Global burden of disease due to smokeless tobacco consumption in adults : analysis of data from 113 countries

BACKGROUND: Smokeless tobacco is consumed in most countries in the world. In view of its widespread use and increasing awareness of the associated risks, there is a need for a detailed assessment of its impact on health. We present the first global estimates of the burden of disease due to consumption of smokeless tobacco by adults. METHODS: The burden attributable to smokeless tobacco use in adults was estimated as a proportion of the disability-adjusted life-years (DALYs) lost and deaths reported in the 2010 Global Burden of Disease study. We used the comparative risk assessment method, which evaluates changes in population health that result from modifying a population's exposure to a risk factor. Population exposure was extrapolated from country-specific prevalence of smokeless tobacco consumption, and changes in population health were estimated using disease-specific risk estimates (relative risks/odds ratios) associated with it. Country-specific prevalence estimates were obtained through systematically searching for all relevant studies. Disease-specific risks were estimated by conducting systematic reviews and meta-analyses based on epidemiological studies. RESULTS: We found adult smokeless tobacco consumption figures for 115 countries and estimated burden of disease figures for 113 of these countries. Our estimates indicate that in 2010, smokeless tobacco use led to 1.7 million DALYs lost and 62,283 deaths due to cancers of mouth, pharynx and oesophagus and, based on data from the benchmark 52 country INTERHEART study, 4.7 million DALYs lost and 204,309 deaths from ischaemic heart disease. Over 85 % of this burden was in South-East Asia. CONCLUSIONS: Smokeless tobacco results in considerable, potentially preventable, global morbidity and mortality from cancer; estimates in relation to ischaemic heart disease need to be interpreted with more caution, but nonetheless suggest that the likely burden of disease is also substantial. The World Health Organization needs to consider incorporating regulation of smokeless tobacco into its Framework Convention for Tobacco Control

Burnout among surgeons before and during the SARS-CoV-2 pandemic: an international survey

Background: SARS-CoV-2 pandemic has had many significant impacts within the surgical realm, and surgeons have been obligated to reconsider almost every aspect of daily clinical practice. Methods: This is a cross-sectional study reported in compliance with the CHERRIES guidelines and conducted through an online platform from June 14th to July 15th, 2020. The primary outcome was the burden of burnout during the pandemic indicated by the validated Shirom-Melamed Burnout Measure. Results: Nine hundred fifty-four surgeons completed the survey. The median length of practice was 10 years; 78.2% included were male with a median age of 37 years old, 39.5% were consultants, 68.9% were general surgeons, and 55.7% were affiliated with an academic institution. Overall, there was a significant increase in the mean burnout score during the pandemic; longer years of practice and older age were significantly associated with less burnout. There were significant reductions in the median number of outpatient visits, operated cases, on-call hours, emergency visits, and research work, so, 48.2% of respondents felt that the training resources were insufficient. The majority (81.3%) of respondents reported that their hospitals were included in the management of COVID-19, 66.5% felt their roles had been minimized; 41% were asked to assist in non-surgical medical practices, and 37.6% of respondents were included in COVID-19 management. Conclusions: There was a significant burnout among trainees. Almost all aspects of clinical and research activities were affected with a significant reduction in the volume of research, outpatient clinic visits, surgical procedures, on-call hours, and emergency cases hindering the training. Trial registration: The study was registered on clicaltrials.gov "NCT04433286" on 16/06/2020

Molecular Network for Regulation of Ovule Number in Plants

In seed-bearing plants, the ovule (“small egg”) is the organ within the gynoecium that develops into a seed after fertilization. The gynoecium located in the inner compartment of the flower turns into a fruit. The number of ovules in the ovary determines the upper limit or the potential of seed number per fruit in plants, greatly affecting the final seed yield. Ovule number is an important adaptive characteristic for plant evolution and an agronomic trait for crop improvement. Therefore, understanding the mechanism and pathways of ovule number regulation becomes a significant research aspect in plant science. This review summarizes the ovule number regulators and their regulatory mechanisms and pathways. Specially, an integrated molecular network for ovule number regulation is constructed, in which phytohormones played a central role, followed by transcription factors, enzymes, other protein and micro-RNA. Of them, AUX, BR and CK are positive regulator of ovule number, whereas GA acts negatively on it. Interestingly, many ovule number regulators have conserved functions across several plant taxa, which should be the targets of genetic improvement via breeding or gene editing. Many ovule number regulators identified to date are involved in the diverse biological process, such as ovule primordia formation, ovule initiation, patterning, and morphogenesis. The relations between ovule number and related characteristics/traits especially of gynoecium/fruit size, ovule fertility, and final seed number, as well as upcoming research questions, are also discussed. In summary, this review provides a general overview of the present finding in ovule number regulation, which represents a more comprehensive and in-depth cognition on it

Self correction fractional least mean square algorithm for application in digital beamforming.

Fractional order algorithms demonstrate superior efficacy in signal processing while retaining the same level of implementation simplicity as traditional algorithms. The self-adjusting dual-stage fractional order least mean square algorithm, denoted as LFLMS, is developed to expedite convergence, improve precision, and incurring only a slight increase in computational complexity. The initial segment employs the least mean square (LMS), succeeded by the fractional LMS (FLMS) approach in the subsequent stage. The latter multiplies the LMS output, with a replica of the steering vector (Ŕ) of the intended signal. Mathematical convergence analysis and the mathematical derivation of the proposed approach are provided. Its weight adjustment integrates the conventional integer ordered gradient with a fractional-ordered. Its effectiveness is gauged through the minimization of mean square error (MSE), and thorough comparisons with alternative methods are conducted across various parameters in simulations. Simulation results underscore the superior performance of LFLMS. Notably, the convergence rate of LFLMS surpasses that of LMS by 59%, accompanied by a 49% improvement in MSE relative to LMS. So it is concluded that the LFLMS approach is a suitable choice for next generation wireless networks, including Internet of Things, 6G, radars and satellite communication

On the Effect of Estimation Error for the Risk-Adjusted Charts

Control charts are a popular statistical process control (SPC) technique for monitoring to detect the unusual variations in different processes. Contrary to the classical charts, control charts have also been modified to include covariates using regression approaches. This study assesses the performance of risk-adjusted control charts under the complexity of estimation error by considering logistic and negative binomial regression models. To be more precise, risk-adjusted Cumulative Sum (CUSUM) and Exponentially Weighted Moving Average (EWMA) charts are used to evaluate the impact of the estimation error. To compute the average run length (ARL), Markov Chain Monte Carlo simulations are conducted. Furthermore, a bootstrap method is also used to compute the ARL assuming different Phase-I data sets to minimize the effect of estimation error on risk-adjusted control charts. The results for cardiac surgery and respiratory disease data sets show that the modified control charts improve the performance in detecting small shifts

Molecular Network for Regulation of Ovule Number in Plants

In seed-bearing plants, the ovule (“small egg”) is the organ within the gynoecium that develops into a seed after fertilization. The gynoecium located in the inner compartment of the flower turns into a fruit. The number of ovules in the ovary determines the upper limit or the potential of seed number per fruit in plants, greatly affecting the final seed yield. Ovule number is an important adaptive characteristic for plant evolution and an agronomic trait for crop improvement. Therefore, understanding the mechanism and pathways of ovule number regulation becomes a significant research aspect in plant science. This review summarizes the ovule number regulators and their regulatory mechanisms and pathways. Specially, an integrated molecular network for ovule number regulation is constructed, in which phytohormones played a central role, followed by transcription factors, enzymes, other protein and micro-RNA. Of them, AUX, BR and CK are positive regulator of ovule number, whereas GA acts negatively on it. Interestingly, many ovule number regulators have conserved functions across several plant taxa, which should be the targets of genetic improvement via breeding or gene editing. Many ovule number regulators identified to date are involved in the diverse biological process, such as ovule primordia formation, ovule initiation, patterning, and morphogenesis. The relations between ovule number and related characteristics/traits especially of gynoecium/fruit size, ovule fertility, and final seed number, as well as upcoming research questions, are also discussed. In summary, this review provides a general overview of the present finding in ovule number regulation, which represents a more comprehensive and in-depth cognition on it

Regularized Urdu Speech Recognition with Semi-Supervised Deep Learning

Automatic Speech Recognition, (ASR) has achieved the best results for English, with end-to-end neural network based supervised models. These supervised models need huge amounts of labeled speech data for good generalization, which can be quite a challenge to obtain for low-resource languages like Urdu. Most models proposed for Urdu ASR are based on Hidden Markov Models (HMMs). This paper proposes an end-to-end neural network model, for Urdu ASR, regularized with dropout, ensemble averaging and Maxout units. Dropout and ensembles are averaging techniques over multiple neural network models while Maxout are units in a neural network which adapt their activation functions. Due to limited labeled data, Semi Supervised Learning (SSL) techniques are also incorporated to improve model generalization. Speech features are transformed into a lower dimensional manifold using an unsupervised dimensionality-reduction technique called Locally Linear Embedding (LLE). Transformed data along with higher dimensional features is used to train neural networks. The proposed model also utilizes label propagation-based self-training of initially trained models and achieves a Word Error Rate (WER) of 4% less than that reported as the benchmark on the same Urdu corpus using HMM. The decrease in WER after incorporating SSL is more significant with an increased validation data size

Regularized Urdu Speech Recognition with Semi-Supervised Deep Learning

Automatic Speech Recognition, (ASR) has achieved the best results for English, with end-to-end neural network based supervised models. These supervised models need huge amounts of labeled speech data for good generalization, which can be quite a challenge to obtain for low-resource languages like Urdu. Most models proposed for Urdu ASR are based on Hidden Markov Models (HMMs). This paper proposes an end-to-end neural network model, for Urdu ASR, regularized with dropout, ensemble averaging and Maxout units. Dropout and ensembles are averaging techniques over multiple neural network models while Maxout are units in a neural network which adapt their activation functions. Due to limited labeled data, Semi Supervised Learning (SSL) techniques are also incorporated to improve model generalization. Speech features are transformed into a lower dimensional manifold using an unsupervised dimensionality-reduction technique called Locally Linear Embedding (LLE). Transformed data along with higher dimensional features is used to train neural networks. The proposed model also utilizes label propagation-based self-training of initially trained models and achieves a Word Error Rate (WER) of 4% less than that reported as the benchmark on the same Urdu corpus using HMM. The decrease in WER after incorporating SSL is more significant with an increased validation data size

Fractional LMS and NLMS Algorithms for Line Echo Cancellation

In long haul communication environments, speech data transmission is severely affected by echoes. This phenomenon results in high bit errors as well as in degraded and annoying performance. Traditionally these problems, including hybrid and acoustic echoes, have been controlled through the use of echo suppressors. These suppressors were subsequently replaced by line echo cancellers using adaptive Finite Impulse Response filters. Fractional calculus has been applied successfully for fixed filtering with constant coefficients and in discrete time adaptive filtering that adjusts the weights according to the environment. This paper presents the Fractional Least Mean Square (FLMS) and Fractional Normalized LMS (FNLMS) algorithms for application in echo cancellation. Moreover, the performances of the FLMS and FNLMS are compared with those provided by the standard LMS, NLMS and Block Discrete Fourier Transform solutions. The mean square error criterion is used as the performance comparison criterion for two types of voice signals namely real and synthetic. The simulation results show a performance improvement of about 50% over the traditional counterparts.info:eu-repo/semantics/publishedVersio

Biological evaluation and docking analysis of daturaolone as potential cyclooxygenase inhibitor

This study deals with the isolation of the active constituent(s) from a methanolic extract of Pistacia integerrima J. L. Stewart barks and it was also oriented to evaluate the in vivo and in silico anti-inflammatory activity. By NMR and crystallography techniques, we have isolated a triterpenoid identified as daturaolone (compound 1). This compound showed in vivo a significant and dose dependent (1-30 mg/kg) anti-inflammatory activity on carrageenan-induced mouse paw oedema (ED50 = 10.1 mg/kg) and on acetic acid-induced writhing responses in mice (ED50 = 13.8 mg/kg). In the in vivo experiments, the effect of tested compound was also evaluated in presence of the reference drug diclofenac (1-30 mg/kg). Moreover, in silico analysis of receptor ligand complex shows that compound 1 interacts with cyclooxygenases (COXs) binding sites displaying an interesting interaction with COX-1. These findings suggest that compound 1 isolated from P. integerrima possesses in vivo anti-inflammatory and antinociceptive potentials, which are supported in silico by an interaction with COXs receptors