High Density Impulse Noise Detection using Fuzzy C-means Algorithm

A new technique for detecting the high density impulse noise from corrupted images using Fuzzy C-means algorithm is proposed. The algorithm is iterative in nature and preserves more image details in high noise environment. Fuzzy C-means is initially used to cluster the image data. The application of Fuzzy C-means algorithm in the detection phase provides an optimum classification of noisy data and uncorrupted image data so that the pictorial information remains well preserved. Experimental results show that the proposed algorithm significantly outperforms existing well-known techniques. Results show that with the increase in percentage of noise density, the performance of the algorithm is not degraded. Furthermore, the varying window size in the two detection stages provides more efficient results in terms of low false alarm rate and miss detection rate. The simple structure of the algorithm to detect impulse noise makes it useful for various applications like satellite imaging, remote sensing, medical imaging diagnosis and military survillance. After the efficient detection of noise, the existing filtering techniques can be used for the removal of noise.

Bibliometric Survey on Effect of Socio-Economic Factors on Spread of Corona virus (COVID-19)

The Novel Coronavirus disease has been rapidly spreading all around the globe, from the time when it was first reported in the Wuhan city of China. The primary focus of this bibliometric survey is to distinguish the documents which have hypothesized and expanded on the effects of various socio-economic factors when it comes to the spread of the Coronavirus. This survey does the evaluation on the 480 documents found. The United Kingdom of the Great Britain and United States have contributed the largest number of publications in this field of research followed closely by India and Italy. The survey includes analysis based on geographical regional, analysis of network, analysis on the basis of type of publication, dialect in which the document is written in. We have also considered the universities, institutes and authors that have contributed in this research area. This bibliometric survey concludes that the highest number publications of “Socio-Economic factors affecting the spread of COVID-19” are from articles, review papers associated with agriculture and biological sciences. The documents that were analyzed are considered from the time period of 2020 to 2021

In-silico molecular docking for Potential herbal leads from Withaniasomnifera L. Dunal for the treatment of Alzheimer’s disease

Alzheimer's disease (AD) poses a significant global health challenge, necessitating novel therapeutic interventions. Withaniasomnifera L. Dunal, commonly known as Ashwagandha, has been historically utilized in traditional medicine for its neuroprotective properties. This study employs computational techniques to explore the potential of W. somnifera compounds in targeting key proteins associated with AD. The reported phytoconstituents of W. somnifera were identified and subjected to molecular docking studies against 5NUU (Torpedo californica acetylcholinesterase in complex with a chlorotacrine-tryptophan hybrid inhibitor), as crucial targets. The results revealed several phytoconstituents of W. somnifera exhibiting favorable binding affinities and promising interactions with the target proteins. These findings provide a valuable foundation for further experimental validation and the development of novel therapeutic agents derived from natural sources for the treatment of Alzheimer's

Molecular Docking studies of chemical constituents of Rauwolfia serpentina on hypertension

Hypertension is still a prevalent cardiovascular disorder which remains a major global health concern. Rauwolfia serpentina, renowned for its therapeutic potential in managing hypertension, harbors a diverse array of bioactive compounds. This study aimed to elucidate the molecular interactions of chemical constituents derived from Rauwolfia serpentina with key hypertensive targets through molecular docking simulations. Utilizing computational tool, a comprehensive library of phytoconstituents obtained from Rauwolfia serpentina was constructed and subjected to molecular docking analyses against human angiotensin receptor (4ZUD) as target protein. The results revealed significant binding affinities between the chemical constituents of Rauwolfia serpentina and the active sites of these molecular targets. This study bridges the knowledge gap regarding the molecular mechanisms underlying the antihypertensive effects of Rauwolfia serpentina's constituents through computational simulations. The identified compounds exhibiting strong binding affinities and favorable interactions serve as promising candidates for further in vitro and in vivo studies, offering avenues for the development of novel therapeutic agents for hypertension management

Exploring the Potential of Chemical Constituents of Datura metel in Breast Cancer from Molecular Docking Studies

Breast cancer remains a pervasive health challenge worldwide, prompting the exploration of novel therapeutic prospects. Datura metel has long been recognized for its pharmacological properties, particularly in containing various bioactive compounds like alkaloids, flavonoids, and terpenoids. This review focuses on the potential of chemical constituents sourced from Datura metel, a traditional medicinal plant, in combating breast cancer, primarily through molecular docking studies. The review meticulously scrutinizes the chemical composition of Datura metel, emphasizing the identified compounds known for their therapeutic attributes. Through an extensive analysis of molecular docking studies, the interactions between these Datura metel constituents and crucial molecular targets associated with breast cancer are elucidated. The phytoconstituents (compound 1-13) were found to be more potent as compare to Tomoxifen citrate as standard anticancer drug. The findings presented herein beckon for further exploration, highlighting a promising avenue in the pursuit of effective and targeted treatments for breast cancer. In conclusion, this review emphasizes the synergistic integration of computational approaches with traditional knowledge, accelerating the discovery and development of innovative breast cancer therapies

Measurement report: Interpretation of wide-range particulate matter size distributions in Delhi

Delhi is one of the world's most polluted cities, with very high concentrations of airborne particulate matter. However, little is known about the factors controlling the characteristics of wide-range particle number size distributions. Here, new measurements are reported from three field campaigns conducted in winter and pre-monsoon and post-monsoon seasons at the Indian Institute of Technology campus in the south of the city. Particle number size distributions were measured simultaneously, using a scanning mobility particle sizer and a GRIMM optical particle monitor, covering 15 nm to >10 μm diameter. The merged, wide-range size distributions were categorized into the following five size ranges: nucleation (15-20 nm), Aitken (20-100 nm), accumulation (100 nm-1 μm), large fine (1-2.5 μm), and coarse (2.5-10 μm) particles. The ultrafine fraction (15-100 nm) accounts for about 52 % of all particles by number (PN10 is the total particle number from 15 nm to 10 μm) but just 1 % by PM10 volume (PV10 is the total particle volume from 15 nm to 10 μm). The measured size distributions are markedly coarser than most from other parts of the world but are consistent with earlier cascade impactor data from Delhi. Our results suggest substantial aerosol processing by coagulation, condensation, and water uptake in the heavily polluted atmosphere, which takes place mostly at nighttime and in the morning hours. Total number concentrations are highest in winter, but the mode of the distribution is largest in the post-monsoon (autumn) season. The accumulation mode particles dominate the particle volume in autumn and winter, while the coarse mode dominates in summer. Polar plots show a huge variation between both size fractions in the same season and between seasons for the same size fraction. The diurnal pattern of particle numbers is strongly reflective of a road traffic influence upon concentrations, especially in autumn and winter, although other sources, such as cooking and domestic heating, may influence the evening peak. There is a clear influence of diesel traffic at nighttime, when it is permitted to enter the city, and also indications in the size distribution data of a mode < 15 nm, which is probably attributable to CNG/LPG vehicles. New particle formation appears to be infrequent and is, in this dataset, limited to 1 d in the summer campaign. Our results reveal that the very high emissions of airborne particles in Delhi, particularly from traffic, determine the variation in particle number size distributions

Bans of WHO Class I Pesticides in Bangladesh –Suicide Prevention without Hampering Agricultural Output

Pesticide self-poisoning is a major problem in Bangladesh. Over the past 20-years, the Bangladesh government has introduced pesticide legislation and banned highly hazardous pesticides (HHPs) from agricultural use. We aimed to assess the impacts of pesticide bans on suicide and on agricultural production.We obtained data on unnatural deaths from the Statistics Division of Bangladesh Police, and used negative binomial regression to quantify changes in pesticide suicides and unnatural deaths following removal of WHO Class I toxicity HHPs from agriculture in 2000. We assessed contemporaneous trends in other risk factors, pesticide usage and agricultural production in Bangladesh from 1996 to 2014.Mortality in hospital from pesticide poisoning fell after the 2000 ban: 15.1% vs 9.5%, relative reduction 37.1% [95% confidence interval (CI) 35.4 to 38.8%]. The pesticide poisoning suicide rate fell from 6.3/100 000 in 1996 to 2.2/100 000 in 2014, a 65.1% (52.0 to 76.7%) decline. There was a modest simultaneous increase in hanging suicides [20.0% (8.4 to 36.9%) increase] but the overall incidence of unnatural deaths fell from 14.0/100 000 to 10.5/100 000 [25.0% (18.1 to 33.0%) decline]. There were 35 071 (95% CI 25 959 to 45 666) fewer pesticide suicides in 2001 to 2014 compared with the number predicted based on trends between 1996 to 2000. This reduction in rate of pesticide suicides occurred despite increased pesticide use and no change in admissions for pesticide poisoning, with no apparent influence on agricultural output.Strengthening pesticide regulation and banning WHO Class I toxicity HHPs in Bangladesh were associated with major reductions in deaths and hospital mortality, without any apparent effect on agricultural output. Our data indicate that removing HHPs from agriculture can rapidly reduce suicides without imposing substantial agricultural costs