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Abstract The purpose of this study is to understand the impact of community mobilization initiatives on citizens’ health-seeking behavior through the lens of COVID-19 vaccination in the state of Madhya Pradesh, India. This study follows a case study approach to understand community mobilization initiatives that led to increased COVID-19 vaccination uptake. The study delineates the process to increase community mobilization to improve the uptake of COVID-19 vaccination. It highlights the key components of community mobilization that were adopted: the involvement of local leadership (at district, block, and village level), support of vaccination/mobilizer team, the role of in-person communication, involvement of influential community heads and social workers, localized strategies and IEC materials, and support from the state administration. Additionally, the key communication themes that played a crucial role in increasing COVID-19 vaccination coverage are identified. The findings of this study will help the policymakers and practitioners in the domains of Health and Medical Humanities, Health Policy and Services, and Social Sciences to clarify the roles of community mobilization activities and initiatives in increasing vaccination coverage. Furthermore, the study provides evidence for the adaptation, integration, and enhancement of these activities based on the four case studies discussed.

Abstract The National Sample Survey (NSS) collects reliable data on morbidity, health care, and health spending through its annual multi-subject and multi-round health surveys. Evidences from these surveys have been extensively used for research and policy. While these surveys collect comprehensive information on morbidity, hospitalisation, health expenditure, information on household consumption expenditure (which is used to explain the economic gradient in health outcomes) is collected through a single question. Literature suggests that having a single question on consumption expenditure results in measurement errors. In this paper, we examine the effect of measurement errors of household consumption expenditure on estimates of catastrophic health expenditure (CHE) in India using data from the 68th round of the consumption survey (2011–12) and the 71st round of the health survey (2014), carried out by the National Sample Survey (NSS). The consumption survey canvassed a detailed schedule on consumption and interviewed 101,651 households, whereas the health survey interviewed 65,932 households from across the country. Descriptive statistics, estimates of CHE, and logistic regression models were used in the analysis. We used both the budget share approach and the capacity-to-pay approach for estimating CHE. The NSS health survey was found to have underestimated monthly per capita consumption expenditure (MPCE) in India by 32%, with the level of underestimation being significant across the states. Using the budget share approach, the CHE of India in 2014–15 was estimated at 23.4% without adjusting for the underestimation of consumption and 21.1% after adjusting for it. Similarly, using the capacity-to-pay approach, CHE was estimated to be 13.4% without adjustment and 10.4% with adjustment. The estimates differed considerably across the states. In general, it was observed that the use of a single question on consumption overestimated CHE in India. The pattern was similar regarding the intensity of CHE. The predictors of CHE were similar using both the methods, but the unadjusted estimates of CHE showed significantly higher predicted probabilities of incurring CHE across household characteristics. It is recommended to include disaggregated questions on household consumption in the future rounds of the NSS-based health surveys. Researchers using NSS data need to be aware of the effect of measurement errors of consumption expenditure on estimates of catastrophic expenditure. Adjusting for the underestimation of MPCE may improve the estimation of CHE in India.

Abstract Though India has achieved near-universal primary school attendance and bridged the gender gap, secondary and technical education schooling remains low. Household ability to pay for education and gender norms at the household and societal levels are possible reasons for variations in school attendance and quality of schooling. Although various studies have examined the gender gap in enrolment and school discontinuation, there are limited studies on gender differentials in educational spending in Indian households. Using data from 174,810 school and college-going children aged 6–22 years from the 75th round of the National Sample Survey Organization, this paper explored the gender gap in household educational spending in India. Descriptive statistics, relative gap, and the Blinder-Oaxaca decomposition method were used in the analysis. The results suggests that school attendance was 82.7% (95% CI 82.0–83.3) among boys and 78.9% (95% CI 78.1–79.7) among girls aged 16–17 years and 63.1% (95% CI 62.6–63.6) among boys and 51.3% (95% CI 50.7–51.9) among girls aged 18–22 years. The difference in the average per capita educational spending between boys and girls was ₹828 at the primary level (ages 6–10), ₹935 at the middle/secondary level (ages 11–15), ₹3189 at the higher secondary level (ages 16–17), and ₹3813 in higher education (age 18–22). The results obtained from the Blinder-Oaxaca Decomposition analysis show that type of educational institution (public or private) contributed more than half of the variation in household educational spending among 6–10- and 11–15-year-old children and was the strongest contributor at every age. The education of the household head considerably reduced the gender gap in higher education. Locational disadvantage and the uniformity in the gender gap across all levels of the economic hierarchy suggest a behavioural bias in the intra-household allocation of educational resources across the states of India.

Abstract Recent advances in multiplexed single‐cell transcriptomics experiments facilitate the high‐throughput study of drug and genetic perturbations. However, an exhaustive exploration of the combinatorial perturbation space is experimentally unfeasible. Therefore, computational methods are needed to predict, interpret, and prioritize perturbations. Here, we present the compositional perturbation autoencoder (CPA), which combines the interpretability of linear models with the flexibility of deep‐learning approaches for single‐cell response modeling. CPA learns to in silico predict transcriptional perturbation response at the single‐cell level for unseen dosages, cell types, time points, and species. Using newly generated single‐cell drug combination data, we validate that CPA can predict unseen drug combinations while outperforming baseline models. Additionally, the architecture's modularity enables incorporating the chemical representation of the drugs, allowing the prediction of cellular response to completely unseen drugs. Furthermore, CPA is also applicable to genetic combinatorial screens. We demonstrate this by imputing in silico 5,329 missing combinations (97.6% of all possibilities) in a single‐cell Perturb‐seq experiment with diverse genetic interactions. We envision CPA will facilitate efficient experimental design and hypothesis generation by enabling in silico response prediction at the single‐cell level and thus accelerate therapeutic applications using single‐cell technologies.

Abstract Microscopy is a powerful tool for characterizing complex cellular phenotypes, but linking these phenotypes to genotype or RNA expression at scale remains challenging. Here, we present Visual Cell Sorting, a method that physically separates hundreds of thousands of live cells based on their visual phenotype. Automated imaging and phenotypic analysis directs selective illumination of Dendra2, a photoconvertible fluorescent protein expressed in live cells; these photoactivated cells are then isolated using fluorescence‐activated cell sorting. First, we use Visual Cell Sorting to assess hundreds of nuclear localization sequence variants in a pooled format, identifying variants that improve nuclear localization and enabling annotation of nuclear localization sequences in thousands of human proteins. Second, we recover cells that retain normal nuclear morphologies after paclitaxel treatment, and then derive their single‐cell transcriptomes to identify pathways associated with paclitaxel resistance in cancers. Unlike alternative methods, Visual Cell Sorting depends on inexpensive reagents and commercially available hardware. As such, it can be readily deployed to uncover the relationships between visual cellular phenotypes and internal states, including genotypes and gene expression programs.

Abstract The genetic causes of congenital hypothyroidism due to thyroid dysgenesis (TD) remain largely unknown. We identified three novel TUBB1 gene mutations that co‐segregated with TD in three distinct families leading to 1.1% of TUBB1 mutations in TD study cohort. TUBB1 (Tubulin, Beta 1 Class VI) encodes for a member of the β‐tubulin protein family. TUBB1 gene is expressed in the developing and adult thyroid in humans and mice. All three TUBB1 mutations lead to non‐functional α/β‐tubulin dimers that cannot be incorporated into microtubules. In mice, Tubb1 knock‐out disrupted microtubule integrity by preventing β1‐tubulin incorporation and impaired thyroid migration and thyroid hormone secretion. In addition, TUBB1 mutations caused the formation of macroplatelets and hyperaggregation of human platelets after stimulation by low doses of agonists. Our data highlight unexpected roles for β1‐tubulin in thyroid development and in platelet physiology. Finally, these findings expand the spectrum of the rare paediatric diseases related to mutations in tubulin‐coding genes and provide new insights into the genetic background and mechanisms involved in congenital hypothyroidism and thyroid dysgenesis.

The relative contribution of promoter architecture and the associated chromatin environment in regulating gene expression noise has remained elusive. In their recent work, Arkin, Schaffer and colleagues (Dey et al, 2015) show that mean expression and noise for a given promoter at different genomic loci are uncorrelated and influenced by the local chromatin environment.

We explored how transcriptional noise propagates in gene‐regulatory pathways by studying the induction of two downstream genes by transcription factors c‐fos and c‐jun. They are produced for a brief period following serum stimulation of cells and then activate the promoters of their target genes by binding to them as heterodimers. We found that, even though they are coordinately expressed at the population level, in individual cells the expression of c‐fos and c‐jun is noisy and uncorrelated with each other. The expression of the downstream genes is also noisy, but there is little or no effect of the noise in the upstream genes on the expression of the downstream genes. The noise is not transmitted, because the number of heterodimers present in single cells is relatively invariant, and the induction of downstream genes is insensitive to the number of heterodimers in individual cells. Sequestration of promoters of the downstream genes within compact chromatin is a likely cause of this insensitivity. These barriers to the propagation and amplification of noise are likely to be commonplace in higher eukaryotes.

Medical image segmentation (MIS) plays an important role in rapid disease detection and prognosis. U-Net, a widely used neural network in the field of MIS, encounters performance limitations when applied to complex datasets. These limitations are primarily due to the basic feature extraction blocks, namely the encoder and decoder, as well as the existence of a semantic gap between these two components. Some U-Net variants, such as Recurrent Residual U-Net, have attempted to solve the problem of simple feature extraction blocks by increasing the network depth. However, this approach does not effectively solve the semantic gap problem. In contrast, another variant, UNET + + , addresses the semantic gap problem by introducing dense skip connections but retains the simplicity of its feature extraction blocks. To overcome these challenges, a new approach is required. Therefore, this research proposed an optimized weight and loss function network called CNN-based Ebola U-Net (EU-Net) architecture for MIS. In the first step, the images are pre-processed for noise reduction using a filtering method called bilateral filtering, which is a hybridization of two Gaussian filters. The CNN-based EU-Net acts as both an encoder and a decoder with convolutional layers. A modification in the skip connection is encouraged to minimize the semantic gaps between the encoder and decoder. In each encoding stage, the edge information of the input images is extracted using the Pyramid Edge Extraction Module (PEEM). Using the extracted information, a segmentation map is generated on the decoder side. The Ebola Optimization Algorithm (EOA) is used to reduce the loss function and overall weight of the proposed network model. To evaluate the performance level of the proposed network, four different datasets are used. The proposed model achieves an overall accuracy of 97.33%, an RMSE of 1.36 and a Dice coefficient of 94.97%. [ABSTRACT FROM AUTHOR]

Recently, every human being has been busy keeping pace with fast changes and is unable to put his attention on a healthy lifestyle. This unhealthy lifestyle leads to several fatal diseases like heart disease (HD), cancer disease, allergies, asthma, breast cancer, etc. These diseases are increasing the morbidity and mortality rate every year, and out of these HD is more fetal. Earlier/Timely prediction of HD is regarded as one of the most crucial tasks in the medical field. In the healthcare industry, a big collection of datasets is available free of cost. Motivated by existing challenges, the authors proposed two intelligent models i.e., HyOPTRF (Model 1), and HyOPTXGBoost Classifier (Model 2), implemented on the Statlog HD dataset with default and hyper-tuned parameters. Both the models recorded maximum Accuracy of 92.59%, Specificity 100%, Precision 100%, Negative Predicted Value 100%, Geometric mean 93.93% F1 Score 93.75%, Training Time 0.02s, and Testing Time 0.005s for the HyOPTRF on Trial no. 2nd and Accuracy of 96.30%, Specificity 100%, Precision 100%, Negative Predicted Value 100%, Geometric mean 96.82%, F1 Score 96.77%, Training Time 0.009s, and Testing Time 0.002s for the HyOPTXGBoost Classifier on Trial no. 33rd. The proposed models were validated by the Stratify Kfold Cross-Validation technique and compared with the other existing models. [ABSTRACT FROM AUTHOR]

The research introduces a novel method for creating drug-loaded hydrogel beads that target anti-aging, anti-oxidative, and anti-inflammatory effects, addressing the interconnected processes underlying various pathological conditions. The study focuses on the development of hydrogel beads containing anti-aging compounds, antioxidants, and anti-inflammatory drugs to effectively mitigate various processes. The synthesis, characterization and in vitro evaluations, and potential applications of these multifunctional hydrogel beads are discussed. A polymeric alginate-orange peel extract (1:1) hydrogel was synthesized for encapsulating fish oil. Beads prepared with variable fish oil concentrations (0.1, 0.3, and 0.5 ml) were characterized, showing no significant decrease in size i.e., 0.5 mm and a reduction in pore size from 23 to 12 µm. Encapsulation efficiency reached up to 98% within 2 min, with controlled release achieved upto 45 to 120 min with increasing oil concentration, indicating potential for sustained delivery. Fourier-transform infrared spectroscopy confirmed successful encapsulation by revealing peak shifting, interaction between constituents. In vitro degradation studies showed the hydrogel's biodegradability improved from 30 to 120 min, alongside anti-inflammatory, anti-oxidative, anti-collagenase and anti-elastase activities, cell proliferation rate enhanced after entrapping fish oil. In conclusion, the synthesized hydrogel beads are a promising drug delivery vehicle because they provide stable and effective oil encapsulation with controlled release for notable anti-aging and regenerative potential. Targeted delivery for inflammatory and oxidative stress-related illnesses is one set of potential uses. Further research may optimize this system for broader applications in drug delivery and tissue engineering. [ABSTRACT FROM AUTHOR]

Present work investigates the DC and Analog/RF characteristics such as the drain current (I D ), Transconductance (g m) , Transconductance Generation Factor (TGF), Cut-off frequency (f T) , Frequency Transconductance Product (FTP), Transit time (τ) , and the total resistance of the source region, drain region, and channel resistance (R S D + C H ) for Dual Metal (DM) Inversion Mode (IM) and Junctionless (JL) Cylindrical Gate All Around (CGAA) Silicon nanowire (SiNW) MOSFETs with 5 nm gate length using Silvaco ATLAS 3D TCAD. In this work, the Non-Equilibrium Green's Function approach along with the self-consistent solution of Schrödinger's equation and Poisson's equation has been considered. The channel is taken to be lightly doped in the case of IM DM CGAA SiNW type of device. The effect of DM Gate work function engineering for SiNW channel of diameter 3 nm with gate oxide (SiO 2) the thickness of 0.8 nm on I D , g m , TGF, f T , τ , FTP and R CH has been studied. Moreover, a comparative study has been made between IMDM and JLDM CGAA SiNW devices with the above-mentioned parameters. For the JL device, the optimization of doping concentration is performed to get the same (i) ION current and (ii) threshold voltage (VTH) as the IM device. About 3.09 times and 21.89 times reduction in IOFF is seen for the same ION and VTH optimized devices respectively as compared to IM device. It has been found that DM Gate variation minimizes drain-induced barrier lowering (DIBL) in IM and JL devices. The JL SiNW showed much lower DIBL ~ 16.46 mV/V, a near ideal SS ~ 60 mV/dec, and higher I ON / I OFF current ratio ~ 7.04 × 108 which is much better as compared to those reported in the literature for cylindrical gate all around (CGAA) devices. Also, it is found that the JL SiNW device performs better than IM in terms of SS, DIBL, I ON / I OFF , g m , TGF, fT, τ , FTP and R S D + C H . [ABSTRACT FROM AUTHOR]

Sugarcane, commercially cultivated for the production of sucrose, is constantly confronted by Colletotrichum falcatum which is accountable for the catastrophic red rot disease. Membrane-bound sucrose transporters are the key controllers of short as well as long-distance transport of sucrose in plants. In the background of some recent reports suggesting the involvement of sucrose transporters and disease resistance proteins in plants' defense responses; we have investigated the abundance of these proteins in the stalk tissues of red rot resistant (BO91), and susceptible (CoJ64) cultivars of sugarcane using nanoLCMS/MS-based approach. The results revealed the presence of eight sucrose transporters and four disease resistance proteins cumulatively in both the cultivars of sugarcane. Further, we observed that C. falcatum inoculation influenced the peptide abundance of these proteins in both the cultivars of sugarcane. Among sucrose transporters, fungal inoculation caused a significant reduction in the peptide abundance of a large number of sucrose transporter proteins in both the cultivars of sugarcane including SUT-1, 5, 6, 7, and 8. In case of disease resistance proteins, the peptide abundance of all the four disease resistance proteins (DRP-1–4) was induced by C. falcatum inoculation in red rot susceptible cultivar. Our study indicated that sucrose transporters are repressed, whereas the disease resistance proteins are induced by C. falcatum infection in both the cultivars of sugarcane. The study might be useful in unraveling the specific roles of sucrose transporters and disease resistance proteins during stress responses in plants. [ABSTRACT FROM AUTHOR]

Life without legal metrology is inconceivable, as legal metrology encompasses every sphere of daily life, from eating to safeguarding health to ensuring public safety and the environment. On the other hand, food plays an important role in human life, and the entire civilisation survives on food. Food laws have been developed over the years with the primary objective of providing consumers with safe and nutritious food. Food laws primarily focus on ensuring the safety and quality of consumables, while legal metrology laws govern the accuracy of weights and measures in commercial transactions, including pre-packed food. The intersection of food laws and legal metrology laws in India presents a complex framework that requires careful analysis. This intersection becomes crucial as discrepancies in quantity, labelling, or packaging can impact consumer rights and public health and appropriate declarations on the packaging help the consumer make an informed choice. This comprehensive analysis involves examining how these regulations merge, identifying potential conflicts, and proposing harmonised measures to enhance regulatory efficiency and consumer protection. Balancing the intricacies of both frameworks is essential to creating a robust system that addresses the unique challenges posed by the intersection of food and metrology regulations in India. [ABSTRACT FROM AUTHOR]

Inadequate availability of quality organic nutrient sources is the major hurdle in adopting organic farming. Hence, a study was conducted during 2019–21 at Sikkim state of the Indian Himalayas to investigate the effect of integrated organic manuring on energy dynamics, profitability, and soil health of maize (Zea mays L.) crops grown in acidic soil. Five organic nutrient management practices viz., T2: 100% recommended dose of nitrogen (RDN) through farmyard manure (FYM); T3: 100% RDN through mixed compost (MC); T4: 100% RDN through vermicompost (VC); T5: 50% RDN through FYM and 50% RDN through MC + biofertilizers (BFs) and T6: 50% RDN through FYM and 50% RDN through VC + BFs were tested in a four-times replicated randomized block design against the farmers' practice (T1: application of 5 Mg ha−1 FYM). Results indicated that the supply of 50% RDN through FYM and 50% RDN through MC along with the BFs consortium improved the green cob and biological yields, net return, production efficiency, energy net return, and energy output efficiency by 26.3%, 24.8%, 29%, 26.4%, 24%, and 24.7%, respectively over farmers' practices. Likewise, the supply of 50% RDN through FYM + 50% RDN through MC + BFs reduced soil bulk density (pb) by 6.87% while increasing total water stable aggregates (TWSA) by 23.1% and mean weight diameter (MWD) by 18.7% over farmers' practice. Similarly, a significant improvement in soil organic carbon, available N, P, K, Zn, Fe, and Mn was observed with 50% RDN through FYM + 50% RDN through MC + BFs over farmmers' practice. Furthermore, integrated nitrogen supply with FYM and MC in equal proportion improved soil enzymatic activity considerably over existing practice. Hence, the study inferred that the supply of 50% RDN through FYM + 50% RDN through MC + BFs may be a viable and soil-supportive approach for cleaner maize production in the acidic soils of Indian Himalayas and other similar ecoregions. [ABSTRACT FROM AUTHOR]

Hybrid algorithms are increasingly being used in optimization strategies. In this paper, a unique hybrid strategy to address the generation scheduling issue, which is non-linear, non-convex, combinatorial, and highly constrained in nature, is given. It makes use of the Chaotic Seagull Optimization Algorithm (CSOA) and the Sine–cosine Optimization Algorithm (SCA). A chaotic search method is used to keep the algorithm from falling towards local minima and to strike a fair balance between the capabilities of exploration and exploitation. This study is examining the consequences of renewable energy sources more closely since they are sustainable and kind to the environment. To handle the stochastic nature of the wind source, an appropriate probability distribution function is employed. The effectiveness and viability of units between 10 and 100 are studied, and the results are compared to those attained utilizing other methods suggested by the literature. The results demonstrate that the proposed technique provides more effective solutions than the other options being thought about. When thermal units are coupled to sources from wind and electric cars, the findings demonstrate that the suggested technique offers excellent economic options. Convergence curves are provided to demonstrate the proposed algorithm's resilience. [ABSTRACT FROM AUTHOR]

The objective of the study was to investigate different factors that influence the intention to purchase green vehicles and prioritize these factors to identify the most critical factor. The use of green vehicles, such as electric or hybrid vehicles, can significantly reduce greenhouse gas emissions, air pollution, and dependence on fossil fuels. Therefore, purchasing green vehicles can contribute to sustainable development by reducing the negative impact of transportation on the environment. Fuzzy AHP is an extension of the traditional AHP method that incorporates fuzzy logic. AHP breaks down complex problems into hierarchies of criteria and alternatives, enabling evaluation and prioritization. The study used fuzzy AHP to rank factors influencing individuals' intention to purchase green vehicles, based on 302 responses. Fuzzy AHP allows for flexible assessment considering uncertainties in decision-making. Harman's single-factor test was conducted to verify common method bias. The results revealed six factors that affect intentions to purchase green vehicles, with environmental concern (EC) being the most crucial and mass media influence (MMI) the least significant factor. However, as the study was conducted in India only, the generalizability of the findings to other countries may be limited. Additionally, the study is cross-sectional, and several factors that could have a significant impact on customers' purchase intentions are not included in the analysis. The study has implications for managers, practitioners, and government agencies. The use of fuzzy AHP in the proposed conceptual framework adds to the literature by providing a better empirical understanding of the primary determinants of customers' purchasing behaviour concerning green vehicles in India. The research also introduces fuzzy AHP as one of the multi-criteria decision-making (MCDM) techniques in the field of green vehicle purchasing, offering a comprehensive view of the significant motivations behind people's favourable attitude towards green vehicles from a theoretical perspective. [ABSTRACT FROM AUTHOR]

Background and Purpose: Automated methods for quantifying brain tissue volumes have gained clinical interest for their objective assessment of neurological diseases. This study aimed to establish reference curves for brain volumes and fractions in the Indian population using Synthetic MRI (SyMRI), a quantitative imaging technique providing multiple contrast-weighted images through fast postprocessing. Methods: The study included a cohort of 314 healthy individuals aged 15–65 years from multiple hospitals/centers across India. The SyMRI-quantified brain volumes and fractions, including brain parenchymal fraction (BPF), gray matter fraction (GMF), white matter fraction (WMF), and myelin. Results: Normative age-stratified quantification curves were created based on the obtained data. The results showed significant differences in brain volumes between the sexes, but not after normalization by intracranial volume. Conclusion: The findings provide normative data for the Indian population and can be used for comparative analysis of brain structure values. Furthermore, our data indicate that the use of fractions rather than absolute volumes in normative curves, such as BPF, GMF, and WMF, can mitigate sex and population differences as they account for individual differences in head size or brain volume. [ABSTRACT FROM AUTHOR]

Since 2019, Lumpy skin disease (LSD) has suddenly spread in many Asian countries, including India. LSD primarily occurs in cattle. However, recent LSD outbreaks in India have also revealed significant morbidity and production losses in buffaloes. This has raised concerns about the role of buffaloes in the epidemiology and transmission of LSD and necessitates the inclusion of buffaloes in the mass vaccination program for the prevention and control of the disease in the country. However, there is no significant data on the immune response in buffaloes following vaccination with the LSD vaccine. In this study, we evaluated antibody- and cell-mediated immune responses following vaccination with a newly developed live-attenuated LSD vaccine (Lumpi-ProVacInd). The detectable amount of anti-LSDV antibodies was observed at 1–2 months following vaccination, with a peak antibody titer at 3 months. Upon stimulation of the peripheral blood mononuclear cells (PBMCs) with the UV-inactivated LSDV antigen, there was a significant increase in CD8 + T cell counts in vaccinated animals as compared to the unvaccinated animals. Besides, vaccinated animals also showed a significant increase in IFN-γ levels upon antigenic stimulation of their PBMCs with LSDV antigen. In conclusion, the buffaloes also mount a potent antibody- and cell-mediated immune response following vaccination with Lumpi-ProVacInd. [ABSTRACT FROM AUTHOR]

The presence of nitrogen dioxide (NO2), a hazardous gas emanating from various sources including vehicles, industrial power plants, indoor combustion appliances, and tobacco smoke, underscores the importance of effective monitoring. Early risk assessment and continuous vigilance are vital to mitigate potential respiratory and cardiovascular consequences associated with prolonged exposure. To address this need, we have developed a cost-effective metal–organic framework (MOF)-derived SnO2-ZnO-based gas sensor for NO2 detection. Different combinations of SnO2-ZnO were synthesized by varying the Sn/Zn molar ratio. The sensor SZ 1–0.5, having Sn/Zn = 1/0.5, demonstrated a superior response (Rg/Ra = 7.37) compared to bare SnO2 and ZnO towards 100 ppm of NO2 at 225°C. The fabricated SZ 1–0.5 sensor showed a good response time of 100 s with outstanding selectivity, cyclability, and repeatability. The enhanced gas-sensing characteristics of the sensor are attributed to the formation of heterojunctions. The gas-sensing mechanism is discussed in detail. [ABSTRACT FROM AUTHOR]

The velocity and volume of MultiSpectral (MS) remote sensing data have recently increased exponentially. In recent times, however, the absence of a spatial data cube to store analysis-ready data (ARD) products for the Indian sensors' data delimits its ready use and depreciates its value. Establishing a framework for storing, managing, and providing online processing ARD products for different sensors is necessary. The current work proposes a framework to produce ARD products by radiometrically correcting the data using the 6 S atmospheric correction and Shepherd Diamond-based terrain correction method to provide normalised surface reflectance. The generated ARD product for LISS-III shows a good correlation with the Planet Lab's surface reflectance ARD product and an excellent correlation with the SACRS2- a Scheme for Atmospheric Correction of ResourceSat-2 corrected product. A frequency-based geometric correction algorithm provides RMSE of less than half a pixel registration error compared to LANDSAT-8 OLI orthorectified imagery. Finally, A Spatial Data Cube (SDC) with CARD4L metadata standard stores the ARD products post ingestion. The paper explains the complete integrated software development with an end-to-end processing chain of LISS III, an Indian optical sensor data. [ABSTRACT FROM AUTHOR]

In the dynamic business environment, firms can adopt emerging technologies to remain relevant and effectively meet changing customer demands. The adoption of these technologies, however, is a complex process that requires strategic flexibility if businesses are to be able to adapt to the changing technology landscape and remain competitive. This study aims to identify critical success factors (CSFs) of emerging technology adoption and strategic flexibility and develop a hierarchical model using evidence data from Twitter (now known as X). A total of 1,76,248 tweets were collected, yielding a sample set of 102,100 unique tweets following the removal of duplicates. This study uses evidence-based total interpretative structural modeling (TISM-E) to develop a hierarchical interrelationship between the CSFs of emerging technology adoption, strategic flexibility, and firm competitiveness. Additionally, this study utilizes UTAUT to explore emerging technology adoption behaviors and understand a firm's perspective on technology acceptance. The resulting six-level hierarchical model can be used in strategic decision-making to achieve firm competitiveness. The findings of this study are important for practitioners and academicians seeking to understand the critical factors that drive technology adoption, strategic flexibility, and firm competitiveness in the changing business landscape. This study helps practitioners understand the relevance of identified CSFs and their interrelationships using a TISM-E approach and evidence data. [ABSTRACT FROM AUTHOR]

Acknowledging the significant impact of temperature deviations on vaccine efficacy, this study examines the prevalence of exposure to non-recommended temperatures, particularly focusing on freeze-sensitive vaccines. This study aims to identify critical points of temperature breaches during transportation from storage to last-mile delivery. In this cross-sectional study, the temperature integrity of vaccines has been examined during transportation across three states in India (Karnataka, Maharashtra, and Rajasthan), utilizing data loggers to track and record temperatures in real time. By analyzing instances of exposure to temperatures beyond the recommended 2–8 °C range, the study aims to identify the critical junctures within the vaccine supply chain susceptible to temperature excursions. The methodology entailed a systematic data logger analysis at each stage of transportation up to the last mile. In this study, descriptive statistics with the analysis of variance test has been applied to investigate the significance of variations in the duration of temperature breaches. The novelty of this study lies in its comprehensive examination of vaccine temperature integrity across critical transportation phases in India, utilizing real-time data-logging technology. Based on the results, the study provides recommendations using the ABCD strategic framework, encompassing awareness, best practices, continuous monitoring, and data management and documentation. This framework will be instrumental in addressing the identified challenges and significantly improving the temperature monitoring flexibility. The findings will inform targeted interventions, optimizing the cold chain to enhance the preservation of vaccine potency and identifying flexible solutions for maintaining the required temperatures from storage to transportation. [ABSTRACT FROM AUTHOR]

Secukinumab is a fully human IgG1 antibody that selectively binds to and neutralizes the proinflammatory cytokine interleukin-17A. Secukinumab is an effective and well-tolerated treatment for plaque psoriasis. There is a limited real-word evidence for dose optimisation of secukinumab based on clinical response. PURE is a multi-national, prospective, observational study in patients with moderate to severe chronic plaque psoriasis in Canada and Latin America, assessing the real-world safety and effectiveness of secukinumab and other indicated therapies. The aim of the current snapshot analysis was to evaluate the effectiveness and safety of on-label dose and updosed secukinumab in patients with plaque psoriasis enrolled in the PURE study. At the time of analysis, 676 patients received secukinumab, of which 84.6% (n = 572) remained on the on-label dose, while 15.4% (n = 104) were updosed. With on-label secukinumab, the absolute Psoriasis Area and Severity Index (PASI) score was reduced from 13.6 at baseline to 1.2 over 36 months, with treatment persistence of 73% at 40 months. At Month 36, 73.2% of the patients receiving on-label secukinumab achieved Investigator's Global Assessment (IGA) 0/1. With updosed secukinumab (300 mg every 2 weeks, 300 mg every 3 weeks, 450 mg every 4 weeks, or 450 mg every 3 weeks), 57.9% of the patients showed improvement in the absolute PASI score at the first visit after updosing, with treatment persistence of 50% at 12 months after updosing. At Month 15, 40% of patients receiving updosed secukinumab achieved IGA 0/1. Patients with previous biologic exposure (odds ratio [OR]: 3.25; 95% confidence interval [CI]: 2.03, 5.18, p < 0.0001) were more likely to be updosed while those with a body weight < 90 kg (OR: 0.49; 95% CI [0.31, 0.77], p = 0.0019) were less likely to be updosed. Previous biologic exposure (HR [hazard ratio]: 1.47; 95% CI [1.24, 1.75], p < 0.0001) and current biologic exposure (secukinumab vs. other indicated therapies: HR 0.57; 95% CI [0.43, 0.75], p = 0.0001) were significantly associated with time to secukinumab updosing. No new or unexpected safety signals were observed with updosed secukinumab. Secukinumab updosing was efficacious and well-tolerated in patients with psoriasis who failed to respond to the approved on-label regimen, suggesting that updosing may be a useful therapeutic option for approved dose non-responders. [ABSTRACT FROM AUTHOR]

The digital revolution in healthcare, amplified by the COVID-19 pandemic and artificial intelligence (AI) advances, has led to a surge in the development of digital technologies. However, integrating digital health solutions, especially AI-based ones, in rare diseases like Waldenström macroglobulinemia (WM) remains challenging due to limited data, among other factors. CURATE.AI, a clinical decision support system, offers an alternative to big data approaches by calibrating individual treatment profiles based on that individual's data alone. We present a case study from the PRECISE CURATE.AI trial with a WM patient, where, over two years, CURATE.AI provided dynamic Ibrutinib dose recommendations to clinicians (users) aimed at achieving optimal IgM levels. An 80-year-old male with newly diagnosed WM requiring treatment due to anemia was recruited to the trial for CURATE.AI-based dosing of the Bruton tyrosine kinase inhibitor Ibrutinib. The primary and secondary outcome measures were focused on scientific and logistical feasibility. Preliminary results underscore the platform's potential in enhancing user and patient engagement, in addition to clinical efficacy. Based on a two-year-long patient enrollment into the CURATE.AI-augmented treatment, this study showcases how AI-enabled tools can support the management of rare diseases, emphasizing the integration of AI to enhance personalized therapy. [ABSTRACT FROM AUTHOR]

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, COVID-19) continues its significant health and economic impact globally. Despite the success of spike-protein vaccines in preventing severe disease, long-lasting protection against emerging variants and the prevention of breakthrough infections and transmission remain elusive. We generate an intranasal live-attenuated SARS-CoV-2 vaccine, CDO-7N-1, using codon deoptimization. CDO-7N-1 shows highly attenuated replication and minimal or no lung pathology in vivo over multiple passages. It induces robust mucosal and systemic neutralizing antibody and T-cell subset responses, in mice (female K18-hACE2 and male HFH4-hACE2 mice), hamsters, and macaques triggered by a single immunization. Mice and hamsters vaccinated with CDO-7N-1 are protected from challenge with wild-type (WT) SARS-CoV-2 and other variants of concern. Serum from vaccinated animals neutralizes WT SARS-CoV-2, variants of concern (beta and delta), variants of interest (omicron XBB.1.5) and SARS-CoV-1. Antibody responses are sustained and enhanced by repeated immunization or infection with WT SARS-CoV-2. Immunity against all SARS-CoV-2 proteins by CDO-7N-1 should improve efficacy against future SARS-CoV-2 variants. Current SARS-CoV-2 vaccines require several injections for optimal immune protection. Here, the authors report an intranasal live-attenuated vaccine that induces long-term immunity following a single dose and protects from SARS-CoV-2 wildtype and variants in non-human primate and small animal models. [ABSTRACT FROM AUTHOR]

Sex differences and age-related changes in the human heart at the tissue, cell, and molecular level have been well-documented and many may be relevant for cardiovascular disease. However, how molecular programs within individual cell types vary across individuals by age and sex remains poorly characterized. To better understand this variation, we performed single-nucleus combinatorial indexing (sci) ATAC- and RNA-Seq in human heart samples from nine donors. We identify hundreds of differentially expressed genes by age and sex and find epigenetic signatures of variation in ATAC-Seq data in this discovery cohort. We then scale up our single-cell RNA-Seq analysis by combining our data with five recently published single nucleus RNA-Seq datasets of healthy adult hearts. We find variation such as metabolic alterations by sex and immune changes by age in differential expression tests, as well as alterations in abundance of cardiomyocytes by sex and neurons with age. In addition, we compare our adult-derived ATAC-Seq profiles to analogous fetal cell types to identify putative developmental-stage-specific regulatory factors. Finally, we train predictive models of cell-type-specific RNA expression levels utilizing ATAC-Seq profiles to link distal regulatory sequences to promoters, quantifying the predictive value of a simple TF-to-expression regulatory grammar and identifying cell-type-specific TFs. Our analysis represents the largest single-cell analysis of cardiac variation by age and sex to date and provides a resource for further study of healthy cardiac variation and transcriptional regulation at single-cell resolution. Analysis of single-nucleus RNA- and ATAC-Seq from healthy human heart revealed age- and sex-specific alterations, defines candidate developmental stage-specific regulatory patterns, and identifies cell type-specific regulatory signatures. [ABSTRACT FROM AUTHOR]

Chalcogenide phase-change materials (PCMs) are showing versatile possibilities in cutting-edge applications, including non-volatile memory, neuromorphic computing, and nano-photonics. However, for embedded phase-change memory applications, conventional PCMs suffer from insufficient thermal stability because of their relatively low crystallization temperatures (Tx). Although doping with additional alloying elements could improve the amorphous stability, it also increases the tendency towards compositional partitioning and phase separation. Recently, a two-dimensional (2D) layered compound CrGeTe3 (CrGT) was developed as a PCM, showing a high Tx ~ 276 °C with an inverse change in resistive-switching character upon phase transition. Here, we report a high-throughput materials screening for 2D layered phase-change chalcogenides. We aim to clarify whether the high Tx and the inverse electrical resistance contrast are intrinsic features of 2D PCMs. In total, twenty-five 2D chalcogenides with CrGT trilayer structures have been identified from a large database. We then focused on selected layered tellurides by performing thorough ab initio simulations and experimental investigations and confirming that their amorphous phase indeed has a much higher Tx than conventional PCMs. We attribute this enhanced amorphous stability to the structurally complex nuclei required to render crystallization possible. Overall, we regard InGeTe3 as a balanced 2D PCM with both high thermal stability and large electrical contrast for embedded memory applications. [ABSTRACT FROM AUTHOR]

Origin recognition complex (ORC)-dependent loading of the replicative helicase MCM2-7 onto replication origins in G1-phase forms the basis of replication fork establishment in S-phase. However, how ORC and MCM2-7 facilitate genome-wide DNA licensing is not fully understood. Mapping the molecular footprints of budding yeast ORC and MCM2-7 genome-wide, we discovered that MCM2-7 loading is associated with ORC release from origins and redistribution to non-origin sites. Our bioinformatic analysis revealed that origins are compact units, where a single MCM2-7 double hexamer blocks repetitive loading through steric ORC binding site occlusion. Analyses of A-elements and an improved B2-element consensus motif uncovered that DNA shape, DNA flexibility, and the correct, face-to-face spacing of the two DNA elements are hallmarks of ORC-binding and efficient helicase loading sites. Thus, our work identified fundamental principles for MCM2-7 helicase loading that explain how origin licensing is realised across the genome. Correct loading of the MCM2-7 helicase is crucial for DNA replication and cell cycle progression. Here, the authors used high-resolution genomics to demonstrate how ORC is displaced from origins, which serves as a mechanism for distributive MCM loading onto DNA. [ABSTRACT FROM AUTHOR]

Brain tumor detection and segmentation from multi-parametric magnetic resonance (MR) scans are crucial for the prognosis and treatment planning of brain tumor patients in current clinical practice. With recent technological advancements, artificial intelligence-based deep learning has proven its indispensable image analysis capability in the most challenging tasks. This study proposes an automated WU-Net + + deep learning model for brain tumor segmentation using multiparametric structural MR scans obtained from the BraTS 2018 dataset. The model was validated through cross-dataset testing for intracranial hemorrhage (ICH) classification (ATLAS V2.1 dataset) and multi-organ segmentation from abdominal images of TCIA and BTCV datasets. The WU-Net++ model, a novel version of U-Net, was developed by adjusting its pooling operation as a weighted function of max and average pooling for brain tumor segmentation. The proposed model (WU-Net + +) achieved an F1 score of 0.94 ± 0.124, a dice score of 0.91 ± 0.132, and an AUC value of 0.915 for whole tumor segmentation. The model also achieved a high accuracy of 0.9949 ± 0.121 in ICH classification and dice scores of 0.912 ± 0.21, 0.844 ± 0.25, and 0.893 ± 0.17 for spleen, esophagus, and portal and splenic vein segmentation, respectively. Our study revealed that WU-Net + + has significant potential to improve the accuracy of segmentation and could be an effective method in the era of precision medicine. [ABSTRACT FROM AUTHOR]

Cigars and cigarillos are emerging as popular tobacco alternatives to cigarettes. However, these products may be equally harmful to human health than cigarettes and are associated with similar adverse health effects. We used 16S rRNA gene amplicon sequencing to extensively characterize the microbial diversity and investigate differences in microbial composition across 23 different products representing three different cigar product categories: filtered cigar, cigarillo, and large cigar. High throughput sequencing of the V4 hypervariable region of the 16 s rRNA gene revealed 2124 Operational Taxonomic Units (OTUs). Our findings showed that the three categories of cigars differed significantly in observed richness and Shannon diversity, with filtered cigars exhibiting lower diversity compared to large cigars and cigarillos. We also found a shared and unique microbiota among different product types. Firmicutes was the most abundant phylum in all product categories, followed by Actinobacteria. Among the 16 genera shared across all product types were Bacillus, Staphylococcus, Pseudomonas, and Pantoea. Nine genera were exclusively shared by large cigars and cigarillos and an additional thirteen genera were exclusive to filtered cigars. Analysis of individual cigar products showed consistent microbial composition across replicates for most large cigars and cigarillos while filtered cigars showed more inter-product variability. These findings provide important insights into the microbial diversity of the different cigar product types. [ABSTRACT FROM AUTHOR]

Tobacco smoking, a significant public health concern globally, is associated with a rise in noncommunicable diseases (NCDs) and preventable deaths, with pronounced impacts in conflict zones like Gaza. A cross-sectional study, conducted in 2020, in Gaza focused on individuals over 40 years of age, aiming to identify predictors of tobacco use and its links to diseases like coronary artery disease (CAD), chronic lung disease (CLD), and stroke using regression analysis. The research, based on the Gaza NCD study data with 4576 participants and a 96.6% response rate, found an overall tobacco smoking prevalence of 19.4%, with higher rates among men. After adjusting for various factors, the study identified significant associations between cigarette smoking in men and adverse health outcomes, such as CAD and CLD, with adjusted odds ratios (OR) of 1.67, 95% CI (1.22–2.29) and 1.68, 95% CI (1.21–2.33) respectively. However, after adjusting for independent variables, shisha smoking in men showed no association with these health outcomes. The findings of this study could assist other researchers in designing interventions aimed at reducing smoking prevalence by utilizing the associated factors identified in our analysis, such as age, education level, physical activity, and body mass index among men in Gaza. [ABSTRACT FROM AUTHOR]

Marek's disease virus (MDV) integrates its genome into the telomeres of host chromosomes and causes fatal lymphomas in chickens. This integration is facilitated by telomeric repeat sequences (TMRs) at the ends of the viral genome, and is crucial for MDV-induced lymphomagenesis. The SB-1 vaccine virus is commonly used in commercial bivalent vaccines against MDV and also contains TMRs at its ends. Here, we demonstrate that SB-1 efficiently integrates its genome into the chromosomes of latently infected T cells. Deletion of the TMRs from the SB-1 genome did not affect virus replication, but severely impaired virus integration and genome maintenance in latently infected T cells and in chickens. Strikingly, the reduced integration and maintenance of latent SB-1 significantly impaired vaccine protection. Taken together, our data revealed that the TMRs facilitate SB-1 integration and that integration and/or maintenance of the latent viral genome is critical for vaccine protection. [ABSTRACT FROM AUTHOR]

The current trend is the ecological consciousness of replacing synthetic fibers with natural fiber. However, several restrictions exist on using natural fibers/fillers, including compatibility, hydrophobicity, and locality. The natural material chosen for the present investigation, Acacia nilotica, is short and cylindrical; the same has been used as reinforcement after being chemically treated with silane and grinded into fillers in the present study. Grinded silane-treated A. nilotica fillers were added in a range of 0, 2, 4, and 6 weight percent to the epoxy matrix. Conventional hand layup was used to manufacture composite materials using epoxy resin and hardener. The manufactured composite's tribological properties were evaluated according to ASTM G99-17 using the pin-on-disk machine under two-body abrasive wear conditions. L-16 orthogonal arrays were used to conduct the research. Load, sliding distance, and material were the process input parameters, and the results were evaluated using the Specific Wear Rate (SWR) and Coefficient of Friction (COF). Analysis of variance (ANOVA) was used to analyze the significant variables. EDAS was used to optimize the output. The abraded surfaces were finally studied using worn surface morphology. According to the findings, silane-treated A. nilotica filler is a new and promising resource as a sustainable raw material for reinforcement in polymer composites manufacturing lightweight materials. Results revealed that the optimized values were 6 wt%, load at 20 N, and sliding distance of 225 m. SWR values decreased by 61.2%, and COF values decreased by 34.3% in the case of 6 wt% composites compared to the unreinforced fiber composites. The rank of the sample was assigned according to performance by the EDAS process. [ABSTRACT FROM AUTHOR]

Towards blended solid fuel processing technologies, the present study is first attempt to utilize de-oiled microalgae, Chlorella pyrenoidosa NCIM2738, as a binder to densify low-rank coal waste to formulate upgraded biomass-blended coal composites. The de-oiled biomass has shown similar gross calorific value (18.62 MJ/kg), sulfur content (1.45%), and low ash content (18%) in comparison to coal. Fuel characteristics of biomass-blended coal composites of 20:80 ratio showed gross calorific value (19.0 MJ/kg), fuel ratio (1.85), and low sulfur content (< 1%). The multi-objective optimization strategy is used to optimize the molding pressure, average particle size, and binder ratio for maximization of the mechanical performance indicators such as compressive strength and drop strength of biomass-blended coal composites. The maximum compressive strength and drop strength of blended composites at multi-objective optimized conditions after model validation (R2 > 0.99) are observed to be 14.6 MPa and 97.8%, respectively. Fourier transform infrared analysis is used to evaluate structural variation during coal-microalgae interaction. Thermogravimetric analysis, derivative thermogravimetric, and differential thermal analysis (TGA-DTG-DTA) are conducted to determine characteristic temperature points and heat involvement during combustion. TGA-DTG-DTA showed remarkable shifting of ignition point from 335 °C (parent coal) to 301–299 °C (blended coal composites), extended burnout temperature (47–82 °C higher than parent coal), and excessive exothermic heat involvement (3305–3363 μVs/mg) during composite combustion. Levenberg-Marquardt algorithm–based artificial neural network model was applied to validate the thermal analysis of coal, microalgae, and blended composites, which offers an excellent tool for studying thermochemical conversions. An in-depth investigation of mechanical-thermal aspect of the coal-biomass energy system will provide new possibilities to select microalgae as binder with optimized binder ratios which can apply in coal-based power plants as sustainable and affordable fuels. [ABSTRACT FROM AUTHOR]

Leachable plasticizers made of petroleum are causing growing environmental and health concerns because they are so dangerous to the environment. Therefore, searching for novel biodegradable plasticizers is of increasing interest of researchers across different domains. So, there is a need to develop bio-based plasticizers from natural sources. Within this context, Agave sisalana act to be real alternatives to the chemical plasticizers because they are biocompatible and biodegradable. In light of these aspects, in this work, we explored a green plasticizer from Agave sisalana, a renewable resource. In this report, a compound was isolated from the residue after extracting from sisal leaf and its structure was investigated. Thermal investigations (TGA), X-ray diffraction, and Fourier transform infrared spectroscopy were used to assess its chemical stability and structure. The surface morphology of A. sisalana was examined in detail by scanning electron microscopy (SEM) and particle size is analyzed by using SEM/EDAX. The results show that the novel bioplasticizers have better qualities than the other traditional plasticizers. The crystallinity index and crystallinity size values of the A. sisalana leaf plasticizer were calculated through X-ray-diffraction for the isolated biomaterials. This exposed its amorphous-crystalline nature. The particle size of the extracted plant material was examined. The kinetic activation parameter was calculated by using TGA/DTG studies. The plasticizing effect of this novel bioplasticizers was also studied by reinforcing this with PLA matrix for biofilm manufacturing for futuristic applications. The conducted investigation showed that these bioplasticizers could be suitable for applications in the bioplastics sector. [ABSTRACT FROM AUTHOR]

Natural fibers derived from plants were gaining favor as a viable alternative to synthetic materials. However, searching for sustainable raw materials with superior qualities is cumbersome because most natural fibers are limited to specific geographical areas. Himalayacalamus falconeri (HF) is a fast-growing plant that is abundant in the hills of Uttarakhand, India. This study is aimed at extracting fibers from the stem of Himalayacalamus falconeri (HF) culms, and investigate their properties by XRD analysis, TGA analysis, AFM analysis, and single-fiber tensile test. The extracted HF fiber was alkali-treated to enhance its mechanical properties. It was observed that the alkali treatment on HF fibers increased the cellulose content by 6%, and density by 4% compared with untreated fiber. Furthermore, the removal of amorphous components from the fiber surface resulted in a decrease in diameter from 103.95 to 94.4 μm. In addition, the alkali treatment on HF fiber enhanced the material's crystallinity index (from 58.92 to 67.79%), tensile strength (from 132 to 196.5 MPa), thermal stability (from 250 to 258 °C), and surface roughness (from 23.478 to 37 nm). The experimental findings confirmed that HF fiber is a suitable replacement material for synthetic reinforcement materials in lightweight polymer composites. [ABSTRACT FROM AUTHOR]

States have turned to novel Medicaid financing to pay for community health worker (CHW) programs, often through fee-for-service or capitated payments. We sought to estimate Medicaid payment rates to ensure CHW program sustainability. A microsimulation model was constructed to estimate CHW salaries, equipment, transportation, space, and benefits costs across the U.S. Fee-for-service rates per 30-min CHW visit (code 98960) and capitated rates were calculated for financial sustainability. The mean CHW hourly wage was $23.51, varying from $15.90 in Puerto Rico to $31.61 in Rhode Island. Overhead per work hour averaged $43.65 nationwide, and was highest for transportation among other overhead categories (65.1% of overhead). The minimum fee-for-service rate for a 30-min visit was $53.24 (95% CI $24.80, $91.11), varying from $40.44 in South Dakota to $70.89 in Washington D.C. The minimum capitated rate was $140.18 per member per month (95% CI $105.94, $260.90), varying from $113.55 in South Dakota to $176.58 in Washington D.C. Rates varied minimally by metro status but more by panel size. Higher Medicaid fee-for-service and capitated rates than currently used may be needed to support financial viability of CHW programs. A revised payment estimation approach may help state officials, health systems and plans discussing CHW program sustainability. [ABSTRACT FROM AUTHOR]

Introduction: This study evaluates the effectiveness of combining the Epley Maneuver with the Dizzy-Fix Training Device in treating Benign Paroxysmal Positional Vertigo (BPPV), aiming to enhance treatment outcomes and patient satisfaction. Methods: In this randomized controlled trial, 50 patients diagnosed with posterior canal BPPV were allocated into two groups: one receiving the traditional Epley Maneuver and the other undergoing the Epley Maneuver supplemented with the Dizzy-Fix Training Device. Key measures included the proportion of symptom-free patients at one month, changes in the Visual Analogue Scale (VAS) and Dizziness Handicap Inventory (DHI) scores, the recurrence rate within one month, and patient satisfaction. Results: The Dizzy-Fix group achieved a significantly higher symptom resolution rate by day 7 (90% vs. 60%) and reported greater patient satisfaction (4.5/5 vs. 3.8/5) compared to the Epley Maneuver alone group. Additionally, this group exhibited a more substantial decrease in DHI scores (from an average of 30 to 5) and a lower recurrence rate (10% vs. 40%) within the first month post-treatment. Conclusion: Incorporating the Dizzy-Fix Training Device with the Epley Maneuver significantly improves the management of BPPV, evidenced by faster symptom resolution, enhanced patient satisfaction, and reduced symptom recurrence. These findings underscore the value of integrating real-time visual feedback technologies in vestibular rehabilitation, promising better patient outcomes, and advancing the quality of care in BPPV treatment. [ABSTRACT FROM AUTHOR]

Identifying auditory impairments early in newborns is essential to prevent developmental delays. Otoacoustic Emissions (OAE) screenings play a critical role in newborn hearing assessments. However, the ideal timing post-birth for these tests remains unclear. This study evaluates the efficacy of OAE screenings within the first five days after birth to determine the most effective timing. An observational study involved 1,013 full-term neonates at a tertiary care centre. These neonates underwent Transient Evoked Otoacoustic Emissions (TEOAE) screenings daily from Day 1 to Day 5, following WHO and JCIH guidelines. The study assessed pass rates, false positives, and false negatives, with follow-up screenings at one and three months for neonates with initial ambiguous results. The study found that screening efficiency significantly increased, with Day 1 pass rates at 8% (81 neonates) and 98% (992 neonates) by Day 5, marking a significant improvement in diagnostic accuracy (p < 0.001). False positive rates dropped from 92% on Day 1 to 2% by Day 5, and false negatives decreased to below 1%. Sensitivity and specificity reached their peak at 98% and 99.5%, respectively, on Day 5. Our study findings advocate for adjusting neonatal hearing screening protocols to include OAE tests on the fifth day post-birth, optimizing clinical efficacy through enhanced diagnostic accuracy and reducing the logistical and emotional burdens on families and healthcare providers. [ABSTRACT FROM AUTHOR]

Introduction: This study investigates the comparative effectiveness of Click Auditory Brainstem Response (Click ABR) and Multiple Auditory Steady-State Response (Multi-ASSR) in identifying hearing impairments in infants. Recognizing auditory issues early is crucial for a child's cognitive and language development, as emphasized by the Joint Committee on Infant Hearing (JCIH) and the American Academy of Audiology (AAA). While Click ABR is widely utilized, Multi-ASSR offers a modern technique for detailed hearing assessment. Methods: A comparative analysis was conducted on 111 infants aged 1–6 months, previously screened for hearing at a tertiary care centre. The study employed both Click ABR and Multi-ASSR to evaluate their respective efficacy in assessing infant hearing. Results: Click ABR detected normal hearing in 87.4% of the infants, slightly higher than Multi-ASSR's 84.7%. A noteworthy finding was the higher incidence of bilateral versus unilateral hearing loss, with Click ABR identifying bilateral loss in 10 infants and unilateral loss in 4, compared to Multi-ASSR, which found bilateral loss in 12 infants and unilateral loss in 5. There was a minor but significant difference in auditory thresholds between the methods, with a mean discrepancy of 1.2 dB and a significant statistical variance (t-value of 15; p < 0.001), indicating variations in sensitivity. Conclusion: Both Click ABR and Multi-ASSR are indispensable tools in paediatric audiology, each with unique advantages. Click ABR excels in efficiency, suitable for rapid assessments and early detection. In contrast, Multi-ASSR offers comprehensive frequency-specific data, facilitating thorough evaluations. Healthcare professionals must grasp these methods' strengths to optimize infant hearing screenings and enhance early intervention strategies, aligning with JCIH and AAA guidelines. [ABSTRACT FROM AUTHOR]

Background: The digit in the noise test is a simple hearing screening tool that can identify hearing loss at the early stage. This screening tool takes less time than traditional pure-tone audiometry. There is a scarcity of hearing health professionals with few resources for carrying out hearing screening on a large scale in India. Hence, a hearing screening tool for the Hindi-speaking population is needed. Aim and Objective: The study aimed to develop and validate the Digit in Noise test in Hindi (DIN-H). Methods: A native Hindi female speaker recorded single digits from 1 to 9 made into triplet combinations, which were binaurally presented to 20 normal hearing subjects having hearing thresholds less than 25 dBHL from 250 to 8000 Hz in the presence of broadband speech-shaped noise. The digit triplets were homogenized using speech intelligibility function for similar difficulty levels across stimuli. The homogenized stimuli were evaluated by obtaining the Speech Recognition Threshold (SRT) of 20 normal hearing subjects. Results: The Mean SRT was − 10.4 and − 11.3 dB before and after homogenization. A strong positive correlation existed between test and retest SRTs (0.78). Mean SRT and slope obtained before and after optimization were comparable to other languages like Korean and English. This test can act as a reliable screening tool for assessing individuals. The test was administered to 106 normal hearing participants. The 95th percentile of the SRT value obtained was − 5.6 dB, which was kept as a cut-off score for the screening test. Conclusion: DIN-H can be used as a screening tool for assessing the integrity of the auditory system on a large scale in less time for the Hindi-speaking population. [ABSTRACT FROM AUTHOR]

Chatter vibration is an undesired phenomenon in machining operations. Chatter can lead to reduced machining quality, productivity, and tool life. The main cause of chatter is the dynamic instability between the cutting tool and the workpiece. Several attempts have been made to detect chatter. Some manual feature extraction methods used to detect the chatter involve wavelet packet transform (WPT), ensemble empirical mode decomposition (EEMD), local mean decomposition (LMD), and variational mode decomposition (VMD). These methods require human expertise for manual feature extraction. In recent time, convolution neural network (CNN) has been evolved as one of the techniques for automatic features extraction. However, CNN uses images and depends on the initial weights and hyperparameters. Creating images from acquired signals for CNN input is still cumbersome and adds one more step in signal processing making it computationally heavy. In this study, a simple, accurate, and robust online chatter detection method is proposed based on wavelet scattering network (WSN). This deep network iterates over standard wavelet transform, nonlinear modulus, and averaging operators of the acoustic signals. Experiments are performed to collect the acoustic signal during the turning operation to train and validate the algorithm. The automatic extracted chatter features are then used in supervised machine learning (ML) algorithms. The results clearly show that the WSN featurization method with SVM algorithm can significantly reduce the complexity of the existing online chatter detection systems without compromising the accuracy. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the role of basal and follicle-stimulating hormone (FSH)-stimulated inhibin B in differentiating premature thelarche from gonadotropin-dependent precocious puberty (GDPP). Method: This was a prospective interventional study. Basal and FSH-stimulated inhibin B levels were estimated in girls presenting with thelarche < 8 years age (n = 10), healthy girls with normal pubertal development (pubertal control) (n = 8) and healthy prepubertal girls (prepubertal control) (n = 7). Girls with early puberty were classified as premature thelarche or GDPP based on GnRH agonist stimulation test. Results: Median (IQR) basal inhibin B levels (pg/mL) in premature thelarche was 5.42 (2.91, 30.58) and FSH-stimulated inhibin B was 236.72 (111.53, 4431.73) (P = 0.043). Median (IQR) basal inhibin B in GDPP was 64.11 (24.96, 792.45) pg/mL and FSH-stimulated inhibin B was 833.66 (500.11–1266.18) pg/mL (P = 0.043). Basal inhibin B was discriminatory between GDPP and premature thelarche (P = 0.032). Median (IQR) basal inhibin B in prepubertal and pubertal controls was 20.36 (9.61, 29.12) and 75.48 (58.55, 165.55) pg/mL, respectively. Conclusion: Basal inhibin B is useful in differentiation of premature thelarche from GDPP while the role of FSH-stimulated inhibin B needs to be further explored in large sample size. [ABSTRACT FROM AUTHOR]

SrB4O7:Eu3+ (0.1–1.0 M%) phosphor materials were synthesized by solid-state diffusion method. Effects of doping concentration, annealing in oxidizing and reducing atmospheres and phase transitions on the luminescence properties of SrB4O7:Eu3+ phosphor were investigated. Thermoluminescence (TL) study of the SrB4O7:Eu3+ (0.2 M%) phosphor material revealed excellent properties, such as, simple glow curve (not changing its structure on irradiation for different doses), high sensitivity (3.6 times more intense than TLD-100 and approximately equal to the intensity of TLD-900), wide and linear dose response (0.5 Gy to 5.0 kGy), good reusability, etc. However, there is high TL fading, which may be occurring due to quantum tunnelling between the defects (traps/luminescence centers) related to Eu2+ and Eu3+ impurity ions. Dosimetry study using CW-OSL technique of SrB4O7:Eu3+ (0.6 M%) was also explored. The phosphor shows similar dosimetry properties like linear CW-OSL dose response (100.0 Gy to 10.0 kGy) but low fading (just 20% in 60 days). Different fading patterns show that the traps involved in the luminescence processes could be different though the material exhibit two different kinds of luminescences. The phosphor material has good dosimetry properties and it could be used as potential TLD as well as OSLD phosphor for dosimetry applications. [ABSTRACT FROM AUTHOR]

Gamma radiation can cause structural defects in metal oxide thin films, affecting their conversion efficiency. This study focuses on synthesizing and characterizing Tungsten oxide (WO3) films for photo-electrochemical (PEC) water-splitting applications. The pristine WO3 film was irradiated with a Co-60 gamma source to improve its photocatalytic activity and was named G-WO3. The resulting G-WO3 thin films showed a decrease in the optical direct band gap and showed higher photocurrent density and lower charge transfer resistance for PEC applications. The carrier charge density (Nd) values were found to be increased after γ-radiation. The synthesized G-WO3 thin films possess good stability and reusability for photo-electrochemical water-splitting applications. [ABSTRACT FROM AUTHOR]