A Lagrangian Description of Flows in Stirred Tanks Via Computer-Automated Radioactive Particle Tracking (CARPT)

In This Study, Computer-Automated Radioactive Particle Tracking (CARPT) is Implemented for the First Time in the Characterization of Flows in Stirred Tanks. Both the Experimental Set-Up Are Discussed. the CARPT Technique is Seen to Capture Qualitatively Most of the Important Flow Phenomena Observed in Stirred Tank Flows, Like the Two Recirculating Loops above and Below the Impeller and the Dead Zones at the Bottom of the Tank. the CARPT Data is Also Used to Extract \u27\u27Sojourn\u27\u27 Time Distributions in Different Zones of the Reactor. These Distributions Are Used to Partially Quantify the Observed Dead and Active Zones in the Tank. © 2001 Elsevier Science Ltd. All Rights Reserved

Atrial Fibrillation Stratification via Fibrillatory Wave Characterization Using the Filter Diagonalization Method

We use the Filter Diagonalization Method (FDM), a harmonic inversion technique, to extract f-wave features in electrocardiographic (ECG) traces for atrial fibrillation (AF) stratification. The FDM detects f-wave frequencies and amplitudes at frame sizes of 0.15 seconds. We demonstrate our method on a dataset comprising of ECG recordings from 23 patients (61.65 ± 11.63 years, 78.26% male) before cryoablation; 2 paroxysmal AF, 16 early persistent AF (12 months duration). Moreover, some of these patients received adenosine to enhance their RR intervals before ablation. Our method extracts features from FDM outputs to train statistical machine learning classifiers. Tenfold cross-validation demonstrates that the Random Forest and Decision Tree models performed best for the pre-ablation without and with adenosine datasets, with accuracy 60.89 ± 0.31% and 59.58% ± 0.04%, respectively. While the results are modest, they demonstrate that f-wave features can be used for AF stratification. The accuracies are similar for the two tests, slightly better for the case without adenosine, showing that the FDM can successfully model short f-waves without the need to concatenate f-wave sequences or adenosine to elongate RR intervals

GlioPredictor: A deep learning model for identification of high-risk adult IDH-mutant glioma towards adjuvant treatment planning

Identification of isocitrate dehydrogenase (IDH)-mutant glioma patients at high risk of early progression is critical for radiotherapy treatment planning. Currently tools to stratify risk of early progression are lacking. We sought to identify a combination of molecular markers that could be used to identify patients who may have a greater need for adjuvant radiation therapy machine learning technology. 507 WHO Grade 2 and 3 glioma cases from The Cancer Genome Atlas, and 1309 cases from AACR GENIE v13.0 datasets were studied for genetic disparities between IDH1-wildtype and IDH1-mutant cohorts, and between different age groups. Genetic features such as mutations and copy number variations (CNVs) correlated with IDH1 mutation status were selected as potential inputs to train artificial neural networks (ANNs) to predict IDH1 mutation status. Grade 2 and 3 glioma cases from the Memorial Sloan Kettering dataset (n = 404) and Grade 3 glioma cases with subtotal resection (STR) from Northwestern University (NU) (n = 21) were used to further evaluate the best performing ANN model as independent datasets. IDH1 mutation is associated with decreased CNVs of EGFR (21% vs. 3%), CDKN2A (20% vs. 6%), PTEN (14% vs. 1.7%), and increased percentage of mutations for TP53 (15% vs. 63%), and ATRX (10% vs. 54%), which were all statistically significant (p \u3c 0.001). Age \u3e 40 was unable to identify high-risk IDH1-mutant with early progression. A glioma early progression risk prediction (GlioPredictor) score generated from the best performing ANN model (6/6/6/6/2/1) with 6 inputs, including CNVs of EGFR, PTEN and CDKN2A, mutation status of TP53 and ATRX, patient\u27s age can predict IDH1 mutation status with over 90% accuracy. The GlioPredictor score identified a subgroup of high-risk IDH1-mutant in TCGA and NU datasets with early disease progression (p = 0.0019, 0.0238, respectively). The GlioPredictor that integrates age at diagnosis, CNVs of EGFR, CDKN2A, PTEN and mutation status of TP53, and ATRX can identify a small cohort of IDH-mutant with high risk of early progression. The current version of GlioPredictor mainly incorporated clinically often tested genetic biomarkers. Considering complexity of clinical and genetic features that correlate with glioma progression, future derivatives of GlioPredictor incorporating more inputs can be a potential supplement for adjuvant radiotherapy patient selection of IDH-mutant glioma patients

A retrospective analysis of acute organophosphorus poisoning cases admitted to the tertiary care teaching hospital in South India

Objectives: We have herein reported our experience with the pattern of presentation of cases of acute organophosphorus (OP) poisoning cases in a tertiary care hospital.Materials and Methods: This retrospective study evaluated the hospital records of patients with acute OP poisoning. In a pre-structured proforma, data regarding age, sex, time elapsed after intake, circumstances of poisoning, duration of hospitalization, severity, complications, and outcome of the patients were recorded. The data were presented as mean ± standard deviation, entered in the open office datasheet, and analyzed with PSPP software.Results: A total 101 patients were included in the study. Young adult males were more commonly involved than females (M:F 2.5:1). The mean age of the patients was 28 years (range 2-72 years, SD ± 14.3 years). Mean time to receive treatment was 5.2 ± 7.4 (range 1-48 h). About 45.5% patients received first aid before coming to the hospital. The reason was suicide in 88.1% cases and accident in 12 (11.9%, all children). Seventy-nine  patients received pralidoxime (PAM) and the mean duration was 1.7 ± 1.1 (range 1-4 days). Atropine was given in all patients. Mean duration was 5.1 ± 3.1 (range 1-19 days). Mean hospital stay was 7.5 ± 4.7 days (range 1-26 days). Mortality was 9.9% in the present series.Conclusion: Although the present study contribute substantial information regarding the epidemiology and outcome of acute OP poisoning in a tertiary care teaching hospital at a district level, its relatively small sample size and the retrospective record-based nature are the major limitations of the present study. There is a further need for prospective studies to understand the underlying socio-economic factors responsible for acute OP poisoning in our population, and, accordingly, address the problems to reduce the incidence of acute OP poisoning cases.Keywords: Acute poisoning, organophosphate poisoning pattern, outcome, tertiary care hospita

Effects of increasing the affinity of CarD for RNA polymerase on Mycobacterium tuberculosis growth, rRNA transcription, and virulence

CarD is an essential RNA polymerase (RNAP) interacting protein in Mycobacterium tuberculosis that stimulates formation of RNAP-promoter open complexes. CarD plays a complex role in M. tuberculosis growth and virulence that is not fully understood. Therefore, to gain further insight into the role of CarD in M. tuberculosis growth and virulence, we determined the effect of increasing the affinity of CarD for RNAP. Using site-directed mutagenesis guided by crystal structures of CarD bound to RNAP, we identified amino acid substitutions that increase the affinity of CarD for RNAP. Using these substitutions, we show that increasing the affinity of CarD for RNAP increases the stability of the CarD protein in M. tuberculosis. In addition, we show that increasing the affinity of CarD for RNAP increases the growth rate in M. tuberculosis without affecting 16S rRNA levels. We further show that increasing the affinity of CarD for RNAP reduces M. tuberculosis virulence in a mouse model of infection despite the improved growth rate in vitro. Our findings suggest that the CarD-RNAP interaction protects CarD from proteolytic degradation in M. tuberculosis, establish that growth rate and rRNA levels can be uncoupled in M. tuberculosis and demonstrate that the strength of the CarD-RNAP interaction has been finely tuned to optimize virulence. IMPORTANCE Mycobacterium tuberculosis, the causative agent of tuberculosis, remains a major global health problem. In order to develop new strategies to battle this pathogen, we must gain a better understanding of the molecular processes involved in its survival and pathogenesis. We have previously identified CarD as an essential transcriptional regulator in mycobacteria. In this study, we detail the effects of increasing the affinity of CarD for RNAP on transcriptional regulation, CarD protein stability, and virulence. These studies expand our understanding of the global transcription regulator CarD, provide insight into how CarD activity is regulated, and broaden our understanding of prokaryotic transcription

Reinforcement Learning-assisted Evolutionary Algorithm: A Survey and Research Opportunities

Evolutionary algorithms (EA), a class of stochastic search methods based on the principles of natural evolution, have received widespread acclaim for their exceptional performance in various real-world optimization problems. While researchers worldwide have proposed a wide variety of EAs, certain limitations remain, such as slow convergence speed and poor generalization capabilities. Consequently, numerous scholars actively explore improvements to algorithmic structures, operators, search patterns, etc., to enhance their optimization performance. Reinforcement learning (RL) integrated as a component in the EA framework has demonstrated superior performance in recent years. This paper presents a comprehensive survey on integrating reinforcement learning into the evolutionary algorithm, referred to as reinforcement learning-assisted evolutionary algorithm (RL-EA). We begin with the conceptual outlines of reinforcement learning and the evolutionary algorithm. We then provide a taxonomy of RL-EA. Subsequently, we discuss the RL-EA integration method, the RL-assisted strategy adopted by RL-EA, and its applications according to the existing literature. The RL-assisted procedure is divided according to the implemented functions including solution generation, learnable objective function, algorithm/operator/sub-population selection, parameter adaptation, and other strategies. Finally, we analyze potential directions for future research. This survey serves as a rich resource for researchers interested in RL-EA as it overviews the current state-of-the-art and highlights the associated challenges. By leveraging this survey, readers can swiftly gain insights into RL-EA to develop efficient algorithms, thereby fostering further advancements in this emerging field.Comment: 26 pages, 16 figure

TRENDS IN IMINO(or AZA)-NAZAROV CYCLIZATION IN THE CONSTRUCTIVE SYNTHESIS OF HETEROCYCLES

This work was financially supported by the Grants Council of the President of the Russian Federation (#NSh-1223.2022.1.3) and Russian Scientific Foundation (Grant # 21-13-00304)

A convenient synthetic approach to 5-(het)arylhydrazine substituted 1,2,4‑triazines

Received: 09.09.2020. Accepted: 20.12.2020. Published:30.12.2020.A convenient synthesis of 1,2,4‑triazines bearing the moieties of (hetero) arylhydrazines at the position of C5 of the 1,2,4‑triazine core is reported