Browse Articles

Robust estimation of the intrinsic dimension of data sets with quantum cognition machine learning

Conceptual design of airborne contra rotating VAWTs for rooftop wind energy

Abstract Co-rotating, counter, and contra-rotating Vertical Axis Wind Turbines (VAWTs) offer higher power yields than singular turbines due to synergetic interactions, making them ideal for rooftop applications. This study focuses on enhancing the efficiency of a Contra-Rotating VAWT (CR-VAWT) using a ducted airborne configuration. A wind gathering device (WGD), optimized via the Taguchi method, is integrated around the CR-VAWT, which is elevated using an oblate spheroid aerostat designed through a force-weight approach. CFD analyses reveal that incorporating the WGD boosts power output by 32% and increases instantaneous torque by 40% during dynamic stall. The WGD enhances flow redirection towards the rotor plane, achieving a 58% power gain at a 10°skew angle. By suppressing blade-tip vortex shedding, the WGD not only improves efficiency but may also reduce noise. This portable system is suitable for rooftops with limited space, requiring fewer turbines to meet energy demands while reducing noise and improving aesthetics. It also allows for integration with other renewable technologies like photovoltaics, promoting sustainability and lowering carbon footprints. A comprehensive implementation framework is presented to support future research on airborne VAWTs.

Root decomposition in Urochloa decumbens pastures fertilized with increasing nitrogen doses or intercropped with Calopogonium mucunoides

PCSK9 with a gain of function D374Y mutation aggravates atherosclerosis by inhibiting PPARα expression

Multinational modelling of PM2.5 and CO exposures from household air pollution in peri-urban Cameroon, Ghana and Kenya

Abstract In sub-Saharan Africa, approximately 85% of the population uses polluting cooking fuels (e.g. wood, charcoal). Incomplete combustion of these fuels generates household air pollution (HAP), containing fine particulate matter (PM2.5 ) and carbon monoxide (CO). Due to large spatial variability, increased quantification of HAP levels is needed to improve exposure assessment in sub-Saharan Africa. The CLEAN-Air(Africa) study included 24-h monitoring of PM2.5 and CO kitchen concentrations (npm2.5 = 248/nCO = 207) and female primary cook exposures (npm2.5 = 245/nCO = 222) in peri-urban households in Obuasi (Ghana), Mbalmayo (Cameroon) and Eldoret (Kenya). HAP measurements were combined with survey data on cooking patterns, socioeconomic characteristics and ambient exposure proxies (e.g. walking time to nearest road) in separate PM2.5 and CO mixed-effect log-linear regression models. Model coefficients were applied to a larger study population (n = 937) with only survey data to quantitatively scale up PM2.5 and CO exposures. The final models moderately explained variation in mean 24-h PM2.5 (R2 = 0.40) and CO (R2 = 0.26) kitchen concentration measurements, and PM2.5 (R2 = 0.27) and CO (R2 = 0.14) female cook exposures. Primary/secondary cooking fuel type was the only significant predictor in all four models. Other significant predictors of PM2.5 and CO kitchen concentrations were cooking location and household size; household financial security and rental status were only predictive of PM2.5 concentrations. Cooking location, household financial security and proxies of ambient air pollution exposure were significant predictors of PM2.5 cook exposures. Including objective cooking time measurements (from temperature sensors) from (n = 143) households substantially improved (by 52%) the explained variability of the CO kitchen concentration model, but not the PM2.5 model. Socioeconomic characteristics and markers of ambient air pollution exposure were strongly associated with mean PM2.5 measurements, while cooking environment variables were more predictive of mean CO levels.

Molecular profiling of breast cancer methylation pattern in triple negative versus non- triple negative breast cancer

Abstract Epigenetic alterations, especially promotor methylation, have a significant impact on gene expression, molecular subtyping, prognosis, and outcome of breast cancer (BC). The methylation profile was assessed for 22 genes of the BC tissue using the EpiTect Methyl II PCR System in 40 triple-negative BC (TNBC) patients compared to 50 non-TNBC group. The data were corelated with the disease-free (DFS) and overall survival (OS) of the patients. Genes that were differentially hypermethylated in TNBC patients compared to those with non-TNBC included CCND2, CDKN2A, ESR1, CDH1, BRCA1, GSTP, RASSF1, SLIT2, MGMT, PTEN, TP73, and PRDM2. These panel achieved 95% sensitivity, 98% specificity, 97.44% positive predictive value (PPV), 94.23% negative predictive value (NPV), and AUC of 0.993. Hypermethylation of BRCA1, CDH1, CDKN2A, ESR1, GSTP, HIC1, MGMT, PRDM2, PTEN, PYCARDM, RASSF1M, THBS1, and TP73 associated significantly with worse OS and DFS in TNBC cohort. Meanwhile, CCNA1 and CDH1 hypermethylation demonstrated significant associations with poor DFS but did not show significant relationships with OS in TNBC patients. PTGS2 and TNFRSF10C methylation were associated with better DFS and OS rates in TNBC patients. On multivariate Cox regression, CCND2 and PTEN hypermethylation were independent predictors of DFS in the overall BC patients. The hypermethylation of BRCA1 and GSTP were independent predictors of DFS, while PTEN hypermethylation was an independent predictor of OS in the TNBC cohort. The identification of hypermethylated genes, such as BRCA1, CCND2, CDH1, ESR1, GSTP, RASSF1, SLIT2, MGMT, and PTEN may serve as potential biomarkers or therapeutic targets for TNBC.

Risk factors and predictive modeling in a US population with sarcopenia: a propensity score cohort study

Topic modeling analysis of the Allen Human Brain Atlas

Uterine and body temperament in women with and without polycystic ovary syndrome: a case-control study

N-benzyl-N-methyldecane-1-amine derived from garlic ameliorates UVB-induced photoaging in HaCaT cells and SKH-1 hairless mice

miR-16-5p, miR-21-5p, and miR-155-5p in circulating vesicles as psoriasis biomarkers

Abstract Psoriasis is a chronic skin disorder marked by fast skin cell growth, leading to thick, red, scaly patches. MicroRNAs are small, non-coding RNA molecules that play a crucial role in post-transcriptional gene regulation. This study investigates miR-16-5p, miR-21-5p, and miR-155-5p expression in psoriasis EVs and assesses their biomarker potential, exploring associated target genes and pathways via bioinformatics. A cross-sectional and case-control study included 40 psoriasis patients, with blood samples collected in EDTA tubes. RNA from extracellular vesicles was isolated using Qiagen kits, and miRNAs were quantified via RT-qPCR. Bioinformatic analysis predicted target genes using databases like miRDB and TargetScan. Gene expression data from GEO was processed, and differentially expressed genes were identified. This study assessed miR-16-5p, miR-21-5p, and miR-155-5p expression in psoriasis patients’ circulating vesicles versus controls, finding significantly lower levels in patients. ROC analysis confirmed their diagnostic potential. A positive correlation of miR-16-5p with the Psoriasis Area Severity Index (PASI) suggests severity marker potential. Bioinformatics identified 378 common dysregulated genes, revealing key pathways and gene interactions in psoriasis. A heat map confirmed miRNA-mediated gene suppression in the disease. This study identifies miR-16-5p, miR-21-5p, and miR-155-5p as potential psoriasis biomarkers, in addition to finding significant gene interactions and pathways involved in psoriasis pathophysiology.

Effects of startle on cognitive performance and physiological activity revealed by fNIRS and thermal imaging

Abstract Sudden and threatening stimuli can trigger a startle reflex, a stereotyped physiological response that may lead to a brief cognitive incapacitation. Better understanding this reaction would be beneficial to safety-critical occupational domains. We characterized some physiological correlates of the startle response while participants completed a difficult task (Toulouse N-back task) tapping executive functions. During the task, loud and threatening sounds were presented unpredictably to trigger a startle reflex. Brain activity and facial skin temperature were measured in 34 participants using functional near-infrared spectroscopy (fNIRS) and functional infrared thermal imaging (fITI), respectively. In the high difficulty condition, participants were generally less efficient, but their performance improved slightly following startle in the high difficulty condition. Brain activity in the right prefrontal cortex was also higher following startle, potentially reflecting a compensatory overactivation to sustain performance. Interestingly, higher trait-anxiety was associated with lower task performance, still following startle in the high difficulty condition. Finally, we found a decrease in temperature of the right eye and right cheek as well as an increase in the nose temperature following startle. These results underscore the complexity of startle-induced cognitive and physiological dynamics, which may have implications for occupational settings where managing sudden stressors is crucial.

Robust fuzzy dynamic integrated environmental-economic-social scheduling considering demand response and user’s satisfaction with electricity under multiple uncertainties

An intelligent network framework for driver distraction monitoring based on RES-SE-CNN

Abstract As the quantity of motor vehicles and drivers experiences a continuous upsurge, the road driving environment has grown progressively more complex. This complexity has led to a concomitant increase in the probability of traffic accidents. Ample research has demonstrated that distracted driving constitutes a primary human - related factor precipitating these accidents. Therefore, the real - time monitoring and issuance of warnings regarding distracted driving behaviors are of paramount significance. In this research, an intelligent driver state monitoring methodology founded on the RES - SE - CNN model architecture is proposed. When compared with three classical models, namely VGG19, DenseNet121, and ResNet50, the experimental outcomes indicate that the RES - SE - CNN model exhibits remarkable performance in the detection of driver distraction. Specifically, it attains a correct recognition rate of 97.28%. The RES - SE - CNN network architecture model is characterized by lower memory occupancy, rendering it more amenable to deployment on vehicle mobile terminals. This study validates the potential application of the intelligent driver distraction monitoring model, which is based on transfer learning, within the actual driving environment.

Genetic variation and heritability of agronomic traits in a native perennial forage species from drylands: breeding potential of Festuca pallescens

Immune dysregulation in COVID-19 induced ARDS in kidney transplant recipients revealed by single-cell RNA sequencing

Impact of high-sensitivity flow cytometry on peri-transplant minimal residual disease kinetics in acute leukemia

Predicting creep failure life in adhesive-bonded single-lap joints using machine learning

Temporal distribution shifts of Chum salmon (Oncorhynchus keta) with sea surface temperature changes at their southern limit in the North Pacific

Understanding the responses of marine organisms to environmental changes at their distribution limits is crucial for predicting climate-change associated habitat changes. This study analyzed the effect of sea surface temperature (SST) on the temporal distribution of Chum salmon (Oncorhynchus keta) in the eastern and southern coastal waters of Korea (ESCK) and on the southern limit of their distribution in the North Pacific. The temporal distribution of Chum in the ESCK and adjacent rivers was statistically compared based on three SST types (T1–T3). Chum were first caught in the northern and then in the southern area, with riverine migration occurring faster in the south than in the north. These migration patterns did not change with SST type. There was no significant difference in the coastal arrival timing of the Chum between T1 and T3, which respectively represented the entire region cooling either rapidly or slowly compared to an average year. In T2, in which the north cooled rapidly and the south cooled slowly, the coastal arrival timing was approximately 4 days earlier compared to T1 and T3. Moreover, as the SST type shifted from T1 to T3, the coastal residence time in the north became shorter, while in the south became longer. These findings help us to understand the adaptation strategies of Chum, and to predict changes in their distribution and resources in the North Pacific under climate change.

Machine learning-based prediction of distant metastasis risk in invasive ductal carcinoma of the breast

More than 90% of deaths due to breast cancer (BC) are due to metastasis-related complications, with invasive ductal carcinoma (IDC) of the breast being the most common pathologic type of breast cancer and highly susceptible to metastasis to distant organs. BC patients who develop cancer metastases are more likely to have a poor prognosis and poor quality of life, so it is extremely important to recognize and diagnose whether distant metastases have occurred in IDC as early as possible. In this study, we develop a non-invasive breast cancer classification system for detecting cancer metastasis. We used Anaconda-Jupyter notebooks to develop various Python programming modules for text mining, data processing, and machine learning (ML) methods. A risk prediction model was constructed based on four algorithms: Random Forest, XGBoost, Logistic Regression, and SVM. Additionally, we developed a hybrid model based on a voting mechanism using these four algorithms as the base models. The models were compared and evaluated by the following metrics: accuracy, precision, recall, F1-score, and area under the ROC curve (AUC) values. The experimental results show that the hybrid model based on the voting mechanism exhibits the best prediction performance (accuracy: 0.867, precision: 0.929, recall: 0.805, F1-score: 0.856, AUC: 0.94). This stable risk prediction model provides a valuable reference support for doctors in assessing and diagnosing the risk of IDC hematogenous metastasis. It also improves the work efficiency of doctors and strives to provide patients with increased chances of survival.