Abstract: BACKGROUND: The present study investigated the association between cerebrovascular diseases and sepsis, including its occurrence, progression, and impact on mortality. However, there is currently a lack of predictive models for 28-day mortality in patients with cerebrovascular disease associated with sepsis. OBJECTIVE: The objective of this study is to examine the mortality rate within 28 days after discharge in this population, while concurrently developing a corresponding predictive model. METHODS: The data for this retrospective cohort study were obtained from the MIMIC-IV database. Patients with sepsis and cerebrovascular disease in the ICU were included. Laboratory indicators, vital signs, and demographic data were…collected within 24 hours of ICU admission. Mortality rates within 28 days after discharge were calculated based on patient death times. Logistic regression analysis was used to identify potential variables for a predictive model. A nomogram visualized the prediction model. The performance of the model was evaluated using ROC curves, Calibration plots, and DCA. RESULTS: The study enrolled a total of 2660 patients diagnosed with cerebrovascular disease complicated by sepsis, consisting of 1434 males (53.91%) with a median age of 70.97 (59.60, 80.73). Among this cohort of patients, a total of 751 fatalities occurred within 28 days following discharge. The multivariate regression analysis revealed that age, creatinine, arterial oxygen partial pressure (Pa O2), arterial carbon dioxide partial pressure (Pa CO2), respiratory rate, white blood cell (WBC) count, Body Mass Index (BMI), and race demonstrated potential predictive variables. The aforementioned model yielded an area under the ROC curve of 0.744, accompanied by a sensitivity of 66.2% and specificity of 71.2%. Furthermore, both calibration plots and DCA demonstrated robust performance in practical applications. CONCLUSION: The proposed prediction model allows clinicians to promptly assess the mortality risk in patients with cerebrovascular disease complicated by sepsis within 28 days after discharge, facilitating early intervention strategies. Consequently, clinicians can implement additional advantageous medical interventions for individuals with cerebrovascular disease and sepsis.
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Abstract: Utilization of residue is a challenge in engineering practice, because unreasonable cutting causes excess materials wasted and increases the production cost. This work considers the residual two-dimensional cutting stock problem with usable leftover in which unused parts of cutting patterns can be used for future orders. We propose an algorithm that combines the iterative sequential value correction heuristic with the beam search heuristic, considering both the accumulation and the reusability of leftovers to reduce the material consumption. Cutting plans are constructed iteratively and the best one are chosen as the solution. Cutting patterns in the cutting plan are generated sequentially…by recursive techniques, and potentially usable leftover are accumulated by beam search heuristic. Item values are corrected after each pattern to diversify cutting plans. Three sets of simulations under different number of periods, over medium and large instances from the literature, are used to demonstrate the effectiveness of the heuristics. Computational results show that the algorithm provides better solutions, which can save a considerable amount of plate in a long-term production period. The utilization of wastages can save a considerable amount of stock plate and contract the production cost of enterprises in the long-term production period.
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Abstract: In the field of face recognition, conventional dictionary learning algorithms mainly focus on reconstructing the training samples and cannot directly associate the learning procedure with the test samples. Thus, they may not well represent the test samples and obtain unsatisfactory classification performance. In addition, though different training samples have various contributions to learn a dictionary, conventional dictionary learning algorithms cannot well exploit these contributions. In order to address these problems, we present a test sample oriented two-phase dictionary learning (TSOTP-DL) algorithm for face recognition. In the first phase of the TSOTP-DL algorithm, we use all training samples to provide a…linear representation of the test sample, and select K ``important'' training samples by using the variety of contributions. In the second phase of the TSOTP-DL algorithm, a dictionary is learned for the test sample by using the selected K$ ``important'' training samples. The TSOTP-DL algorithm utilizes the testing sample to select a subset of the training samples for learning a dictionary, which can reduce the influence of noise. Thus, the training samples are refined according to their contributions to the test sample in our algorithm, and it can improve the discriminative ability of the learned dictionary. In order to further improve the discriminative ability of the learned dictionary, a label embedding of atoms is constructed to encourage the same class training samples to have more similar coding coefficients than different classes. Experiment results demonstrate that our proposed algorithm achieves better classification results than some state-of-the-art dictionary learning and sparse coding algorithms on four public face databases.
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Keywords: Dictionary learning, sparse coding, face recognition
Abstract: In this paper, we present quality of service (QoS) metrics for various network applications based on human factors and technology attributes. The first term, human factors, addresses human perception of different kinds of media, such as conventional text, audio and video. The second term, technology attributes, represents the different technological aspects of these network applications, such as time-dependence and symmetry. Both of these terms are key factors that lead to variations of requirements for QoS. Establishing these requirements is paramount to providing QoS on computer networks and the Internet. With the metrics presented in this paper we can provide the…criteria necessary for such QoS assurance.
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Abstract: A novel similarity measure, proposed for clustering data with arbitrary distribution shapes, is developed. Such a new measure of similarity is employed in a dynamic model to collectively measure similarity among pattern vectors, which can help to achieve a more robust clustering performance than using the existing measures that are staticly and individually based on the distances among the isolated pairwise data. The experiment results demonstrated that the proposed neural network based on the new similarity measure has the capability to robustly and quickly cluster data on which Cluster-Detection-and-Labeling neural network fails.
Keywords: unsupervised learning, clustering, association clustering
Abstract: Swarm intelligence optimization algorithm has been proved to perform well in the field of parameter optimization. In order to further improve the performance of intelligent optimization algorithm, this paper proposes an improved and adaptive tunicate swarm algorithm (IMATSA) based on tunicate swarm algorithm (TSA). IMATSA improves TSA in the following four aspects: population diversity, local search convergence speed, jumping out of local optimal position, and balancing global and local search. Firstly, IMATSA adopts Tent map and quadratic interpolation to initialize population and enhance the diversity. Secondly, IMATSA uses Golden-Sine algorithm to accelerate the convergence of local search. Thirdly, in the…process of global development, IMATSA adopts Levy flight and the improved Gauss disturbance method to adaptively improves and coordinates the ability of global development and local search. Then, this paper verifies the performance of IMATSA based on 14 benchmark functions experiment, ablation experiment, parameter optimization experiments of Support Vector Machine (SVM) and Gradient Boosting Decision Tree (GBDT), Wilcoxon signed rank test and image multi-threshold segmentation experiment with the performance metrics are convergence speed, convergence value, significance level P -value, Peak Signal-to-Noise Ratio (PSNR) and Standard Deviation (STD). Experimental results show that IMATSA performs better in three kinds of benchmark functions; each component of IMATSA has a positive effect on the performance; IMATSA performs better in parameter optimization experiments of SVM experiment and GBDT; there is significant difference between IMATSA and other algorithms by Wilcoxon signed rank test; in image segmentation, the performance is directly proportional to the number of thresholds, and compared with other algorithms, IMATSA has better comprehensive performance.
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Abstract: OBJECTIVE: This study aimed to explore the correlation of circular RNA ABCB10 (circ-ABCB10) expression with clinicopathological features and survival, as well as its impact on regulating cell proliferation and apoptosis in epithelial ovarian cancer (EOC). METHODS: A total of 103 EOC patients were consecutively recruited, then their tumor tissues were obtained for circ-ABCB10 detection using qRT-PCR. Additionally, 53 EOC adjacent tissues were collected as control. Patients’ clinicopathological and survival data were recorded. In vitro , circ-ABCB10 expression was detected in OVCAR3, UWB1.289, SKOV3, CAOV3 and IOSE80 cell lines by RT-qPCR, and the effect of circ-ABCB10 on cell proliferation and apoptosis…was detected through circ-ABCB10 overexpression and silencing by plasmids transfection into SKOV3 cells. RESULTS: Circ-ABCB10 was upregulated in tumor tissues compared with adjacent tissues, and presented with good value in distinguishing tumor tissues from adjacent tissues (AUC = 0.766, 95% CI: 0.690–0.842). Circ-ABCB10 high expression was correlated with poor differentiation, large tumor size and advanced International Federation of Gynecology and Obstetrics (FIGO) stage in EOC patients. As for survival, circ-ABCB10 was correlated with worse OS. In vitro experiments revealed that circ-ABCB10 was upregulated and promoted cell proliferation but reduced cell apoptosis, and negatively regulated miR-1271, miR-1252 and miR-203 in EOC cells. CONCLUSIONS: Circ-ABCB10 correlates with advanced clinicopathological features and unfavorable survival, and promotes proliferation, reduces apoptosis and negatively regulated miR-1271, miR-1252 and miR-203 in EOC.
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Keywords: Circ-ABCB10, EOC, disease risk, OS, cell proliferation and apoptosis
Abstract: BACKGROUND: Numerous studies reveal the clinical significance of tumor microenvironment (TME) in multiple cancers. The association between TME in oral squamous cell carcinoma (OSCC) and clinical outcomes remains unsolved. OBJECTIVE: This study aims to exhibit the TME of OSCC and identified the prognostic marker. METHODS: Gene expression profile and clinical data OSCC patients were from the TCGA database. The validated stage data was from the Gene Expression Omnibus (GSE65858). Immune/stromal scores of each patient were calculated by ESTIMATE algorithm. Biological functional prediction was conducted. Prognostic genes identified by survival analysis. Nomogram and Receiver operating characteristic curves were employed to test…the predicting power. TIMER database was applied to evaluated the immune infiltrates. RESULTS: Lower immune scores were observed in male patients (P = 0.0107) and different primary tumor sites of oral cavity with different stromal scores (P = 0.0328). The Differentially expressed genes (DEGs) were involved in immune related pathways. HGF gene (hepatocyte growth factor) was prognostic related and with a better prognostic performance when combined with clinical features (AUC= TCGA 0.638, AUC= GEO 0.714). HGF was significantly related with B cell, CD4ï ¼ T cell, CD8+ T cell, macrophage, neutrophils, and dendritic cell infiltration. CONCLUSION : The current study analyzed the TME and presented immune related prognostic biomarkers for OSCC.
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Abstract: Probabilistic Uncertain Linguistic Term Set (PULTS), as an emerging and effective linguistic expression tool, can appropriately describe the complex evaluation information of decision makers. The cloud model is powerful in handling complex cognitive linguistic information, based on which, this paper proposes two new Multicriteria Decision-Making (MCDM) Methods with PULTSs. Firstly, to avoid the problem of information loss in traditional linguistic conversion methods, Probabilistic Uncertainty Trapezium Cloud (PUTC) is proposed to quantify linguistic evaluation information. Secondly, the Probabilistic Uncertainty Trapezium Cloud Weighted Bonferroni mean (PUTCWBM) operator is defined, while presenting a new cloud score function and similarity measures. Additionally, two ranking…methods are proposed, one on the basis of the similarity measures of PUTCs and ideal solutions, the other on the basis of the PUTCWBM operator and the cloud score function. Finally, the two methods are verified with an example of evaluation on masks, and the effectiveness and superiority of the methods are further illustrated through sensitivity analysis and method comparison.
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Keywords: Multicriteria decision-making, probabilistic uncertain linguistic term set, probabilistic uncertain Trapezium cloud, similarity measure, cloud score function