Growth Analysis of Baby Corn (Zea mays L.) Under the Effect of Integrated Nutrient Management

Maize (Zea mays L.) is the most versatile crop having wider adaptability in varied ecologies. Presently baby corn is gaining popularity among Indian farming communities mainly due to its short duration, high market rate, nutritive value and also its multiuse. Baby corn requires higher population and plant nutrition than normal grain corn. Therefore the nutrient management is of immense importance for higher corn production. The present study was thus carried out during Kharif season 2015 at the Instructional Dairy Farm (IDF), Nagla, Govind Ballabh Pant University of Agriculture and Technology, Pantnagar, Uttarakhand to analyse the growth of baby corn under the effect of integrated nutrient management. The experimental design was Randomized Block Design with 11 treatments consisting of sole application of NPK fertilizer, sole application of Azotobacter and Azospirillum, and application of Azotobacter and Azospirillum along with NPK fertilizer. The study revealed that Leaf area index was significantly higher at 50 DAS and harvest under 100% NPK+Azot+Azos. Application of 75% NPK+Azot+Azos had significantly higher at 25-50 DAS while 100% NPK+Azot+Azos gave significantly higher at 50 DAS – harvest. The values remained non significant at both the stages, however the highest was recorded at application of 100% NPK+Azot+Azos. The remained non significant at 25 – 50 DAS but at 50 DAS – harvest, the values recorded significantly higher at 100% NPK+Azot+Azos that remained non significant with all the treatments except control and seed treatment with Azotobacter. The too was recorded non significant by different integrated nutrient management practices at 25-50 DAS but at 50 DAS-harvest, the significantly highest was recorded under control, whereas the lowest was found at application of 100% NPK that remained statistically at par with all other treatments except control. Higher dose of nitrogen coupled with biofertilizers improved the plant growth

Detection of retinal hemorrhages in the presence of blood vessels

Segmentation of hemorrhages helps in improving the efficiency of computer assisted image analysis of diseases like diabetic retinopathy and hypertensive retinopathy. Hemorrhages are blood leakages lying in close proximity to blood vessels, which makes their delineation from blood vessels challenging. We use multiresolution morphological processing with a view of achieving perceptual grouping of the hemorrhagic candidates occurring in variable shapes, sizes and textures. We propose a novel method of suppression of candidates lying on blood vessels while attaining a good segmentation of true hemorrhages including the ones attached to the vessels. Evaluated on 191 images having different degrees of pathological severity, our method achieved > 82% sensitivity at < 7 false positives per image (FPPI). We further observe that the sensitivity is higher for candidates with bigger sizes

Taguchi based Design of Sequential Convolution Neural Network for Classification of Defective Fasteners

Fasteners play a critical role in securing various parts of machinery. Deformations such as dents, cracks, and scratches on the surface of fasteners are caused by material properties and incorrect handling of equipment during production processes. As a result, quality control is required to ensure safe and reliable operations. The existing defect inspection method relies on manual examination, which consumes a significant amount of time, money, and other resources; also, accuracy cannot be guaranteed due to human error. Automatic defect detection systems have proven impactful over the manual inspection technique for defect analysis. However, computational techniques such as convolutional neural networks (CNN) and deep learning-based approaches are evolutionary methods. By carefully selecting the design parameter values, the full potential of CNN can be realised. Using Taguchi-based design of experiments and analysis, an attempt has been made to develop a robust automatic system in this study. The dataset used to train the system has been created manually for M14 size nuts having two labeled classes: Defective and Non-defective. There are a total of 264 images in the dataset. The proposed sequential CNN comes up with a 96.3% validation accuracy, 0.277 validation loss at 0.001 learning rate.Comment: 13 pages, 6 figure

Efficacy of fecal microbiota therapy in steroid dependent ulcerative colitis: a real world intention-to-treat analysis

Background/Aims Four high-quality randomized controlled trials have proven the efficacy of fecal microbiota transplantation (FMT) in active ulcerative colitis (UC). We assessed the efficacy of FMT in a real-world setting involving steroid-dependent patients with UC. Methods This was a single-center prospective analysis of data from steroid-dependent patients with UC treated with FMT from September 2015 to September 2017 at the Dayanand Medical College, a tertiary care center in India. Fecal samples from random unrelated donors were administered through colonoscopy at weeks 0, 2, 6, 10, 14, 18, and 22. The primary outcome was achievement of steroid-free clinical remission, and the secondary outcomes were clinical response and endoscopic remission at 24 weeks. Modified intention-to-treat analysis was performed, which included subjects who underwent at least 1 FMT. Results Of 345 patients with UC treated during the study period, 49 (14.2%) had steroid-dependent UC. Of these 49 patients, 41 underwent FMT: 33 completed 7 sessions over 22 weeks according to the protocol, and 8 discontinued treatment (non-response, 5; lost to follow-up, 2; and fear of adverse effects, 1). At week 24, steroid-free clinical remission was achieved in 19 out of 41 (46.3%) patients, whereas clinical response and endoscopic remission were achieved in 31 out of 41 (75.6%) and 26 out of 41 (63.4%) patients, respectively. All patients with clinical response were able to withdraw steroids. There were no serious adverse events necessitating discontinuation. Conclusions A multisession FMT via the colonoscopic route is a promising therapeutic option for patients with steroid-dependent UC, as it can induce clinical remission and aid in steroid withdrawal

Targeted bisulfite sequencing by solution hybrid selection and massively parallel sequencing

We applied a solution hybrid selection approach to the enrichment of CpG islands (CGIs) and promoter sequences from the human genome for targeted high-throughput bisulfite sequencing. A single lane of Illumina sequences allowed accurate and quantitative analysis of ~1 million CpGs in more than 21 408 CGIs and more than 15 946 transcriptional regulatory regions. Of the CpGs analyzed, 77–84% fell on or near capture probe sequences; 69–75% fell within CGIs. More than 85% of capture probes successfully yielded quantitative DNA methylation information of targeted regions. Differentially methylated regions (DMRs) were identified in the 5′-end regulatory regions, as well as the intra- and intergenic regions, particularly in the X-chromosome among the three breast cancer cell lines analyzed. We chose 46 candidate loci (762 CpGs) for confirmation with PCR-based bisulfite sequencing and demonstrated excellent correlation between two data sets. Targeted bisulfite sequencing of three DNA methyltransferase (DNMT) knockout cell lines and the wild-type HCT116 colon cancer cell line revealed a significant decrease in CpG methylation for the DNMT1 knockout and DNMT1, 3B double knockout cell lines, but not in DNMT3B knockout cell line. We demonstrated the targeted bisulfite sequencing approach to be a powerful method to uncover novel aberrant methylation in the cancer epigenome. Since all targets were captured and sequenced as a pool through a series of single-tube reactions, this method can be easily scaled up to deal with a large number of samples

Global burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

Background: Understanding the health consequences associated with exposure to risk factors is necessary to inform public health policy and practice. To systematically quantify the contributions of risk factor exposures to specific health outcomes, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 aims to provide comprehensive estimates of exposure levels, relative health risks, and attributable burden of disease for 88 risk factors in 204 countries and territories and 811 subnational locations, from 1990 to 2021. Methods: The GBD 2021 risk factor analysis used data from 54 561 total distinct sources to produce epidemiological estimates for 88 risk factors and their associated health outcomes for a total of 631 risk–outcome pairs. Pairs were included on the basis of data-driven determination of a risk–outcome association. Age-sex-location-year-specific estimates were generated at global, regional, and national levels. Our approach followed the comparative risk assessment framework predicated on a causal web of hierarchically organised, potentially combinative, modifiable risks. Relative risks (RRs) of a given outcome occurring as a function of risk factor exposure were estimated separately for each risk–outcome pair, and summary exposure values (SEVs), representing risk-weighted exposure prevalence, and theoretical minimum risk exposure levels (TMRELs) were estimated for each risk factor. These estimates were used to calculate the population attributable fraction (PAF; ie, the proportional change in health risk that would occur if exposure to a risk factor were reduced to the TMREL). The product of PAFs and disease burden associated with a given outcome, measured in disability-adjusted life-years (DALYs), yielded measures of attributable burden (ie, the proportion of total disease burden attributable to a particular risk factor or combination of risk factors). Adjustments for mediation were applied to account for relationships involving risk factors that act indirectly on outcomes via intermediate risks. Attributable burden estimates were stratified by Socio-demographic Index (SDI) quintile and presented as counts, age-standardised rates, and rankings. To complement estimates of RR and attributable burden, newly developed burden of proof risk function (BPRF) methods were applied to yield supplementary, conservative interpretations of risk–outcome associations based on the consistency of underlying evidence, accounting for unexplained heterogeneity between input data from different studies. Estimates reported represent the mean value across 500 draws from the estimate's distribution, with 95% uncertainty intervals (UIs) calculated as the 2·5th and 97·5th percentile values across the draws. Findings: Among the specific risk factors analysed for this study, particulate matter air pollution was the leading contributor to the global disease burden in 2021, contributing 8·0% (95% UI 6·7–9·4) of total DALYs, followed by high systolic blood pressure (SBP; 7·8% [6·4–9·2]), smoking (5·7% [4·7–6·8]), low birthweight and short gestation (5·6% [4·8–6·3]), and high fasting plasma glucose (FPG; 5·4% [4·8–6·0]). For younger demographics (ie, those aged 0–4 years and 5–14 years), risks such as low birthweight and short gestation and unsafe water, sanitation, and handwashing (WaSH) were among the leading risk factors, while for older age groups, metabolic risks such as high SBP, high body-mass index (BMI), high FPG, and high LDL cholesterol had a greater impact. From 2000 to 2021, there was an observable shift in global health challenges, marked by a decline in the number of all-age DALYs broadly attributable to behavioural risks (decrease of 20·7% [13·9–27·7]) and environmental and occupational risks (decrease of 22·0% [15·5–28·8]), coupled with a 49·4% (42·3–56·9) increase in DALYs attributable to metabolic risks, all reflecting ageing populations and changing lifestyles on a global scale. Age-standardised global DALY rates attributable to high BMI and high FPG rose considerably (15·7% [9·9–21·7] for high BMI and 7·9% [3·3–12·9] for high FPG) over this period, with exposure to these risks increasing annually at rates of 1·8% (1·6–1·9) for high BMI and 1·3% (1·1–1·5) for high FPG. By contrast, the global risk-attributable burden and exposure to many other risk factors declined, notably for risks such as child growth failure and unsafe water source, with age-standardised attributable DALYs decreasing by 71·5% (64·4–78·8) for child growth failure and 66·3% (60·2–72·0) for unsafe water source. We separated risk factors into three groups according to trajectory over time: those with a decreasing attributable burden, due largely to declining risk exposure (eg, diet high in trans-fat and household air pollution) but also to proportionally smaller child and youth populations (eg, child and maternal malnutrition); those for which the burden increased moderately in spite of declining risk exposure, due largely to population ageing (eg, smoking); and those for which the burden increased considerably due to both increasing risk exposure and population ageing (eg, ambient particulate matter air pollution, high BMI, high FPG, and high SBP). Interpretation: Substantial progress has been made in reducing the global disease burden attributable to a range of risk factors, particularly those related to maternal and child health, WaSH, and household air pollution. Maintaining efforts to minimise the impact of these risk factors, especially in low SDI locations, is necessary to sustain progress. Successes in moderating the smoking-related burden by reducing risk exposure highlight the need to advance policies that reduce exposure to other leading risk factors such as ambient particulate matter air pollution and high SBP. Troubling increases in high FPG, high BMI, and other risk factors related to obesity and metabolic syndrome indicate an urgent need to identify and implement interventions

THz Range Perforated Metasurface-integrated Multiband Fabry-Perot Microstrip Patch Antenna

We have proposed a THz range, multi-band, metasurface-integrated Fabry- Perot cavity antenna. The perforated single layer metasurface provides 40% wide stop bandwidth and it is used as superstrate. The metasurface integrated antenna resonates at the frequencies of 180.0 GHz, 189.46 GHz, 199.02 GHz and 208.82 GHz. The maximum peak gain of 13 dBi is at 189.46 GHz among the four bands. Nearly 5% of gain enhancement is achieved in all four bands after loading the metasurface on the antenna

DCA‐based unimodal feature‐level fusion of orthogonal moments for Indian sign language dataset

Sign language recognition system classifies signs made by hand gestures. An adequate number of features are required to represent the shape variations of sign language. As compared to individual feature set, a combination of features can be effective due to the fact that a particular feature set represents different shape information. A simple concatenation results in large feature vector size and increases the classification computational complexity. Discriminant correlation analysis (DCA)‐based unimodal feature‐level fusion has been applied on uniform as well as complex background Indian sign language datasets. DCA is a feature‐level fusion technique that takes into account the class associations while combining the feature sets. It maximises the inter‐class separability of two feature sets and also minimises the intra‐class separability while performing the feature fusion. The objective of DCA‐based unimodal feature fusion technique is to combine different feature sets into a single feature vector with more discriminative power. The performance of proposed framework is compared with individual orthogonal moment‐based feature sets and canonical correlation analysis (CCA)‐based feature fusion technique. Results show that in comparison to individual features and CCA‐based fused features, DCA is an effective technique in terms of improved accuracy, reduced feature vector size and smaller classification time

An integral equation involving Saigo-Maeda operator

The aim of this paper is to obtain a solution of integral equation of the Saigo- Maeda operator which contain Appell-hypergeometric function as a kernel. The integral equation and its solution gives new form of generalised fractional integral and generalised fractional derivative. Further various consequences also investigated.Publisher's Versio