Efficient three layer secured adaptive video steganography method using chaotic dynamic systems.

In light of the unprecedented growth in internet usage, safeguarding data from unauthorized access has emerged as a paramount concern. Cryptography and steganography stand as pivotal methods for ensuring data security during transmission. This study introduces an innovative adaptive video steganography approach featuring three tiers of security for extracting concealed information, thereby facilitating secure communication. The embedding process operates within the spatial domain of cover video frames, enabling a remarkable hiding ratio of up to 28.125% (equivalent to 2.25 bits per pixel in payload) without compromising the quality of video frames. Users are afforded the flexibility to select between partial or full embedding capacity of CVF through the proposed adaptive control block (ACB). The chaotic key generator (CKG), which combines a logistic map and sine map, is employed to generate highly sensitive initial seeds for permutation order (PO), frame selection (FS), and random position for hiding (RPH), thereby ensuring three levels of security. Prior to transmission, both CVF and hidden data (SD) are encrypted using PO. Encrypted CVFs are then randomly selected using FS for embedding, with RPH employed during the embedding process. Subsequently, for transmitting the stego-video frame, embedded CVFs are decrypted using the same PO. Experimental results demonstrate the efficacy of the proposed approach, achieving an adaptive hiding ratio ranging from 7.1 to 28.125% (equivalent to 0.56 to 2.25 bits per pixel in payload) and maintaining a peak signal-to-noise ratio (PSNR) within the range of 50.25 to 62.05 dB.

Biomarkers and Cognition Study, Singapore (BIOCIS): Protocol, Study Design, and Preliminary Findings.

BACKGROUND: The focus of medicine is shifting from treatment to preventive care. The expression of biomarkers of dementia and Alzheimer's disease (AD) appear decades before the onset of observable symptoms, and evidence has emerged supporting pharmacological and non-pharmacological interventions to treat modifiable risk factors of dementia. However, there is limited research on the epidemiology, clinical phenotypes, and underlying pathobiology of cognitive diseases in Asian populations. OBJECTIVES: The objectives of the Biomarkers and Cognition Study, Singapore(BIOCIS) are to characterize the underlying pathobiology of Cognitive Impairment through a longitudinal study incorporating fluid biomarker profiles, neuroimaging, neuropsychological and clinical outcomes in a multi-ethnic Southeast Asian population. DESIGN, SETTING, PARTICIPANTS: BIOCIS is a 5-year longitudinal study where participants are assessed annually. 2500 participants aged 30 to 95 will be recruited from the community in Singapore. To investigate how pathology presents with or without minimal clinical symptoms and vice versa, CI and unimpaired individuals will be recruited. Participants will undergo assessments to characterise biomarkers of dementia through neuroimaging, fluid biomarkers, cognitive assessments, behavioural and lifestyle profiles, retinal scans and microbiome indicators. RESULTS: Since commencement of recruitment in February 2022, 1148 participants have been enrolled, comprising 1012 Chinese, 62 Indian, and 35 Malay individuals. Mean age and education is 61.32 years and 14.34 years respectively with 39.8% males. 47.9 % of the cohort are employed and 32.06% have a family history of dementia. The prevalence of cerebral small vessel disease is 90.2% with a mean modified Fazekas white matter hyperintensity score of 4.1. CONCLUSION: The BIOCIS cohort will help identify novel biomarkers, pathological trajectories, epidemiology of dementia, and reversible risk factors in a Southeast Asian population. Completion of BIOCIS longitudinal data could provide insights into risk-stratification of Asians populations, and potentially inform public healthcare and precision medicine for better patient outcomes in the prevention of Alzheimer's disease and dementia.

Neutron spectrum measurements near KAMINI reactor south beam vault door using nested neutron spectrometer.

KAlpakkam MINI reactor (KAMINI) is a 233U fuelled research reactor has various neutron irradiation locations for experimental purposes. The pit at the south beam end of KAMINI reactor is being extensively utilised for neutron attenuation experiments in prospective shielding materials as well as for neutron radiography. During reactor operation, it will be closed by a movable shield. A vault door is located above the shield and the movable shield is used to attenuate streaming neutrons and gamma-rays during reactor operation. Even with the shield, there exists significant dose because of streaming neutrons and gamma rays. Its variation depends on the power of the reactor. The neutron and gamma dose rates close to the south beam vault door have recently been found to be 275-300 µSv/h and 175-200 µSv/h, respectively, when the reactor is operating at 10 kW. In order to characterise the streaming neutron spectra of vault door place for the first time, measurements are done using the Nested Neutron Spectrometer. Along with the neutron flux, neutron mean energy and ambient dose-equivalent rate are also measured and compared with earlier measurements carried out inside the south beam pit. It is observed that the presence of paraffin shield reduces the neutron average energy from 370 to 178 keV. Apart from energy reduction, 10 kW normalised neutron flux of south beam pit is also attenuated by the shield by 25 000 times and it is found that the neutron spectrum of the measured location is also more thermalized. Neutron reference data of the location are generated.

Outcome of tailored therapy in rheumatic heart disease with persistent atrial fibrillation (RHD-AF).

INTRODUCTION: Rheumatic heart disease with persistent atrial fibrillation (RHD-AF) is associated with increased morbidity. However, there is no standardized approach for the maintenance of sinus rhythm (SR) in them. We aimed to determine the utility of a stepwise approach to achieve SR in RHD-AF. METHODS: Consecutive patients with RHD-AF from July 2021 to August 2023 formed the study cohort. The stepwise approach included pharmacological rhythm control and/or electrical cardioversion (Central illustration). In patients with recurrence, additional options included AF ablation or pace and ablate strategy with conduction system pacing or biventricular pacing. Clinical improvement, NT-proBNP, 6-Minute Walk Test (6MWT), heart failure (HF) hospitalizations, and thromboembolic complications were documented during follow-up. RESULTS: Eighty-three patients with RHD-AF (mean age 56.13 ± 9.51 years, women 72.28%) were included. Utilizing this approach, 43 (51.81%) achieved and maintained SR during the study period of 11.04 ± 7.14 months. These patients had improved functional class, lower NT-proBNP, better distance covered for 6MWT, and reduced HF hospitalizations. The duration of AF was shorter in patients who achieved SR, compared to those who remained in AF (3.15 ± 1.29 vs 6.93 ± 5.23, p = 0.041). Thirty-five percent (29) maintained SR after a single cardioversion over the study period. Only one underwent AF ablation. Of the 24 who underwent pace and ablate strategy, atrial lead was implanted in 22 (hybrid approach), and 50% of these achieved and maintained SR. Among these 24, none had HF hospitalizations, but patients who maintained SR had further improvement in clinical and functional parameters. CONCLUSIONS: RHD-AF patients who could achieve SR with a stepwise approach, had better clinical outcomes and lower HF hospitalizations.

Involvement of ObRb receptor, nitric oxide, and BKCa channel signaling pathways in leptin-induced relaxation of pregnant mouse uterus.

The purpose of this study was to determine the receptor subtype and the underlying mechanisms involved in the relaxant effect to leptin in mid- and late-pregnant mouse uterus. We determined the relative mRNA expression of receptor subtypes, eNOS, and BKCa channel by quantitative PCR and also the overall receptor expression by immunohistochemistry. Isometric tension studies were conducted to evaluate the effects of leptin and to delineate its mechanisms. A selective siRNA for the ObRb receptor was used to determine the participation of the receptor subtype in biochemical and molecular effects of leptin. The relaxant response to leptin was greater in mid-pregnancy compared to late pregnancy and was mediated by the activation of BKCa channels by eNOS-derived nitric oxide in an ObRb receptor-dependent manner. In comparison to mid-pregnancy, expression of short forms (mainly ObRa receptor) of the receptor was significantly increased in late pregnancy, whereas ObRb receptor expression was similar in both phases. The results of the study suggest that ObRb receptor mediates leptin-induced increase in eNOS expression and NO synthesis. Leptin-induced eNOS expression and activation cause cGMP-independent stimulation of BKCa channels causing uterine relaxation. Increased short forms of the receptors and reduced BKCa channels exert a negative effect on uterine relaxation in late pregnancy. Leptin may have a physiological role in maintaining uterine quiescence in mid-pregnancy and its reduced relaxant response in late gestation may facilitate labor. Further, ObRb receptor agonists may be useful in the management of preterm labor.

Biomass Derived Biofluorescent Carbon Dots for Energy Applications: Current Progress and Prospects.

Biomass resources are often disposed of inefficiently and it causes environmental degradation. These wastes can be turned into bio-products using effective conversion techniques. The synthesis of high-value bio-products from biomass adheres to the principles of a sustainable circular economy in a variety of industries, including agriculture. Recently, fluorescent carbon dots (C-dots) derived from biowastes have emerged as a breakthrough in the field, showcasing outstanding fluorescence properties and biocompatibility. The C-dots exhibit unique quantum confinement properties due to their small size, contributing to their exceptional fluorescence. The significance of their fluorescent properties lies in their versatile applications, particularly in bio-imaging and energy devices. Their rapid and straight-forward production using green/chemical precursors has further accelerated their adoption in diverse applications. The use of green precursors for C-dot not only addresses the biomass disposal issue through a scientific approach, but also establishes a path for a circular economy. This approach not only minimizes biowaste, which also harnesses the potential of fluorescent C-dots to contribute to sustainable practices in agriculture. This review explores recent developments and challenges in synthesizing high-quality C-dots from agro-residues, shedding light on their crucial role in advancing technologies for a cleaner and more sustainable future.

Assessing the Impact of AI-Enhanced Diagnostic Tools on the Treatment Planning of Orthodontic Cases: An RCT.

Background: Orthodontic treatment planning involves the precise assessment of dental and skeletal anomalies, which can be facilitated by AI-enhanced diagnostic tools. Materials and Methods: A total of 100 orthodontic cases were included in this RCT. Patients were randomly assigned to two groups: an AI-enhanced diagnostic group and a traditional diagnostic group. The AI-enhanced diagnostic group underwent orthodontic assessment with the aid of AI-powered software, which provided automated cephalometric analysis, 3D model evaluations, and treatment suggestions. The traditional diagnostic group received conventional diagnostic assessments by orthodontists. The primary outcome measures included treatment planning accuracy, treatment time, and patient satisfaction. Secondary outcomes included the number of appointments required and treatment cost. Results: The AI-enhanced diagnostic group demonstrated a significantly higher accuracy in treatment planning compared to the traditional diagnostic group (P < 0.05). The AI group also required fewer appointments (mean ± SD: 10.2 ± 2.1 vs. 12.8 ± 3.4) and had a shorter treatment time (mean ± SD: 14.6 ± 3.2 months vs. 18.9 ± 4.5 months) (P < 0.001 for both comparisons). Additionally, patient satisfaction scores were higher in the AI group (mean ± SD: 9.2 ± 0.6 vs. 8.1 ± 0.8) (P < 0.001). However, the AI-enhanced diagnostic group had a slightly higher treatment cost. Conclusion: AI-enhanced diagnostic tools significantly enhance the accuracy of treatment planning in orthodontic cases, leading to reduced treatment time, fewer appointments, and increased patient satisfaction.

Exploring Macroscopic Dipoles of Designed Cyclic Peptide Ordered Assemblies to Harvest Piezoelectric Properties.

Crystalline materials exhibiting non-centrosymmetry and possessing substantial surface dipole moments play a critical role in piezoelectricity. Designing biocompatible self-assembled materials with these attributes is particularly challenging when compared to inorganic materials and ceramics. In this study, we elucidate the crystal conformations of novel cyclic peptides that exhibit self-assembly into tubular structures characterized by unidirectional hydrogen bonding and piezoelectric properties. Unlike cyclic peptides derived from alternating L- and D-amino acids, those derived from new δ-amino acids demonstrate the formation of self-assembled tubes with unidirectional hydrogen bonds. Further, the tightly packed tubular assemblies and higher macrodipole moments result in superior piezoelectric coefficients compared to peptides with lower macrodipole moments. Our findings underscore the potential for designing cyclic peptides with unidirectional hydrogen bonds, thereby paving the way for their application in design of biocompatible piezo- and ferroelectric materials.

Cryopreservation alters buffalo sperm kinematics and mitochondrial parameters, acrosome and intra-cellular calcium status.

BACKGROUND: Little is known about the effects of different seasons on the cryopreservation success of buffalo sperm in terms of kinematics and sperm functional parameters. OBJECTIVE: To study the effect of three seasons (winter, comfort and summer) and cryopreservation on sperm kinematics and functional properties in buffalo bulls. MATERIALS AND METHODS: Semen ejaculates (n = 90) collected during three seasons i.e. winter (n = 30), comfort (n = 30), summer (n = 30) were evaluated for sperm kinematics and functional properties. RESULTS: Sperm kinematics with respect to total (TM), progressive (PM) and rapid motility (RM) was higher (P < 0.05) in fresh sperm compared to sperm that had been frozen-thawed. Similarly, all kinematic parameters [viz. average path velocity (VAP), straight linear velocity (VSL), curvilinear velocity (VCL), beats cross frequency (BCF), lateral head displacement (ALH), linearity (LIN) and straightness (STR)] were higher (P < 0.01) at the fresh stage. With respect to season, frozen-thawed semen TM (57.67 ± 115 %), PM (50.2 ± 1.15 %) and RM (51.6 ± 1.19 %) were higher (P < 0.01) when using sperm collected during winter. The stage of cryopreservation (i.e., equilibration and freeze-thawing) also showed significant effects (P < 0.01) on mitochondrial superoxide positive status (MSPS), mitochondrial membrane potential (MMP), acrosome status and intra-cellular calcium status. CONCLUSION: The season of sperm collection and cryopreservation have significant effects on buffalo bull sperm kinematics and functional properties. Doi.org/10.54680/fr24410110612.

Artificial Intelligence-Based, Wavelet-Aided Prediction of Long-Term Outdoor Performance of Perovskite Solar Cells.

The commercial development of perovskite solar cells (PSCs) has been significantly delayed by the constraint of performing time-consuming degradation studies under real outdoor conditions. These are necessary steps to determine the device lifetime, an area where PSCs traditionally suffer. In this work, we demonstrate that the outdoor degradation behavior of PSCs can be predicted by employing accelerated indoor stability analyses. The prediction was possible using a swift and accurate pipeline of machine learning algorithms and mathematical decompositions. By training the algorithms with different indoor stability data sets, we can determine the most relevant stress factors, thereby shedding light on the outdoor degradation pathways. Our methodology is not specific to PSCs and can be extended to other PV technologies where degradation and its mechanisms are crucial elements of their widespread adoption.

"Heyde's enigma" in a patient with congenital annular aortic stenosis and its therapeutic challenges.

Heyde's syndrome is described as angio-dysplastic gastrointestinal (GI) bleeding in elderly patients with degenerative severe calcific aortic stenosis (AS), resulting in anaemia. It was first reported by Edward C. Heyde in 1958 and thus carried his name. Although this condition is considered to develop in 10-20% of severe AS, it is a less familiar entity in clinical practice. With the rising geriatric population in the communities, there is a proportionate increase in the incidence of AS and accompanying Heyde's syndrome. Heyde's syndrome has also been associated with hypertrophic cardiomyopathy, left ventricular assist device, ventricular septal defect, and patent ductus arteriosus. This article reports a case of Heyde's syndrome associated with congenital annular AS, successfully treated by aortic root enlargement and valve replacement. Supplementary Information: The online version contains supplementary material available at 10.1007/s12055-023-01636-y.

Comparative Analysis of Machine Learning Algorithms Evaluating the Single Nucleotide Polymorphisms of Metabolizing Enzymes with Clinical Outcomes Following Intravenous Paracetamol in Preterm Neonates with Patent Ductus Arteriosus.

AIMS: Pharmacogenomics has been identified to play a crucial role in determining drug response. The present study aimed to identify significant genetic predictor variables influencing the therapeutic effect of paracetamol for new indications in preterm neonates. BACKGROUND: Paracetamol has recently been preferred as a first-line drug for managing Patent Ductus Arteriosus (PDA) in preterm neonates. Single Nucleotide Polymorphisms (SNPs) in CYP1A2, CYP2A6, CYP2D6, CYP2E1, and CYP3A4 have been observed to influence the therapeutic concentrations of paracetamol. OBJECTIVES: The purpose of this study was to evaluate various Machine Learning Algorithms (MLAs) and bioinformatics tools for identifying the key genotype predictor of therapeutic outcomes following paracetamol administration in neonates with PDA. METHODS: Preterm neonates with hemodynamically significant PDA were recruited in this prospective, observational study. The following SNPs were evaluated: CYP2E1*5B, CYP2E1*2, CYP3A4*1B, CYP3A4*2, CYP3A4*3, CYP3A5*3, CYP3A5*7, CYP3A5*11, CYP1A2*1C, CYP1A2*1K, CYP1A2*3, CYP1A2*4, CYP1A2*6, and CYP2D6*10. Amongst the MLAs, Artificial Neural Network (ANN), C5.0 algorithm, Classification and Regression Tree analysis (CART), discriminant analysis, and logistic regression were evaluated for successful closure of PDA. Generalized linear regression, ANN, CART, and linear regression were used to evaluate maximum serum acetaminophen concentrations. A two-step cluster analysis was carried out for both outcomes. Area Under the Curve (AUC) and Relative Error (RE) were used as the accuracy estimates. Stability analysis was carried out using in silico tools, and Molecular Docking and Dynamics Studies were carried out for the above-mentioned enzymes. RESULTS: Two-step cluster analyses have revealed CYP2D6*10 and CYP1A2*1C to be the key predictors of the successful closure of PDA and the maximum serum paracetamol concentrations in neonates. The ANN was observed with the maximum accuracy (AUC = 0.53) for predicting the successful closure of PDA with CYP2D6*10 as the most important predictor. Similarly, ANN was observed with the least RE (1.08) in predicting maximum serum paracetamol concentrations, with CYP2D6*10 as the most important predictor. Further MDS confirmed the conformational changes for P34A and P34S compared to the wildtype structure of CYP2D6 protein for stability, flexibility, compactness, hydrogen bond analysis, and the binding affinity when interacting with paracetamol, respectively. The alterations in enzyme activity of the mutant CYP2D6 were computed from the molecular simulation results. CONCLUSION: We have identified CYP2D6*10 and CYP1A2*1C polymorphisms to significantly predict the therapeutic outcomes following the administration of paracetamol in preterm neonates with PDA. Prospective studies are required for confirmation of the findings in the vulnerable population.

Infrared thermal images using PCSAN-Net-DBOA: An approach of breast cancer classification.

This manuscript proposes thermal images using PCSAN-Net-DBOA Initially, the input images are engaged from the database for mastology research with infrared image (DMR-IR) dataset for breast cancer classification. The adaptive distorted Gaussian matched-filter (ADGMF) was used in removing noise and increasing the quality of infrared thermal images. Next, these preprocessed images are given into one-dimensional quantum integer wavelet S-transform (OQIWST) for extracting Grayscale statistic features like standard deviation, mean, variance, entropy, kurtosis, and skewness. The extracted features are given into the pyramidal convolution shuffle attention neural network (PCSANN) for categorization. In general, PCSANN does not show any adaption optimization techniques to determine the optimal parameter to offer precise breast cancer categorization. This research proposes the dung beetle optimization algorithm (DBOA) to optimize the PCSANN classifier that accurately diagnoses breast cancer. The BCD-PCSANN-DBO method is implemented using Python. To classify breast cancer, performance metrics including accuracy, precision, recall, F1 score, error rate, RoC, and computational time are considered. Performance of the BCD-PCSANN-DBO approach attains 29.87%, 28.95%, and 27.92% lower computation time and 13.29%, 14.35%, and 20.54% greater RoC compared with existing methods like breast cancer diagnosis utilizing thermal infrared imaging and machine learning approaches(BCD-CNN), breast cancer classification from thermal images utilizing Grunwald-Letnikov assisted dragonfly algorithm-based deep feature selection (BCD-VGG16) and Breast cancer detection in thermograms using deep selection based on genetic algorithm and Gray Wolf Optimizer (BCD-SqueezeNet), respectively. RESEARCH HIGHLIGHTS: The input images are engaged from the breast cancer dataset for breast cancer classification. The ADQMF was used in removing noise and increasing the quality of infrared thermal images. The extracted features are given into the PCSANN for categorization. DBOA is proposed to optimize PCSANN classifier that classifies breast cancer precisely. The proposed BCD-PCSANN-DBO method is implemented using Python.

Relationship of emotional intelligence and capability of answering higher-order knowledge questions in physiology among first-year medical students.

Emotional intelligence (EI) has a positive correlation with the academic performance of medical students. However, why there is a positive correlation needs further exploration. We hypothesized that the capability of answering higher-order knowledge questions (HOQs) is higher in students with higher EI. Hence, we assessed the correlation between EI and the capability of medical students to answer HOQs in physiology. First-year undergraduate medical students (n = 124) from an Indian medical college were recruited as a convenient sample. EI was assessed by the Schutte Self-Report Emotional Intelligence Test (SSEIT), a 33-item self-administered validated questionnaire. A specially designed objective examination with 15 lower-order and 15 higher-order multiple-choice questions was conducted. The correlation between the examination score and the EI score was tested by Pearson's correlation coefficient. Data from 92 students (33 females and 59 males) with a mean age of 20.14 ± 1.87 yr were analyzed. Overall, students got a percentage of 53.37 ± 14.07 in the examination, with 24.46 ± 9.1 in HOQs and 28.91 ± 6.58 in lower-order knowledge questions (LOQs). They had a mean score of 109.58 ± 46.2 in SSEIT. The correlation coefficient of SSEIT score with total marks was r = 0.29 (P = 0.0037), with HOQs was r = 0.41 (P < 0.0001), and with LOQs was r = 0.14 (P = 0.19). Hence, there is a positive correlation between EI and the capability of medical students to answer HOQs in physiology. This study may be the foundation for further exploration of the capability of answering HOQs in other subjects.NEW & NOTEWORTHY This study assessed the correlation between emotional intelligence (EI) and the capability of medical students to answer higher-order knowledge questions (HOQs) in the specific context of physiology. The finding reveals one of the multifaceted dimensions of the relationship between EI and academic performance. This novel perspective opens the door to further investigations to explore the relationship in other subjects and other dimensions to understand why students with higher EI have higher academic performance.

Interactions between the protein barnase and co-solutes studied by NMR.

Protein solubility and stability depend on the co-solutes present. There is little theoretical basis for selection of suitable co-solutes. Some guidance is provided by the Hofmeister series, an empirical ordering of anions according to their effect on solubility and stability; and by osmolytes, which are small organic molecules produced by cells to allow them to function in stressful environments. Here, NMR titrations of the protein barnase with Hofmeister anions and osmolytes are used to measure and locate binding, and thus to separate binding and bulk solvent effects. We describe a rationalisation of Hofmeister (and inverse Hofmeister) effects, which is similar to the traditional chaotrope/kosmotrope idea but based on solvent fluctuation rather than water withdrawal, and characterise how co-solutes affect protein stability and solubility, based on solvent fluctuations. This provides a coherent explanation for solute effects, and points towards a more rational basis for choice of excipients.

Hypertension treatment cascade among men and women of reproductive age group in India: analysis of National Family Health Survey-5 (2019-2021).

Background: Only a proportion of adults with hypertension are diagnosed and receive recommended prescriptions despite the availability of inexpensive and efficacious treatment. We aimed to estimate the prevalence of different stages of hypertension treatment cascade among the reproductive age groups in India at the national and state levels. We also identified the predictors of different stages of the hypertension treatment cascade. Methods: We used the nationally representative data from National Family Health Survey (NFHS)-5. We included all the males (15-54 years) and females aged 15-49. Socio-demographic factors, anthropometric measurements, habits, comorbid conditions, and healthcare access stratified the stages of the hypertension treatment cascade among hypertensives. We used multinomial logistic regression to identify the determinants of the treatment cascade levels. Findings: We had data from 1,267,786 individuals. The national prevalence of hypertension was 18.3% (95% CI: 18.1%-18.4%). Men (21.6%, 95% CI: 21.5%-21.7%) were found to have a higher prevalence as compared to women (14.8%, 95% CI: 14.7%-14.9%). Among hypertensive individuals, 70.5% (95% CI: 70.3%-70.7%) had ever received a BP measurement ("screened"), 34.3% (95% CI: 34.1%-34.5%) had been diagnosed prior to the survey ("aware"), 13.7% (95% CI: 13.5%-13.8%) reported taking a prescribed anti-hypertensive drug ("under treatment"), and 7.8% (95% CI: 7.7%-7.9%) had their BP under control ("controlled"). Males, illiterates, poor, never married, residents of rural areas, smokers/tobacco users, and alcoholic users were less likely to be in any of the treatment cascades. Interpretation: The prevalence of hypertension in India is high. The "Rule of half" of hypertension does not apply to India as the proportion of people screened, aware of their hypertension status, treated, and controlled are lower than 50% at each stage. Program managers must improve access to hypertension diagnosis and treatment, especially among men in rural areas and populations with lower household wealth. Funding: None.

Boosting algorithms for projecting streamflow in the Lower Godavari Basin for different climate change scenarios.

The present study investigates the ability of five boosting algorithms, namely Adaptive Boosting (AdaBoost), Categorical Boosting (CatBoost), Light Gradient Boosting (LGBoost), Natural Gradient Boosting (NGBoost), and eXtreme Gradient Boosting (XGBoost) for simulating streamflow in the Lower Godavari Basin, India. Monthly rainfall, temperatures, and streamflow from 1982 to 2020 were used for training and testing. Kling Gupta Efficiency (KGE) was deployed to assess the ability of the boosting algorithms. It was observed that all the boosting algorithms had shown good simulating ability, having KGE values of AdaBoost (0.87, 0.85), CatBoost (0.90, 0.78), LGBoost (0.95, 0.93), NGBoost (0.95, 0.95), and XGBoost (0.91, 0.90), respectively, in training and testing. Thus, all the algorithms were used for projecting streamflow in a climate change perspective for the short-term projections (2025-2050) and long-term projections (2051-2075) for four Shared Socioeconomic Pathways (SSPs). The highest streamflow for all four SSPs in the case of NGBoost is more than the historical scenario (9382 m3/s), whereas vice-versa for the remaining four. The effect of ensembling the outputs of five algorithms is also studied and compared with that of individual algorithms.

A destabilizing Y891D mutation in activated EGFR impairs sensitivity to kinase inhibition.

EGFR tyrosine kinase inhibitors (TKIs) have transformed the treatment of EGFR-mutated non-small cell lung carcinoma (NSCLC); however, therapeutic resistance remains a clinical challenge. Acquired secondary EGFR mutations that increase ATP affinity and/or impair inhibitor binding are well-described mediators of resistance. Here we identify a de novo EGFR Y891D secondary alteration in a NSCLC with EGFR L858R. Acquired EGFR Y891D alterations were previously reported in association with resistance to first generation EGFR TKIs. Functional studies in Ba/F3 cells demonstrate reduced TKI sensitivity of EGFR L858R + Y891D, with the greatest reduction observed for first and second generation TKIs. Unlike other EGFR mutations associated with TKI resistance, Y891D does not significantly alter ATP affinity or promote steric hindrance to inhibitor binding. Our data suggest that the Y891D mutation destabilizes EGFR L858R, potentially generating a population of misfolded receptor with preserved signaling capacity but reduced sensitivity to EGFR inhibitors. These findings raise the possibility of protein misfolding as a mechanism of resistance to EGFR inhibition in EGFR-mutated NSCLC.