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Objective To establish the indicator and fuzzy evaluation model for the procurement demonstration of key instruments and equipment, in order to provide a scientific and reliable method for the procurement demonstration of equipment in blood centers. Methods Through literature review and Delphi method, the procurement evaluation indexes of key instruments and equipment in blood centers were established. The weights of each index were calculated using analytic hierarchy process (AHP) by sending questionnaires to experts. According to the established indicator and fuzzy comprehensive evaluation method, a scoring model of procurement demonstration of key instruments and equipment was constructed. The blood screening total laboratory automation was analyzed and verified using the established demonstration model. Results A total of 4 first-level evaluation indicators and 16 second-level evaluation indicators were established. The weights of urgency, social benefit, economic benefit and technical condition were 0.496, 0.300, 0.051 and 0.153, respectively. For the blood screening assembly line system to be procured, the evaluation value was 0.629, and it passed the procurement demonstration. Conclusion The quantitative and scientific evaluation of the procurement evaluation can be carried out by establishing the evaluation index and fuzzy comprehensive evaluation method for the key instruments and equipment of blood centers.

It is commonly believed that the atmosphere is decoupled from the solid Earth. Thus, it is difficult for the seismic wave energy inside the Earth to propagate into the atmosphere, and atmospheric pressure wave signals excited by earthquakes are unlikely to exist in atmospheric observations. An increasing number of studies have shown that earthquakes, volcanoes, and tsunamis can perturb the Earth's atmosphere due to various coupling effects. However, the observations mainly focus on acoustic waves with periods of less than 10 min and inertial gravity waves with periods of greater than 1 h. There are almost no clear observations of gravity waves that coincide with observations of low-frequency signals of the Earth's free oscillation frequency band within 1 h. This paper investigates atmospheric gravity wave signals within 1 h of surface-atmosphere observations using the periodogram method based on seismometer and microbarometer observations from the global seismic network before and after the July 29, 2021 MW8.2 Alaska earthquake in the United States. The numerical results show that the atmospheric gravity wave signals with frequencies similar to those of the Earth's free oscillations 0S2 and 0T2 can be detected in the microbarometer observations. The results confirm the existence of atmospheric gravity waves, indicating that the atmosphere and the solid Earth are not decoupled within this frequency band and that seismic wave energy excited by earthquakes can propagate from the interior of the Earth to the atmosphere and enhance the atmospheric gravity wave signals within 1 h.

Background: Liposarcoma is a malignant tumor that originates from adipose tissue and can occur in any part of the body. There is currently no clear conclusion on whether there are significant differences in prognosis between liposarcoma at different anatomical locations, especially retroperitoneal liposarcoma (RLPS) and non retroperitoneal liposarcoma (NRLPS). The aim of this study is to reveal whether there are differences in prognosis between these two locations of liposarcoma, and further explore the fundamental reasons behind these differences. Methods: We conducted an in-depth investigation into the factors affecting the prognosis of patients with liposarcoma by analyzing the data from the Surveillance, Epidemiology, and End Results Program (SEER) database. Then, we used propensity score matching (PSM) to balance these prognostic factors for comparative analysis of survival between RLPS and NRLPS. In addition, by analyzing transcriptome and whole exome data from TCGA and the Japan Genotypic Phenotype Archive (JGA), we identified genes with significant expression differences and explored changes in the immune microenvironment. Result: Through analysis of RLPS and NRLPS patients in the SEER database, we observed significant prognostic differences between the two groups, with RLPS exhibiting worse prognosis (p

Abstract Here, an approach to produce a hierarchical porous Fe‐N‐C@TABOH catalyst with densely accessible high intrinsic active FeNx sites is proposed. The method involves a single‐step pyrolysis of Zn/Fe‐zeolitic imidazolate framework (Zn/Fe‐ZIF‐H) with tetrabutylammonium hydroxide (TABOH) micelles, which is obtained by utilizing TABOH as a structural template and electronic mediator at room temperature for a brief duration of 16 min. Notably, the yield of Zn/Fe‐ZIF‐H is 3.5 times that of Zn/Fe‐ZIF‐N prepared by conventional method. Results indicate that in addition to expediting synthesis and increasing yield of the Zn/Fe‐ZIF‐H, the TABOH induces a hierarchical porous structure and fosters the formation of more and higher intrinsic active FeNx moieties in Fex‐N‐C@TABOH, showing that TABOH is a multifunctional template. Crucially, the increased mesoporosity/external surface area and optimized microenvironment of Fe‐N‐C@TABOH significantly enhance ORR activity by facilitating the formation of high intrinsic active FeNx sites, increasing accessible FeNx sites, and reducing mass transfer resistance. Through structure tailoring and microenvironment optimization, the resulting Fe‐N‐C@TABOH exhibits superior ORR performance. DFT calculation further validates that the synergistic effect of these two factors leads to low ORR barrier and optimized *OH adsorption energy. This study underscores the importance of structure and electronic engineering in the development of highly active ORR catalysts.

Abstract Introduction Triptolide (TP), a natural product derived from the herbal medicine Tripterygium wilfordii, exhibits potent immunosuppressive activity. However, the mechanisms underlying its effects in rheumatoid arthritis remain incompletely understood. Methods Collagen‐induced arthritis (CIA) model was induced in Sprague−Dawley rats by immunization with bovine type II collagen, and TP was administrated as treatment. The therapeutic effect of TP was evaluated based on paw swelling, histopathology, and serum levels of inflammatory factors. Exosomes isolated from rat serum were characterized by transmission electron microscopy, dynamic light scattering, and western blot analysis. Proteomic profiling of exosomes was analyzed by direct DIA quantitative proteomics analysis. Gene ontology and the Kyoto Encyclopedia of Genes and Genomes databases were employed for enrichment analysis related to molecular function, biological processes, and signaling pathways. Western blot analysis was used to analyze differentially expressed proteins. Results TP treatment ameliorated arthritic phenotypes in CIA rats as evidenced by reduced arthritis score, paw swelling, pathological injury severity scores, and serum levels of inflammatory cytokines. The proteomic analysis revealed that TP treatment significantly inhibited complement and coagulation cascades, interleukin‐17 signaling pathway, and cholesterol metabolism, which were reactivated in CIA rats. Importantly, lipocalin 2 (LCN2) and myeloperoxidase (MPO) levels were markedly upregulated in the CIA group but suppressed upon TP administration. Furthermore, in synovial tissues, LCN2 and MPO expression levels were also elevated in the CIA group but decreased following TP treatment. Conclusion Our findings demonstrate that TP alleviates CIA, possibly through modulation of exosomal LCN2 and MPO proteins.

Drilling into fractured formations can easily lead to mud loss, which significantly affects drilling efficiency and increases drilling costs. In high-temperature and high-pressure environments, changes in mud properties render mud loss more uncontrollable. On the basis of a mud performance test in a high-temperature and high-pressure environment, and considering the time-varying characteristics of wellbore temperature and pressure and the distribution law of fracture temperature and pressure, a coupling model of temperature and pressure in the wellbore and fracture was established in this study. The developed model was solved numerically using the finite difference approximation, and the characteristics of the mud loss were analyzed. The effects of geothermal gradient, fracture parameters (fracture width, fracture length, and deformation capacity), mud properties (viscosity, yield value, and specific heat capacity), and operating conditions on mud loss were investigated. The model was validated using field measurement-while-drilling tool data of the LD1 well in the South China Sea. The results show that the average errors between the calculated temperature and pressure results of the numerical model and the actual data are 2.45% and 0.57%, respectively, which proves that the model has high accuracy in the prediction of the bottom hole temperature and pressure. A comparison of the predicted data of mud loss with the actual measured data indicates that the average error of the mud leakage model is 4.23%. This also demonstrates that the model has a high accuracy in predicting mud loss. In addition, the results show that the mud performance changes significantly under high-temperature and high-pressure conditions, which has a significant influence on mud loss. The geothermal gradient affected the mud properties and thus significantly affected the mud loss characteristics. The larger the mud pumping rate, the larger the bottomhole pressure difference and the more serious the mud loss. Blindly increasing mud viscosity is not advisable because it may lead to more serious losses. With an increase in the mud yield value, the mud flow resistance in the fractures can be significantly increased to inhibit mud loss. The larger the mud-specific heat capacity, the less mud loss is affected by the temperature. With an increase in the fracture width, the degree of mud loss gradually decreased. The larger the fracture length and deformation capacity, the more serious the mud loss. The fracture length and width could be inverted using the developed model.

IntroductionThe proportion of retroperitoneal malignant peripheral nerve sheath tumours (RMPNST) in retroperitoneal tumors is less than 5%, but the mortality rate is very high. However, there is no relevant research focused on RMPNST only.MethodsWe retrospectively analyzed data from the SEER database of patients with primary RMPNST from 2000 to 2019, by leveraging the advantages of the Seer database, we can explore the prognosis of such rare diseases. Kaplan-Meier method was used to construct the survival curve, and cox regression model was used to analyze the factors affecting the prognosis of patients. In addition, a model was developed to distinguish high-risk and low-risk patients.ResultsThis study included a total of 52 patients, with a median survival time of 39 months (95% CI 12.740–65.260) and a 5-year survival rate of 44.2% (95% CI 0.299–0.565). Radiotherapy (p = 0.004, OR: 1.475, 95% CI 0.718–3.033), metastasis disease (p = 0.002, OR: 5.596, 95% CI 2.449–47.079) and surgery (p = 0.003, OR: 5.003, 95% CI 0.011–0.409) were associated with overall survival (OS). The 5-year distant metastasis rate was 36% (95% CI 0.221–0.499). We used the above risk factors to separate patients into high and low groups and evaluate the results through the receiver operating characteristic (ROC) curve. This model is beneficial for guiding the selection of treatment strategies.ConclusionThe majority of RMPNST patients have a good prognosis after surgery, and the establishment of high-low group is helpful for clinical decision-making.

Objectives Delirium is a form of brain dysfunction with high incidence and is associated with many negative outcomes in the intensive care unit. However, few studies have been large enough to reliably examine the associations between body mass index (BMI) and delirium, especially in critically ill patients. The objective of this study was to investigate the association between BMI and delirium incidence in critically ill patients.Design A retrospective cohort study.Setting Data were collected from the Medical Information Mart for Intensive Care-IV V2.0 Database consisting of critically ill participants between 2008 and 2019 at the Beth Israel Deaconess Medical Center in Boston.Participants A total of 20 193 patients with BMI and delirium records were enrolled in this study and were divided into six groups.Primary outcome measure Delirium incidence.Results Generalised linear models and restricted cubic spline analysis were used to estimate the associations between BMI and delirium incidence. A total of 30.81% of the patients (6222 of 20 193) developed delirium in the total cohort. Compared with those in the healthy weight group, the patients in the different groups (underweight, overweight, obesity grade 1, obesity grade 2, obesity grade 3) had different relative risks (RRs): RR=1.10, 95% CI=1.02 to 1.19, p=0.011; RR=0.93, 95% CI=0.88 to 0.97, p=0.003; RR=0.88, 95% CI=0.83 to 0.94, p

Ectopic thyroid-stimulating hormone (TSH)-secreting tumors are extremely rare, with only 15 reported cases in the literature. Herein, we described a 60-year-old female patient with thyrotoxicosis and elevated or unsuppressed levels of TSH. Family history and laboratory and genetic tests did not support a diagnosis of resistance to thyroid hormone (RTH). Given the unsuppressed TSH, TSH-secreting tumor was suspected, and magnetic resonance imaging (MRI) of the pituitary gland was performed. Surprisingly, the MRI scans revealed a nodule in the nasopharynx rather than a pituitary tumor in the sella region. Further evaluation using Gallium-68 DOTATATE positron emission tomography/computed tomography (68Ga-DOTATATE PET/CT) demonstrated increased DOTATATE uptake in the nasopharyngeal nodule. Additionally, an octreotide suppression test (OST) revealed an obvious reduction in TSH levels, further supporting the suspicion of the nasopharyngeal mass as the cause of inappropriate TSH secretion. To prepare for surgery, the patient received preoperative administration of octreotide, resulting in the normalization of TSH and thyroid hormone levels. The patient subsequently underwent successful surgical removal of the nasopharyngeal mass. Following the procedure, the patient experienced complete resolution of hyperthyroidism symptoms, with TSH declined and thyroid hormone levels returned to normal. Histochemistry analysis of the tumor revealed positive staining for TSH, growth hormone (GH), prolactin (PRL), luteinizing hormone (LH), and somatostatin receptor 2 (SSTR2). We discussed differential diagnosis of hyperthyroidism due to inappropriate TSH secretion, with a particular emphasis on the importance of 68Ga-DOTATATE PET/CT in combination with OST for identifying ectopic pituitary tumors.

Background: Seasonal influenza vaccine (SIV) uptake (receipt of vaccine) in Manitoba, Canada is consistently low notwithstanding vaccine availability and free-of-charge vaccination. Despite, there is a lack of published evidence on the determinants of uptake of the vaccine. We sought to assess the association between SIV uptake and certain population and primary care physician (PCP) characteristics in Manitoba. Methods: We conducted a longitudinal study utilizing Manitoba administrative health databases. We summarized SIV uptake from 2000/01–2019/20 influenza seasons across subpopulations defined by socioeconomic, health-related and PCP characteristics. Utilizing multivariable generalized estimating equation logistic regression models, we assessed the association between SIV uptake and the socioeconomic, health-related and PCP characteristics, stratified by age group (

For networking of big data applications, an essential issue is how to represent networks in vector space for further mining and analysis tasks, e.g., node classification, clustering, link prediction, and visualization. Most existing studies on this subject mainly concentrate on monoplex networks considering a single type of relation among nodes. However, numerous real-world networks are naturally composed of multiple layers with different relation types; such a network is called a multiplex network. The majority of existing multiplex network embedding methods either overlook node attributes, resort to node labels for training, or underutilize underlying information shared across multiple layers. In this paper, we propose Multiplex Network Infomax (MNI), an unsupervised embedding framework to represent information of multiple layers into a unified embedding space. To be more specific, we aim to maximize the mutual information between the unified embedding and node embeddings of each layer. On the basis of this framework, we present an unsupervised network embedding method for attributed multiplex networks. Experimental results show that our method achieves competitive performance on not only node-related tasks, such as node classification, clustering, and similarity search, but also a typical edge-related task, i.e., link prediction, at times even outperforming relevant supervised methods, despite that MNI is fully unsupervised.

Background Joint local context that is primarily processed by pre-trained models has emerged as a prevailing technique for text classification. Nevertheless, there are relatively few classification applications on small sample of industrial text datasets. Methods In this study, an approach of employing global enhanced context representation of the pre-trained model to classify industrial domain text is proposed. To achieve the application of the proposed technique, we extract primary text representations and local context information as embeddings by leveraging the BERT pre-trained model. Moreover, we create a text information entropy matrix through statistical computation, which fuses features to construct the matrix. Subsequently, we adopt BERT embedding and hyper variational graph to guide the updating of the existing text information entropy matrix. This process is subjected to iteration three times. It produces a hypergraph primary text representation that includes global context information. Additionally, we feed the primary BERT text feature representation into capsule networks for purification and expansion as well. Finally, the above two representations are fused to obtain the final text representation and apply it to text classification through feature fusion module. Results The effectiveness of this method is validated through experiments on multiple datasets. Specifically, on the CHIP-CTC dataset, it achieves an accuracy of 86.82% and an F1 score of 82.87%. On the CLUEEmotion2020 dataset, the proposed model obtains an accuracy of 61.22% and an F1 score of 51.56%. On the N15News dataset, the accuracy and F1 score are 72.21% and 69.06% respectively. Furthermore, when applied to an industrial patent dataset, the model produced promising results with an accuracy of 91.84% and F1 score of 79.71%. All four datasets are significantly improved by using the proposed model compared to the baselines. The evaluation result of the four dataset indicates that our proposed model effectively solves the classification problem.

PurposeTo investigate the potential role of the unique sound of tennis in alleviating anxiety. Previous research has consistently shown that exercise can mitigate anxiety, primarily attributed to the impact of increased physical activity on hormonal and neurostructural changes. However, in daily life we find that one of the reasons people are drawn to tennis is its distinctive sound. In this study, we specifically examined the influence of this sound on anxiety.Methods and resultsIn a randomized controlled experiment involving 96 participants reporting chronic anxiety (n1 = n2 = 48), we found that the control group exhibited an average reduction of 0.00156 in anxiety scores 4 weeks before and after the study. On the other hand, the experimental group, exposed to tennis stroke sound stimuli, showed an average reduction of 0.02896 in anxiety scores after 4 weeks, with some individuals even experiencing a decrease from anxiety to mild anxiety. Furthermore, the analysis of sound data revealed that the sound of tennis exhibited a pleasing timbre, with the primary sound frequencies ranging from 100 to 2,800 Hz. The rhythm of the sound had an average interval of approximately 1.758″ (± 0.41), corresponding to speed of approximately 93.6 km/h. The sound exhibited a steady rhythm, orderly variations in pitch, and a soothing timbre.ConclusionThis study confirms that the sound of tennis alone contributes to anxiety relief, attributed to its suitable loudness, steady rhythm, and orderly variations in pitch, all of which align with human auditory characteristics. This indicates that a considerable portion of the anxiety-alleviating effects of tennis attributed to its comforting sound.

Although the fusion of multispectral (MS) and hyperspectral (HS) images in remote sensing has become relatively mature, and different types of fusion methods have their own characteristics in terms of fusion effect, data dependency, and computational efficiency, few studies have focused on the impact of radiance extreme areas, which widely exist in real remotely sensed scenes. To this end, this paper proposed a novel method called radiance extreme area compensation fusion (RECF). Based on the architecture of spectral unmixing fusion, our method uses the reconstruction of error map to construct local smoothing constraints during unmixing and utilizes the nearest-neighbor multispectral data to achieve optimal replacement compensation, thereby eliminating the impact of overexposed and underexposed areas in hyperspectral data on the fusion effect. We compared the RECF method with 11 previous published methods on three sets of airborne hyperspectral datasets and HJ2 satellite hyperspectral data and quantitatively evaluated them using 5 metrics, including PSNR and SAM. On the test dataset with extreme radiance interference, the proposed RECF method achieved well in the overall evaluation results; for instance, the PSNR metric reached 47.6076 and SAM reached 0.5964 on the Xiong’an dataset. In addition, the result shows that our method also achieved better visual effects on both simulation and real datasets.

In the realm of ballistic target analysis, micro-motion attributes, such as warhead precession, nutation, and decoy oscillations, play a pivotal role. This paper addresses these critical aspects by introducing an advanced analytical model for assessing the Doppler power spectra of convex quadric revolution bodies during precession. Our model is instrumental in calculating the Doppler shifts pertinent to both precession and swing cones. Additionally, it extends to delineate the Doppler power spectra for configurations involving cones and sphere–cone combinations. A key aspect of our study is the exploration of the effects exerted by geometric parameters and observation angles on the Doppler spectra, offering a comparative perspective of various micro-motion forms. The simulations distinctly demonstrate how different micro-motion patterns of a cone influence the Doppler power spectra and underscore the significance of geometric parameters and observational angles in shaping these spectra. This research not only contributes to enhancing LIDAR target identification methodologies but also lays a groundwork for future explorations into complex micro-motions like nutation.

Abstract Background Cattle (Bos taurus) are a major large livestock, however, compared with other species, the transcriptional specificity of bovine oocyte development has not been emphasised. Results To reveal the unique transcriptional signatures of bovine oocyte development, we used integrated multispecies comparative analysis and weighted gene co-expression network analysis (WGCNA) to perform bioinformatic analysis of the germinal follicle (GV) and second meiosis (MII) gene expression profile from cattle, sheep, pigs and mice. We found that the expression levels of most genes were down-regulated from GV to MII in all species. Next, the multispecies comparative analysis showed more genes involved in the regulation of cAMP signalling during bovine oocyte development. Moreover, the green module identified by WGCNA was closely related to bovine oocyte development. Finally, integrated multispecies comparative analysis and WGCNA picked up 61 bovine-specific signature genes that participate in metabolic regulation and steroid hormone biosynthesis. Conclusion In a short, this study provides new insights into the regulation of cattle oocyte development from a cross-species comparison.

Abstract Horizontal wells with a slim hole (HW‐SH), characterized by high productivity, high environmental protection, and low cost are gradually being introduced into oil and gas extraction processes in high‐temperature and high‐pressure formations. However, the HW‐SH annulus is narrow, and the distributions of temperature and pressure in the wellbore are very different from those of conventional horizontal wells. Furthermore, the mud properties change under the influence of high temperature and pressure. In this study, a numerical model of the transient temperature‐pressure coupling field in an HW‐SH wellbore was developed based on high‐temperature and high‐pressure performance tests of mud, combined with the characteristics of a narrow annulus in HW‐SH. Subsequently, the effects of the ground temperature gradient, displacement, drill pipe speed, annulus size, and joint size on the temperature‐pressure coupling field were analyzed. Measured data from the HW‐SH drilled in the Mahu field in China were used to validate the developed model. The results showed that the ground temperature had a significant effect on the mud density and rheological properties. As the temperature increased, the mud density decreased, and its rheological properties improved. Although pressure also has a large effect on density, it has a negligible effect on the rheological properties of the mud. The coupled temperature‐pressure field model for HW‐SH considering the effects of the temperature and narrow annulus can predict the annulus temperature and pressure with an accuracy of up to 97%. The ground temperature gradient has the greatest influence on the HW‐SH annular temperature, larger than that of the displacement. In addition, the annular size, rotation speed of the drill pipe, and joint size have almost no influence on the annular temperature. Compared to those of conventional horizontal wells, the rotational speed, annulus size, and joint size of the HW‐SH have a greater impact on the annulus pressure.

UAV transmission tower inspection is the use of UAV technology for regular inspection and troubleshooting of towers on transmission lines, which helps to improve the safety and reliability of transmission lines and ensures the stability of the power supply. From the traditional manual tower boarding to the current way of manually selecting target camera shooting points from 3D point clouds to plan the inspection path of the UAV, operational efficiency has drastically improved. However, indoor planning work is still labor-consuming and expensive. In this paper, a deep learning-based point cloud transmission tower segmentation (PCTTS) model combined with the corresponding target point localization algorithm is proposed for automatic segmentation of transmission tower point cloud data and automatically localizing the key inspection component as the target point for UAV inspection. First, we utilize octree sampling with unit ball normalization to simplify the data and ensure translation invariance before putting the data into the model. In the feature extraction stage, we encode the point set information and combine Euclidean distance and cosine similarity features to ensure rotational invariance. On this basis, we adopt multi-scale feature extraction, construct a local coordinate system, and introduce the offset-attention mechanism to enhance model performance further. Then, after the feature propagation module, gradual up-sampling is used to obtain the features of each point to complete the point cloud segmentation. Finally, combining the segmentation results with the target point localization algorithm completes the automatic extraction of UAV inspection target points. The method has been applied to six kinds of transmission tower point cloud data of part segmentation results and three kinds of transmission tower point cloud data of instance segmentation results. The experimental results show that the model achieves mIOU of 94.1% on the self-built part segmentation dataset and 86.9% on the self-built instance segmentation dataset, and the segmentation accuracy outperforms that of the methods for point cloud segmentation, such as PointNet++, DGCNN, Point Transformer, and PointMLP. Meanwhile, the experimental results of UAV inspection target point localization also verify the method’s effectiveness in this paper.

Multi-spectral imaging technologies have made great progress in the past few decades. The development of snapshot cameras equipped with a specific multi-spectral filter array (MSFA) allow dynamic scenes to be captured on a miniaturized platform across multiple spectral bands, opening up extensive applications in quantitative and visualized analysis. However, a snapshot camera based on MSFA captures a single band per pixel; thus, the other spectral band components of pixels are all missed. The raw images, which are captured by snapshot multi-spectral imaging systems, require a reconstruction procedure called demosaicing to estimate a fully defined multi-spectral image (MSI). With increasing spectral bands, the challenge of demosaicing becomes more difficult. Furthermore, the existing demosaicing methods will produce adverse artifacts and aliasing because of the adverse effects of spatial interpolation and the inadequacy of the number of layers in the network structure. In this paper, a novel multi-spectral demosaicing method based on a deep convolution neural network (CNN) is proposed for the reconstruction of full-resolution multi-spectral images from raw MSFA-based spectral mosaic images. The CNN is integrated with the channel attention mechanism to protect important channel features. We verify the merits of the proposed method using 5 × 5 raw mosaic images on synthetic as well as real-world data. The experimental results show that the proposed method outperforms the existing demosaicing methods in terms of spatial details and spectral fidelity.

Plant-endophytic microbes affect plant growth, development, nutrition, and resistance to pathogens. However, how endophytic microbial communities change in different strawberry plant compartments after Fusarium pathogen infection has remained elusive. In this study, 16S and internal transcribed spacer rRNA amplicon sequencing were used to systematically investigate changes in the bacterial and fungal diversity and composition in the endophytic compartments (roots, stems, and leaves) of healthy strawberries and strawberries with Fusarium wilt, respectively. The analysis of the diversity, structure, and composition of the bacterial and fungal communities revealed a strong effect of pathogen invasion on the endophytic communities. The bacterial and fungal community diversity was lower in the Fusarium-infected endophytic compartments than in the healthy samples. The relative abundance of certain bacterial and fungal genera also changed after Fusarium wilt infection. The relative abundance of the beneficial bacterial genera Bacillus, Bradyrhizobium, Methylophilus, Sphingobium, Lactobacillus, and Streptomyces, as well as fungal genera Acremonium, Penicillium, Talaromyces, and Trichoderma, were higher in the healthy samples than in the Fusarium wilt samples. The relative abundance of Fusarium in the infected samples was significantly higher than that in the healthy samples, consistent with the field observations and culture isolation results for strawberry wilt. Our findings provide a theoretical basis for the isolation, identification, and control of strawberry wilt disease.

Increasing environmental pollution has led to the spread of many endocrine-disrupting chemicals (EDCs) around the world, which are toxic substances in the form of compounds that pose a great threat to the reproductive health of mammals and become a potential cause of many reproductive function-related diseases. In the past decade, the rapid development of single-cell RNA sequencing (scRNA-seq) technology has greatly promoted the study of the toxic mechanisms of EDCs in the mammalian reproductive system, including DEHP, ZEN, BPA, and BDE47. These studies aim to resolve the interference of EDCs in critical stages of reproductive development, including prepubertal and pubertal in males, meiosis I and early follicle formation in females. This paper introduces the sequencing process and analysis methods of current mainstream scRNA-seq technology, systematically reviews the outstanding contributions and specific research ideas of this technology in the study of reproductive system toxicity, lists representative cases of using this technology to explore reproductive damage caused by EDCs, and summarizes in detail the connection between environmental pollution and reproductive development disorders. It provides an important theoretical basis and direction for further exploring the mechanism of damage to the physiological functions of toxic substances on the reproductive system and the prevention and treatment of reproductive diseases.

Casing is inevitably damaged during perforation operation, and the perforation parameters will have a significant impact on the degree of casing damage, which will directly affect the stability of casing structure in the process of hydraulic fracturing. A three-dimensional numerical model, which was composed of perforating charge, casing–cement sheath, and formation and consistent with the engineering practice, was established, considering the dynamic characteristics of materials in the process of explosion impact. The numerical simulation results were verified by physical model tests and showed that the stress superposition between adjacent perforations increases the damage degree of casing. Perforation density and phase angle directly affect the range of stress superposition area, with the increase of perforation density or decrease of phase angle, the damage area and damage coefficient of casing are increasing. Based on the results of numerical simulation, an innovative perforation parameter optimization method considering casing yield strength was proposed, which could effectively reduce the stress superposition area between adjacent perforations and keep casing damage within a reasonable range. Finally, perforation parameter optimization based on real wells was carried out, and analysis results showed that the casing stress was lower than the yield strength after parameter optimization, which could ensure the stability of the casing. The research results can provide an important reference for the optimization of perforation design and field operation.

Abstract Sepsis is a public cause of death in intensive care unit patients. Probiotics were widely used to increase the survival rate of sepsis by a series of clinical research. The purpose of this research was to investigate the therapeutic effects of Lactobacillus johnsonii 6084 in septic mice. Sepsis mouse model was induced by LPS treatment. The influence of L. johnsonii 6084 on the protection of organ injury induced by sepsis was explored. Moreover, the composition of gut microbiota was studied to clarify the mechanism of L. johnsonii 6084 therapeutic effect on sepsis. L. johnsonii 6084 treatment could conspicuously decrease the mortality and organ injury of sepsis. The reduction of gut microbial diversity and richness in septic mice were moderated by the administration of 6084. The abundance of Bacteroidetes and Proteobacteria were change by LPS treatment while restored by L. johnsonii 6084. To conclude, probiotic 6084 may has optimistic result on reducing mortality of sepsis through rebalancing gut microbiota.

The special temperature-pressure environment and narrow safety density window of the deepwater formation pose a great challenge to wellbore pressure control during drilling. The downhole variable-gradient drilling method is an effective idea to solve this challenge. However, previous studies mainly focused on the theoretical adaptability analysis of this method, while the experimental feasibility verification has not yet been reported. In this paper, based on the segmental control principle of wellbore fluid density, we set up an indoor physical experiment system to investigate the feasibility of variable-gradient drilling method based on downhole cyclone separator. Using this experimental system, we investigated the effect of drilling fluid physical parameters, hollow glass microsphere (HGMS) performance parameters and engineering parameters on the separator separation efficiency, wellbore pressure gradient and HGMS slip rate. The experimental results revealed that the separator could separate the HGMSs into the annulus from the fluid mixture in the drill pipe. The separation efficiency increased with the increase in the HGMS diameter, HGMS concentration and flow rate, while decreased with the increase in the HGMS density and fluid viscosity. The HGMS density and HGMS concentration were the key factors affecting the annulus pressure gradient value, and the separator separation efficiency was the main factor determining the difference of the annulus pressure gradient. The HGMS slip rate was highly sensitive to its diameter and fluid viscosity, and a combination of optimal HGMS diameter and fluid viscosity could avoid the drilling risk caused by the HGMS slippage. Therefore, the variable-gradient drilling method based on downhole separator is feasible, which is able to create two or more pressure gradients in the annulus. This study could provide guidance for the optimization of key parameters and the development of equipment related to the variable-gradient drilling.

Knowledge of wellbore multiphase flow characteristics is important for early gas invasion identification and treatment, especially in oil-based drilling fluid. The wellbore multiphase flow law in downhole dual-gradient drilling (DDGD) is more complex than that in conventional single-gradient drilling. There was no relevant literature report at present. In this paper, considering the synergistic feedback relationship of multiphase flow, interphase mass transfer, and wellbore temperature, a fully transient wellbore multiphase flow model was established based on oil-based drilling fluid in deepwater DDGD. The model also coupled the variable temperature–mass flows caused by the dynamic transfer of hollow glass balls and formation gas. The bubble interface mass transfer theory was adopted to describe the interphase mass transfer rate. The model was verified through the experimental measurement data. The calculated results indicated that the dynamic transfer of hollow glass balls varied the wellbore pressure gradient and flow rate in DDGD, resulting in a sudden decrease in gas fraction and gas solubility along the flow direction at the separator. Additionally, the transient interphase mass transfer and wellbore temperature had large impact on the wellbore multiphase flow behaviors. Moreover, the model based on bubble interface mass transfer theory could more accurately characterize the development of the wellbore multiphase flow. This research could provide a certain theoretical basis for the detection and treatment of gas invasion in deepwater DDGD.

Abstract The Yinggehai Basin (YGH) in the South China Sea is a Cenozoic, high‐temperature, and high‐pressure basin. Exploration and development of natural gas resources in the YGH show that owing to the diapir structures make the formation pressure system is complex, overpressure characteristics are obvious, prediction is difficult, and spatial distribution is irregular, which severely restricts the development of natural gas. To evaluate the influence of diapir structures on the formation and distribution of overpressure, we compared and analyzed the ancient and modern geothermal temperatures, organic matter maturity, clay mineral evolution, burial depth, and pressure coefficient in the diapir and nondiapir zones of the YGH. The results showed that of the diapir structures in the YGH occurred in the following order: Uplift–spiking–piercing–depositing. The Dongfang area is dominated by low‐amplitude turtle‐back diapir structures with weak energy, whereas the Ledong area is dominated by high‐amplitude spiked diapir structures with strong energy, both of which pressurize the upper formation. The Changnan area is dominated by high‐amplitude pierced/depositing diapirs with strong energy, which can relieve pressure. The influence of the diapir and its associated structures on the formation of overpressure can be attributed to the transport of the diapir body and the associated fault‐fracture system and the local thermal effect caused by the upwelling of hot fluids. Under the influence of the diapir structure, the overpressure top surface of the basin was shallower. According to the degree of influence and intensity of the diapir structure, variations in formation pressure coefficient with depth can be divided into four evolution modes: constant pressure, slow pressurization, “Z” pressurization, and “S” pressurization types. This study provides some guidance and references for the identification and accurate prediction of the overpressure formation mechanism in basins containing diapirs, and helps in formulating efficient drilling and production plans.

Abstract Sand deposition in the wellbore is one of the most common problems in oil–gas and hydrate exploitation work. A detailed understanding of the migration characteristics of the sand bed and accurate prediction of the starting velocity of the sand bed are key aspects of effective sand carrying. Therefore, this study conducted a large‐scale outdoor gas–water–sand three‐phase flow experiment. The effects of critical factors such as flow pattern, sand size, sand bed height, and pipe inclination angle on sand bed startup velocity and migration were studied experimentally. The experimental results indicated that during the startup process of the sand bed, sand particles exhibited three states: rolling, jumping, and suspension. During the experiment, the sand bed morphology included a static bed, rolling bed, jumping bed, and suspension bed. Compared to a stratified flow, intermittent flow and agitated flow were more beneficial for the startup velocity of sand beds. An increase in pipe inclination also reduced the starting velocity of the sand bed. With an increase in sand size, the startup velocity of the sand bed increased correspondingly. However, there was no significant influence on the migration morphology of the sand bed. Additionally, the greater the thickness of the sand bed, the lower the starting velocity of the sand bed. Based on the stress states of sand grains, in combination with experimental data, this study used least‐squares estimation to establish a prediction model for the startup velocity of sand beds. The model prediction results were in good agreement with the experimental data. This study will provide a basis for predicting the startup velocity of sand beds during the gas–liquid two‐phase sand‐carrying process.

Abstract Formation pressure is the most fundamental data in oil and gas drilling and production; it has an important position in the entire cycle of oil and gas extraction. However, most current prediction methods are limited to parametric methods with fixed models; such that the accuracy does not meet requirements. This is especially true for deeper layers of marine sedimentary basins where the safety density window is extremely narrow. In this study, we propose a novel method to predict pore pressure using machine learning techniques. For the first time, the effective stress (direct output variable) was accurately predicted by a combination of four input variables (2900 sets of data, of which 90% is the training subset and 10% is the testing subset), including longitudinal velocity, porosity, mud content, and density. As such, an accurate prediction of the formation pressure was achieved based on the effective stress theorem. The performance of machine learning techniques was verified by comparing and analyzing the prediction results with traditional parametric single and multivariate models; whereby the best algorithm was chosen by structural optimization and comparative analysis of five algorithms (multilayer perceptron neural network, radial basis neural network, support vector machine, random forest, and gradient boosting machine). Compared with the methods based on parametric one‐dimensional and multivariate models, the machine learning‐based method was determined to possess high accuracy, adequate self‐adaptation, and high fault tolerance (D2 = 0.9981, RMSE = 0.00718 g/cm3). Moreover, the multilayer perceptual neural network algorithm outperformed other machine learning algorithms in terms of goodness of fit, generalization, and prediction accuracy, with D2 = 0.9981 and RMSE = 0.00709 g/cm3. The formation pressure prediction model developed in this study is not affected by the mechanical depositional environment and is applicable to sandy mudstone formations, such that it can be a useful and highly accurate alternative to the traditional formation pressure prediction methods with fixed parameter forms.

Long non-coding RNAs (lncRNAs) have been extensively studied, and their crucial roles in adipogenesis, lipid metabolism, and gene expression have been revealed. However, the exact regulatory or other mechanisms by which lncRNAs influence the functioning of mesenteric adipose tissue (MAT) remain largely unknown. In this paper, we report the identification of a new lncRNA, named G8110, from the MAT of Bama pigs. The coordinated expression levels of lncRNA G8110 and NFE2L1 were significantly decreased in the MAT of obese Bama pigs compared with those in the MAT of lean pigs. Using a bone mesenchymal stem cell adipogenic differentiation model, we found that lncRNA G8110 played a role in adipocyte differentiation by positively regulating NFE2L1. We also found that lncRNA G8110 inhibited the formation of intracellular lipid synthesis, promoted lipid metabolism, and inhibited the expression of inflammatory cytokines. Our findings regarding lipid synthesis may further promote the role of lncRNAs in driving adipose tissue remodeling and maintaining metabolic health.

Hyperspectral anomaly detection (HAD) is an important technique used to identify objects with spectral irregularity that can contribute to object-based image analysis. Latterly, significant attention has been given to HAD methods based on Autoencoders (AE). Nevertheless, due to a lack of prior information, transferring of modeling capacity, and the “curse of dimensionality”, AE-based detectors still have limited performance. To address the drawbacks, we propose a Multi-Prior Graph Autoencoder (MPGAE) with ranking-based band selection for HAD. There are three main components: the ranking-based band selection component, the adaptive salient weight component, and the graph autoencoder. First, the ranking-based band selection component removes redundant spectral channels by ranking the bands by employing piecewise-smooth first. Then, the adaptive salient weight component adjusts the reconstruction ability of the AE based on the salient prior, by calculating spectral-spatial features of the local context and the multivariate normal distribution of backgrounds. Finally, to preserve the geometric structure in the latent space, the graph autoencoder detects anomalies by obtaining reconstruction errors with a superpixel segmentation-based graph regularization. In particular, the loss function utilizes ℓ2,1-norm and adaptive salient weight to enhance the capacity of modeling anomaly patterns. Experimental results demonstrate that the proposed MPGAE effectively outperforms other state-of-the-art HAD detectors.

Objective To establish scrap indicators for key equipment in blood screening laboratory of blood centers and quantitatively assess the running status of key equipment, so as to provide a scientific method for equipment scrap. Methods Through the literature review and Delphi method, the scrap indicators of key equipment in blood screening laboratories were established in terms of applicability, economy and advancement of the equipment. The weights of relevant indicators were calculated by analysis hierarchy process (AHP), and the quantitative assessment model of equipment scrap priority was established according to the indicators and its weight. The equipment running data from January 2020 to December 2020 of Laboratory Department were collected and analyzed using the model, and its accuracy was verified based on experience. Results Thirteen second-level scrap indicators were established, and the weights of the three first-level indexes of applicability, economy and advancement were 0.582, 0.114 and 0.306, respectively. Among the total 30 key equipment, the model score of 4 equipment was less than 0.5, and the running status after manually checking met the scrap standard. Conclusion The model can accurately assess the scrap priority of key equipment and facilitate the procurement budget and scrapping identification in advance, which can avoid the waste of resources and ensure safe, efficient and orderly laboratory work.

Background: Copper is an indispensable mineral element involved in many physiological metabolic processes. Cuproptosis is associated with a variety of cancer such as hepatocellular carcinoma (HCC). The objective of this study was to examine the relationships between the expression of cuproptosis-related genes (CRGs) and tumor characteristics, including prognosis and microenvironment of HCC.Methods: The differentially expressed genes (DEGs) between high and low CRGs expression groups in HCC samples were identified, and further were analyzed for functional enrichment analysis. Then, CRGs signature of HCC was constructed and analyzed utilizing LASSO and univariate and multivariate Cox regression analysis. Prognostic values of CRGs signature were evaluated by Kaplan-Meier analysis, independent prognostic analysis and nomograph. The expression of prognostic CRGs was verified by Real-time quantitative PCR (RT-qPCR) in HCC cell lines. In addition, the relationships between prognostic CRGs expression and the immune infiltration, tumor microenvironment, antitumor drugs response and m6A modifications were further explored using a series of algorithms in HCC. Finally, ceRNA regulatory network based on prognostic CRGs was constructed.Results: The DEGs between high and low CRG expression groups in HCC were mainly enriched in focal adhesion and extracellular matrix organization. Besides, we constructed a prognostic model that consists of CDKN2A, DLAT, DLST, GLS, and PDHA1 CRGs for predicting the survival likelihood of HCC patients. And the elevated expression of these five prognostic CRGs was substantially in HCC cell lines and associated with poor prognosis. Moreover, immune score and m6A gene expression were higher in the high CRG expression group of HCC patients. Furthermore, prognostic CRGs have higher mutation rates in HCC, and are significantly correlated with immune cell infiltration, tumor mutational burden, microsatellite instability, and anti-tumor drug sensitivity. Then, eight lncRNA-miRNA-mRNA regulatory axes that affected the progression of HCC were predicted.Conclusion: This study demonstrated that the CRGs signature could effectively evaluate prognosis, tumor immune microenvironment, immunotherapy response and predict lncRNA-miRNA-mRNA regulatory axes in HCC. These findings extend our knowledge of cuproptosis in HCC and may inform novel therapeutic strategies for HCC.

Alzheimer’s disease (AD) is a degenerative disease of the central nervous system, the most common type of dementia in old age, which causes progressive loss of cognitive functions such as thoughts, memory, reasoning, behavioral abilities and social skills, affecting the daily life of patients. The dentate gyrus of the hippocampus is a key area for learning and memory functions, and an important site of adult hippocampal neurogenesis (AHN) in normal mammals. AHN mainly consists of the proliferation, differentiation, survival and maturation of newborn neurons and occurs throughout adulthood, but the level of AHN decreases with age. In AD, the AHN will be affected to different degrees at different times, and its exact molecular mechanisms are increasingly elucidated. In this review, we summarize the changes of AHN in AD and its alteration mechanism, which will help lay the foundation for further research on the pathogenesis and diagnostic and therapeutic approaches of AD.

Lithium dendrite growth due to uneven electrodeposition may penetrate the separator and solid electrolyte, causing inner short circuit and potential thermal runaway. Despite great electrochemical phase-field simulation efforts devoted to exploring the dendrite growth mechanism under the temperature field, no unified picture has emerged. For example, it remains open how to understand the promotion, inhibition, and dual effects of increased temperature on dendrite growth when using different electrolyte types. Here, by comprehensively considering the temperature-dependent Li+ diffusion coefficient, electrochemical reaction coefficient, and initial temperature distribution in phase-field model, we propose that the activation–energy ratio, defined as the ratio of electrochemical reaction activation energy to electrolyte Li+ diffusion activation energy, can be used to quantify the effect of temperature on dendrite morphology. Specifically, we establish a mechanism diagram correlating the activation–energy ratio, uniform initial temperature, and maximum dendrite height, which unifies the seemingly contradictory simulation results. Furthermore, results based on nonuniform initial temperature distribution indicate that a positive temperature gradient along the discharging current facilitates uniform Li+ deposition and local hotspot should be avoided. These findings provide valuable insights into the temperature-dependent Li dendrite growth and contribute to the practical application of Li metal batteries.

Hypoxic-ischemic white matter injury (WMI) pathogenesis in preterm infants is not well established, and iron-related proteins in the brain may play an important role in imbalanced iron metabolism. We aimed to investigate the iron-related protein changes in neonatal rats after hypoxia-ischemia (HI), clarify the role of iron-related proteins in hypoxic-ischemic WMI, and potentially provide a new target for the clinical treatment of hypoxic-ischemic WMI in preterm infants. We adopted a WMI animal model of bilateral common carotid artery electrocoagulation combined with hypoxia in neonatal 3-day-old Sprague-Dawley rats. We observed basic myelin protein (MBP) and iron-related protein expression in the brain (ferritin, transferrin receptor [TfR], and membrane iron transporter 1 [FPN1]) via Western blot and double immunofluorescence staining. The expression of MBP in the WMI group was significantly downregulated on postoperative days (PODs) 14, 28, and 56. Ferritin levels were significantly increased on PODs 3, 7, 14, and 28 and were most significant on POD 28, returning to the sham group level on POD 56. FPN1 levels were significantly increased on PODs 7, 28, and 56 and were still higher than those in the sham group on POD 56. TfR expression was significantly upregulated on PODs 1, 7, and 28 and returned to the sham group level on POD 56. Immunofluorescence staining showed that ferritin, TfR, and FPN1 were expressed in neurons, blood vessels, and oligodendrocytes in the cortex and corpus callosum on POD 28. Compared with the sham group, the immune-positive markers of three proteins in the WMI group were significantly increased. The expression of iron-related proteins in the brain (ferritin, FPN1, and TfR) showed spatiotemporal dynamic changes and may play an important role in hypoxic-ischemic WMI.

Background: The unprecedented COVID-19 pandemic has been spreading for a long time. However, it is unclear whether the pandemic influenced admission in a surgical pediatric intensive care unit (PICU) that never received COVID-19 patients during the early outbreak in China. Methods: A retrospective study was conducted in a surgical PICU in a tertiary hospital in Chengdu, China. We sought to describe the trend in admission numbers from January 2018 to April 2021. We explored the impact of the COVID-19 outbreak on PICU admission characteristics by including all patients younger than 18 years admitted to the PICU between January 23 and April 8 in 2020 and those admitted in the same time periods in prepandemic years (2018 and 2019) and in 2021. Results: The percentage of patients admitted to the PICU from the Chengdu region increased from 34.2 percent in 2019 to 40.4 percent in 2020, whereas that from other provinces decreased from 11.7 percent in 2019 to 5.8 percent in 2020 (P = 0.012). The median length of stay (LOS) in the PICU was significantly longer in the 2020 cohort (4.0 days) than in the 2019 cohort (2.0 days) (P < 0.001); the median hospital LOS was also significantly longer in the former (12.0 days) than in the latter (8.0 days) (P < 0.001). Hospital outcomes (P = 0.005) and primary diagnosis distributions (P = 0.025) between the 2020 and 2019 cohorts were both statistically significant. Conclusions: In a surgical PICU that never received COVID-19 patients, the onset of the 2020 major outbreak was accompanied by changes in the composition of the regional sources of patients, longer PICU and hospital stays, increased proportions of unauthorized discharges, and changes in the distribution of primary diseases for admission. These findings have yet to be strengthened by additional studies involving similar healthcare backgrounds.

A novel high-speed transceiver based on 7 bit correlated non-return-to-zero(CNRZ-7) with high-bandwidth-density(bandwidth per unit length) and low-power for Die-to-Die(D2D) communication is proposed. In order to further improve the SNR and the bandwidth of the CNRZ-5 in D2D communication, a CNRZ-7 based transmitter matrix and receiver matrix are proposed firstly, which are derived from Walsh Hadamard(W-H) transform and inverse transformation. In addition, to reduce the power consumption of the transmitter, the encoding driver based on CNRZ-7 transmitter matrix is designed with source series terminated drivers(SST). To further improve the SNR of the receiver, the decoding circuit based on CNRZ-7 receiver matrix is designed with a special multi-input comparators(MIC), which contain equalizer circuits. This transceiver is designed with a 28nm CMOS technology and the core area is 0.66mm2. The post-simulation results show that this transceiver can operate at 280 Gb/s, and the data rate is 35 Gb/s/wire. The worst width of the receiver’s eye-diagrams is 0.45UI when the transceiver operates at a 50 mm PCB channel with a 10dB@20GHz insertion loss, and the total BER is less than 1E-15. The power consumption is 1.1pJ/b under a normal corner.

Real-world problems always change over time. In the past two decades, many researchers have become increasingly interested in studying evolutionary optimization algorithms for dynamic problems owing to their great importance in real-world applications. Currently, there are no unified and mature test functions for dynamic problems, and the existing dynamic test functions are incapable of simulating a real dynamic environment. This study constructs a new benchmark test function generator for simulating dynamic environments that combine existing static benchmark functions and their related properties to form new dynamic test functions. It is simple to construct and conveniently control the dynamic properties by adjusting the corresponding parameters. Through a series of comparisons with the dynamic benchmark of the generalized dynamic benchmark generator (GDBG), the simulation results show that the dynamic test functions constructed by this generator can reflect different types of changing intensity.

Abstract Developing an efficient and quality remote sensing (RS) technology using volume and efficient modelling in different aircraft RS images is challenging. Generative models serve as a natural and convenient simulation method. Because aircraft types belong to the fine class under the rough class, the issue of feature entanglement may occur while modelling multiple aircraft classes. Our solution to this issue was a novel first-generation realistic aircraft type simulation system (ATSS-1) based on the RS images. It realised fine modelling of the seven aircraft types based on a real scene by establishing an adaptive weighted conditional attention generative adversarial network and joint geospatial embedding (GE) network. An adaptive weighted conditional batch normalisation attention block solved the subclass entanglement by reassigning the intra-class-wise characteristic responses. Subsequently, an asymmetric residual self-attention module was developed by establishing a remote region asymmetric relationship for mining the finer potential spatial representation. The mapping relationship between the input RS scene and the potential space of the generated samples was explored through the GE network construction that used the selected prior distribution z, as an intermediate representation. A public RS dataset (OPT-Aircraft_V1.0) and two public datasets (MNIST and Fashion-MNIST) were used for simulation model testing. The results demonstrated the effectiveness of ATSS-1, promoting further development of realistic automatic RS simulation.

With the continuous development of surface observation methods and technologies, we can acquire multiple sources of data more effectively in the same geographic area. The quality and availability of these data have also significantly improved. Consequently, how to better utilize multi-source data to represent ground information has become an important research question in the field of geoscience. In this paper, a novel model called multi-modal transformer cascaded fusion net (MMTCFN) is proposed for fusion and classification of multi-modal remote sensing data, Hyperspectral Imagery (HSI) and LiDAR data. Feature fusion and feature extraction are the two stages of the model. First, in the feature extraction stage, a three-branch cascaded Convolutional Neural Network (CNN) framework is employed to fully leverage the advantages of convolutional operators in extracting shallow-level local features. Based on this, we generated multi-modal long-range integrated deep features utilizing the transformer-based vectorized pixel group transformer (VPGT) module during the feature fusion stage. In the VPGT block, we designed a vectorized pixel group embedding that preserves the global features extracted from the three branches in a non-overlapping multi-space manner. Moreover, we introduce the DropKey mechanism into the multi-head self-attention (MHSA) to alleviate overfitting caused by insufficient training samples. Finally, we employ a probabilistic decision fusion strategy to integrate multiple class estimations, assigning a specific category to each pixel. This model was experimented on three HSI-LiDAR datasets with balanced and unbalanced training samples. The proposed model outperforms the other seven SOTA approaches in terms of OA performance, proving the superiority of MMTCFN for the HSI-LiDAR classification task.

The North Konawe region in Indonesia, known for its lateritic nickel (Ni) deposits, holds significant potential for obtaining Ni resources. However, the complex topographic conditions of this area pose challenges. Exploring the application of remote sensing technology to reveal the spectral response mechanism of Ni grade from high-precision multispectral data and inversion of Ni grade represents a novel direction in future Ni resource exploration. Traditional remote sensing inversion methods solely consider the spectral characteristics of sample data and ignore vital geospatial location information. As a result, efficiently obtaining regional details of target substance content over large areas has become challenging. The introduction of the geographically weighted regression (GWR) method offers an opportunity for fine-grained Ni grade inversion based on remote sensing. This study focused on the E and K blocks within the lateritic Ni mining area in North Konawe. Through utilizing the WordView-3 multispectral data which exhibits immense potential in quantitative remote sensing inversion studies, GWR was employed to integrate spectral features and spatial information. The goal was to reveal the correlation between multispectral remote sensing data and Ni grade. The obtained results were then compared and analyzed with multiple linear regression (MLR) and back propagation neural network (BPNN) models. The findings revealed that GWR achieved the highest coefficient of determination R2 of 0.96, surpassing MLR and BPNN values of 0.05 and 0.17, respectively. Additionally, GWR exhibited the lowest root mean square error of 0.04, which was lower than those of MLR and BPNN with the values of 0.25 and 0.23, respectively. These results confirmed the enhanced stability and accuracy of the GWR method compared to MLR and BPNN. Furthermore, GWR effectively mapped the spatial distribution trends of Ni grades in the study area, providing evidence of the method’s effectiveness in Ni grade inversion. The study also delved into the inversion effect of the GWR method in areas with varying weathering crust thickness and vegetation cover. The research revealed that higher values of weathering crust thickness negatively impacted the inversion effect. However, the influence mechanism of vegetation cover on Ni grade inversion necessitated further investigation. These results served as a significant demonstration of the remote sensing inversion of mineral resource grades in similar areas. They provided valuable insights for future exploration and decision-making processes.

Micro-LED display technology is considered to be the next generation of display technology, which has the characteristics of high miniaturization, thin film and integration, as well as the advantages of high brightness, high contrast, fast response speed and long service life. However, in the development of Micro-LED display technology, there are still some technical and cost problems to be solved. This paper focuses on the key technologies involved in Micro-LED display technology, such as chip technology, mass transfer, full-color display, bonding and driving technology, the research history and frontier progress of these technologies are reviewed in detail. For chip epitaxy technology, the wavelength uniformity, current density and defect control are emphasized; for chip process, the two main chip structures and the challenges brought by miniaturization are discussed emphatically; for chip integration, full-color display, backplane bonding and driving, several mainstream technical schemes are summarized in turn. Finally, the chip detection and repair technologies and commercial application are introduced.

Accurate soil organic carbon (SOC) maps are helpful for guiding forestry production and management. Different ecological landscape areas within a large region may have different soil–landscape relationships, so models specifically for these areas may capture these relationships more accurately than the global model for the entire study area. The aim of this study was to investigate the role of zonal modelling in predicting forest SOC and to produce highly accurate forest SOC distribution maps. The prediction objects were SOC at five soil depths (0–20, 20–40, 40–60, 60–80, and 80–100 cm). First, the forest type map and soil texture class map were used to divide the relative homogeneous regions in Shaoguan City, Guangdong Province, China. Second, seven terrain variables derived from a 12.5-m digital elevation model (DEM) and five vegetation variables generated from 10-m Sentinel-2 remote sensing images were used as predictors to develop regional artificial neural network (ANN) models for each homogeneous region, as well as a global ANN model for the entire study area (1000 sample points). Finally, 10-fold cross-validation was used to assess the ANN prediction model performance, and independent validation was used to evaluate the produced forest SOC prediction maps (194 additional samples). The cross-validation results showed that the accuracies of the regional models were better than that of the global model. Independent validation results also showed that the precision (R2) of 0- to 100-cm forest SOC maps generated by forest type modelling had an improvement of 0.05–0.15, and that by soil texture class modelling had an improvement of 0.07–0.13 compared to the map generated by the global model. In conclusion, delineating relatively homogeneous regions via simple methods can improve prediction accuracy when undertaking soil predictions over large areas, especially with complex forest landscapes. In addition, SOC in the study area is generally more abundant in broadleaf forest and clay areas, with overall levels decreasing with soil depth. Accurate SOC distribution information can provide references for fertilization and planting. Plants with particularly high soil fertility requirements may perhaps be planted in broadleaf forests or clay areas, and plants with particularly developed roots may require furrow application of a small amount of SOC.

Warburg effect is a pivotal hallmark of cancers and appears prevalently in renal cell carcinoma (RCC). FBP1 plays a negative role in Warburg effect as a rate-limiting enzyme in gluconeogenesis, yet its mechanism in RCC remains to be further characterized. Herein, we revealed that FBP1 was downregulated in RCC tissue samples and was related to the poor survival rate of RCC. Strikingly, miR-24-1 whose DNA locus is overlapped with enhancer region chr9:95084940-95087024 was closely linked with the depletion of FBP1 in RCC. Of note, miRNAs like miR-24-1 whose DNA loci are enriched with H3K27ac and H3K4me1 modifications are belonging to nuclear activating miRNAs (NamiRNAs), which surprisingly upregulate target genes in RCC through enhancer beyond the conventional role of repressing target gene expression. Moreover, miR-24-1 reactivated the expression of FBP1 to suppress Warburg effect in RCC cells, and subsequently inhibited proliferation and metastasis of RCC cells. In mechanism, the activating role of miR-24-1 was dependent on enhancer integrity by dual luciferase reporter assay and CRISPR/Cas9 system. Ultimately, animal assay in vivo validated the suppressive function of FBP1 on 786-O and ACHN cells. Collectively, the current study highlighted that activation of FBP1 by enhancer-overlapped miR-24-1 is capable of contributing to Warburg effect repression through which RCC progression is robustly blocked, providing an alternative mechanism for RCC development and as well implying a potential clue for RCC treatment strategy.

IntroductionThe detailed association between albumin levels and mortality has not been studied in critically ill children. The aim of this study was to reveal an association between albumin levels in detail and mortality in critically ill children.Materials and methodsWe retrospectively collected data from children admitted to four pediatric intensive care units (PICUs) in China between January 2015 and October 2020. Restricted cubic spline curves based on logistic regression models were generated to evaluate the detailed associations between serum albumin levels and PICU mortality. Threshold effect analysis was performed using two piecewise regression models.ResultsThe study included 9,123 children. The overall mortality was 5.3%. The detailed association between serum albumin levels and the risk of mortality followed a U-shape. The risk of mortality decreased with increasing serum albumin levels (OR = 0.919; 95% CI: 0.886, 0.954) in children with serum albumin levels < 43.2 g/L and increased with increasing serum albumin levels (OR = 1.174; 95% CI: 1.044, 1.316) in children with serum albumin levels ≥ 43.2 g/L.ConclusionThere was a U-shaped association between serum albumin levels and mortality in critically ill children in the PICU.

Background: Renal cell carcinoma (RCC) is the predominant type of malignant tumor in kidney cancer. Finding effective biomarkers, particularly those based on the tumor immune microenvironments (TIME), is critical for the prognosis and diagnosis of RCC. Increasing evidence has revealed that long non-coding RNAs (lncRNAs) play a crucial role in cancer immunity. However, the comprehensive landscape of immune infiltration-associated lncRNAs and their potential roles in the prognosis and diagnosis of RCC remain largely unexplored.Methods: Based on transcriptomic data of 261 RCC samples, novel lncRNAs were identified using a custom pipeline. RCC patients were classified into different immune groups using unsupervised clustering algorithms. Immune-related lncRNAs were obtained according to the immune status of RCC. Competing endogenous RNAs (ceRNA) regulation network was constructed to reveal their functions. Expression patterns and several tools such as miRanda, RNAhybrid, miRWalk were used to define lncRNAs-miRNAs-mRNAs interactions. Univariate Cox, LASSO, and multivariate Cox regression analyses were performed on the training set to construct a tumorigenesis-immune-infiltration-related (TIR)-lncRNA signature for predicting the prognosis of RCC. Independent datasets involving 531 RCC samples were used to validate the TIR-lncRNA signature.Results: Tens of thousands of novel lncRNAs were identified in RCC samples. Comparing tumors with controls, 1,400 tumorigenesis-related (TR)-lncRNAs, 1269 TR-mRNAs, and 192 TR-miRNAs were obtained. Based on the infiltration of immune cells, RCC patients were classified into three immune clusters. By comparing immune-high with immune-low groups, 241 TIR-lncRNAs were identified, many of which were detected in urinary samples. Based on lncRNA-miRNA-mRNA interactions, we constructed a ceRNA network, which included 25 TR-miRNAs, 28 TIR-lncRNAs, and 66 TIR-mRNAs. Three TIR lncRNAs were identified as a prognostic signature for RCC. RCC patients in the high-risk group exhibited worse OS than those in the low-risk group in the training and testing sets (p < 0.01). The AUC was 0.9 in the training set. Univariate and multivariate Cox analyses confirmed that the TIR-lncRNA signature was an independent prognostic factor in the training and testing sets.Conclusion: Based on the constructed immune-related lncRNA landscape, 241 TIR-lncRNAs were functionally characterized, three of which were identified as a novel TIR-lncRNA signature for predicting the prognosis of RCC.

Bauhinia variegata petals are colorful, rich in anthocyanins, and have ornamental, nutritional, and medicinal value. However, the regulatory mechanism of anthocyanin accumulation in B. variegata remains unclear. In this study, a combined analysis of the metabolome and transcriptome was performed in red and white B. variegata cultivars in the early, middle, and blooming stages. A total of 46 different anthocyanins were identified, of which 27 showed marked differences in accumulation between the two cultivars, and contribute to their different petal colors. Malvidin 3-O-galactoside, peonidin 3-O-galactoside, cyanidin 3-O-glucoside, cyanidin 3-O-galactoside, and malvidin 3-O-glucoside were much more abundant in the second stage of flowering. In the blooming stage, except for the anthocyanins mentioned, delphinidin 3-O-galactoside and petunidin 3-O-galactoside were the most abundant anthocyanins in the red flowers, indicating that malvidin, peonidin, cyanidin, delphinidin, and petunidin were all responsible for the red color of petals in B. variegata. RNA sequencing identified 2,431 differentially expressed genes (DEGs), of which 26 were involved in the anthocyanin synthesis pathway. Correlations between the anthocyanin biosynthesis-related DEGs and anthocyanin contents were explored, and the DEGs involved in anthocyanin accumulation in B. variegata petals were identified. Eighteen of these DEGs encoded key catalytic enzymes, such as anthocyanidin reductase (ANR) and flavonoid-3′5′-hydroxylase (F3′5′H), and 17 of them encoded transcription factors (TFs) belonging to 14 families (including MYB, NAC, SPL, ERF, and CHR28). These results improve our understanding of the roles of anthocyanins, catalytic enzymes, and TFs in B. variegata petal-color expression.

BackgroundAlthough erectile dysfunction (ED) often occurs simultaneously with depression, not all patients with ED suffer major depression (MD), with a PHQ-9 score ≥15 indicating MD. Because the PHQ-9 questionnaire includes phrases such as “I think I am a loser” and “I want to commit suicide,” the psychological burdens of ED patients are likely to increase inevitably after using the PHQ-9, which, in turn, may affect ED therapeutic effects. Accordingly, we endeavored to develop a nomogram to predict individual risk of PHQ-9 score ≥15 in these patients.MethodsThe data of 1,142 patients with ED diagnosed in Xijing Hospital and Northwest Women and Children's Hospital from January 2017 to May 2020 were analyzed. While the Least Absolute Shrinkage and Selection Operator regression was employed to screen PHQ-9 score ≥15 related risk factors, multivariate logistic regression analysis was performed to verify these factors and construct the nomogram. The training cohort and an independent cohort that comprised 877 prospectively enrolled patients were used to demonstrate the efficacy of the nomogram.ResultsThe IIEF-5 score, PEDT score, physical pain score, frequent urination, and feeling of endless urination were found to be independent factors of PHQ-9 score ≥15 in patients with ED. The nomogram developed by these five factors showed good calibration and discrimination in internal and external validation, with a predictive accuracy of 0.757 and 0.722, respectively. The sensitivity and specificity of the nomogram in the training cohort were 0.86 and 0.52, respectively. Besides, the sensitivity and specificity of the nomogram in the validation cohort were 0.73 and 0.62, respectively. Moreover, based on the nomogram, the sample was divided into low-risk and high-risk groups.ConclusionThis study established a nomogram to predict individual risk of PHQ-9 score ≥15 in patients with ED. It is deemed that the nomogram may be employed initially to avoid those with a low risk of MD completing questionnaires unnecessarily.