Blind Source Separation Based on Covariance Ratio and Artificial Bee Colony Algorithm

The computation amount in blind source separation based on bioinspired intelligence optimization is high. In order to solve this problem, we propose an effective blind source separation algorithm based on the artificial bee colony algorithm. In the proposed algorithm, the covariance ratio of the signals is utilized as the objective function and the artificial bee colony algorithm is used to solve it. The source signal component which is separated out, is then wiped off from mixtures using the deflation method. All the source signals can be recovered successfully by repeating the separation process. Simulation experiments demonstrate that significant improvement of the computation amount and the quality of signal separation is achieved by the proposed algorithm when compared to previous algorithms

Multi-scale modeling of drug binding kinetics to predict drug efficacy

Optimizing drug therapies for any disease requires a solid understanding of pharmacokinetics (the drug concentration at a given time point in different body compartments) and pharmacodynamics (the effect a drug has at a given concentration). Mathematical models are frequently used to infer drug concentrations over time based on infrequent sampling and/or in inaccessible body compartments. Models are also used to translate drug action from in vitro to in vivo conditions or from animal models to human patients. Recently, mathematical models that incorporate drug-target binding and subsequent downstream responses have been shown to advance our understanding and increase predictive power of drug efficacy predictions. We here discuss current approaches of modeling drug binding kinetics that aim at improving model-based drug development in the future. This in turn might aid in reducing the large number of failed clinical trials

Quantitative Computed Tomography Study of Bone Mineral Density for Adults Residents in Foshan

Objective: To study the relation between adult vertebral bone mineral density (BMD) and gender and age in Foshan, to explore the average BMD of each age group and osteoporosis (OP) incidence distribution in the region. Methods: Quantitative computed tomography (QCT) was used to examine and record the vertebral BMD of 1065 (male 648, female 417) healthy adults who underwent physical examination in Foshan Hospital of TCM from October 2021 to March 2023, and grouped according to age, T test and variance analysis were used on BMD in different sex and age group. Chi square analysis and correlation analysis were used on the incidence of OP in different sex and age group. Results: There was significant difference in the BMD between male and female of 20~29, 30~39, 40~49 and 60~69, 70~79 years old, women had higher peak BMD than man in young to middle age. In the male groups of 40~49、50~59, 60~69, 70~79 and female groups of 30~39, 40~49, 50~59, 60~69 years old, there were also significant differences in BMD in different age groups of the same sex, and decreased with age. There was significant difference in the incidence of OP between different age groups in the male and female groups. Age was positively related to the incidence of OP (r=0.517 for male and r=0.636 for female). Conclusion: The vertebral BMD derived from QCT for healthy adults in Foshan are associated with sex and age, peaked at 20~29 years old, and decreased with age. The incidence of OP for male and female increased with age from 40~49 years old

Text classification in fair competition law violations using deep learning

IntroductionEnsuring fair competition through manual review is a complex undertaking. This paper introduces the utilization of Long Short-Term Memory (LSTM) neural networks and TextCNN to establish a text classifier for classifying and reviewing normative documents.MethodsThe experimental dataset used consists of policy measure samples provided by the antitrust division of the Guangdong Market Supervision Administration. We conduct a comparative analysis of the performance of LSTM and TextCNN classification models.ResultsIn three classification experiments conducted without an enhanced experimental dataset, the LSTM classifier achieved an accuracy of 95.74%, while the TextCNN classifier achieved an accuracy of 92.7% on the test set. Conversely, in three classification experiments utilizing an enhanced experimental dataset, the LSTM classifier demonstrated an accuracy of 96.36%, and the TextCNN classifier achieved an accuracy of 96.19% on the test set.DiscussionThe experimental results highlight the effectiveness of LSTM and TextCNN in classifying and reviewing normative documents. The superior accuracy achieved with the enhanced experimental dataset underscores the potential of these models in real-world applications, particularly in tasks involving fair competition review

GATA2 mutant variant allele frequency may reflect prognosis in Chinese adult patients with de novo cytogenetically normal acute myeloid leukemia

In this study, we analyzed GATA2 mutations (GATA2mut) and co-mutations in 166 Chinese patients with cytogenetically normal acute myeloid leukemia. This was done through targeted next-generation sequencing of 34 genes associated with myeloid leukemia. GATA2mut was identified in 17 (10%) patients being significantly correlated with co-mutations in CCAAT/enhancer-binding protein alpha (CEBPA) double mutation (P = 0.001). We observed that the N-terminal zinc finger domain (ZF1) was linked to CEBPA mutations, while the C-terminal zinc finger domain (ZF2) was associated with Wilms' tumor 1 (WT1) mutations. It was also noted that patients with GATA2mut had lower platelet counts at diagnosis (P = 0.032). In the entire cohort, GATA2mut had no significant prognostic impact on overall survival (OS) (P = 0.762) and relapse-free survival (RFS) (P = 0.369) compared to patients with GATA2wt. The OS (P = 0.737) and RFS (P = 0.894) of the ZF1 mutation were similar to those of the ZF2 mutation. Most patients with GATA2 mutations were classified in the ELN2022 favorable- and intermediate-risk groups. GATA2mut patients in the favorable-risk group were divided into GATA2High and GATA2Low groups using a median cutoff variant allele frequency (VAF) of 40.13%. GATA2High patients were associated with worse OS (P = 0.031) and RFS (P = 0.021) than GATA2Low patients. In the intermediate-risk group, the high median VAF of GATA2 (≥38.51%) had no significant effect in OS and RFS compared with the low median VAF (<38.51%). This study offers new insights on the prognosis of GATA2mut in the favorable-risk group, where VAF can be used as a guide

Longitudinal Association between Selenium Levels and Hypertension in a Rural Elderly Chinese Cohort

Objectives Results from previous studies have been inconsistent on the association between selenium and hypertension, and very few studies on this subject have focused on the elderly population. The purpose of this study is to examine the relationship between selenium level and hypertension in a rural elderly Chinese cohort. Design A longitudinal study was implemented and data were analyzed using logistic regression models and Cox proportional hazards regression model adjusting for potential confounders. The associations between selenium level and prevalent hypertension at baseline and between selenium and incident hypertension were examined. Setting Community-based setting in four rural areas in China. Subjects A total of 2000 elderly aged 65 years and over (mean 71.9±5.6 years) participated in this study. Measurements Nail selenium levels were measured in all subjects at baseline. Blood pressure measures and self-reported hypertension history were collected at baseline, 2.5 years and 7 years later. Hypertension was defined as systolic blood pressure 140 mmHg or higher, diastolic blood pressure 90 mmHg or higher, or reported use of anti-hypertensive medication. Results The rate of baseline hypertension was 63.50% in this cohort and the mean nail selenium level is 0.413±0.183µg/g. Multi-covariate adjusted cross-sectional analyses indicated that higher selenium level was associated with higher blood pressure measures at baseline and higher rates of hypertension. For the 635 participants with normal blood pressure at baseline, 360 had developed hypertension during follow-up. The incidence rate for hypertension was 45.83%, 52.27%, 62.50%, 70.48%, and 62.79% from the first selenium quintile to the fifth quintile respectively. Comparing to the lowest quintile group, the hazard ratios were 1.41 (95%CI: 1.03 to1.94), 1.93 (95%CI: 1.40 to 2.67), 2.35 (95%CI: 1.69 to 3.26) and 1.94 (95%CI: 1.36 to 22.77) for the second selenium quintile to the fifth quintile respectively. Conclusions Our findings suggest that high selenium may play a harmful role in the development of hypertension. Future studies are needed to confirm our findings and to elucidate a plausible biological mechanism

An allosteric modulator activates BK channels by perturbing coupling between Ca2+ binding and pore opening

BK type C