Browse Articles

A fuzzy system based self-adaptive memetic algorithm using population diversity control for evolutionary multi-objective optimization

Abstract Simulated by nature’s evolution, numerous evolutionary algorithms had been proposed. These algorithms perform better for a particular problem domain and extensive parameter fine tuning and adaptations are required in optimizing problems of varied domain. This paper aims to develop robust and self-adaptive memetic algorithm by combining Differential Evolution based algorithm, a popular population based global search method with the Controlled Local search procedure to solve multi-objective optimization problems. Memetic Algorithm is an enhanced evolutionary algorithm, it combines global search method with local search techniques for faster convergence. Memetic algorithm improves both exploration and exploitation, preventing premature convergence and also refines the current best solutions efficiently. Proposed algorithm is named as Fuzzy based Memetic Algorithm using Diversity control (F-MAD). In F-MAD, population diversity is controlled through the control parameters self-adaptation of Differential Evolution algorithm (DE) such as, crossover rate and scaling factor by using two fuzzy systems. A controlled local search procedure is adapted for guiding convergence process thus balancing explore-exploit cycle. The control parameter self-adaptation and enhanced selection method with controlled local search method aid population diversity control in decision space and attaining optimal solutions with uniform distribution in terms of diversity and convergence metrics in objective space. These characteristics help the proposed method suitable to be extended to different application domain without the need of trial-and-error fine tuning of the parameters. The performance is tested through standard benchmark test problems-CEC 2009 test problems and DTLZ test problem and further validated through performance metrics and statistical test. It is compared with popular optimization algorithms and experiment results indicate that F-MAD perform well than State of-The-Art (SOTA) algorithms taken for comparison. F-MAD algorithm attains better results for 8 out of 10 CEC 2009 test problems (UF1-UF10) when compared to 20 other algorithms taken for comparison. For DTLZ problems, F-MAD attains better results for ALL 7 problems (DTLZ 1-DTLZ7) when compared to 8 other SOTA algorithms. The performance is further evaluated using Friedman rank test and the proposed F-MAD significantly outperformed other algorithms.

PDE4B promotes JNK/NLRP3 activation in the nucleus pulposus and mediates intervertebral disc degeneration

A new approach to treatment of stress urinary incontinence using non-ablative transurethral laser

Acidification potential estimation for small hydropower using LCA methodology in India

What are the best AI tools for research? Nature’s guide

Optimizing oil properties and asphaltene management using Fe3O4-based nanohybrids under microwave radiation

Scalable intracellular delivery via microfluidic vortex shedding enhances the function of chimeric antigen receptor T-cells

Abstract Adoptive chimeric antigen receptor T-cell (CAR-T) therapy is transformative and approved for hematologic malignancies. It is also being developed for the treatment of solid tumors, autoimmune disorders, heart disease, and aging. Despite unprecedented clinical outcomes, CAR-T and other engineered cell therapies face a variety of manufacturing and safety challenges. Traditional methods, such as lentivirus transduction and electroporation, result in random integration or cause significant cellular damage, which can limit the safety and efficacy of engineered cell therapies. We present hydroporation as a gentle and effective alternative for intracellular delivery. Hydroporation resulted in 1.7- to 2-fold higher CAR-T yields compared to electroporation with superior cell viability and recovery. Hydroporated cells exhibited rapid proliferation, robust target cell lysis, and increased pro-inflammatory and regulatory cytokine secretion in addition to improved CAR-T yield by day 5 post-transfection. We demonstrate that scaled-up hydroporation can process 5 × 10 8 cells in less than 10 s, showcasing the platform as a viable solution for high-yield CAR-T manufacturing with the potential for improved therapeutic outcomes.

Multi-omics analysis identifies novels genes involved in glioma prognosis

A novel UPLC-based method to identify elephant and mammoth ivory

Shallow burial of drip irrigation tape improves the water use efficiency of potatoes in semi-arid areas

Predicting visual field global and local parameters from OCT measurements using explainable machine learning

Abstract Glaucoma is characterised by progressive vision loss due to retinal ganglion cell deterioration, leading to gradual visual field (VF) impairment. The standard VF test may be impractical in some cases, where optical coherence tomography (OCT) can offer predictive insights into VF for multimodal diagnoses. However, predicting VF measures from OCT data remains challenging. To address this, five regression models were developed to predict VF measures from OCT, Shapley Additive exPlanations (SHAP) analysis was performed for interpretability, and a clinical software tool called OCT to VF Predictor was developed. To evaluate the models, a total of 268 glaucomatous eyes (86 early, 72 moderate, 110 advanced) and 226 normal eyes were included. The machine learning models outperformed recent OCT-based VF prediction deep learning studies, with correlation coefficients of 0.76, 0.80 and 0.76 for mean deviation, visual field index and pattern standard deviation, respectively. Introducing the pointwise normalisation and step-size concept, a mean absolute error of 2.51 dB was obtained in pointwise sensitivity prediction, and the grayscale prediction model yielded a mean structural similarity index of 77%. The SHAP-based analysis provided critical insights into the most relevant features for glaucoma diagnosis, showing promise in assisting eye care practitioners through an explainable AI tool.

Fracture properties of dolomite and prediction of fracture toughness based on BP-ANN

Unraveling the causal association between inflammatory bowel diseases and uveitis through mendelian randomization analysis

Abstract To investigate the causal relationship between inflammatory bowel disease (IBD) and uveitis, we conducted a two-sample bidirectional Mendelian randomization (MR) analysis utilizing summary data from genome-wide association studies (GWAS). The primary statistical analysis was performed using the inverse-variance weighted (IVW) method. False discovery rate (FDR) correction was used to control for false positives in multiple testing. In addition, sensitivity analyses were carried out using the MR Egger intercept test and Cochran’s Q test. The MR analysis revealed that genetically determined IBD (OR = 1.141, 95% CI 1.080–1.205, P = 2.21 × 10−6, PFDR = 6.90 × 10−6), ulcerative colitis (UC) (OR = 1.113, 95% CI 1.032–1.201, P = 0.006, PFDR = 0.009), and Crohn’s diseases (CD) (OR = 1.073, 95% CI 1.017–1.133, P = 0.010, PFDR = 0.011) had a causal effect on uveitis. Conversely, the reverse MR analysis did not reveal significant causal link of uveitis on IBD, including its two subtypes. Furthermore, the results of the MR-Egger and weighted median methods were consistent with the IVW method. No evidence of heterogeneity or pleiotropy was detected by sensitivity analysis. Our findings confirm that IBD and its main subtypes had a causal connection with uveitis. Further research is needed to elucidate the underlying pathophysiological mechanisms driving this association.

Machine learning-based identification of co-expressed genes in prostate cancer and CRPC and construction of prognostic models

Apolipoprotein E-ε4 allele is associated with perihematomal brain edema and poor outcomes in patients with intracerebral hemorrhage

Cross-sectional study on the association between serum uric acid levels and non-alcoholic fatty liver disease in an elderly population

Predicting preterm birth using machine learning methods

Identification of IGF2BPs-related mRNA signature for predicting the overall survival of lung adenocarcinoma

Solid-liquid interface charge transfer for generation of H2O2 and energy

A multiparametric anti-aging CRISPR screen uncovers a role for BAF in protein synthesis regulation

Abstract Progeria syndromes are very rare, incurable premature aging conditions recapitulating most aging features. Here, we report a whole genome, multiparametric CRISPR screen, identifying 43 genes that can rescue multiple cellular phenotypes associated with progeria. We implement the screen in fibroblasts from Néstor-Guillermo Progeria Syndrome male patients, carrying a homozygous A12T mutation in BAF. The hits are enriched for genes involved in protein synthesis, protein and RNA transport and osteoclast formation and are validated in a whole-organism Caenorhabditis elegans model. We further confirm that BAF A12T can disrupt protein synthesis rate and fidelity, which could contribute to premature aging in patients. This work highlights the power of multiparametric genome-wide suppressor screens to identify genes enhancing cellular resilience in premature aging and provide insights into the biology underlying progeria-associated cellular dysfunction.