Image Enhancement through Denoising and Retrieval of Vegetation Parameters from Landsat8

This paper proposed the enhancement of Landsat8 imagery through an Un-decimated Dual-Tree Complex Wavelet Transform (UDT-CWT) based denoising method and modified homographic filter for edge preservation. This work has been extended by estimating several vegetation parameters like Normalized Difference of Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Modified Soil Adjusted Vegetation Index (MASVI), and Soil & Atmospherically Resistant Vegetation Index (SARVI). Once the estimation of these parameters was done, the effect of noise was verified. Wavelet decomposes the image into frequency subbands and de-noises each subband separately. These subbands help to increase the resolution. The general problem of the homomorphic filter is that it doesn’t enhance the Low-frequency components which also play a key role in estimating Vegetation Indices (VI).So it was modified to enhance the high-frequency components as well as low-frequency details. Monitoring of vegetation parameters using remote sensing is one of the prominent ways in the estimation of crop yield, Land Use Land Cover (LULC), Water resource management, Drought management, etc. The high-resolution image is more preferable than moderate resolution image to retrieve VI. Image denoising and enhancing the spatial resolution helps to retrieve the parameters well and accurate. The proposed algorithm was working on the images of Landsat8

Denoising of Locally Received NOAA images for Remote Sensing Applications

Remote Sensing means capturing images of earth’s surface using satellites. Remote Sensing finds its applications in agriculture sector, climate studies, forest fire detection, pollution monitoring and oceanography etc. In this paper, NOAA images are considered as Remote Sensing images. NOAA images are directly received by using L Band antenna, located at Sri Venkateswara University, Tirupati, Andhra Pradesh state, India. The received NOAA images are denoised using spatial and frequency domain denoising techniques with modified soft thresholding. The proposed thresholding technique preserves the green content of the image even after denoising by which accuracy of outcome can be increased in remote sensing applications. Comparison of the performance is done to prove that the proposed techniques are better than existing methods

Protective Effect of Abutilon indicum L. (Malvaceae) Against Cisplatin Induced Nephrotoxicity in Rats

Cisplatin (CDDP) is an effective antineoplastic agent in the treatment of solid malignant tumors. But, Its clinical use is limited because of various side effects including sensorineural hearing loss. Several agents have been proposed to reduce these side effects.  The present study reported that the etanolic extract of Abutilon indicum scavenge superoxide and hydroxyl radicals, resulting in a reduction of lipid peroxidation. The purpose of the present study was to evaluate EEAI's efficacy as a protective agent against cisplatin-induced ototoxicity. Albino wistar rats were used in this study and were divided into five treatment groups: 1) animals administered 2% v/v aqueous tween 80 solution (5ml/kg, p.o) – control sroup(Group I), 2) animals administered 2% v/v aqueous tween 80 solution (5ml/kg, p.o) + 6 mg/kg via the                                               i.p route of Cisplatin (Group II), 3) animals received Cystone (5ml/kg, p.o) [Standard] (Group III), 4) animals received 200 mg/kg EEAI suspended in 2% v/v aqueous tween 80 solution, p.o + 6 mg/kg, i.p of cisplatin (Group IV), 5) animals received 400 mg/kg EEAI suspended in 2% v/v aqueous tween 80 solution, p.o 6 mg/kg, i.p of cisplatin (Group V). The protective effect of EEAI on CDDP-induced nephrotoxicity was evaluated. Nephrotoxicity was evaluated by means of measurement of serum BUN and creatinine and histopathological examination of the kidney. There were significant differences in serum BUN and creatinine levels between control Group and cisplatin treated Groups. The result suggested that EEAI at 200 and 400mg/kg administered 7 days before cisplatin treatment significantly prevented the increase of serum creatinine, blood urea nitrogen, uric acid, total proteins, total cholesterol, alkaline phosphatase, and albumin concentrations and markedly decreased cisplatin-induced renal damage as confirmed by biochemical assays and histopathological studies. In the present study, Key words:Antioxidants, Cisplatin, nephrotoxiciity, Abutilon indicu

PROTECTIVE EFFECT OF ABUTILON INDICUM L. (MALVACEAE) AGAINST ACETAMINOPHEN INDUCED NEPHROTOXICITY IN RATS

Acetaminophen overdose can cause nephrotoxicity with oxidative stress as one of the possible mechanisms. The effects of ethanolic extract of Abutilon Indicum[200 mg per kg of body weight (mg/kg) and 400 mg/kg] on Acetaminophen induced nephrotoxicity were evaluated. Rats were divided into five groups containing 6 rats each. The control group received distilled water while other groups were treated with extract alone (400 mg/kg), Acetaminophen alone (750 mg/kg), 750 mg/kg Acetaminophen+200 mg/kg extract (Acetaminophen+ 200-extract), and 750 mg/kg Acetaminophen+400 mg/kg extract (Acetaminophen+400-extract), respectively, for seven consecutive days. The EEAI was given orally concurrent with oral administration of Acetaminophen Treatment with EEAI at doses of 200 and 400 mg/kg prevented the Acetaminophen-induced nephrotoxicity and oxidative impairments of the kidney, as evidenced by a significantly reduced (P<0.05) level of Serum creatinine, BUN, serum alkaline phosphatase, Serum uric acid, serum total proteins and total cholesterol. The nephroprotective effects of EEAI were confirmed by a reduced intensity of renal cellular damage, as evidenced by histological findings. Moreover, EEAI administered at 400 mg/kg was found to show greater protective effects than that at 200 mg/kg. In conclusion, EEAI has a protective role against Acetaminophen-induced nephrotoxicity and the process is probably mediated through its antioxidant properties.Key words: Antioxidants, Cisplatin, nephrotoxiciity, Abutilon indicu

Mixed Layer Budget Terms on Acoustic Propagation A Study based on the Butterfly Track Experiment in the South Eastern Arabian Sea

A butterfly type of repeat track cruise was carried out in the South Eastern Arabian Sea (off Minicoy) onboard INS Sagardhwani during July 2016 to Aug 2016. We have also made use of the data from OMNI buoy, AD09, which is about 6 km close to the centre station of butterfly track. Air sea flux, the horizontal current data from AD09 and the time series data collected from the butterfly experiment were analyzed to compute the mixed layer heat and salt budget. The short-term thermo-haline variability off Minicoy, relative contribution of heat/salt budget terms in MLD and its effects on acoustic propagation are addressed in this paper. In this study, we found that most dominating term in the mixed layer heat budget estimation is net surface heat flux followed by the advective terms. However the salinity in the mixed layer is dominated by the contribution of buoyancy mixing due to night time evaporative cooling. During the calm, sunny day, the so-called afternoon effect due to the diurnal heating restricts the sonar range. But during the windy day, the wind/wave mixing prevents the warming of the surface layer which in turn enhances the sonar range. Similarly, the night time cooling also enhance the acoustic propagation range. The presence of Arabian Sea High Salinity Watermass in the surface layer also enhances the acoustic propagation

Statistical Analysis and Deep Learning Associated Modeling for Early stage Detection of Carinoma

The high death rate and overall complexity of the cancer epidemic is a global health crisis. Progress in cancer prediction based on gene expression has increased in light of the speedy advancement using modern high-throughput sequencing methods and a wide range of machine learning techniques, bringing insights into efficient and precise treatment decision-making. Therefore, it is of significant interest to create machine learning systems that accurately identify cancer patients and healthy people. Although several classification systems have been applied to cancer prediction, no single strategy has proven superior. This research shows how to apply deep learning to an optimization method that uses numerous machine learning models. Statistical analysis has helped us choose informative genes, and we've been feeding those to five different categorization models. The results from the five different classifiers are ensembled in the next step using a deep learning technique. The three most common types of adenocarcinoma are those of the lungs, stomach, and breasts. The suggested deep learning-based inter-ensembles model was tested with deep learning-based algorithms on Carcinoma data. The results of the tests show that relative to using only one set of classifiers or the simple consensus algorithm, it improves the precision of cancer prognosis in every analyzed carcinoma dataset. The suggested deep learning-based inter-ensemble approach is demonstrated to be reliable and efficient for cancer diagnosis by entirely using diverse classifiers

Global burden of chronic respiratory diseases and risk factors, 1990–2019: an update from the Global Burden of Disease Study 2019

Background: Updated data on chronic respiratory diseases (CRDs) are vital in their prevention, control, and treatment in the path to achieving the third UN Sustainable Development Goals (SDGs), a one-third reduction in premature mortality from non-communicable diseases by 2030. We provided global, regional, and national estimates of the burden of CRDs and their attributable risks from 1990 to 2019. Methods: Using data from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019, we estimated mortality, years lived with disability, years of life lost, disability-adjusted life years (DALYs), prevalence, and incidence of CRDs, i.e. chronic obstructive pulmonary disease (COPD), asthma, pneumoconiosis, interstitial lung disease and pulmonary sarcoidosis, and other CRDs, from 1990 to 2019 by sex, age, region, and Socio-demographic Index (SDI) in 204 countries and territories. Deaths and DALYs from CRDs attributable to each risk factor were estimated according to relative risks, risk exposure, and the theoretical minimum risk exposure level input. Findings: In 2019, CRDs were the third leading cause of death responsible for 4.0 million deaths (95% uncertainty interval 3.6–4.3) with a prevalence of 454.6 million cases (417.4–499.1) globally. While the total deaths and prevalence of CRDs have increased by 28.5% and 39.8%, the age-standardised rates have dropped by 41.7% and 16.9% from 1990 to 2019, respectively. COPD, with 212.3 million (200.4–225.1) prevalent cases, was the primary cause of deaths from CRDs, accounting for 3.3 million (2.9–3.6) deaths. With 262.4 million (224.1–309.5) prevalent cases, asthma had the highest prevalence among CRDs. The age-standardised rates of all burden measures of COPD, asthma, and pneumoconiosis have reduced globally from 1990 to 2019. Nevertheless, the age-standardised rates of incidence and prevalence of interstitial lung disease and pulmonary sarcoidosis have increased throughout this period. Low- and low-middle SDI countries had the highest age-standardised death and DALYs rates while the high SDI quintile had the highest prevalence rate of CRDs. The highest deaths and DALYs from CRDs were attributed to smoking globally, followed by air pollution and occupational risks. Non-optimal temperature and high body-mass index were additional risk factors for COPD and asthma, respectively. Interpretation: Albeit the age-standardised prevalence, death, and DALYs rates of CRDs have decreased, they still cause a substantial burden and deaths worldwide. The high death and DALYs rates in low and low-middle SDI countries highlights the urgent need for improved preventive, diagnostic, and therapeutic measures. Global strategies for tobacco control, enhancing air quality, reducing occupational hazards, and fostering clean cooking fuels are crucial steps in reducing the burden of CRDs, especially in low- and lower-middle income countries

Low light image enhancement using curvelet transform and iterative back projection

Abstract With the advancement of technology in image capturing, people are accustomed to high-resolution images. One of the primary necessities of an image capturing system is to provide the same. However, in many cases, the image resolution may not be reaching the expectations of the user which leads to a decrease in user experience. This is a common phenomenon that occurs when the images are captured in low light or if the image encounters a distortion either because of lack of exposure or the image capturing devices may be equipped with a small size sensor. In this work, a resolution enhancement technique using the concepts of curvelet transform and iterative back projection is presented. Sparse representation of images can be enhanced using a combination of curvelet transforms with iterative back projection. Application of curvelet transform along with iterative back projection algorithm on low light images results in enhancing the resolution of the images. The resultant images from here then passed through the inverse transform block and gives an image with contrast enhancement which leads to the user experience improvement. The antiquated image enhancement with improvement in the resolution is validated with the measurement of peak signal-to-noise ratio and structural similarity index. The usage of curvelet transform with iterative back projection leads to the restoration of the image resolution by minimizing the distortions, thus leading to an enhanced image whose edge details are retained