IBGJO: Improved Binary Golden Jackal Optimization with Chaotic Tent Map and Cosine Similarity for Feature Selection.

Feature selection is a crucial process in machine learning and data mining that identifies the most pertinent and valuable features in a dataset. It enhances the efficacy and precision of predictive models by efficiently reducing the number of features. This reduction improves classification accuracy, lessens the computational burden, and enhances overall performance. This study proposes the improved binary golden jackal optimization (IBGJO) algorithm, an extension of the conventional golden jackal optimization (GJO) algorithm. IBGJO serves as a search strategy for wrapper-based feature selection. It comprises three key factors: a population initialization process with a chaotic tent map (CTM) mechanism that enhances exploitation abilities and guarantees population diversity, an adaptive position update mechanism using cosine similarity to prevent premature convergence, and a binary mechanism well-suited for binary feature selection problems. We evaluated IBGJO on 28 classical datasets from the UC Irvine Machine Learning Repository. The results show that the CTM mechanism and the position update strategy based on cosine similarity proposed in IBGJO can significantly improve the Rate of convergence of the conventional GJO algorithm, and the accuracy is also significantly better than other algorithms. Additionally, we evaluate the effectiveness and performance of the enhanced factors. Our empirical results show that the proposed CTM mechanism and the position update strategy based on cosine similarity can help the conventional GJO algorithm converge faster.

Exosomal circ_0048856 derived from non-small cell lung cancer contributes to aggressive cancer progression through downregulation of miR-1287-5p.

AIM: Present study was aimed to explore the diagnostic value and mechanism of exosome derived circular RNA (circ)_0048856 in non-small cell lung cancer (NSCLC) development. METHODS: Exosomes protein markers, CD63 and CD9, were examined by Western blot. Real time quantitative reverse transcriptase - polymerase chain reaction (qRT-PCR) was used to detect the relative expression of circ_0048856 and miR-1287-5p. Proliferation of NSCLC cell was detected by MTT assay. Transwell assay was utilized to evaluated the migration and invasion of NSCLC cells. Apoptosis rate was examined by Flow cytometry. Double luciferase report assay was used to assess the targeting association between circ_0048856 and miR-1287-5p. Diagnostic value of circ_0048856 for NSCLC was estimated by receiver operating characteristic (ROC) curve. Animal models were constructed to explore the function of exosomal circ_0048856 in NSCLC. RESULTS: Diameter of exosomes in NSCLC serum was ranged between 75 and 150 nm. Exosomes circ_0048856 was enhanced in NSCLC serum and cell lines (P < 0.001). Exosome derived circ_0048856 had high diagnostic value for NSCLC. Area under the curve (AUC) was 0.943 (95%CI=0.904-0.982, P < 0.001). at the optimal cutoff value of 1.800, the sensitivity was 0.880, and specificity was 0.800. Exosome circ_0048856 facilitated proliferation, migration and invasion, inhibited apoptosis of NSCLC cells. MiR-1287-5p could be effaced by circ_0048856. MiR-1287-5p reversed the biological behavior changes of NSCLC cells which induced by circ_0048856. In vivo, exosomal circ_0048856 could significantly promote tumor growth (P < 0.001). CONCLUSION: Exosomes derived circ_0048856 was elevated in NSCLC serum and cell lines. Exosome circ_0048856 promoted the NSCLC development by targeting miR-1287-5p. Exosome circ_0048856 had high diagnostic value for NSCLC.

Long non-coding RNA PRNCR1 modulates non-small cell lung cancer cell proliferation, apoptosis, migration, invasion, and EMT through PRNCR1/miR-126-5p/MTDH axis.

BACKGROUND: Non-small cell lung cancer (NSCLC) is a highly malignant tumor. Accumulating evidence suggested that prostate cancer non-coding RNA 1 (PRNCR1) participated in the pathogenesis of NSCLC, whereas the elaborate mechanism remains unclear. Hence, the role of PRNCR1 in the progression of NSCLC was investigated. METHODS: Levels of PRNCR1, microRNA-126-5p (miR-126-5p), and metadherin (MTDH) were examined by quantitative real-time polymerase chain reaction (qRT-PCR). Cell proliferation was measured using Cell Counting Kit-8 (CCK-8). Flow cytometry was conducted to determine cell apoptosis. Besides, transwell assay was performed to detect cell migration and invasion in NSCLC cells. The expression levels of E-cadherin, N-cadherin, Vimentin, and MTDH were detected via Western blot. Dual-luciferase reporter, RNA immunoprecipitation, and RNA pull down assays were employed to verify the relationship between miR-126-5p and PRNCR1 or MTDH. RESULTS: PRNCR1 and MTDH levels were highly expressed, while miR-126-5p expression was lowly expressed in NSCLC tissues and cell lines. Knockdown of PRNCR1 promoted cell apoptosis, impeded proliferation, migration, invasion, and epithelial-mesenchymal transition (EMT) in NSCLC cells, and these effects were abrogated by its target gene of miR-126-5p inhibitor. Moreover, MTDH as the target of PRNCR1, its overexpression reversed the impacts of miR-126-5p mimic on cell behaviors and EMT in vitro. Finally, PRNCR1 and miR-126-5p regulated MTDH expression. CONCLUSION: PRNCR1 modified cell behaviors and EMT via miR-126-5p/MTDH axis in NSCLC cells, providing a novel thinking for clinical treatment of NSCLC.

Structure-based design of peptides against HER2 with cytotoxicity on colon cancer.

In this study, we found that four novel peptides designed by molecular modeling techniques were successfully applicated with cytotoxicity on colon cancer cells sw620. First, the interactions between the Herstatin and the HER2 were explored by ational-designed approaches, which were combined with homology modeling, protein/protein docking, and structural superimposition analysis. Then, based on the results derived from theoretical analysis, four novel peptides were designed, synthesized, and experimentally evaluated for biological function; it was found that they showed a remarkable enhancement on Herceptin to inhibit the genesis and development of colon cancers, and no significant side effects on normal colon cells NCM460 were observed but Doxorubicin had. These results indicated that it is a feasible way to use the well-designed peptides derived from Herstatin to enhance the efficacy of clinical drugs Herceptin and to kill colon cancer cells selectively without harming normal colon cells. We believe that our research might provide a new way to develop the potential therapies for colon cancers.

The Joint Adaptive Kalman Filter (JAKF) for Vehicle Motion State Estimation.

This paper proposes a multi-sensory Joint Adaptive Kalman Filter (JAKF) through extending innovation-based adaptive estimation (IAE) to estimate the motion state of the moving vehicles ahead. JAKF views Lidar and Radar data as the source of the local filters, which aims to adaptively adjust the measurement noise variance-covariance (V-C) matrix 'R' and the system noise V-C matrix 'Q'. Then, the global filter uses R to calculate the information allocation factor 'ß' for data fusion. Finally, the global filter completes optimal data fusion and feeds back to the local filters to improve the measurement accuracy of the local filters. Extensive simulation and experimental results show that the JAKF has better adaptive ability and fault tolerance. JAKF enables one to bridge the gap of the accuracy difference of various sensors to improve the integral filtering effectivity. If any sensor breaks down, the filtered results of JAKF still can maintain a stable convergence rate. Moreover, the JAKF outperforms the conventional Kalman filter (CKF) and the innovation-based adaptive Kalman filter (IAKF) with respect to the accuracy of displacement, velocity, and acceleration, respectively.

AST: Activity-Security-Trust driven modeling of time varying networks.

Network modeling is a flexible mathematical structure that enables to identify statistical regularities and structural principles hidden in complex systems. The majority of recent driving forces in modeling complex networks are originated from activity, in which an activity potential of a time invariant function is introduced to identify agents' interactions and to construct an activity-driven model. However, the new-emerging network evolutions are already deeply coupled with not only the explicit factors (e.g. activity) but also the implicit considerations (e.g. security and trust), so more intrinsic driving forces behind should be integrated into the modeling of time varying networks. The agents undoubtedly seek to build a time-dependent trade-off among activity, security, and trust in generating a new connection to another. Thus, we reasonably propose the Activity-Security-Trust (AST) driven model through synthetically considering the explicit and implicit driving forces (e.g. activity, security, and trust) underlying the decision process. AST-driven model facilitates to more accurately capture highly dynamical network behaviors and figure out the complex evolution process, allowing a profound understanding of the effects of security and trust in driving network evolution, and improving the biases induced by only involving activity representations in analyzing the dynamical processes.

Selection of interdependent genes via dynamic relevance analysis for cancer diagnosis.

Microarray analysis is widely accepted for human cancer diagnosis and classification. However the high dimensionality of microarray data poses a great challenge to classification. Gene selection plays a key role in identifying salient genes from thousands of genes in microarray data that can directly contribute to the symptom of disease. Although various excellent selection methods are currently available, one common problem of these methods is that genes which have strong discriminatory power as a group but are weak as individuals will be discarded. In this paper, a new gene selection method is proposed for cancer diagnosis and classification by retaining useful intrinsic groups of interdependent genes. The primary characteristic of this method is that the relevance between each gene and target will be dynamically updated when a new gene is selected. The effectiveness of our method is validated by experiments on six publicly available microarray data sets. Experimental results show that the classification performance and enrichment score achieved by our proposed method is better than those of other selection methods.

[Trachea,bronchus,blood vessel and left atrium plasty in the treatment of lung cancer].

BACKGROUND: The trachea, bronchus, blood vessel and left atrium plasty can maximumly remove tumor tissus and preserve the functional pulmonary tissues so as to improve patients' quality of life and prolong their survival. METHODS: From January 1990 to August 2004, 125 patients with lung cancer underwent trachea, bronchus, bloood vessel and left atrium plasty. RESULTS: There were 116 patients who underwent bronchoplasty, in which angioplasty was performed concurrently in 26 cases. And reconstruction of superior vena cava was performed in 3 patients, and left atrium plasty in 6 patients. Three patients died after operation. CONCLUSIONS: Trachea, bronchus, blood vessel and left atrium plasty may be a promising direction in thoracic surgery.