Neizrazita strategija optimizacije potrošnje energije za paralelno hibridno električno vozilo korištenjem kaotičnog nedominirajućeg genetskog algoritma sortiranja

This paper presented a parallel hybrid electric vehicle (HEV) equipped with a hybrid energy storage system. To handle complex energy flow in the powertrain system of this HEV, a fuzzy-based energy management strategy was established. A chaotic multi-objective genetic algorithm, which optimizes the parameters of fuzzy membership functions, was also proposed to improve fuel economy and HC, CO, and NOx emissions. The main target of this algorithm is to escape from local optima and obtain high quality trade-off solutions. Chaotic initialization operator, chaotic crossover and mutation operators, chaotic disturbance operator, and chaotic local search operator were integrated into non-dominated sorting genetic algorithm II (NSGA-II) to form this new algorithm named chaotic NSGA-II (C-NSGA-II). Simulation results and comparisons demonstrated that chaotic operators can enhance searching ability for optimal solutions. In conclusion, C-NSGA-II is suitable for solving HEV energy management optimization problem.Ovaj rad prikazuje paralelno hibridno električno vozilo (HEV) opremljeno hibridnim spremnikom energije. Kako bi se omogućila funkcionalnost pogonskog sklopa ovakvog HEV-a korištena je strategija raspolaganja energijom zasnovana na neizrazitoj logici. Također, prikazan je više kriterijski genetski algoritam kaosa za optimiranje parametara neizrazite funkcije povezanih s ekonomskim pokazateljem te pokazateljima emisije HC-a, CO-a i NOx-a. Osnovni cilj algoritma je omogućiti izlazak iz lokalnih optimuma i uspostavljanjem kompromisa omogućiti dosezanje boljih rješenja. Kaotični inicijalizacijski operator, kaotično križanje i operator mutacije, kaotični operator poremećaja i kaotični operator lokalnog pretraživanje uključeni su u nedominirajući genetski algoritam sortiranja II (NSGA-II) u svrhu formulacije novog problema nazvanog kaotični NSGA-II (C-NSGA-II). Simulacijski rezultati i usporedbe prikazuju kako kaotični operator može povećati uspješnost traženja optimalnog rješenja. Zaključno, C-NSGA-II je primjeren za rješavanje problema raspolaganja energijom u HEV-u

Current development and future challenges in microplastic detection techniques: a bibliometrics-based analysis and review.

Microplastics have been considered a new type of pollutant in the marine environment and have attracted widespread attention worldwide in recent years. Plastic particles with particle size less than 5 mm are usually defined as microplastics. Because of their similar size to plankton, marine organisms easily ingest microplastics and can threaten higher organisms and even human health through the food chain. Most of the current studies have focused on the investigation of the abundance of microplastics in the environment. However, due to the limitations of analytical methods and instruments, the number of microplastics in the environment can easily lead to overestimation or underestimation. Microplastics in each environment have different detection techniques. To investigate the current status, hot spots, and research trends of microplastics detection techniques, this review analyzed the papers related to microplastics detection using bibliometric software CiteSpace and COOC. A total of 696 articles were analyzed, spanning 2012 to 2021. The contributions and cooperation of different countries and institutions in this field have been analyzed in detail. This topic has formed two main important networks of cooperation. International cooperation has been a common pattern in this topic. The various analytical methods of this topic were discussed through keyword and clustering analysis. Among them, fluorescent, FTIR and micro-Raman spectroscopy are commonly used optical techniques for the detection of microplastics. The identification of microplastics can also be achieved by the combination of other techniques such as mass spectrometry/thermal cracking gas chromatography. However, these techniques still have limitations and cannot be applied to all environmental samples. We provide a detailed analysis of the detection of microplastics in different environmental samples and list the challenges that need to be addressed in the future

Eugenol modulates the NOD1-NF-κB signaling pathway via targeting NF-κB protein in triple-negative breast cancer cells

BackgroundThe most aggressive subtype of breast cancer, triple-negative breast cancer (TNBC), has a worse prognosis and a higher probability of relapse since there is a narrow range of treatment options. Identifying and testing potential therapeutic targets for the treatment of TNBC is of high priority.MethodsUsing a transcriptional signature of triple-negative breast cancer collected from Gene Expression Omnibus (GEO), CMap was utilized to reposition compounds for the treatment of TNBC. CCK8 and colony formation experiments were performed to detect the effect of the candidate drug on the proliferation of TNBC cells. Meanwhile, transwell and wound healing assay were implemented to detect cell metastasis change caused by the candidate drug. Moreover, the proteomic approach was presently ongoing to evaluate the underlying mechanism of the candidate drug in TNBC. Furthermore, drug affinity responsive target stability (DARTS) coupled with LC-MS/MS was carried out to explore the potential drug target candidate in TNBC cells.ResultsWe found that the most widely used medication, eugenol, reduced the growth and metastasis of TNBC cells. According to the underlying mechanism revealed by proteomics, eugenol could inhibit TNBC cell proliferation and metastasis via the NOD1-NF-κB signaling pathway. DARTS experiment further revealed that eugenol may bind to NF-κB in TNBC cells.ConcludesOur findings pointed out that eugenol was a potential candidate drug for the treatment of TNBC

Development and validation of risk prediction and neural network models for dilated cardiomyopathy based on WGCNA

BackgroundDilated cardiomyopathy (DCM) is a progressive heart condition characterized by ventricular dilatation and impaired myocardial contractility with a high mortality rate. The molecular characterization of DCM has not been determined yet. Therefore, it is crucial to discover potential biomarkers and therapeutic options for DCM.MethodsThe hub genes for the DCM were screened using Weighted Gene Co-expression Network Analysis (WGCNA) and three different algorithms in Cytoscape. These genes were then validated in a mouse model of doxorubicin (DOX)-induced DCM. Based on the validated hub genes, a prediction model and a neural network model were constructed and validated in a separate dataset. Finally, we assessed the diagnostic efficiency of hub genes and their relationship with immune cells.ResultsA total of eight hub genes were identified. Using RT-qPCR, we validated that the expression levels of five key genes (ASPN, MFAP4, PODN, HTRA1, and FAP) were considerably higher in DCM mice compared to normal mice, and this was consistent with the microarray results. Additionally, the risk prediction and neural network models constructed from these genes showed good accuracy and sensitivity in both the combined and validation datasets. These genes also demonstrated better diagnostic power, with AUC greater than 0.7 in both the combined and validation datasets. Immune cell infiltration analysis revealed differences in the abundance of most immune cells between DCM and normal samples.ConclusionThe current findings indicate an underlying association between DCM and these key genes, which could serve as potential biomarkers for diagnosing and treating DCM

A novel prognostic model for patients with colon adenocarcinoma

BackgroundColon adenocarcinoma (COAD) is a highly heterogeneous disease, which makes its prognostic prediction challenging. The purpose of this study was to investigate the clinical epidemiological characteristics, prognostic factors, and survival outcomes of patients with COAD in order to establish and validate a predictive clinical model (nomogram) for these patients.MethodsUsing the SEER (Surveillance, Epidemiology, and End Results) database, we identified patients diagnosed with COAD between 1983 and 2015. Disease-specific survival (DSS) and overall survival (OS) were assessed using the log-rank test and Kaplan–Meier approach. Univariate and multivariate analyses were performed using Cox regression, which identified the independent prognostic factors for OS and DSS. The nomograms constructed to predict OS were based on these independent prognostic factors. The predictive ability of the nomograms was assessed using receiver operating characteristic (ROC) curves and calibration plots, while accuracy was assessed using decision curve analysis (DCA). Clinical utility was evaluated with a clinical impact curve (CIC).ResultsA total of 104,933 patients were identified to have COAD, including 31,479 women and 73,454 men. The follow-up study duration ranged from 22 to 88 months, with an average of 46 months. Multivariate Cox regression analysis revealed that age, gender, race, site_recode_ICD, grade, CS_tumor_size, CS_extension, and metastasis were independent prognostic factors. Nomograms were constructed to predict the probability of 1-, 3-, and 5-year OS and DSS. The concordance index (C-index) and calibration plots showed that the established nomograms had robust predictive ability. The clinical decision chart (from the DCA) and the clinical impact chart (from the CIC) showed good predictive accuracy and clinical utility.ConclusionIn this study, a nomogram model for predicting the individualized survival probability of patients with COAD was constructed and validated. The nomograms of patients with COAD were accurate for predicting the 1-, 3-, and 5-year DSS. This study has great significance for clinical treatments. It also provides guidance for further prospective follow-up studies

A new measuring method for friction factor by using ring with inner boss compression test

To overcome the disadvantage of the bulging effect due to non-uniform deformation in dimension measurement of the conventional ring compression test (RCT), a new measuring method for the friction factor called ring compression test with inner boss (RCT-IB) was proposed. The compression behavior of the ring with an inner boss was investigated and results showed that the change of inner boss was sensitive to friction. The non-concave profile of inner boss allows the dimensional changes to be easily and precisely measured. The calibration curves of RCT-IB were constructed and compared with those of RCT showing similar level of sensitivities at most friction conditions. The RCT-IB method was successfully used to measure the friction factors under four different lubricating conditions

Assisted reproductive technology and interactions between serum basal FSH/LH and ovarian sensitivity index

ObjectivesThis study aimed to investigate whether the FSH (follicle-stimulating hormone)/LH (Luteinizing hormone) ratio correlates with ovarian response in a cross-sectional retrospective study of a population with normal levels of anti-Müllerian hormone (AMH).MethodsThis was a retrospective cross‐sectional study with data obtained from medical records from March 2019 to December 2019 at the reproductive center in the Affiliated Hospital of Southwest Medical University. The Spearmans correlation test evaluated correlations between Ovarian sensitivity index (OSI) and other parameters. The relationship between basal FSH/LH and ovarian response was analyzed using smoothed curve fitting to find the threshold or saturation point for the population with mean AMH level (1.1<AMH<6μg/L). The enrolled cases were divided into two groups according to AMH threshold. Cycle characteristics, cycle information and cycle outcomes were compared. The Mann-Whitney U test was used to compare different parameters between two groups separated by basal FSH/LH in the AMH normal group. Univariate logistic regression analysis and multivariate logistic regression analysis were performed to find the risk factor for OSI.ResultsA total of 428 patients were included in the study. A significant negative correlation was observed between OSI and age, FSH, basal FSH/LH, Gn total dose, and Gn total days, while a positive correlation was found with AMH, AFC, retrieved oocytes, and MII egg. In patients with AMH <1.1 ug/L, OSI values decreased as basal FSH/LH levels increased, while in patients with 1.1<AMH<6 ug/L, OSI values remained stable with increasing basal FSH/LH levels. Logistic regression analysis identified age, AMH, AFC, and basal FSH/LH as significant independent risk factors for OSI.ConclusionsWe conclude that increased basal FSH/LH in the AMH normal group reduces the ovarian response to exogenous Gn. Meanwhile, basal FSH/LH of 3.5 was found to be a useful diagnostic threshold for assessing ovarian response in people with normal AMH levels. OSI can be used as an indicator of ovarian response in ART treatment

Incorporation of a machine learning pathological diagnosis algorithm into the thyroid ultrasound imaging data improves the diagnosis risk of malignant thyroid nodules

ObjectiveThis study aimed at establishing a new model to predict malignant thyroid nodules using machine learning algorithms.MethodsA retrospective study was performed on 274 patients with thyroid nodules who underwent fine-needle aspiration (FNA) cytology or surgery from October 2018 to 2020 in Xianyang Central Hospital. The least absolute shrinkage and selection operator (lasso) regression analysis and logistic analysis were applied to screen and identified variables. Six machine learning algorithms, including Decision Tree (DT), Extreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), Naive Bayes Classifier (NBC), Random Forest (RF), and Logistic Regression (LR), were employed and compared in constructing the predictive model, coupled with preoperative clinical characteristics and ultrasound features. Internal validation was performed by using 10-fold cross-validation. The performance of the model was measured by the area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, F1 score, Shapley additive explanations (SHAP) plot, feature importance, and correlation of features. The best cutoff value for risk stratification was identified by probability density function (PDF) and clinical utility curve (CUC).ResultsThe malignant rate of thyroid nodules in the study cohort was 53.2%. The predictive models are constructed by age, margin, shape, echogenic foci, echogenicity, and lymph nodes. The XGBoost model was significantly superior to any one of the machine learning models, with an AUC value of 0.829. According to the PDF and CUC, we recommended that 51% probability be used as a threshold for determining the risk stratification of malignant nodules, where about 85.6% of patients with malignant nodules could be detected. Meanwhile, approximately 89.8% of unnecessary biopsy procedures would be saved. Finally, an online web risk calculator has been built to estimate the personal likelihood of malignant thyroid nodules based on the best-performing ML-ed model of XGBoost.ConclusionsCombining clinical characteristics and features of ultrasound images, ML algorithms can achieve reliable prediction of malignant thyroid nodules. The online web risk calculator based on the XGBoost model can easily identify in real-time the probability of malignant thyroid nodules, which can assist clinicians to formulate individualized management strategies for patients

Identification of the Signature Associated With m6A RNA Methylation Regulators and m6A-Related Genes and Construction of the Risk Score for Prognostication in Early-Stage Lung Adenocarcinoma

BackgroundN6-methyladenosine (m6A) RNA modification is vital for cancers because methylation can alter gene expression and even affect some functional modification. Our study aimed to analyze m6A RNA methylation regulators and m6A-related genes to understand the prognosis of early lung adenocarcinoma.MethodsThe relevant datasets were utilized to analyze 21 m6A RNA methylation regulators and 5,486 m6A-related genes in m6Avar. Univariate Cox regression analysis, random survival forest analysis, Kaplan–Meier analysis, Chi-square analysis, and multivariate cox analysis were carried out on the datasets, and a risk prognostic model based on three feature genes was constructed.ResultsRespectively, we treated GSE31210 (n = 226) as the training set, GSE50081 (n = 128) and TCGA data (n = 400) as the test set. By performing univariable cox regression analysis and random survival forest algorithm in the training group, 218 genes were significant and three prognosis-related genes (ZCRB1, ADH1C, and YTHDC2) were screened out, which could divide LUAD patients into low and high-risk group (P < 0.0001). The predictive efficacy of the model was confirmed in the test group GSE50081 (P = 0.0018) and the TCGA datasets (P = 0.014). Multivariable cox manifested that the three-gene signature was an independent risk factor in LUAD. Furthermore, genes in the signature were also externally validated using the online database. Moreover, YTHDC2 was the important gene in the risk score model and played a vital role in readers of m6A methylation.ConclusionThe findings of this study suggested that associated with m6A RNA methylation regulators and m6A-related genes, the three-gene signature was a reliable prognostic indicator for LUAD patients, indicating a clinical application prospect to serve as a potential therapeutic target