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Background: Streptococcus pneumoniae (Spn) is a major causative agent of pneumonia, which can disseminate to the bloodstream and brain. Pneumonia remains a leading cause of death among children aged 1–59 months worldwide. This study aims to investigate the role of Kruppel-like factor 2 (KLF2) in lung injury caused by Spn in young mice. Methods: Young mice were infected with Spn to induce pneumonia, and the bacterial load in the bronchoalveolar lavage fluid was quantified. KLF2 expression in lung tissues was analyzed using real-time quantitative polymerase chain reaction and Western blotting assays. Following KLF2 overexpression, lung tissues were assessed for lung wet-to-dry weight ratio and Myeloperoxidase activity. The effects of KLF2 on lung injury and inflammation were evaluated through hematoxylin and eosin staining and enzyme-linked immunosorbent assay. Chromatin immunoprecipitation and dual-luciferase assay were conducted to examine the binding of KLF2 to the promoter of microRNA (miR)-222-3p and cyclin-dependent kinase inhibitor 1B (CDKN1B), as well as the binding of miR-222-3p to CDKN1B. Levels of miR-222-3p and CDKN1B in lung tissues were also determined. Results: In young mice with pneumonia, KLF2 and CDKN1B were downregulated, while miR-222-3p was upregulated in lung tissues. Overexpression of KLF2 reduced lung injury and inflammation, evidenced by decreased bacterial load, reduced lung injury, and lower levels of proinflammatory factors. Co-transfection of miR-222-3p-WT and oe-KLF2 significantly reduced luciferase activity, suggesting that KLF2 binds to the promoter of miR-222-3p and suppresses its expression. Transfection of CDKN1B-WT with miR-222-3p mimics significantly reduced luciferase activity, indicating that miR-222-3p binds to CDKN1B and downregulates its expression. Overexpression of miR-222-3p or downregulation of CDKN1B increased bacterial load in BALF, lung wet/dry weight ratio, MPO activity, and inflammation, thereby reversing the protective effect of KLF2 overexpression on lung injury in young mice with pneumonia. Conclusions: KLF2 alleviates lung injury in young mice with Spn-induced pneumonia by transcriptional regulation of the miR-222-3p/CDKN1B axis. [ABSTRACT FROM AUTHOR]

Information fusion enables the integration and transformation of complimentary data from different sources, providing a unified representation for centralized knowledge discovery, which can contribute to effective decision-making, classification, prediction, and more. Multi-source interval-valued data, represented in the form of intervals to capture uncertainty phenomenons, is a common type of symbolic data that finds extensive applications in the real world. This paper aims to investigate the effective fusion of multi-source interval-valued data and to design dynamic updating algorithms for the situations involving multiple dimensions. The objective is to enhance the efficiency of fusion processes. Firstly, this paper use the Kullback–Leibler divergence to measure the dissimilarity between interval distributions, and construct fuzzy similarity relation. Furthermore, we define a fuzzy information granule structure of interval-valued. Secondly, the concept of fuzzy similarity relations is utilized to construct fuzzy decision-making for objects. Subsequently, based on the aforementioned fuzzy information granule structure and fuzzy decision-making, we propose a novel measure called fuzzy approximate conditional entropy and design a corresponding entropy fusion model. Finally, we discuss various scenarios where dynamic changes occur simultaneously in the attributes and information sources of dynamic multi-source interval-valued data. We design corresponding dynamic update algorithms for these situations. Numerical experiments are conducted on nine UCI datasets to validate our proposed fusion method. The experimental results indicate that our fusion approach exhibits improved classification performance compared to the common fusion methods. The designed dynamic update algorithms are also capable of reducing computation time and enhancing fusion efficiency. [ABSTRACT FROM AUTHOR]

Background: Lung cancer remains one of the most prevalent cancer types worldwide, with a high mortality rate. Upregulation of programmed cell death protein 1 (PD-1) and its ligand (PD-L1) may represent a key mechanism for evading immune surveillance. Immune checkpoint blockade (ICB) antibodies against PD-1 or PD-L1 are therefore widely used to treat patients with lung cancer. However, the mechanisms by which lung cancer and neutrophils in the microenvironment sustain PD-L1 expression and impart stronger inhibition of CD8+ T cell function remain unclear. Methods: We investigated the role and underlying mechanism by which PD-L1+ lung cancer and PD-L1+ neutrophils impede the function of CD8+ T cells through magnetic bead cell sorting, quantitative real-time polymerase chain reaction (RT-PCR), western blotting, enzyme-linked immunosorbent assays, confocal immunofluorescence, gene silencing, flow cytometry, etc. In vivo efficacy and safety studies were conducted using (Non-obeseDiabetes/severe combined immune deficiency) SCID/NOD mice. Additionally, we collected clinical and prognostic data from 208 patients who underwent curative lung cancer resection between 2017 and 2018. Results: We demonstrated that C-X-C motif chemokine ligand 5 (CXCL5) is markedly overexpressed in lung cancer cells and is positively correlated with a poor prognosis in patients with lung cancer. Mechanistically, CXCL5 activates the phosphorylation of the Paxillin/AKT signaling cascade, leading to upregulation of PD-L1 expression and the formation of a positive feedback loop. Moreover, CXCL5 attracts neutrophils, compromising CD8+ T cell-dependent antitumor immunity. These PD-L1+ neutrophils aggravate CD8+ T cell exhaustion following lung cancer domestication. Combined treatment with anti-CXCL5 and anti-PD-L1 antibodies significantly inhibits tumor growth in vivo. Conclusions: Our findings collectively demonstrate that CXCL5 promotes immune escape through PD-L1 upregulation in lung cancer and neutrophils chemotaxis through autocrine and paracrine mechanisms. CXCL5 may serve as a potential therapeutic target in synergy with ICBs in lung cancer immunotherapy. [ABSTRACT FROM AUTHOR]

Background: Diffuse large B-cell lymphoma (DLBCL) is a diffuse malignant hyperplastic disease of large B-cell lymphoma characterized by high heterogeneous and aggressiveness and has a poor prognosis. Objectives: To explore the pathogenesis of DLBCL and identify the effective therapeutic targets. Results: The expression of ubiquitin-specific peptidase 13 (USP13) was significantly increased in peripheral blood of children with DLBCL and in DLBCL cells. USP13 overexpression promoted the capacity of proliferation, migration, invasion of DLBCL cells and human umbilical vein endothelial cell (HUVEC) tube formation, while knockdown of USP13 suppressed the proliferation, migration, invasion, and angiogenesis of DLBCL cells. USP13 positively regulated the expression of myeloid cell leukemia 1 (MCL-1) in DLBCL cells. Overexpression of MCL-1 attenuated the inhibition effects of USP13 knockdown on DLBCL cells. Conclusion: USP13 modulated DLBCL cell proliferation, migration, invasion, and angiogenesis by promoting MCL-1 expression. [ABSTRACT FROM AUTHOR]

As human activities continue to expand, wildlife persistence faces escalating threats from roads. In Wolong area of Giant Panda National Park, the local giant pandas (Ailuropoda melanoleuca) are divided into two population groups along the National Highway G350 (NHG). Therefore, selecting suitable areas to help those giant pandas communicate across the NHG is necessary. In this research, we evaluated the presence of human activities and simulated their absence to analyze how they affect the giant panda's habitat in Wolong. Subsequently, based on the kernel density estimation (KDE) for giant pandas and the main human distribution locations, we selected suitable areas for the population link between the two road sections on the NHG. We simulated the absence of human activities on the two road sections to compare changes in the habitat suitability index (HSI) and connectivity value (CV) relative to their presence. We aimed to carefully select the area for future giant panda corridor plans and simulate whether eliminating human activities will significantly improve the HSI and CV of the area. Our results show that: (1) Human activities presence has led to subtle changes in the landscape pattern of suitable habitats and a decrease in Wolong by 78.76 km2 compared to their absence. (2) Human activities presence significantly reduced HSI and CV in the 1000 m buffer along the NHG compared to their absence. (3) The HSI and CV of the 1000 m buffer in the simulated absence of human activities for the two road sections were significantly higher than their presence. This research identified the optimal road section for crossing the NHG to link giant panda population groups and habitats in Wolong. These insights are significant for formulating conservation decisions and corridor plans and for promoting wildlife conservation in reserves amid high levels of human activity. [ABSTRACT FROM AUTHOR]

Purpose: To explore the early diagnostic value of superb microvascular imaging (SMI) features within the rotator cuff gap for frozen shoulder. Patients and Methods: This prospective study enrolled patients with acute early-stage frozen shoulder seeking treatment at Zhabei Central Hospital in Jing'an District, Shanghai, between July 2021 and December 2022 were enrolled in this study. Healthy controls were collected in a 1:1 ratio from the same hospital's physical examination center. All participants underwent SMI and power Doppler ultrasound (PDUS) of the rotator cuff gap. Results: The study included 79 patients with frozen shoulder and 77 healthy controls. Compared with the healthy control group, the patient group had a higher proportion of hypoechoic rotator cuff gap (81.0% vs 48.1%, P< 0.001), a thicker coracohumeral ligament (2.60± 1.01 vs 2.03± 0.97, P< 0.001), a thicker glenohumeral joint capsule (3.10± 0.99 vs 2.46± 1.17, P< 0.001), and elevated blood grading using SMI (P< 0.001) and PDUS (P=0.014). The highest area under the curve (AUC) was observed for SMI blood flow grading (AUC=0.824, 95% CI: 0.755– 0.880, P< 0.001), resulting in 82% sensitivity and 77% specificity when using a cutoff of 1. SMI blood flow grading was associated with external rotation < 30° (P=0.007) and abduction < 30° (P=0.013) but not with internal rotation < 30° (P=0.630) or flexion < 30° (P=0.562). Conclusion: The grading of SMI blood flow may emerge as a valuable predictive indicator for the early stages of frozen shoulder. This simple ultrasound technique holds the potential to enhance the diagnostic process, enabling early initiation of treatment and potentially improving patient outcomes. [ABSTRACT FROM AUTHOR]

SPL (SQUAMOSA promoter binding protein-like), as one family of plant transcription factors, plays an important function in plant growth and development and in response to environmental stresses. Despite SPL gene families having been identified in various plant species, the understanding of this gene family in peanuts remains insufficient. In this study, thirty-eight genes (AhSPL1-AhSPL38) were identified and classified into seven groups based on a phylogenetic analysis. In addition, a thorough analysis indicated that the AhSPL genes experienced segmental duplications. The analysis of the gene structure and protein motif patterns revealed similarities in the structure of exons and introns, as well as the organization of the motifs within the same group, thereby providing additional support to the conclusions drawn from the phylogenetic analysis. The analysis of the regulatory elements and RNA-seq data suggested that the AhSPL genes might be widely involved in peanut growth and development, as well as in response to environmental stresses. Furthermore, the expression of some AhSPL genes, including AhSPL5, AhSPL16, AhSPL25, and AhSPL36, were induced by drought and salt stresses. Notably, the expression of the AhSPL genes might potentially be regulated by regulatory factors with distinct functionalities, such as transcription factors ERF, WRKY, MYB, and Dof, and microRNAs, like ahy-miR156. Notably, the overexpression of AhSPL5 can enhance salt tolerance in transgenic Arabidopsis by enhancing its ROS-scavenging capability and positively regulating the expression of stress-responsive genes. These results provide insight into the evolutionary origin of plant SPL genes and how they enhance plant tolerance to salt stress. [ABSTRACT FROM AUTHOR]

Background: Many patients experience lower-extremity swelling following total knee arthroplasty (TKA), which impedes recovery. Diosmin is a semisynthetic flavonoid that is often utilized to treat swelling and pain caused by chronic venous insufficiency. We aimed to evaluate the efficacy and safety of diosmin in reducing lower-extremity swelling and pain as well as in improving functional outcomes following TKA. Methods: This study was designed as a randomized, controlled multicenter trial and conducted in 13 university-affiliated tertiary hospitals. A total of 330 patients undergoing TKA were randomized to either receive or not receive diosmin postoperatively. The diosmin group received 0.9 g of diosmin twice per day for 14 consecutive days starting on the day after surgery, whereas the control group received neither diosmin nor a placebo postoperatively. The primary outcome was lower-extremity swelling 1, 2, 3, and 14 days postoperatively. The secondary outcomes were postoperative pain assessed with use of a visual analogue scale, Hospital for Special Surgery score, range of knee motion, levels of the inflammatory biomarkers C-reactive protein and interleukin-6, and complications. Results: At all postoperative time points, diosmin was associated with significantly less swelling of the calf, thigh, and upper pole of the patella as well as with significantly lower pain scores during motion. However, no significant differences in postoperative pain scores at rest, Hospital for Special Surgery scores, range of motion, levels of inflammatory biomarkers, or complication rates were found between the diosmin and control groups. Conclusions: The use of diosmin after TKA reduced lower-extremity swelling and pain during motion and was not associated with an increased incidence of short-term complications involving the outcomes studied. However, further studies are needed to continue exploring the efficacy and safety of diosmin use in TKA. Level of Evidence: Therapeutic Level I. See Instructions for Authors for a complete description of levels of evidence. [ABSTRACT FROM AUTHOR]

To solve the problem of path planning in blood transportation by unmanned aerial vehicles (UAVs), a path planning model for blood transportation by UAVs is established with the goal of minimizing total transportation distance of UAVs. Considering the safe time interval of continuous take-off and landing of UAVs under the condition that the number of UAVs take-off and landing platforms is limited, a scheduling strategy of UAVs take-off sequence is designed to reduce the total time consumption for UAVs to complete transportation. And an imperialist competitive algorithm based on the imperialist reform is proposed. A sinusoidal disturbance strategy and an imperialist reform are introduced to improve the search accuracy of the algorithm. The acceptance criteria related to solution quality is designed to increase the diversity of the population. The benchmark example and the UAVs transporting blood example are used for verification. The results show that the proposed algorithm can provide transport tasks for the blood transportation by UAVs that meets various constraints and without UAVs take-off and landing conflicts. In addition, using the scheduling strategy of UAVs take-off sequence can reduce the total time spent by UAVs to actually complete the tasks effectively. [ABSTRACT FROM AUTHOR]

Rough set theory is a mathematical tool widely employed in various fields to handle uncertainty. Feature selection, as an essential and independent research area within rough set theory, aims to identify a small subset of important features by eliminating irrelevant, redundant, or noisy ones. In human life, data characteristics constantly change over time and other factors, resulting in ordered datasets with varying features. However, existing feature extraction methods are not suitable for handling such datasets since they do not consider previous reduction results when features change and need to be recomputed, leading to significant time consumption. To address this issue, the incremental attribute reduction algorithm utilizes prior reduction results effectively reducing computation time. Motivated by this approach, this paper investigates incremental feature selection algorithms for ordered datasets with changing features. Firstly, we discuss the dominant matrix and the dominance conditional entropy while introducing update principles for the new dominant matrix and dominance diagonal matrix when features change. Subsequently, we propose two incremental feature selection algorithms for adding (IFS-A) or deleting (IFS-D) features in ordered data set. Additionally, nine UCI datasets are utilized to evaluate the performance of our proposed algorithm. The experimental results validate that the average classification accuracy of IFS-A and IFS-D under four classifiers on twelve datasets is 82.05% and 80.75%, which increases by 5.48% and 3.68% respectively compared with the original data. [ABSTRACT FROM AUTHOR]

Stereodivergent engineering of one enzyme to create stereocomplementary variants for synthesizing optically pure molecules with tailor‐made (R) or (S) configurations on an optional basis is highly desirable and challenging. This study aimed to engineer fatty acid photodecarboxylase from Chlorella variabilis (CvFAP) using the focused rational iterative site‐specific mutagenesis (FRISM) strategy to obtain two highly stereocomplementary variants with excellent selectivity (both giving products with up to 99 % e.e.). These variants were used for the CvFAP‐catalyzed light‐driven kinetic resolution of oxalates or oxamic acids prepared from the corresponding sec‐alcohols or amines, providing a new biotransformation process for preparing chiral sec‐alcohols and amines. Molecular dynamics simulation, kinetic data and transient spectra revealed the source of selectivity. This study represents the first example of the kinetic resolution of sec‐alcohols or amines catalyzed by a pair of stereocomplementary CvFAPs. [ABSTRACT FROM AUTHOR]

Stereodivergent engineering of one enzyme to create stereocomplementary variants for synthesizing optically pure molecules with tailor‐made (R) or (S) configurations on an optional basis is highly desirable and challenging. This study aimed to engineer fatty acid photodecarboxylase from Chlorella variabilis (CvFAP) using the focused rational iterative site‐specific mutagenesis (FRISM) strategy to obtain two highly stereocomplementary variants with excellent selectivity (both giving products with up to 99 % e.e.). These variants were used for the CvFAP‐catalyzed light‐driven kinetic resolution of oxalates or oxamic acids prepared from the corresponding sec‐alcohols or amines, providing a new biotransformation process for preparing chiral sec‐alcohols and amines. Molecular dynamics simulation, kinetic data and transient spectra revealed the source of selectivity. This study represents the first example of the kinetic resolution of sec‐alcohols or amines catalyzed by a pair of stereocomplementary CvFAPs. [ABSTRACT FROM AUTHOR]

Rough set theory is a mathematical tool widely employed in various fields to handle uncertainty. Feature selection, as an essential and independent research area within rough set theory, aims to identify a small subset of important features by eliminating irrelevant, redundant, or noisy ones. In human life, data characteristics constantly change over time and other factors, resulting in ordered datasets with varying features. However, existing feature extraction methods are not suitable for handling such datasets since they do not consider previous reduction results when features change and need to be recomputed, leading to significant time consumption. To address this issue, the incremental attribute reduction algorithm utilizes prior reduction results effectively reducing computation time. Motivated by this approach, this paper investigates incremental feature selection algorithms for ordered datasets with changing features. Firstly, we discuss the dominant matrix and the dominance conditional entropy while introducing update principles for the new dominant matrix and dominance diagonal matrix when features change. Subsequently, we propose two incremental feature selection algorithms for adding (IFS-A) or deleting (IFS-D) features in ordered data set. Additionally, nine UCI datasets are utilized to evaluate the performance of our proposed algorithm. The experimental results validate that the average classification accuracy of IFS-A and IFS-D under four classifiers on twelve datasets is 82.05% and 80.75%, which increases by 5.48% and 3.68% respectively compared with the original data. [ABSTRACT FROM AUTHOR]

The bronchopleural fistula (BPF) is a pathological communication between the bronchus and the pleural space. Diagnosing BPF poses a significant challenge for physicians, particularly when identifying multifocal BPFs. Traditionally, retrograde instillation of methylene blue (MB) into the pleural cavity with simultaneous observation with a bronchoscope has been used to locate a BPF. However, MB instillation is not effective in identifying multifocal BPFs. In this article, we report a new method for locating multifocal BPFs which involves placing the endobronchial valve (EBV) in reverse combined with retrograde MB instillation. First, the thoracic cavity is filled with MB solution. Then, using bronchoscopy, the location of a BPF can be identified as the MB solution flows into the bronchus. Secondly, an EBV is deployed in reverse in the bronchus where the identified BPF is located. Retrograde MB instillation is then repeated to locate any additional BPFs until no new ones are found. Two cases were reported using this novel method to identify multifocal BPFs, and each case was ultimately diagnosed with 2 BPFs. After precisely locating all the BPFs, the EBVs are then removed and placed forward in the target bronchi for treating the BPFs. During the follow‐up period, no recurrence of BPFs was observed. We conclude that reversed placement of EBVs combined with retrograde MB instillation appears to be an effective approach for locating multifocal BPFs. [ABSTRACT FROM AUTHOR]

Intestinal epithelia impairment of inflammatory bowel disease (IBD) leads to the leakage of bacteria and antigens and the consequent persistent immune imbalance. Restoring the epithelial barrier is a promising therapeutic target but lacks effective and safe clinical interventions. By identifying the catalase (CAT) presence in the IBD pathological environment, we herein develop a CAT-catalyzed pathologically coating on the damaged epithelial barrier to inhibit intestinal leakage for IBD therapy. With the codelivery of CaO2 (a CAT substrate) and dopamine, the nanosystem can enable CAT-catalyzed oxygen (O2) production and in-situ polymerization of dopamine and then yield a thin and integrative polydopamine (PDA) coating on the intestinal barrier due to the highly adhesive property of PDA. In vivo study demonstrates that PDA coating provides not only a protective barrier by restricting intestinal leakage but also a favorable anti-inflammation effect. Beyond drug management, this work provides a physical repair strategy via catalyzed coating for IBD therapy. [ABSTRACT FROM AUTHOR]

Background: T cell factor‐1 (TCF‐1) + stem‐like tumor‐infiltrating lymphocytes (stem‐like TILs) are important memory cells in the tumor microenvironment. However, their relationship with clinicopathological features, CD8+ TIL densities, immune checkpoint inhibitors (ICs), and prognostic values remain unknown for lung adenocarcinomas (LUADs). In this study, we aimed to characterize TCF‐1+ TILs and their prognostic significance in patients with surgically resected LUADs. Methods: Expression of TCF‐1, CD8, and ICs including programmed death‐1 (PD‐1), lymphocyte activating‐3 (LAG‐3), and T cell immunoglobulin and mucin‐domain containing‐3 (TIM‐3) in TILs were estimated using immunohistochemistry of resected LUADs. The association between TCF‐1 expressions and clinicopathological characteristics of patient prognoses were analyzed. Results: Positive TCF‐1 expression significantly correlated with advanced pathological stage, tumor grade, CD8+ TILs density, TIM‐3 expression, LAG‐3 expression, and PD‐1 expression. TCF‐1 positivity was significantly associated with a better recurrence‐free survival (RFS), and overall survival (OS). Subgroup analysis revealed that the TCF‐1+/CD8+ group had the best RFS and OS, while the TCF‐1‐/CD8‐ group had the worst RFS and OS. Similarly, patients with TCF‐1 + PD‐1‐ had the best prognoses and patients with TCF‐1‐PD‐1+ had the worst prognoses. Conclusion: TCF‐1 had relatively high positive expression and special clinicopathological features in patients with LUAD. TCF‐1+ TILs were related to CD8 density, TIM‐3 expression, LAG‐3 expression, and PD‐1 expression, and were associated with better prognoses in LUAD patients. A combination of TCF‐1 and CD8 densities or PD‐1 expression further stratified patients into different groups with distinct prognoses. [ABSTRACT FROM AUTHOR]

Concepts learning is the most fundamental unit in the process of human cognition in philosophy. Granularity is one of the fundamental concepts of human cognition. The combination of granular computing and concept learning is critical in the cognitive process. Meanwhile, efficiently and accurately using the information collected from different sources is the focus of data mining in the contemporary. Hence, how to sufficiently learn concepts under a multi-sources context is an essential concern in the field of cognition. This paper offers a new thought for two-way concept-cognitive learning based on granular computing in multi-source fuzzy decision tables. Firstly, based on the best possible guarantee of the classification ability, original information from different sources is fused by conditional entropy, which is the kind of multi-source fusion method (i.e., CE-fusion). Secondly, we learn concepts from a given object set, attribute set, or pair of object and attribute sets in the fused information table, and these three types of concept learning algorithms are designed. This analysis shows that two-way concept learning based on multi-source information fusion is a suitable method of multi-source concept learning. Some examples are valuable for applying these theories to deal with practical issues. Our work will provide a convenient novel tool for researching concept-cognitive learning methods with multi-source fuzzy context. [ABSTRACT FROM AUTHOR]

Objective To investigate the expression and clinical significance of phosphatidylinoinosidine-3-kinase (PI3K) and matrix metalloproteinase 9 (MMP-9) in bronchoscopic alveolar lavage fluid of children with severe pneumonia in pediatric intensive care unit (PICU). Methods A total of 103 children with severe pneumonia admitted to PICU of the hospital from January 2021 to June 2022 were enrolled. All patients underwent fiberbronchoscopic alveolar lavage. Real-time fluorescence quantitative polymerase chain reaction was used to determine the expression of PI3K, and enzyme-linked immunosorption was used to determine the expression of MMP-9 in alveolar lavage fluid. The patients were followed up for 21 days after admission to evaluate the prognosis. Logistic regression analysis was used to analyze the factors affecting the prognosis. Pearson correlation was used to analyze the correlation between the expression levels of PI3K and MMP-9 in the bubble lavage solution and the child Critical Case Score (PCIS) and child Organ Dysfunction 2 (PELOD-2) Scores. The values of PI3K and MMP-9 expression levels in alveolar lavage fluid to predict the prognosis were analyzed by receiver operating curve (ROC). Results Among the 103 children, 29 cases had poor prognosis vs 74 cases had good prognosis. Logistic multivariate regression analysis showed that combined shock (OR = 4.104, 95%CI: 1.689 ~ 9.974), PELOD-2 score (OR = 4.764, 95%CI: 1.960 ~ 11.577), PCIS score (OR = 5.068, 95%CI: 2.085 ~ 12.318), PI3K (OR = 3.924, 95%CI: 1.614 ~ 9.536) and MMP-9 (OR = 3.873, 95%CI: 1.594 ~ 9.412) were the prognostic factors of PICU children with severe pneumonia (P < 0.05). Pearson correlation analysis showed that the expression levels of PI3K and MMP-9 in alveolar lavage fluid were negatively correlated with PCIS scores (r = -0.598, P = 0.000; r = -0.537, P = 0.000), PI3K and MMP-9 expression levels in alveolar lavage fluid were positively correlated with PELOD-2 scores (r = 0.546, P = 0.000; r = 0.571, P = 0.000). ROC curve results showed that the sensitivity of PI3K, MMP-9 and their combination to predict the prognosis of PICU children with severe pneumonia was 75.86% (95%CI: 56.08% ~ 88.98%), 72.41% (95%CI: 52.51% ~ 86.55%) and 82.76% (95%CI: 63.51% ~ 93.47%), the specificity was 75.68% (95%CI: 64.07% ~ 84.58%), 79.73% (95%CI: 68.47% ~ 87.85%), 89.19% (95%CI: 79.28% ~ 94.88%), and the AUC was 0.762 (95%CI: 0.666 ~ 0.859), 0.767 (95%CI: 0.670 ~ 0.873), 0.909 (95%CI: 0.848 ~ 0.971). Conclusion The expression of PI3K and MMP-9 in bronchoscopic alveolar lavage fluid was related to the prognosis of children with severe pneumonia in PICU, and the combination in predicting the prognosis was effective, so it has certain clinical value to use them as a sensitive indicator to predict the prognosis of children with severe pneumonia in PICU. [ABSTRACT FROM AUTHOR]

Recent developments in big data applications have heightened the need for understanding and processing high-dimensional data. It is necessary to extract some excellent features that effect the learning performance in high-dimensional data. Feature selection algorithm based on rough set theory as an important preprocessing method has been widely used in practical applications. Meanwhile, it should be noted that different attributes have different effects on model evaluation. Nevertheless, each feature or attribute has the same degree of importance in the interval-valued information system by using rough set models, ignoring the imbalance between features. Moreover, the monotonic classification effect of interval-valued data is easily affected by noise. For these two issues, we introduce different weights into neighborhood relations and propose a novel approach for feature selection-based weighted neighborhood rough sets for interval-valued information systems in this study. First, weighted neighborhood relations and some important properties are proposed by considering different attribute weights in the interval-valued information system. Then, we construct an interval-valued-based weighted neighborhood rough set (IVWNRS) model to solve the contradiction between the degree of dependency and the classification ability of the attribute subset. Furthermore, a heuristic algorithm is designed according to the degree of dependency to select an attribute subset that has both strong correlation and high dependency. Finally, we compare it with six other representative feature selection algorithms on fifteen public datasets to evaluate the performance of the proposed algorithm. Experimental results on different classifiers show that the IVWNRS algorithm has higher classification performance and is significantly effective. [ABSTRACT FROM AUTHOR]

The purpose of this study was to compare an oblique bikini-incision via direct anterior approach (BI-DAA) to a conventional posterolateral approach (PLA) during simultaneous bilateral total hip arthroplasty (simBTHA) in terms of early patient outcomes, postoperative functional recovery, and complications. From January 2017 to January 2020, 106 patients receiving simBTHA were enrolled and randomly allocated to the BI-DAA or PLA group. Primary outcomes were measured using hemoglobin (HGB) drop, transfusion rate, the length of stay (LOS), the visual analog scale (VAS) for pain, the Harris hip score, Western Ontario and McMaster Universities Osteoarthritis Index, and the scar cosmesis assessment and rating scale. Secondary outcomes were the operative time, radiographic measurements, including femoral offset, femoral anteversion, stem varus/valgus angle, and leg length discrepancy (LLD). The occurrence of postoperative complications was also recorded. There were no differences in demographic or clinical characteristics before surgery. Compared to the PLA, the patients in the BI-DAA group had lower HGB drop (24.7 ± 13.3 g/L vs. 34.7 ± 16.7, P <.01) and transfusion rates (9/50 vs. 18/50, P =.04) and a shorter LOS (5.12 ± 1.5 vs. 6.40 ± 2.0 days, P <.01) without increasing the operative time (169.7 ± 17.3 vs. 167.5 ± 21.8 min, P =.58). The BI-DAA group yielded a smaller LLD (2.1 ± 2.3 vs. 3.8 ± 3.0 mm, P <.01) and less variability in component orientation than the PLA group (100% vs. 93%, P =.01). As for the scar, the BI-DAA group produced a shorter incision length (9.7 ± 1.6 vs. 10.8 ± 2.0 mm, P <.01) and higher postoperative recovery satisfaction than the PLA group. Furthermore, the BI-DAA group had a reduced VAS score one week after surgery and had better functional recovery in three months postoperatively. The BI-DAA group had a higher incidence of LFCN dysesthesia (12/100 vs. 0/100 thighs, P <.01), while other complications did not differ significantly between the two groups. For simBTHA, the bikini incision offers early recovery, less variance in components orientation, better postoperative outcomes, and scar healing than the PLA. Therefore, the bikini incision could be a safe and feasible option for simBTHA recipients. [ABSTRACT FROM AUTHOR]

Dominance-based neighborhood rough set (DNRS) is capable to give qualitative and quantitative descriptions of the relations between ordered objects. In spite of its effectiveness in feature selection, DNRS ignores the various significance of features. In fact, different features exert different impacts on decision-making. Once we explore these differences in advance, it is easier to find out features with high correlation and dependency. Likewise, it is inevitable that in big-data era the objects may update from time to time, which calls for efficient attribute reduction. However, the existing approaches are inappropriate for the weighted and ordered data. Motivated by these two deficiencies, first, we assign different weights to conditional attributes and establish the weighted dominance-based neighborhood rough set (WDNRS). Then a kind of conditional entropy in matrix form and ensuing updating principles are put forward to evaluate the significance of the attributes. In addition, grounded on the entropy, we come up with the heuristic algorithm and corresponding incremental mechanism when objects increase. Finally, twelve experiments are carried out to verify that it is effective and efficient for the designed method to select features in dynamic datasets. [ABSTRACT FROM AUTHOR]

Drilling trajectory control technology for near-horizontal directional drilling in coal mines is mainly determined empirically by manual skew data, and the empirical results are only qualitative and variable, meanwhile possessing great instability and uncertainty. In order to improve the accuracy and efficiency of drilling trajectory prediction, this paper investigates a random forest regression-based drilling trajectory prediction method after applying numerous machine learning methods to experimental data for comparison. In the selection of prediction features, this paper replaces all feature variables of the borehole trajectory with feature values with higher cumulative influence weights, and screens out three variables with high importance, azimuth, inclination and bend at the present moment of the drill, as the input variables of the model, and the increments in the borehole in a horizontal direction, left and right direction, and up and down direction at the present moment and the next moment as the output variables of the model. In the model training, the model in this paper uses the bootstrap self-service method resampling technique to collect training sample data, constructs a random forest regression model, and takes the mean value of the decision tree output as the result of the borehole trajectory prediction. To further improve the model accuracy, the paper improves the prediction performance of the model by adjusting the parameters of the random forest model such as tree, depth, minimum sample of leaf nodes, minimum sample number of internal node division, etc. The model is also evaluated by using common machine learning evaluation metrics R2 score, RAE, RMSE and MSE. The experimental results show that the average absolute error of the model drops to 1% on the prediction of the horizontal direction and up and down direction; the average absolute error of the model drops to 9% on the prediction of the left and right direction, and this error rate reaches the error requirement in the actual construction process, so the model can effectively improve the prediction accuracy of borehole trajectory while improving the safety of directional construction. [ABSTRACT FROM AUTHOR]

The article discusses Sustainable Development Goal 15 (SDG 15), which aims to protect and restore terrestrial ecosystems, manage forests sustainably, combat desertification, and halt biodiversity loss. The article highlights that many of the indicators for SDG 15 are off track or going in reverse, indicating a lack of progress. However, the article also mentions the use of big data, such as earth observation and citizen science, to address data gaps and improve monitoring of SDG 15. The article concludes by introducing a special issue that showcases advancements in using cutting-edge technologies for evaluating SDG 15 indicators. [Extracted from the article]

Dominance-based rough set approach (DRSA) is an effective knowledge acquisition tool for ordered data, and to meet the needs of practical applications, the DRSA-based extended models have been widely studied. Nevertheless, the collected data in practical applications are always polluted by noise and most of these extended models are very susceptible to noise. In addition, with the arrival of new attributes and the removal of obsolete attributes, the ordered data have the characteristic of attributes evolving over time (i.e., dynamic ordered data). How to efficiently mining knowledge from dynamic ordered data is a meaningful topic. Motivated by these two issues, we first propose a robust fuzzy dominance rough set model to combat noise interference and then develop incremental update approximation mechanisms based on the proposed model for dynamic ordered data. Firstly, the vaguely quantified fuzzy dominance rough set (VQFDRS) and its matrix representation are introduced. Subsequently, we develop the incremental mechanisms to update the approximations of VQFDRS when multiple attributes are added to or deleted from an ordered data, respectively. Then, on this basis, a static calculation approximations algorithm and two incremental calculation approximations algorithms are designed, respectively. Finally, a series of numerical comparative experiments are performed on six public datasets. Experimental results indicate that the VQFDRS has good robustness, and the incremental algorithms are highly efficient compared with static algorithm under the dynamic data environment. [ABSTRACT FROM AUTHOR]

The development of mild, efficient, and enantioselective methods for preparing chiral fluorinated compounds has been a long‐standing challenge. Herein, we report a promiscuous cyclohexanone monooxygenase (CHMO) for the photoinduced synthesis of chiral α‐fluoroketones via enantioselective reductive dehalogenation of α,α‐halofluoroketones. Wild‐type CHMO from Acinetobacter sp. possesses this promiscuous ability innately; however, the yield and stereoselectivity are low. A structure‐guided rational design of CHMO improved the yield and stereoselectivity remarkably. Mechanistic studies and molecular simulations demonstrated that this photoinduced CHMO catalyzes the reductive dehalogenation via a novel electron transfer (ET)/proton transfer (PT) mechanism, distinct from that of previously reported reductases with similar promiscuity. This methodology was expanded to various substrates, and desirable chiral α‐fluoroketones were obtained in high yields (up to 99 %) and e.r. values (up to 99:1). [ABSTRACT FROM AUTHOR]

The development of mild, efficient, and enantioselective methods for preparing chiral fluorinated compounds has been a long‐standing challenge. Herein, we report a promiscuous cyclohexanone monooxygenase (CHMO) for the photoinduced synthesis of chiral α‐fluoroketones via enantioselective reductive dehalogenation of α,α‐halofluoroketones. Wild‐type CHMO from Acinetobacter sp. possesses this promiscuous ability innately; however, the yield and stereoselectivity are low. A structure‐guided rational design of CHMO improved the yield and stereoselectivity remarkably. Mechanistic studies and molecular simulations demonstrated that this photoinduced CHMO catalyzes the reductive dehalogenation via a novel electron transfer (ET)/proton transfer (PT) mechanism, distinct from that of previously reported reductases with similar promiscuity. This methodology was expanded to various substrates, and desirable chiral α‐fluoroketones were obtained in high yields (up to 99 %) and e.r. values (up to 99:1). [ABSTRACT FROM AUTHOR]

Monotonic classification is a common task in the field of multicriteria decision-making, in which features and decision obey a monotonic constraint. The dominance-based rough set theory is an important mathematical tool for knowledge acquisition in monotonic classification tasks (MCTs). However, existing dominance-based rough set models are very sensitive to noise information, and only a misclassified sample will lead to large errors in acquiring knowledge. This unstable phenomenon does not meet the requirements of practical applications. On the other hand, feature selection is supposedly an effective dimensionality reduction approach for classification tasks. In the real world, feature combinations with multiple correlations can often provide important classification information, where the multiple correlations include redundancy, complementarity, and interaction between features. To the best of our knowledge, most of the existing feature selection methods for MCTs only consider the relevance between features and decision, while ignoring the multiple correlations. To overcome these two drawbacks, in this article, we propose a robust fuzzy dominance rough set model, and develop a feature selection method that considers multiple correlations based on the robust model for MCTs. First, a soft fuzzy dominance rough set (SFDRS) with robustness is proposed. Second, a feature evaluation index considering multiple correlations is presented. Finally, a feature selection algorithm based on SFDRS is designed to select an optimal feature subset. Extensive experiments are conducted on 12 public datasets, and the results show that the SFDRS model has good robustness and the proposed feature selection algorithm has excellent classification performance. [ABSTRACT FROM AUTHOR]

Data clustering refers to partition the original data set into some subsets such that every vertex belongs to one or more subsets at the same time. For graph data that composed by attribute information of vertices as well as structural information between vertices, how to make an efficient clustering is not an easy thing. In this paper, we propose a novel method of how to partition graph data into some overlapping subgraph data in aspect of rough set theory. At first, we introduce a detailed description about the global similarity measurement of vertices. After that, an objective-function oriented optimization model is constructed in terms of updating fuzzy membership degree and cluster center that based on the theory of rough set. Obviously, the determined cluster is no longer a fuzzy set, but a rough set, that is to say, the cluster is expressed by the upper approximation set and lower approximation set. Finally, eleven real-world graph data and four synthetic graph data are applied to verify the validity of the proposed fuzzy clustering algorithm. The experimental results show that our algorithm is better than existing clustering approach to some extent. [ABSTRACT FROM AUTHOR]

Background: To retrospectively characterize the clinical characteristics and efficacy of total hip arthroplasty and the important factors needing attention in hypophosphatemic osteomalacia (HO) patients with hip involvement. Patients and methods: We performed a review of seven patients (two women and five men) referred to our clinic with a final diagnosis of HO who received total hip arthroplasty between 2010 and 2018. Five patients (Group 1) received proper medical management with or without aetiologic therapy, while the other two patients (Group 2) did not receive due to misdiagnosis. The mean follow-up duration was 5.1 ± 2.0 years. Results: The patients in Group 1 had significant relief of pain and improved laboratory results. The mean Harris Hip Score of Group 1 increased from 44.2 ± 6.0 to 94.0 ± 3.0, and the mean VAS score decreased from 8.8 ± 0.4 to 1.8 ± 0.7. However, the progressive extensive pain score in Group 2 had no obvious improvement, with the Harris Hip Score increasing from 45.5 ± 0.5 to 60 ± 28.0 and the VAS score decreasing from 9.0 ± 1.0 to 6.5 ± 2.5. Conclusion: THA appears to be an effective method for hip arthritis or joint deformities resulting from hypophosphatemic osteomalacia. A satisfactory outcome of the surgery depends on the early etiological identification, the treatment of hypophosphatemia, a careful operation, and the operative strategies, as well as proper medical treatment. [ABSTRACT FROM AUTHOR]

To successfully implement the platoon control of connected and automated vehicles (CAVs), the model uncertainties, external disturbances, and time delays must be addressed. In this study, we propose a distributed Model Predictive Control scheme to achieve offset-free tracking for CAVs with model uncertainties and mixed disturbances which contains both stochastic and deterministic noises. A simple Vehicle-to-Vehicle communication flow with the short-distance and low-volume information exchange is adopted to reduce the effect of the communication delay and dropout. First, the deterministic and stochastic perturbances are addressed separately in constraints handling with an integrated control law based on the mismatched prediction model. Additional constraints on coupling input and output are imposed to guarantee the assumption about forecast trajectory. Then an observer gives an unbiased estimate of the state and integrated disturbance. Based on the estimation, the target calculator eliminates the steady tracking offset by designing a new target in real-time and the Min-Max Model Predictive Control steers the uncertain systems to the new target under the worst case within the domain of disturbances. Lastly, the Optimal Control Problem is reformulated and can be efficiently solved by existing solvers. Simulation results indicate that the proposed method shows superiority in interference rejection and offset-free tracking. [ABSTRACT FROM AUTHOR]

Diapause, an important strategy used by insects to avoid adverse environments, is regulated by various cell signaling pathways. The results of our previous studies demonstrated that the transforming growth factor‐β (TGF‐β) signaling pathway regulated pupal diapause in Helicoverpa armigera, which was accompanied by downregulation of proteins in TGF‐β signaling. However, to date the mechanism underlying this phenomenon remains unknown. Here, we cloned the E3 ubiquitin ligases gene Smurf. In vitro experiments showed that Smurf directly bound to TGF‐β receptor type I (TGFβRI) and Smad2. Overexpressing Smurf promoted ubiquitination of TGFβRI and Smad2, thereby downregulating their protein levels. Conversely, silencing of the Smurf gene suppressed ubiquitination of TGFβRI and Smad2 thereby increasing their protein levels. Results from in vivo co‐immunoprecipitation assays revealed that the binding of Smurf to TGFβRI or Smad2 was stronger in diapause pupae than in nondiapause pupae. Injection of Smurf inhibitor A01 into diapause pupae markedly upregulated expression of TGFβRI and Smad2 proteins, leading to resumption of development in diapause pupae. Taken together, these findings suggested that ubiquitin ligase E3 Smurf participated in H. armigera diapause by regulating TGF‐β signaling, and thus could be playing a crucial role in insect diapause. [ABSTRACT FROM AUTHOR]

Objective. Acute lung injury (ALI) is a severe respiratory disorder causing alveolar-capillary barrier, leading to a high rate of morbidity and death in critically ill individuals. microRNAs (miRNAs)-mediated mechanism in the pathogenesis of ALI has attracted much interest. Herein, we attempt to characterize a candidate miRNA and its downstream target that is linked to the pathogenesis of ALI. Methods. LPS-conditioned MH-S cells were treated with miR-29a-1-5p mimic, inhibitor, and RNT4 expression vector, and the ALI animal model was injected with agomir and antagomir of miR-29b-1-5p and RNT4 expression vector, in which the pro-inflammatory cytokine production, cell viability and apoptosis, myeloperoxidase (MPO) activity, wet/dry (W/D) ratio, and expression of TGF-β1, α-smooth muscle actin (α-SMA), E-cadherin, and vimentin were examined. miR-29a-1-5p inhibition of RTN4 translation was confirmed by luciferase activity assays. Results. An elevated miR-29a-1-5p expression was demonstrated in LPS-conditioned MH-S cells. miR-29a-1-5p inhibitor transfection attenuated the production of pro-inflammatory cytokines and MH-S cell viability but enhanced the apoptosis. miR-29a-1-5p inhibition of RTN4 translation was demonstrated in the setting of LPS-induced ALI. LPS-induced murine models demonstrated upregulated miR-29a-1-5p. Intravenous injection of miR-29b-1-5p agomir attenuated mouse lung injury and pulmonary fibrosis. RTN4 overexpression resisting to miR-29a-1-5p overexpression was demonstrated in LPS-induced murine models. Conclusion. The findings obtained from the study that disturbing the action of miR-29a-1-5p may be a novel therapeutic strategy for preventing ALI. [ABSTRACT FROM AUTHOR]

The development of biomass energy obtained from nonfossil and renewable feedstocks is an important means to deal with the fossil energy crisis. The preparation of gasoline-range bio-hydrocarbons from natural abundant and renewable lipids has high potential. However, it is much more difficult than the preparation of widely reported biodiesel and represents a challenge to green chemistry. Compared with traditional methods usually involving harsh reaction conditions, a photo-chemo-enzymatic multi-step combination route was developed in this study for the highly efficient and environment-friendly preparation of biogasoline from cheap and sustainable triolein using solar energy and atmospheric O2 under mild conditions. This whole catalytic sequence was composed of lipase-catalyzed hydrolysis of triolein, fatty acid photodecarboxylase (CvFAP)-catalyzed decarboxylation of oleic acid, photocatalytic oxidative cleavage of long-chain alkene, and the final decarboxylation of the medium- or short-chain fatty acids catalyzed by CvFAP mutant. After optimizing the reaction conditions in detail, the practicability of the method was proved by a gram-scale experiment. This protocol was successfully applied to the conversion of various long-chain unsaturated acids and long-chain alkenes into biogasoline, implying its wide substrate scope. This photo-chemo-biocatalytic sequential reaction exhibited great potential to generate high value-added gasoline alkanes from natural abundant lipids and even wasted or non-edible oil. [ABSTRACT FROM AUTHOR]

Radial neural network is based in the world with the characteristics of active adaptation, active learning, active recognition, low error rate, and thought mapping and plays an important role in personalized regulation. However, in practical applications, due to the existence of pattern recognition, motion control, and a large amount of combined knowledge, the traditional methods are difficult to solve, even powerless, and cannot be effectively solved. Although the traditional BP network is more widely used, the BP neural network is easy to enter the regional minimum value during the training process, which leads to a lower training learning speed and low efficiency. The RBF network (radial neural network) is in a certain sense, and it can detect both known intrusions and unpredictable intrusions. At the same time, it is superior to BP neural network in data collection, pattern recognition, and personality customization. Through detection and comparison, it is found that the radial network has improved the analysis speed by about 20%, and the degree of privacy protection of athletes is as high as 99%, which is close to the full value, and the accuracy of the psychological control scheme is also improved by about 15% on the basis of the classic network. Athletes can be adjusted as soon as possible to achieve the best state; so, it will be an ideal choice to use a radial neural network for the psychological adjustment detection system. [ABSTRACT FROM AUTHOR]

Decision implication is a basic form of knowledge representation of Formal Concept Analysis in the setting of decision-making. Inference rules are generally utilized to infer new decision implications from a given set of decision implications. Three inference rules have been proposed for the deduction process on decision implications, i.e., Augmentation, Combination and Con-Combination. How to apply the inference rules, however, is not discussed in literature. To this end, we studied the properties of the inference rules and found that in the deduction process, Augmentation should be applied only once and that both Combination and Con-Combination need to be applied at most ⌈ log 2 m ⌉ times. Moreover, by analyzing the interchangeabilities of the inference rules, we found that Augmentation and Combination are interchangeable, but Augmentation and Con-Combination are not. Based on these results, three inference methods were then proposed and their efficiencies were verified by experiments. The experimental results show that one of the inference methods, namely, applying Augmentation once and then applying Con-Combination at most ⌈ log 2 m ⌉ times, is the most efficient inference method. [ABSTRACT FROM AUTHOR]

As a typical generalization of classical rough set, variable precision fuzzy neighborhood rough set (VPFNRS) can effectively handle the datasets with noise. At the same time, the three-way regions induced by VPFNRS can determine some decision rules. However, data usually changes over time in real life, such as the addition of new objects and the removal of obsolete objects. Therefore, the rules determined by the three-way regions may change with time. To address this issue, we investigate the dynamic maintenance of the variable precision fuzzy neighborhood three-way regions under the background of interval-valued fuzzy decision system (IvFDS), aiming to effectively update the three-way regions in dynamic environment. Firstly, the δ -fuzzy neighborhood relation and its induced δ -fuzzy neighborhood class are defined. On this basis, a novel VPFNRS model suitable for IvFDS is proposed. Secondly, the variable precision fuzzy neighborhood three-way regions induced by the proposed model and its matrix representations are introduced. Subsequently, the matrix-based incremental mechanisms to update the three-way regions when the objects change are constructed. Meanwhile, corresponding incremental algorithms are designed. Finally, a series of numerical comparative experiments are executed on nine datasets, and the results indicate that incremental algorithms are not only effective, but also highly efficient under the dynamic data environment. [ABSTRACT FROM AUTHOR]

Largemouth bass virus (LMBV), belonging to the genus Ranavirus, causes high mortality and heavy economic losses in largemouth bass aquaculture. In the present study, a novel cell line, designated as MsF, was established from the fin of largemouth bass (Micropterus salmoides), and applied to investigate the characteristics of cell death induced by LMBV. MsF cells showed susceptibility to LMBV, evidenced by the occurrence of a cytopathic effect (CPE), increased viral gene transcription, protein synthesis, and viral titers. In LMBV-infected MsF cells, two or more virus assembly sites were observed around the nucleus. Notably, no apoptotic bodies occurred in LMBV-infected MsF cells after nucleus staining, suggesting that cell death induced by LMBV in host cells was distinct from apoptosis. Consistently, DNA fragmentation was not detected in LMBV-infected MsF cells. Furthermore, only caspase-8 and caspase-3 were significantly activated in LMBV-infected MsF cells, suggesting that caspases were involved in non-apoptotic cell death induced by LMBV in host cells. In addition, the disruption of the mitochondrial membrane potential (ΔΨm) and reactive oxygen species (ROS) generation were detected in both LMBV-infected MsF cells and fathead minnow (FHM) cells. Combined with our previous study, we propose that cell death induced by LMBV infection was cell type dependent. Although LMBV-infected MsF cells showed the characteristics of non-apoptotic cell death, the signal pathways might crosstalk and interconnect between apoptosis and other PCD during LMBV infection. Together, our results not only established the in vitro LMBV infection model for the study of the interaction between LMBV and host cells but also shed new insights into the mechanisms of ranavirus pathogenesis. [ABSTRACT FROM AUTHOR]

As two important expanded quantification rough sets models, the multigranulation decision-theoretic rough sets mainly uses conditional probability to show relative quantitative information in multigranulation framework, and the graded multigranulation rough set is used to measure absolute quantitative information. However, they only consider the absolute quantitative (relative quantitative) information in granular structure, but do not consider the relative quantitative (absolute quantitative) information. It means that they cannot reflect a complete information. In order to overcome the defect, this paper proposes two pairs of multigranulation double-quantitative decision-theoretic rough sets models based on Bayesian decision and graded multigranulation rough sets, which essentially indicate the relative and absolute information quantification. After further studies to discuss decision rules and the inner relationship between these two models. Furthermore, we introduce an illustrative case to show the effectiveness and superiority of our proposed models, and the results show that our methods are effective for dealing with practical problems. Finally, we present some experiments based on UCI data sets showing the advantages of our proposed models in classification performance. [ABSTRACT FROM AUTHOR]

The approximation space in rough set theory is important for dealing with uncertainties. As the information contained in various information systems is constantly updated and changed with the development of information technology, how to effectively obtain the approximation space in dynamic environments is essential. The local rough set as an excellent model avoids unnecessary calculation of information granules, and can significantly improve learning efficiency. In this paper, we mainly investigate a dynamic approximation update mechanism of multigranulation data from local viewpoint. We first define a support and inclusion function to construct local generalized multigranulation neighborhood rough set model. Then, the dynamic updating process of global rough set and local rough set is analyzed when object chandes. Meanwhile, the corresponding dynamic update algorithms for dynamic objects are proposed based on local generalized multigranulation rough set model. The complexity analysis about them theoretically proves the efficiency of local dynamic algorithm compared with global algorithm and static algorithm. To illustrate the effectiveness of proposed algorithms, twelve datasets from UCI are adopted to contrast experiments. [ABSTRACT FROM AUTHOR]

Attribute reduction is one of the most important preprocessing steps in machine learning and data mining. As a key step of attribute reduction, attribute evaluation directly affects classification performance, search time, and stopping criterion. The existing evaluation functions are greatly dependent on the relationship between objects, which makes its computational time and space more costly. To solve this problem, we propose a novel separability-based evaluation function and reduction method by using the relationship between objects and decision categories directly. The degree of aggregation (DA) of intraclass objects and the degree of dispersion (DD) of between-class objects are first defined to measure the significance of an attribute subset. Then, the separability of attribute subsets is defined by DA and DD in fuzzy decision systems, and we design a sequentially forward selection based on the separability (SFSS) algorithm to select attributes. Furthermore, a postpruning strategy is introduced to prevent overfitting and determine a termination parameter. Finally, the SFSS algorithm is compared with some typical reduction algorithms using some public datasets from UCI and ELVIRA Biomedical repositories. The interpretability of SFSS is directly presented by the performance on MNIST handwritten digits. The experimental comparisons show that SFSS is fast and robust, which has higher classification accuracy and compression ratio, with extremely low computational time. [ABSTRACT FROM AUTHOR]

Incremental feature selection approaches can improve the efficiency of feature selection used for dynamic datasets, which has attracted increasing research attention. Nevertheless, there is currently no work on incremental feature selection approaches for dynamic ordered data. Moreover, the monotonic classification effect of ordered data is easily affected by noise, so a robust feature evaluation metric is needed for feature selection algorithm. Motivated by these two issues, we investigate incremental feature selection approaches using a new conditional entropy with robustness for dynamic ordered data in this study. First, we propose a new rough set model, i.e., fuzzy dominance neighborhood rough sets (FDNRS). Second, a conditional entropy with robustness is defined based on FDNRS model, which is used as evaluation metric for features and combined with a heuristic feature selection algorithm. Finally, two incremental feature selection algorithms are designed on the basis of the above researches. Experiments are performed on ten public datasets to evaluate the robustness of the proposed metric and the performance of the incremental algorithms. Experimental results verify that the proposed metric is robust and our incremental algorithms are effective and efficient for updating reducts in dynamic ordered data. [ABSTRACT FROM AUTHOR]

The phosphorus-silicon synergistic flame-retardant epoxy resins were prepared separately by using two types of polyhedral oligomeric silsesquioxane (POSS) combined with 9, 10-dihydro-9-oxa-10-phosphaphenanthrene-10-oxide (DOPO)-based organophosphorus flame retardants (D-bp), and their flame retardant, thermal properties, mechanical, and dynamic mechanical properties were investigated. The results indicated that the phosphorus-silicon synergistic flame retardant epoxy resins obtained a UL 94 V-0 rating at a phosphorus content of only 0. 25 wt%. The epoxy resins exhibited a peak heat release rate of 31,3. 9 kW/m², total heat release of 118. 2 MJ/m², and average effective combustion heat of 23. 1 MJ/kg, which decreased by 66. 6 %, 41. 4 % and 26. 9 % respectively when the Phenyl-POSS was compounded with D-bp. The combination of Epoxy-POSS and D-bp could simultaneously improve the flame retardant, thermal, mechanical, and dynamic mechanical properties of phosphorus-silicon synergistic flame retardant epoxy resins, indicating excellent comprehensive performance. [ABSTRACT FROM AUTHOR]

In this highly volatile, uncertain, complex, and ambiguous (VUCA) environment, a fuzzy inventory optimization problem is a hot topic that received the most attention from both academicians and practitioners. In this paper, the authors have studied the integrated inventory problems under trade credit in fuzzy environments, where the demand rate and deterioration rate are taken here as triangular fuzzy numbers. Accordingly, three types of fuzzy programming models as fuzzy expected cost minimization model, fuzzy α-cost minimization model, and credibility maximization model are built according to different management goals. Since the problem is shown to be NP-hard, this research focuses on proposing a hybrid intelligent algorithm by integrating fuzzy simulation, neural network, and genetic algorithm for solving these models. Then, some numerical examples are presented to illustrate the benefits of the models and evaluate the efficiency of the algorithms. The computational experiments show that the trade credit strategy has a positive impact on improving the supply chain's performance and reducing its total cost. In addition, the maximum relative error of the objective values is less than 1%, which implies that the hybrid intelligent algorithm is robust to the parameter settings and effective in solving our models. Finally, the practical implications are discussed and useful managerial insights are given. [ABSTRACT FROM AUTHOR]

Concept-cognitive learning (CCL) is to make machines like human beings have the ability of summarizing and reasoning. Automatically learn and find concepts from given information clues is a research focus of CCL. The existing researches mainly focuses on the concept learning methods in classical and fuzzy formal contexts, but there are few researches on the CCL of interval-valued contexts. In view of the universality of interval values in practical applications, we study the mechanism of CCL in interval-valued formal contexts. Firstly, we propose interval-valued formal contexts and a pair of dual cognitive operators as the fundamental foundation of concept learning. Then we mine the relationship between interval-valued information granules and concepts from cognitive learning and granular computing perspective. Then we systematically study the mechanism of interval-valued CCL from the establishment of interval-valued information granules (IvIGs) and its mathematical properties, and the transformation between different information granules (IGs) and clue oriented concept learning. Moreover, three algorithms are established to automatically learn concepts from different clue information. Finally, we download eight public data sets to verify the effectiveness and feasibility of the proposed algorithms from the perspective of the size of extension of concepts, running time of concept learning algorithms and the number of concepts learned by the concept learning algorithms. The experimental comparison indicates that the proposed algorithms are effective and feasible for interval-valued CCL. [ABSTRACT FROM AUTHOR]