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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.

BackgroundShort stature is a complex disorder with phenotypic and genetic heterogeneity. This study aimed to investigate clinical phenotypes and molecular basis of a cohort of patients with short stature.MethodsTrio whole-exome sequencing (Trio-WES) was performed to explore the genetic aetiology and obtain a molecular diagnosis in twenty Chinese probands with syndromic and isolated short stature.ResultsOf the twenty probands, six (6/20, 30%) patients with syndromic short stature obtained a molecular diagnosis. One novel COMP pathogenic variant c.1359delC, p.N453fs*62 and one LZTR1 likely pathogenic variant c.509G>A, p.R170Q were identified in a patient with short stature and skeletal dysplasia. One novel de novo NAA15 pathogenic variant c.63T>G, p.Y21X and one novel de novo KMT2A pathogenic variant c.3516T>A, p.N1172K was identified in two probands with short stature, intellectual disability and abnormal behaviours, respectively. One patient with short stature, cataract, and muscle weakness had a de novo POLG pathogenic variant c.2863 T>C, p.Y955H. One PHEX pathogenic variant c.1104G>A, p.W368X was identified in a patient with short stature and rickets. Maternal uniparental disomy 7 (mUPD7) was pathogenic in a patient with pre and postnatal growth retardation, wide forehead, triangular face, micrognathia and clinodactyly. Thirteen patients with isolated short stature had negative results.ConclusionTrio-WES is an important strategy for identifying genetic variants and UPD in patients with syndromic short stature, in which dual genetic variants are existent in some individuals. It is important to differentiate between syndromic and isolated short stature. Genetic testing has a high yield for syndromic patients but low for isolated patients.

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.

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 (

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.

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.

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.

In the process of constructing domain-specific knowledge graphs, the task of relational triple extraction plays a critical role in transforming unstructured text into structured information. Existing relational triple extraction models facemultiple challenges when processing domain-specific data, including insufficient utilization of semantic interaction information between entities and relations, difficulties in handling challenging samples, and the scarcity of domain-specific datasets. To address these issues, our study introduces three innovative components: Relation semantic enhancement, data augmentation, and a voting strategy, all designed to significantly improve the model's performance in tackling domain-specific relational triple extraction tasks. We first propose an innovative attention interaction module. This method significantly enhances the semantic interaction capabilities between entities and relations by integrating semantic information fromrelation labels. Second,we propose a voting strategy that effectively combines the strengths of large languagemodels (LLMs) and fine-tuned small pre-trained language models (SLMs) to reevaluate challenging samples, thereby improving the model's adaptability in specific domains. Additionally, we explore the use of LLMs for data augmentation, aiming to generate domain-specific datasets to alleviate the scarcity of domain data. Experiments conducted on three domain-specific datasets demonstrate that our model outperforms existing comparative models in several aspects, with F1 scores exceeding the State of the Art models by 2%, 1.6%, and 0.6%, respectively, validating the effectiveness and generalizability of our approach. [ABSTRACT FROM AUTHOR]

Regulatory proteins involved in human cellular division and proliferation, cyclin-dependent kinases 4 and 6 (CDK4/6) are overexpressed in numerous cancers, including triple-negative breast cancer (TNBC). TNBC is a common pathological subtype of breast cancer that is prone to recurrence and metastasis, and has a single treatment method. As one of the CDK4/6 inhibitors, abemaciclib can effectively inhibit the growth of breast tumors. In this study, we synthesized LA-D-B1, a derivative of Abemaciclib, and investigated its anti-tumor effects in breast cancer. Methods: Cellular viability was assessed using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. Cell cloning and migration abilities were determined by colony formation assay and wound healing assay. Cell invasion abilities and adhesion were determined by cell invasion assay and cell adhesion assay. The impact of compound LA-D-B1 on cell proliferation and the cell cycle was analyzed through Western blotting, which quantified the levels of proteins associated with the cyclin-dependent kinase (CDK) 4/6-cyclin D-Rb-E2F pathway. The in vivo anti-tumor activity of compound LA-D-B1 was investigated using a chick chorioallantoic membrane (CAM) model. Results: The study demonstrated that LA-D-B1 effectively suppressed breast cancer cell proliferation, induced apoptosis, and caused cell cycle arrest. Furthermore, LA-D-B1 reduced the expression of key proteins in the CDK4/6-cyclin D-Rb-E2F pathway, including CDK4, CDK6 and E2F1. The results also indicated significant antitumor activity of LA-D-B1 in a transplanted tumor model. Conclusion: In this study, LA-D-B1 demonstrated a potent anti-tumor effect by effectively suppressing cell proliferation and inhibiting cell cycle progression in breast cancer. These findings highlight the potential of LA-D-B1 as a valuable compound for enhancing therapeutic outcomes and controlling the progression of breast cancer. [ABSTRACT FROM AUTHOR]

Abstract In order to explore the mechanism of rockburst in coal seam with rock parting, a combination of on-site and numerical experiment is used to study the failure and instability process, crack propagation mechanism, and influencing factors. The following four points were addressed: (1) the instability is a process that roadway in coal seam with rock parting go through from stable locking in the initial stress unloading stage to slipping unlocking, and then to spatter ejection in slipping dynamic load disturbance stage. (2) The fracture development caused by unloading excavation of coal seam with rock parting will change from shear crack to tensile crack. In this process, coal-rock contact surface slip and coal-rock fracture are coupled with each other. (3) The greater the mining depth is, the greater the lateral pressure coefficient is, and the higher the rockburst risk is. On the contrary, the lower the risk of rockburst. (4) When choosing the support form of roadway in coal seam with rock parting, the two supporting forms of bolting (cable) and supplementary masonry support should be preferred. The results enrich the theory of the dynamics of surrounding rock fracture in coal mine, further clarify the potential dangers to mining-area roadways and working faces, and provide technical information to ensure the safe and efficient mining of bifurcated coal seam.