vulnerability detection tasks within smart contracts.

The global demand for sustainable and nutritious food sources has catalyzed interest in legumes, known for their rich repertoire of health-promoting compounds. This review delves into the diverse array of bioactive peptides, protein subunits, isoflavones, antinutritional factors, and saponins found in the primary legume protein sources—soybeans, peas, chickpeas, and mung beans. The current state of research on these compounds is critically evaluated, with an emphasis on the potential health benefits, ranging from antioxidant and anticancer properties to the management of chronic diseases such as diabetes and hypertension. The extensively studied soybean is highlighted and the relatively unexplored potential of other legumes is also included, pointing to a significant, underutilized resource for developing health-enhancing foods. The review advocates for future interdisciplinary research to further unravel the mechanisms of action of these bioactive compounds and to explore their synergistic effects. The ultimate goal is to leverage the full spectrum of benefits offered by legumes, not only to advance human health but also to contribute to the sustainability of food systems. By providing a comprehensive overview of the nutraceutical potential of legumes, this manuscript sets a foundation for future investigations aimed at optimizing the use of legumes in the global pursuit of health and nutritional security.

Abstract Background The electrophoretic mobility shift assay (EMSA) is a common technology to detect DNA-protein interactions. However, in most cases, the protein used in EMSA is obtained via prokaryotic expression, and rarely from plants. At the same time, the proteins expressed from prokaryotic systems usually cannot fold naturally and have no post translationally modification, which may affect the binding of proteins to DNA. Results Here, we develop a technique to quickly isolate proteins of interest from host plants and then analyze them using fluorescent EMSA. This technology system is called: protein from plants fluorescent EMSA method (PPF-EMSA). In PPF-EMSA, a special transient transformation method is employed to transiently deliver genes into the plant, enabling efficient synthesis the encoded proteins. Then, the target protein is isolated using immunoprecipitation, and the DNA probes were labeled with cyanine 3 (Cy3). Both fluorescent EMSA and super-shift fluorescent EMSA can be performed using the proteins from plants. Three kinds of plants, Betula platyphylla, Populus. davidiana×P. bolleana and Arabidopsis thaliana, are used in this study. The proteins isolated from plants are in a natural state, can fold naturally and are posttranslationally modified, enabling true binding to their cognate DNAs. Conclusion As transient transformation can be performed quickly and not depended on whether stable transformation is available or not, we believe this method will have a wide application, enabling isolation of proteins from host plant conveniently.

Background: We created and validated a computed tomography (CT)-based radiomic model using both clinical factors and the radiomic signature for assessing the strangulation risk of acute intestinal obstruction. This would assist surgeons in accurately predicting intestinal ischemia and strangulation in patients with intestinal obstruction. Methods: We recruited 289 patients with acute intestinal obstruction admitted in the Affiliated Hospital of Qingdao University from January 2019 to February 2022. The patients were allocated to a training (n = 226) and validation cohort (n = 63). Radiomic features were collected from CT images, and the radiomic signature was extracted and used to calculate a radiomic score (Rad-score). A nomogram was constructed using the clinical features and the Rad-score, and the performance of the clinical, radiomics, and nomogram models was assessed in the two cohorts. Results: Six robust features were used to construct the radiomic signature. The nomogram incorporating hemoglobin levels, C-reactive protein levels, American Society of Anesthesiologists score, time of obstruction, CT image of mesenteric fluid (P < 0.05), and the signature demonstrated good predictive ability for intestinal ischemia in patients with acute intestinal obstruction, with areas under the curve of 0.892 (95% confidence interval, 0.837–0.947) and 0.781 (95% confidence interval, 0.619–0.944) for the training and validation sets, respectively. The decision curve analysis showed that this model outperformed the clinical and radiomic signature models. Conclusion: The radiomic nomogram may effectively predict intestinal ischemia in patients with acute intestinal disease and may assist clinical decision-making.

Abstract Background Solid nodules (SN) had more aggressive features and a poorer prognosis than part-solid nodules (PSN). This study aimed to evaluate the specific impacts of nodule radiological appearance (SN vs. PSN) on lymph node metastasis and prognosis based on solid size in cT1 non-small cell lung cancer (NSCLC). Methods Patients with cT1 NSCLC who underwent anatomical resection between 2010 and 2019 were retrospectively screened. Univariable and multivariable logistic regression analyses were adopted to evaluate the associations between nodule radiological appearance and lymph node metastasis. The log-rank test and Cox regression analyses were applied for prognostic evaluation. The cumulative recurrence risk was evaluated by the competing risk model. Results There were 958 and 665 NSCLC patients with PSN and SN. Compared to the PSN group, the SN arm had a higher overall lymph node metastasis rate (21.7% vs. 2.7%, P

In recent years, the application of deep learning to change detection (CD) has significantly progressed in remote sensing images. CD tasks have mostly used architectures, such as CNN and Transformer to locate image changes. However, these architectures have shortcomings in representing boundary details and are prone to false alarms and missed detections under complex lighting and weather conditions. For that, we propose a new network, Siamese meets diffusion network (SMDNet), a CD model that combines discriminative and generative architecture. By leveraging the power of the Siam-U2Net feature differential encoder (SU-FDE) and denoising diffusion implicit model (DDIM), it not only improves the accuracy of object edge detection but also enhances the data through iterative denoising and thinning reconstruction detail detection accuracy. Improves the model's robustness under environmental changes. First, we propose an SU-FDE module that uses shared weight features to capture differences between time series images, refine edge detection, and combine it with the attention mechanism to identify vital coarse features, thereby improving model sensitivity and accuracy. Finally, the progressive sampling of DDIM is used to integrate further these key features, and the adaptability of the model in different environments is enhanced with the help of the denoising ability of the diffusion model and the accurate capture of the probability distribution of image data. The performance evaluation of SMDNet on LEVIR-CD, DSIFN-CD, and CDD datasets yields validated F1 scores of 89.17%, 88.48%, and 88.23%, respectively. This substantiates the advanced capabilities of our model in accurately identifying variations and intricate details.

Traditional grass-roofed dwellings are important components of Chinese vernacular architecture. Building a comprehensive nationwide database of traditional grass-roofed dwellings is crucial for the inherence of this cultural heritage and its traditional ecological technologies. This study proposes classifying traditional Chinese grass-roofed dwellings into three types according to recognizable appearance features. Based on the YOLOv8 deep learning framework, a recognition model is constructed to recognize and spatially locate various grass-roofed dwellings from the image dataset on a county-level. Further, by conducting spatial overlap analysis with a variety of natural and socio-environmental factors on ArcGIS, their influences on the distribution pattern of traditional grass-roofed dwellings were examined. The study findings are as follows: (1) Traditional grass-roofed dwellings are concentrated on the southeast side of the Hu Line with different distribution patterns according to their types. (2) The natural environment influences the original construction and distribution of traditional grass-roofed dwellings in terms of the growth of grass resources and the ecological adaptability of grass material. (3) The development of economy, population, and urbanization pose challenges to the retention of grass-roofed dwellings. This research provides useful references for the precise preservation of various grass-roofed dwellings and introduced a novel approach for the classification of traditional buildings.

PurposeCamptodactyly, clasped thumbs, and windblown hands are distinctive features of distal arthrogryposis (DA). Current therapeutic interventions often yield suboptimal effects, predisposing patients to relapses and complications. This study explicates a corrective approach involving a progressive extension brace for the management of DA and evaluates its clinical outcomes.MethodsBetween 2015 and 2023, progressive extension braces were used in 32 DA patients, with an average follow-up of 4.8 years. Patients were stratified by age into four groups: 0–1, 1–3, 3–7, and above 7 years. The correction of camptodactyly was assessed based on the total active movement (TAM) of metacarpophalangeal joints (MPJ) and proximal interphalangeal joints (PIPJ), as well as the extensor lag of PIPJ. Clasped thumb correction was evaluated by measuring the thumb-to-index finger metacarpal angle (M1M2 angle) and the degree of deviation at the first MPJ (M1P1 angle). The quality of life for the children was measured using PedsQL 4.0, while parental satisfaction was gauged using the FACE questionnaire.ResultsEarlier intervention with a progressive extension brace yielded superior corrective results. Infants aged 0–1 year and toddlers aged 1–3 years achieved average TAM scores of 152° and 126° after correction; however, patients older than 3 years experienced a significant decrease in TAM with the same treatment. Infants and toddlers with DA showed improvement in the average extensor lag from 46° to 6°. The M1M2 angle increased from an average of 38° to 65°, with the M1P1 angle decreasing from an average of 43° to 5°. After the treatment, average PedsQL scores of 94.7 (parent-reported) and 89.3 (child-reported) were achieved. Among the 32 parents, 24 expressed high satisfaction, 5 expressed moderate satisfaction, and 3 expressed fair satisfaction.ConclusionThe early, progressive, and consistent use of an extension brace significantly improved joint mobility and corrected camptodactyly and clasped thumbs. It can be an effective approach to addressing hand deformities in patients with DA.

BackgroundPostoperative complications in adhesive small bowel obstruction (ASBO) significantly escalate healthcare costs and prolong hospital stays. This study endeavors to construct a nomogram that synergizes computed tomography (CT) body composition data with inflammatory-nutritional markers to forecast postoperative complications in ASBO.MethodsThe study’s internal cohort consisted of 190 ASBO patients recruited from October 2017 to November 2021, subsequently partitioned into training (n = 133) and internal validation (n = 57) groups at a 7:3 ratio. An additional external cohort comprised 52 patients. Body composition assessments were conducted at the third lumbar vertebral level utilizing CT images. Baseline characteristics alongside systemic inflammatory responses were meticulously documented. Through univariable and multivariable regression analyses, risk factors pertinent to postoperative complications were identified, culminating in the creation of a predictive nomogram. The nomogram’s precision was appraised using the concordance index (C-index) and the area under the receiver operating characteristic (ROC) curve.ResultsPostoperative complications were observed in 65 (48.87%), 26 (45.61%), and 22 (42.31%) patients across the three cohorts, respectively. Multivariate analysis revealed that nutrition risk score (NRS), intestinal strangulation, skeletal muscle index (SMI), subcutaneous fat index (SFI), neutrophil-lymphocyte ratio (NLR), and lymphocyte-monocyte ratio (LMR) were independently predictive of postoperative complications. These preoperative indicators were integral to the nomogram’s formulation. The model, amalgamating body composition and inflammatory-nutritional indices, demonstrated superior performance: the internal training set exhibited a 0.878 AUC (95% CI, 0.802–0.954), 0.755 accuracy, and 0.625 sensitivity; the internal validation set displayed a 0.831 AUC (95% CI, 0.675–0.986), 0.818 accuracy, and 0.812 sensitivity. In the external cohort, the model yielded an AUC of 0.886 (95% CI, 0.799–0.974), 0.808 accuracy, and 0.909 sensitivity. Calibration curves affirmed a strong concordance between predicted outcomes and actual events. Decision curve analysis substantiated that the model could confer benefits on patients with ASBO.ConclusionA rigorously developed and validated nomogram that incorporates body composition and inflammatory-nutritional indices proves to be a valuable tool for anticipating postoperative complications in ASBO patients, thus facilitating enhanced clinical decision-making.

Submersible underwater towed systems usually need to transition from steady-state motion to maneuvering motion during operation while dynamically adjusting the length of the towed cable. The lumped mass approach was employed to convert the dragged cable into a model with a concentrated mass. Analyzed utilizing a computational simulation tool, the motion response of the towed system was examined for both simple and composite maneuvering motions. By comparing the changes in tension at the end of the towed cable and the depth of the towed body motion under different motion states and cable retraction and deployment speeds, the motion response law of the system when the length of the towed cable changes during the submarine maneuver motion is obtained. The maximum tension value occurred at 1.0 m/s during the acceleration maneuver when the velocity change of the submersible ended at the same time as the cable length change. After the deployment maneuver in a circular rotating motion, the range of tension fluctuations decreased by 93%, greatly improving the stability of the towed system. An analysis was conducted to examine the impact of various motion and structural parameters on the motion response. The study revealed that the buoyancy-to-gravity ratio of the towed body, the acceleration time of the accelerated motion, and the rotational speed of the circular rotational motion had a notable influence on the outcomes. When the buoyancy-to-gravity ratio of the towed body is 1.0, the maximum tension value of the towed cable is minimized, and the depth change of the towed body is closer to 0 m.

The hydrodynamic wake generated by the underwater vehicle’s motion has a considerable impact on the movement of the towed system underwater. This paper utilizes the lumped mass method to model the towed cable in order to improve the accuracy of predicting its position and attitude in the wake, and to determine the suitable cable-towed position. Velocity is transferred from the flow field to the cable dynamic model in an innovative way to imitate the motion of the cable. Several iterations are conducted to enhance the dynamic reactivity of the cable system. Numerical simulations are used to model both the straight towed and turning movements. The numerical calculation provides the characteristics of vorticity in the flow field formed by the energy exchange between the vorticity and the cable, as well as the gradually dissipating vorticity and momentum exchange characteristics at the trailing edge of the enclosure. The results indicate that the best location for the cable towed is where its motion does not cause any adhesion. On the other hand, the disadvantageous scenario for cable-towed systems occurs when the cable’s movement causes substantial adhesion. This paper innovatively establishes a model of mechanical energy exchange, describes the characteristics of energy exchange between the cable and fluid dynamics, and divides the four regions of cable motion. In the manipulation state, the dynamic energy exchange between the cable and the wake results in the transient vibration response of the cable. The fluid structure coupling method can accurately determine the separation region of the towed point of the vehicle based on its compatibility (non-adhesive) and incompatibility (adhesive). The boundary of the region is defined to distinguish a free tow point from a wall-attached tow point. A transition zone has the possibility to change the pattern from a free tow to a wall-attached tow. The wake can be divided into free tow region, transition zone, and adjacent wall tow region by this fluid structure interaction assessment method.

PurposeTo establish a model combining radiomic and clinicopathological factors based on magnetic resonance imaging to predict pathological complete response (pCR) after neoadjuvant chemotherapy in breast cancer patients.MethodMRI images and clinicopathologic data of 329 eligible breast cancer patients from the Affiliated Hospital of Qingdao University from August 2018 to August 2022 were included in this study. All patients received neoadjuvant chemotherapy (NAC), and imaging examinations were performed before and after NAC. A total of 329 patients were randomly allocated to a training set and a test set at a ratio of 7:3. We mainly studied the following three types of prediction models: radiomic models, clinical models, and clinical-radiomic models. All models were evaluated using subject operating characteristic curve analysis and area under the curve (AUC), decision curve analysis (DCA) and calibration curves.ResultsThe AUCs of the clinical prediction model, independent imaging model and clinical combined imaging model in the training set were 0.864 0.968 and 0.984, and those in the test set were 0.724, 0.754 and 0.877, respectively. According to DCA and calibration curves, the clinical-radiomic model showed good predictive performance in both the training set and the test set, and we found that we had developed a more concise clinical-radiomic nomogram.ConclusionWe have developed a clinical-radiomic model by integrating radiomic features and clinical factors to predict pCR after NAC in breast cancer patients, thereby contributing to the personalized treatment of patients.

The space–air–ground integrated network can provide services to ground users in remote areas by utilizing high-altitude platform (HAP) drones to support stable user access and using low earth orbit (LEO) satellites to provide large-scale traffic backhaul. However, the rapid movement of LEO satellites requires dynamic maintenance of the matching relationship between LEO satellites and HAP drones. Additionally, different traffic types generated at HAP drones hold varying levels of values. Therefore, a tripartite matching problem among LEO satellites, HAP drones, and traffic types jointly considering multi-dimensional characteristics such as remaining visible time, channel condition, handover latency, and traffic storage capacity is formulated as mixed integer nonlinear programming to maximize the average transmitted traffic value. The traffic generation state for HAP drones is modeled as a mixture of stochasticity and determinism, which aligns with real-world scenarios, posing challenges for traditional optimization solvers. Thus, the original problem is decoupled into two independent sub-problems: traffic–drone matching and LEO–drone matching, which are addressed by mathematical simplification and multi-agent deep reinforcement learning with centralized training and decentralized execution, respectively. Simulation results verify the effectiveness and superiority of the proposed tripartite matching approach.

As links between genotype and phenotype, small-molecule metabolites are attractive biomarkers for disease diagnosis, prognosis, classification, drug screening and treatment, insight into understanding disease pathology and identifying potential targets. Metabolomics technology is crucial for discovering targets of small-molecule metabolites involved in disease phenotype. Mass spectrometry-based metabolomics has implemented in applications in various fields including target discovery, explanation of disease mechanisms and compound screening. It is used to analyze the physiological or pathological states of the organism by investigating the changes in endogenous small-molecule metabolites and associated metabolism from complex metabolic pathways in biological samples. The present review provides a critical update of high-throughput functional metabolomics techniques and diverse applications, and recommends the use of mass spectrometry-based metabolomics for discovering small-molecule metabolite signatures that provide valuable insights into metabolic targets. We also recommend using mass spectrometry-based metabolomics as a powerful tool for identifying and understanding metabolic patterns, metabolic targets and for efficacy evaluation of herbal medicine.

Trajectory prediction is a crucial step in the pipeline for autonomous driving because it not only improves the planning of future routes, but also ensures vehicle safety. On the basis of deep neural networks, numerous trajectory prediction models have been proposed and have already achieved high performance on public datasets due to the well-designed model structure and complex optimization procedure. However, the majority of these methods overlook the fact that vehicles’ limited computing resources can be utilized for online real-time inference. We proposed a Lane Transformer to achieve high accuracy and efficiency in trajectory prediction to tackle this problem. On the one hand, inspired by the well-known transformer, we use attention blocks to replace the commonly used Graph Convolution Network (GCN) in trajectory prediction models, thereby drastically reducing the time cost while maintaining the accuracy. In contrast, we construct our prediction model to be compatible with TensorRT, allowing it to be further optimized and easily transformed into a deployment-friendly form of TensorRT. Experiments demonstrate that our model outperforms the baseline LaneGCN model in quantitative prediction accuracy on the Argoverse dataset by a factor of $10\times $ to $25\times $ . Our $7ms$ inference time is the fastest among all open source methods currently available. Our code is publicly available at: https://github.com/mmdzb/Lane-Transformer.

[Introduction] With the establishment of China's "carbon peak and neutrality" goal and the urgent need to build a clean, low-carbon, safe, and efficient modern energy system, the proportion of renewable energy in the power grid will increase year by year. However, due to its randomness and intermittent characteristics, the contribution of renewable energy to power peak shaving is extremely limited. To improve its ability to absorb renewable energy, coal-fired power units with good peak-shaving potential are playing an important role in the basic regulation energy. Among them, the coal-fired boiler is the front-end core system of the coal-fired power unit, and its low (variable) load operation performance directly affects the overall peak-shaving capacity of the coal-fired power unit. [Method] Therefore, starting from the combustion stability, operating reliability, environmental protection, and economic requirements during low (variable) load operation of coal-fired boilers, this paper analyzed and summarized the current main research content and progress from four aspects, including combustion stability under low (variable) load operation, environmental protection and economy, adaptability and safety of main auxiliary equipment, and thermo-electrolytic coupling of the thermal power unit. [Result] On this basis, the research and development prospects of the technologies for deepening the peak shaving capacity of coal-fired boilers are put forward. [Conclusion] Through the analysis, it is concluded that to give full play to the first-mover advantage of coal-fired boilers in improving the power peak-shaving capacity, we should focus on the overall system of coal-fired boilers, comprehensively carry out monitoring and evaluation of the operation status of main auxiliary machinery, research on key influencing factors of combustion performance, conduct process intelligent optimization control and prediction. Thereby improving the overall performance of coal-fired boilers in low-load operation, rapid start-stop, and load-lifting.

Abstract Purpose Langaco Lake (LGL) is a soda lake located at an altitude of 4548 m in the Qinghai-Tibet Plateau in China. LGL exhibits unique hydrochemical characteristics among soda lakes, but little is known about the microbial diversity of LGL and the microbial interactions with environmental factors. Methods The water samples were filtered using chemical-grade cellulose acetate membrane (pore size of 0.45 μm), and the hydrochemical characteristics were analyzed. Community DNA was extracted, and then high-throughput sequencing of 16S rRNA genes was conducted to evaluate the composition of the microbial community. Results The high-throughput sequencing of 16S rRNA genes revealed that the bacterial diversity in LGL consisted of 327 genera in 24 phyla (4871 operational taxonomic units (OTUs); Shannon index values of 5.20–6.07), with a significantly higher diversity than that of the Archaea (eight phyla and 29 genera comprising 1008 OTUs; Shannon index values of 2.98–3.30). The bacterial communities were dominated by Proteobacteria (relative abundances of 42.79–53.70%), followed by Bacteroidetes (11.13–15.18%), Planctomycetes (4.20–12.82%), Acidobacteria (5.91–9.50%), Actinobacteria (2.60–5.80%), and Verrucomicrobia (2.11–4.08%). Furthermore, the archaeal communities were dominated by Crenarchaeota (35.97–58.29%), Euryarchaeota (33.02–39.89%), and Woesearchaeota (6.50–21.57%). The dominant bacterial genus was Thiobacillus (8.92–16.78%), and its abundances were most strongly correlated with the total phosphorus (TP) content, pH value, CO3 2− concentration, and temperature. The most abundant archaeal genus was Methanoregula (21.40–28.29%), and its abundances were the most highly correlated with the total organic carbon (TOC) content, total salinity (TS), and K+ and Na+ concentrations. Conclusions The results of this study provide valuable insights for developing a more comprehensive understanding of microbial diversity in these unique carbonate alkaline environments, as well as a better understanding of the microbial resources on the Qinghai-Tibet Plateau.

As one of the most prominent gene families, R2R3-MYB transcription factors significantly regulate biochemical and physiological processes under salt stress. However, in Sophora alopecuroides, a perennial herb known for its exceptional saline alkali resistance, the comprehensive identification and characterization of SaR2R3-MYB genes and their potential functions in response to salt stress have yet to be determined. We investigated the expression profiles and biological functions of SaR2R3-MYB transcription factors in response to salt stress, utilizing a transcriptome-wide mining method. Our analysis identified 28 SaR2R3-MYB transcription factors, all sharing a highly conserved R2R3 domain, which were further divided into 28 subgroups through phylogenetic analysis. Some SaR2R3-MYB transcription factors showed induction under salt stress, with SaR2R3-MYB15 emerging as a potential regulator based on analysis of the protein–protein interaction network. Validation revealed the transcriptional activity and nuclear localization of SaR2R3-MYB15. Remarkably, overexpression of SaR2R3-MYB15 in transgenic plants could increase the activity of antioxidant enzymes and the accumulation of proline but decrease the content of malondialdehyde (MDA), compared with wild-type plants. Moreover, several salt stress-related genes showed higher expression levels in transgenic plants, implying their potential to enhance salt tolerance. Our findings shed light on the role of SaR2R3-MYB genes in salt tolerance in S. alopecuroides.

The digestibility of soybean meal can be severely impacted by trypsin inhibitor (TI), one of the most abundant anti-nutritional factors present in soybean seeds. TI can restrain the function of trypsin, a critical enzyme that breaks down proteins in the digestive tract. Soybean accessions with low TI content have been identified. However, it is challenging to breed the low TI trait into elite cultivars due to a lack of molecular markers associated with low TI traits. We identified Kunitz trypsin inhibitor 1 (KTI1, Gm01g095000) and KTI3 (Gm08g341500) as two seed-specific TI genes. Mutant kti1 and kti3 alleles carrying small deletions or insertions within the gene open reading frames were created in the soybean cultivar Glycine max cv. Williams 82 (WM82) using the CRISPR/Cas9-mediated genome editing approach. The KTI content and TI activity both remarkably reduced in kti1/3 mutants compared to the WM82 seeds. There was no significant difference in terms of plant growth or maturity days of kti1/3 transgenic and WM82 plants in greenhouse condition. We further identified a T1 line, #5-26, that carried double homozygous kti1/3 mutant alleles, but not the Cas9 transgene. Based on the sequences of kti1/3 mutant alleles in #5-26, we developed markers to co-select for these mutant alleles by using a gel-electrophoresis-free method. The kti1/3 mutant soybean line and associated selection markers will assist in accelerating the introduction of low TI trait into elite soybean cultivars in the future.

Natural products derived from herbal medicine are a fruitful source of lead compounds because of their structural diversity and potent bioactivities. However, despite the success of active compounds derived from herbal medicine in drug discovery, some approaches cannot effectively elucidate the overall effect and action mechanism due to their multi-component complexity. Fortunately, mass spectrometry-based metabolomics has been recognized as an effective strategy for revealing the effect and discovering active components, detailed molecular mechanisms, and multiple targets of natural products. Rapid identification of lead compounds and isolation of active components from natural products would facilitate new drug development. In this context, mass spectrometry-based metabolomics has established an integrated pharmacology framework for the discovery of bioactivity-correlated constituents, target identification, and the action mechanism of herbal medicine and natural products. High-throughput functional metabolomics techniques could be used to identify natural product structure, biological activity, efficacy mechanisms, and their mode of action on biological processes, assisting bioactive lead discovery, quality control, and accelerating discovery of novel drugs. These techniques are increasingly being developed in the era of big data and use scientific language to clarify the detailed action mechanism of herbal medicine. In this paper, the analytical characteristics and application fields of several commonly used mass spectrometers are introduced, and the application of mass spectrometry in the metabolomics of traditional Chinese medicines in recent years and its active components as well as mechanism of action are also discussed.

Abstract The cingulate cortex is part of the conserved limbic system, which is considered as a hub of emotional and cognitive control. Accumulating evidence suggested that involvement of the cingulate cortex is significant for cognitive impairment of Parkinson's disease (PD). However, mechanistic studies of the cingulate cortex in PD pathogenesis are limited. Here, transcriptomic and regulatory network analyses were conducted for the cingulate cortex in PD. Enrichment and clustering analyses showed that genes involved in regulation of membrane potential and glutamate receptor signalling pathway were upregulated. Importantly, myelin genes and the oligodendrocyte development pathways were markedly downregulated, indicating disrupted myelination in PD cingulate cortex. Cell‐type‐specific signatures revealed that myelinating oligodendrocytes were the major cell type damaged in the PD cingulate cortex. Furthermore, downregulation of myelination pathways in the cingulate cortex were shared and validated in another independent RNAseq cohort of dementia with Lewy bodies (DLB). In combination with ATACseq data, gene regulatory networks (GRNs) were further constructed for 32 transcription factors (TFs) and 466 target genes among differentially expressed genes (DEGs) using a tree‐based machine learning algorithm. Several transcription factors, including Olig2, Sox8, Sox10, E2F1, and NKX6‐2, were highlighted as key nodes in a sub‐network, which control many overlapping downstream targets associated with myelin formation and gliogenesis. In addition, the authors have validated a subset of DEGs by qPCRs in two PD mouse models. Notably, seven of these genes,TOX3, NECAB2 NOS1, CAPN3, NR4A2, E2F1 and FOXP2, have been implicated previously in PD or neurodegeneration and are worthy of further studies as novel candidate genes. Together, our findings provide new insights into the role of remyelination as a promising new approach to treat PD after demyelination.

Image dehazing, a fundamental problem in computer vision, involves the recovery of clear visual cues from images marred by haze. Over recent years, deploying deep learning paradigms has spurred significant strides in image dehazing tasks. However, many dehazing networks aim to enhance performance by adopting intricate network architectures, complicating training, inference, and deployment procedures. This study proposes an end-to-end U-Net dehazing network model with recursive gated convolution and attention mechanisms to improve performance while maintaining a lean network structure. In our approach, we leverage an improved recursive gated convolution mechanism to substitute the original U-Net’s convolution blocks with residual blocks and apply the SK fusion module to revamp the skip connection method. We designate this novel U-Net variant as the Dehaze Recursive Gated U-Net (DRGNet). Comprehensive testing across public datasets demonstrates the DRGNet’s superior performance in dehazing quality, detail retrieval, and objective evaluation metrics. Ablation studies further confirm the effectiveness of the key design elements.

CRISPR/Cas9-based genome editing system is a powerful tool for plant genetic improvement. However, the variable efficiency of guide RNA(s) (gRNA) represents a key limiting factor that hampers the broad application of the CRISPR/Cas9 system in crop improvement. Here, we employed the Agrobacterium-mediated transient assays to evaluate the effectiveness of gRNAs for editing genes in Nicotiana benthamiana and soybean. We designed a facile screening system based on indels that can be introduced by CRISPR/Cas9-mediated gene editing. A gRNA binding sequence (23 nucleotides) was inserted into the open reading frame of yellow fluorescent protein (YFP) gene (gRNA-YFP), which disrupted the YFP reading frame and results in no fluorescent signal when it was expressed in plant cells. Transiently co-expression of Cas9 and a gRNA targeting the gRNA-YFP gene in plant cells could restore the YFP reading frame and recover the YFP signals. We evaluated five gRNAs targeting Nicotiana benthamiana and soybean genes and confirmed the reliability of the gRNA screening system. The effective gRNAs targeting NbEDS1, NbWRKY70, GmKTI1, and GmKTI3 had been used to generate transgenic plants and resulted in expected mutations on each gene. While a gRNA targeting NbNDR1 was confirmed to be ineffective in transient assays. This gRNA indeed failed to trigger target gene mutations in stable transgenic plants. Thus, this new transient assay system can be used to validate the effectiveness of gRNAs before generating stable transgenic plants.

The sucrose and Alanine (Ala) content in edamame beans significantly impacts the sweetness flavor of edamame-derived products as an important attribute to consumers’ acceptance. Unlike grain-type soybeans, edamame beans are harvested as fresh beans at the R6 to R7 growth stages when beans are filled 80-90% of the pod capacity. The genetic basis of sucrose and Ala contents in fresh edamame beans may differ from those in dry seeds. To date, there is no report on the genetic basis of sucrose and Ala contents in the edamame beans. In this study, a genome-wide association study was conducted to identify single nucleotide polymorphisms (SNPs) related to sucrose and Ala levels in edamame beans using an association mapping panel of 189 edamame accessions genotyped with a SoySNP50K BeadChip. A total of 43 and 25 SNPs was associated with sucrose content and Ala content in the edamame beans, respectively. Four genes (Glyma.10g270800, Glyma.08g137500, Glyma.10g268500, and Glyma.18g193600) with known effects on the process of sucrose biosynthesis and 37 novel sucrose-related genes were characterized. Three genes (Gm17g070500, Glyma.14g201100 and Glyma.18g269600) with likely relevant effects in regulating Ala content and 22 novel Ala-related genes were identified. In addition, by summarizing the phenotypic data of edamame beans from three locations in two years, three PI accessions (PI 532469, PI 243551, and PI 407748) were selected as the high sucrose and high Ala parental lines for the perspective breeding of sweet edamame varieties. Thus, the beneficial alleles, candidate genes, and selected PI accessions identified in this study will be fundamental to develop edamame varieties with improved consumers’ acceptance, and eventually promote edamame production as a specialty crop in the United States.

IntroductionThis study aims to investigate the long-term effects of spirulina supplementation in a high-fat diet (HFD) on rumen morphology, rumen fermentation, and the composition of rumen microbiota in lambs. Spirulina is a blue-green microalgae that has been shown to have high nutritional value for livestock.MethodsFifty-four lambs were randomly divided into three groups: a normal chow diet (NCD) group, a high-fat diet (HFD) group, and a high-fat diet supplemented with 3% spirulina (HFD+S) group. Rumen morphology, rumen fermentation, and rumen microbiota were analyzed at the end of the study.ResultsSpirulina supplementation improved the concentration of volatile fatty acids and rumen papilla length. Additionally, there was a tendency for an increase in rumen weight and an upregulation of the genes Claudin-1, Claudin-4, and Occludin in the HFD+S group. Pyrosequencing of the 16S ribosomal RNA gene also showed that spirulina supplementation significantly changed the rumen microbiota composition in the HFD group, with a decrease in richness and diversity. Specifically, the relative abundance of Prevotella 9 and Megasphaera was significantly increased in the HFD group compared to the NCD group, while spirulina supplementation reversed these changes.DiscussionThis study suggests that 3% spirulina supplementation can improve rumen development and fermentation, and effectively relieve rumen microbe disorders in lambs caused by a high-fat diet. However, further research is needed to confirm the findings and to examine the long-term effects of spirulina supplementation in different types of livestock and under different dietary conditions.

N6-methyladenosine (m6A) modification plays a critical role in mammalian development. However, the role of m6A in the skeletal muscle development remains largely unknown. Here, we report a global m6A modification pattern of goat skeletal muscle at two key development stages and identified that the m6A modification regulated the expression of the growth arrest and DNA damage-inducible 45B (GADD45B) gene, which is involved in myogenic differentiation. We showed that GADD45B expression increased during myoblast differentiation, whereas the downregulation of GADD45B inhibits myogenic differentiation and mitochondrial biogenesis. Moreover, the expression of GADD45B regulates the expression of myogenic regulatory factors and peroxisome proliferator-activated receptor gamma coactivator 1 alpha by activating the p38 mitogen-activated protein kinase (MAPK) pathway. Conversely, the inactivation of p38 MAPK abolished the GADD45B-mediated myogenic differentiation. Furthermore, we found that the knockdown of fat mass and obesity-associated protein (FTO) increases GADD45B m6A modification and decreases the stability of GADD45B mRNA, which impairs myogenic differentiation. Our results indicate that the FTO-mediated m6A modification in GADD45B mRNA drives skeletal muscle differentiation by activating the p38 MAPK pathway, which provides a molecular mechanism for the regulation of myogenesis via RNA methylation.

Water supply and demand in leaves are primarily determined by stomatal density (SD, water demand) and minor leaf vein density (VLA, water supply). Thus, covariation between them is essential for maintaining water balance. However, there is debate over whether these two traits vary in a coordinated way. Here, we gathered SD and VLA data from 194 species over four altitudinal gradients, and investigated their relationships across all species, growth forms, and different altitudes. Our findings demonstrated that SD and VLA were positively associated across all species, independent on plant phylogeny. Moreover, the reliability of this SD-VLA relationship increased with altitudes. Although the stomatal number per minor vein length (SV) remained stable across different altitudes and growth forms, the positive SD-VLA relationship was found only in shrubs and herbs, but not in trees. Differently, a strong coordination between total stomatal number and total leaf vein length was observed across all species, trees, shrubs and herbs. These findings suggested that coordinating stomatal number and minor vein length within one leaf, rather than stomatal and vein density, may be a common choice of plants in the fluctuating environment. Therefore, to explore the relationship between total number of stomata and total length of leaf veins seems to better reflect the linkage between stomata and leaf veins, especially when covering different growth forms.

The GRAS gene family is a plant-specific family of transcription factors and play a vital role in many plant growth processes and abiotic stress responses. Nevertheless, the functions of the GRAS gene family in woody plants, especially in Betula platyphylla (birch), are hardly known. In this study, we performed a genome-wide analysis of 40 BpGRAS genes (BpGRASs) and identified typical GRAS domains of most BpGRASs. The BpGRASs were unevenly distributed on 14 chromosomes of birch and the phylogenetic analysis of six species facilitated the clustering of 265 GRAS proteins into 17 subfamilies. We observed that closely related GRAS homologs had similar conserved motifs according to motif analysis. Besides, an analysis of the expression patterns of 26 BpGRASs showed that most BpGRASs were highly expressed in the leaves and responded to salt stress. Six BpGRASs were selected for cis-acting element analysis because of their significant upregulation under salt treatment, indicating that many elements were involved in the response to abiotic stress. This result further confirmed that these BpGRASs might participate in response to abiotic stress. Transiently transfected birch plants with transiently overexpressed 6 BpGRASs and RNAi-silenced 6 BpGRASs were generated for gain- and loss-of-function analysis, respectively. In addition, overexpression of BpGRAS34 showed phenotype resistant to salt stress, decreased the cell death and enhanced the reactive oxygen species (ROS) scavenging capabilities and proline content under salt treatment, consistent with the results in transiently transformed birch plants. This study is a systematic analysis of the GRAS gene family in birch plants, and the results provide insight into the molecular mechanism of the GRAS gene family responding to abiotic stress in birch plants.

Microplastics (MPs) have been a significant worldwide issue, becoming persistent and emerging contaminants in the environment. While MPs have been the subject of extensive scrutiny in the aquatic environment, their presence, mobility, and potential for pollution control, notably in African, Latin American, Oceanian, and some Asian agro-ecosystems, remain unknown. Most MPs in croplands come from composite pesticides and fertilizers, organic waste and compost, mulch films, wastewater irrigation, and atmospheric deposition. MPs' ultimate mobility and fate in the soil ecosystem are driven mostly by MPs' physicochemical properties, soil properties, farming techniques, and soil biota heterogeneity. In different global agro-ecosystems, this review explores the MPs' origins, mobility, fate, and pollution control in the soil agro-ecosystems and explores MPs' contaminant-transport capacity and toxicity impact on soil organisms. Future studies should focus on deleterious implications on animals and humans, irregular mobility and behavior in the soil agro-ecosystems, optimum management techniques, and worldwide agricultural policies to promote sustainable development.

Long non-coding RNAs (lncRNAs) regulate the development of follicles and reproductive diseases, but the mechanisms by which lncRNAs regulate ovarian functions and fertility remain elusive. We profiled the expression of lncRNAs in ovarian tissues of Hu sheep with different prolificacy and identified 21,327 lncRNAs. Many of the lncRNAs were differentially expressed in different groups. We further characterized an lncRNA that was predominantly expressed in the ovaries of the low prolificacy FecB+ (LPB+) group and mainly present in granulosa cells (GCs), and the expression of this lncRNA decreased during follicular development, which we named follicular development-associated lncRNA (FDNCR). Next, we found that FDNCR directly binds miR-543-3p, and decorin (DCN) was identified as a target of miR-543-3p. FDNCR overexpression promoted GC apoptosis through increased expression of DCN, which could be attenuated by miR-543-3p. Furthermore, miR-543-3p increased and FDNCR reduced the expression of transforming growth factor-β (TGF-β) pathway-related genes, including TGF-β1 and inhibin beta A (INHBA), which were upregulated upon DCN silencing. Our results demonstrated that FDNCR sponges miR-543-3p in GCs and prevents miR-543-3p from binding to the DCN 3′ UTR, resulting in DCN transactivation and TGF-β pathway inhibition and promotion of GC apoptosis in Hu sheep. These findings provide insights into the mechanisms underlying prolificacy in sheep.

Calibration of sensors is critical for the precise functioning of lidar–IMU systems. However, the accuracy of the system can be compromised if motion distortion is not considered. This study proposes a novel uncontrolled two-step iterative calibration algorithm that eliminates motion distortion and improves the accuracy of lidar–IMU systems. Initially, the algorithm corrects the distortion of rotational motion by matching the original inter-frame point cloud. Then, the point cloud is further matched with IMU after the prediction of attitude. The algorithm performs iterative motion distortion correction and rotation matrix calculation to obtain high-precision calibration results. In comparison with existing algorithms, the proposed algorithm boasts high accuracy, robustness, and efficiency. This high-precision calibration result can benefit a wide range of acquisition platforms, including handheld, unmanned ground vehicle (UGV), and backpack lidar–IMU systems.

Determination of Cas9 cutting efficiency to the target sites is important for genome editing. However, this determination can only be made via an in vitro method, as the purification of Cas protein and synthesis of gRNA are necessary. Here, we developed an in vivo method, called transient CRISPR/Cas editing in plants (TCEP) to determine Cas9 cutting efficiency. The CRISPR/Cas vector for plant transformation mediated by Agrobacterium tumefaciens was constructed as normal. Using the transient transformation method we built, the Cas9 protein and gRNA were transiently expressed and formed a complex to cut its target sites, resulting in dynamic DNA breakage. The broken DNA was quantified using qPCR to measure the efficiencies of Cas9 cutting. We studied the Cas9 cutting efficiencies to different target sites in Betula platyphylla and Populus davidiana×P. bolleana plants using TCEP and an in vitro method. The results of TCEP were consistent with those of the in vitro method, suggesting that the TCEP method is reliable in determining cutting efficiency. Additionally, using the TCEP method, we showed that both heat and sonication treatment significantly improved CRISPR/Cas efficiency. Therefore, the TCEP method has broad application value and can not only be used to analyze the CRISPR/Cas efficiency but also to determine the factors involved in Cas9 cutting.

Abstract Background Heterosis refers to superior traits exhibiting in a hybrid when compared with both parents. Generally, the hybridization between parents can change the expression pattern of some proteins such as non-additive proteins (NAPs) which might lead to heterosis. ‘Zhongdan808’ (ZD808) and ‘Zhongdan909’ (ZD909) are excellent maize hybrids in China, however, the heterosis mechanism of them are not clear. Proteomics has been wildly used in many filed, and comparative proteomic analysis of hybrid and its parents is helpful for understanding the mechanism of heterosis in the two maize hybrids. Results Over 2000 protein groups were quantitatively identified from second seedling leaves of two hybrids and their parents by label-free quantification. Statistical analysis of total identified proteins, differentially accumulated proteins (DAPs) and NAPs of the two hybrids revealed that both of them were more similar to their female parents. In addition, most of DAPs were up-regulated and most of NAPs were high parent abundance or above-high parent abundance in ZD808, while in ZD909, most of DAPs were down-regulated and most of NAPs were low parent abundance or below-low parent abundance. Pathway enrichment analysis showed that more of stress response-related NAPs in ZD808 were high parent abundance or above-high parent abundance, and most of PS related NAPs in ZD909 were high parent abundance or above-high parent abundance. Finally, four stress response-related proteins and eight proteins related to PS were verified by PRM, ten of them had significant differences between hybrid and midparent value. Conclusions Even though every one of the two hybrids were more similar to its female parent at proteome level, the biological basis of heterosis is different in the two maize hybrids. In comparison with their parents, the excellent agronomic traits of hybrid ZD808 is mainly correlated with the high expression levels of some proteins related to stress responses and metabolic functions, while traits of ZD909 is mainly correlated with high expressed proteins related to photosynthesis. Our proteomics results support previous physiological and morphological research and have provided useful information in understanding the reason of valuable agronomic traits.

The proliferation of the Internet of Things (IoT) has paved the way for the future of smart cities. The large volume of data over the IoT can enable decision-making for various applications such as smart transportation, smart parking, and smart lighting. The key to the success of smart cities is data collection and aggregation over the IoT. Recently, crowdsensing has become a new data collection paradigm over the IoT, which can realize large-scale and fine-grained data collection with low cost for various applications. For example, we can leverage the power of the crowd to build a real-time noise map with microphones on smartphones. Despite the advantages of crowdsensing and the IoT, there are many challenges to utilize crowdsensing over the IoT for smart cities, such as how to allocate tasks to appropriate users to provide high-quality sensing data, how to incentivize users to participate in crowdsourcing, how to detect the reliability of the crowdsourced data, and how to protect the privacy of users.

The discovery of ferroelectricity in hafnium zirconium oxide (HZO) thin films has attracted wide attention from academia to industry due to the application in ferroelectric non-volatile random access memories (FeRAM) with prominent performance in scalability and CMOS process compatibility. Dielectric behavior of ferroelectric HZO thin films is a key factor affecting the dynamic effect, piezoelectric and electrostrictive effect. Interface between HZO and capping electrodes plays an important role in regulating the dielectric properties. In this paper, the impedance frequency response and dielectric spectrum of ferroelectric HZO thin films were analyzed. Parameters of the interface were extracted to analyze the regulating effect on the dielectric properties based on an impedance model with constant phase element (CPE). Besides, dielectric spectrums at elevated temperatures were identified to verify this analysis.

There are several challenges in diabetes care management including optimizing the currently used therapies, educating patients on selfmanagement, and improving patient lifestyle and systematic healthcare barriers. The purpose of performing a systems approach to implementation science aided by artificial intelligence techniques in diabetes care is two-fold: 1) to explicate the systems approach to formulate predictive analytics that will simultaneously consider multiple input and output variables to generate an ideal decision-making solution for an optimal outcome; and 2) to incorporate contextual and ecological variations in practicing diabetes care coupled with specific health educational interventions as exogenous variables in prediction. A similar taxonomy of modeling approaches proposed by Brennon et al (2006) is formulated to examining the determinants of diabetes care outcomes in program evaluation. The discipline-free methods used in implementation science research, applied to efficiency and quality-of-care analysis are presented. Finally, we illustrate a logically formulated predictive analytics with efficiency and quality criteria included for evaluation of behavioralchange intervention programs, with the time effect included, in diabetes care and research.

Objective The Yip1 domain family (YIPF) proteins were proposed to function in endoplasmic reticulum (ER) to Golgi transport and maintenance of the morphology of the Golgi, which were homologues of yeast Yip1p and Yif1p. YIPF3, the member 3 of YIPF family was a homolog of Yif1p. The aim of present study was to investigate the expression and regulation mechanism of porcine YIPF3. Methods Quantitative realtime polymerase chain reaction (qPCR) was used to analyze porcine YIPF3 mRNA expression pattern in different tissues and pig kidney epithelial (PK15) cells stimulated by polyinosine-polycytidylic acid (poly [I:C]). Site-directed mutations combined with dual luciferase reporter assays and electrophoretic mobility shift assay (EMSA) were employed to reveal transcription regulation mechanism of porcine YIPF3. Results Results showed that the mRNA of porcine YIPF3 (pYIPF3) was widely expressed with the highest levels in lymph and lung followed by spleen and liver, while weak in heart and skeletal muscle. Subcellular localization results indicated that it expressed in Golgi apparatus and plasma membranes. Upon stimulation with poly (I:C), the level of this gene was dramatically up-regulated in a time- and concentration-dependent manner. pYIPF3 core promoter region harbored three cis-acting elements which were bound by ETS proto-oncogene 2 (ETS2), zinc finger and BTB domain containing 4 (ZBTB4), and zinc finger and BTB domain containing 14 (ZBTB14), respectively. In which, ETS2 and ZBTB4 both promoted pYIPF3 transcription activity while ZBTB14 inhibited it, and these three transcription factors all played important regulation roles in tumorigenesis and apoptosis. Conclusion The pYIPF3 mRNA expression was regulated by ETS2, ZBTB4, and ZBTB14, and its higher expression in immune organs might contribute to enhancing ER to Golgi transport of proteins, thus adapting to the immune response.

Although gait recognition has been greatly improved by efforts from many researchers in recent years, its performance is still unsatisfactory due to the lack of gait information under the real scenariowhere only one or two images may be used for recognition. In this paper, a new gait recognition framework is brought about which can combine the long-short-term attention modules on silhouette images over the whole sequence and the real human physiological information calculated by a monocular image. The contributions of this work include the following: (1) Fusing the global long-term attention (GLTA) and local short-term attention (LSTA) over the whole query sequence to improve the gait recognition accuracy, where both the short-term gait feature (from two or three frames) and long-term feature (from the whole sequence) are extracted; (2) presenting a method to calculate the real personal static and dynamic physiological features through a single monocular image; (3) by efficiently applying the human physiological information, a new physiological feature extraction (PFE) network is proposed to concatenate the physiological information with silhouette for gait recognition. Through the experiments between the CASIA-B and Multi-state Gait datasets, the effectiveness and efficiency of the proposed method are proven. Under three different walking conditions of the CASIA-B dataset, the mean accuracy of rank-1 in our method is up to 89.6%, and in the Multi-state Gait dataset, wearing different clothes, the mean accuracy of rank-1 in our method is 2.4% higher than the other works.

**Background:** Deep Learning (DL) has not been well-established as a method to identify high-risk patients among patients with heart failure (HF). **Objectives:** This study aimed to use DL models to predict hospitalizations, worsening HF events, and 30-day and 90-day readmissions in patients with heart failure with reduced ejection fraction (HFrEF). **Methods:** We analyzed the data of adult HFrEF patients from the IBM® MarketScan® Commercial and Medicare Supplement databases between January 1, 2015 and December 31, 2017. A sequential model architecture based on bi-directional long short-term memory (Bi-LSTM) layers was utilized. For DL models to predict HF hospitalizations and worsening HF events, we utilized two study designs: with and without a buffer window. For comparison, we also tested multiple traditional machine learning models including logistic regression, random forest, and eXtreme Gradient Boosting (XGBoost). Model performance was assessed by area under the curve (AUC) values, precision, and recall on an independent testing dataset. **Results:** A total of 47 498 HFrEF patients were included; 9427 with at least one HF hospitalization. The best AUCs of DL models without a buffer window in predicting HF hospitalizations and worsening HF events in the total patient cohort were 0.977 and 0.972; with a 7-day buffer window the best AUCs were 0.573 and 0.608, respectively. The best AUCs in predicting 30- and 90-day readmissions in all adult patients were 0.597 and 0.614, respectively. An AUC of 0.861 was attained for prediction of 90-day readmission in patients aged 18-64. For all outcomes assessed, the DL approach outperformed traditional machine learning models. **Discussion:** The DL approach can automate feature engineering during the model learning, which can increase the clinical applicability and lead to comparable or better model performance. However, the lack of granular clinical data, and sample size and imbalance issues may have limited the model’s performance. **Conclusions:** A DL approach using Bi-LSTM was shown to be a feasible and useful tool to predict HF-related outcomes. This study can help inform the future development and deployment of predictive tools to identify high-risk HFrEF patients and ultimately facilitate targeted interventions in clinical practice.

Among various Phase I clinical trial designs, rule-based standard 3 + 3 designs are the most widely utilized for their simplicity and robustness. It is necessary to define crucial operating characteristics of a Phase I clinical trial before it starts. Based on the assumed probability of dose limiting toxicity (DLT) at each tested dose level, Lin and Shih elaborated formulas to calculate the five key operating characteristics of Phase I clinical trials using the two subtypes of standard 3 + 3 designs (with vs without dose de-escalation): probability of each dose level being chosen as the maximum tolerated dose (MTD); expected number of patients treated at each dose level; expected number of patients experiencing DLT at each dose level; target toxicity level (TTL) (expected probability of DLT at MTD); expected total number of patients experiencing DLT. Understanding these formulas requires advanced statistical knowledge and the formulas are too complicated to be used directly. To facilitate their application, we have developed stand-alone interactive software for convenient calculation of these key operating characteristics. The calculated results are presented in tables and plots that can be saved and easily edited for further use. Some examples of calculation using the software are presented and discussed. Keywords: Standard 3+3 design, Statistical software, Operating characteristics, Cancer phase I clinical trials, Maximum tolerated dose, Dose limiting toxicity

Pituitary gonadotropins play a pivotal role in reproduction. Long noncoding RNAs (lncRNAs) have been identified as important regulators in the hypothalamic–pituitary–ovarian (HPO) axis associated with reproduction. However, the contributions of lncRNAs to pituitary gonadotropin secretion remain largely unknown. Therefore, this work was performed to uncover the functional mechanisms of the novel lncRNA TCONS_00083279 (lncRNA SM2) and its potential targeting pathway oar-miR-16b/TGF-beta/SMAD2, which is associated with gonadotropin secretion in sheep pituitary cells. In the present study, the lncRNA SM2 showed high expression levels in the sheep pituitary gland, and it was located in both the nucleus and the cytoplasm of pituitary cells. lncRNA SM2 knockdown inhibited pituitary cell proliferation and FSH and LH secretion. The function of the lncRNA SM2 was sponged by oar-miR-16b, and this regulated the growth and gonadotropin secretion of pituitary cells by modulating SMAD2, as shown by the dual-luciferase reporter assay. FSH and LH levels were both upregulated by SMAD2 overexpression. Moreover, the levels of the lncRNA SM2, SMAD2 and TGFR1, as well as FSH and LH, in sheep pituitary cells increased significantly under gonadotropin-releasing hormone (GnRH) stimulation (p < 0.05). This work illustrates that the lncRNA SM2 regulates gonadotropin secretion in the Hu sheep anterior pituitary by targeting the oar-miR-16b/TGF-β/SMAD2 signaling pathway, providing a valuable resource for understanding the molecular mechanisms underlying sheep reproduction.

A method of calculating the outline of metallographic diagram and statistical outline shape features such as area and slenderness ratio is introduced to reflect the influence of different process parameters on the microstructure of metals. After binarizing the image, the model of similarity matrix is established to describe the overall similarity of the lattice growth and detect possible anomalies for the microstructures with uniform lattice growth and good connectivity. By comparing the statistical values of the outline characteristics of the crystal structure and the characteristic area of the surrounding structure, these techniques can quantitatively analyze the effect of process parameters on the crystal structure.

Variation in leaf veins along environmental gradients reflects an important adaptive strategy of plants to the external habitats, because of their crucial roles in maintaining leaf water status and photosynthetic capacity. However, most studies concentrate on a few species and their vein variation across horizontal spatial scale, we know little about how vein traits shift along the vertical scale, e.g., elevational gradient along a mountain, and how such patterns are shaped by plant types and environmental factors. Here, we aimed to investigate the variation in leaf vein traits (i.e., vein density, VD; vein thickness, VT; and vein volume per unit leaf area, VV) of 93 woody species distributed along an elevational gradient (1,374–3,375 m) in a temperate mountain in China. Our results showed that altitude-related trends differed between growth forms. Tree plants from higher altitudes had lower VD but higher VT and VV than those from lower altitude; however, the opposite tend was observed in VD of shrubs, and no significant altitudinal changes in their VT or VV. Plant phylogenetic information at the clade level rather than climate explained most of variation in three leaf vein traits (17.1–86.6% vs.