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To break the productivity barriers in Bt cotton (Gossypium spp.), adoption of optical sensor based nitrogen (N) management practices is the need of the hour. In light of this, a field experiment was conducted at the main Agricultural Research Station, Dharwad, Karnataka during rainy (kharif) season 2019 and 2020 to optimize the response index (RI) and sufficiency index (SI) for real time nitrogen management in Bt cotton through optical sensors. Experiment was conducted in split plot design with 16 treatment combinations having 2 genotypes (main plot) and 8 N management practices (subplot). Pooled data indicated that non-significant (P=0.3480) difference in seed cotton yield was observed with genotypes (First Class and Ajeet 155 recorded 3313 and 3159 kg/ha, respectively). Seed cotton yields varied significantly (P.9999). Similarly, N supplementation at 1.1–1.5 RI and 81–90% SI recorded significantly (P

‘Big data’ is massive amounts of information that can work wonders. It has become a topic of special interest for the past two decades because of a great potential that is hidden in it. Various public and private sector industries generate, store, and analyze big data with an aim to improve the services they provide. In the healthcare industry, various sources for big data include hospital records, medical records of patients, and results of medical examinations, and devices that are a part of internet of things. Biomedical research also generates a significant portion of big data relevant to public healthcare. This data requires proper management and analysis in order to derive meaningful information. Otherwise, seeking solution by analyzing big data quickly becomes comparable to finding a needle in the haystack. There are various challenges associated with each step of handling big data which can only be surpassed by using high-end computing solutions for big data analysis. That is why, to provide relevant solutions for improving public health, healthcare providers are required to be fully equipped with appropriate infrastructure to systematically generate and analyze big data. An efficient management, analysis, and interpretation of big data can change the game by opening new avenues for modern healthcare. That is exactly why various industries, including the healthcare industry, are taking vigorous steps to convert this potential into better services and financial advantages. With a strong integration of biomedical and healthcare data, modern healthcare organizations can possibly revolutionize the medical therapies and personalized medicine.

BACKGROUND Abnormal fusion or canalisation of mullerian duct during embryonic life results in congenital uterine malformations. Prevalence of congenital uterine malformations is approximately 2 - 4% in reproductive age group and 5 - 25% in women with adverse pregnancy outcomes. Diagnosis of uterine anomalies has to be made precisely which requires diagnostic modalities like ultrasonography, magnetic resonance imaging, hysterosalpingogram, and hysterolaparoscopy. This observational study is conducted to determine the frequency and types of congenital uterine anomalies discovered during Caesarean section done for abnormal presentations. METHODS This is a retrospective observational study conducted in the Department of Obstetrics and Gynaecology of JSS Hospital, Mysuru. A total number of 108 cases were included in the study over a period of 2 years. Patients who underwent Caesarean section due to abnormal presentation were included in the study. After delivery of the foetus and the placenta, uterus was examined for the presence or absence of congenital malformations by digital palpation of the uterine cavity and inspection of fundus of uterus after exteriorisation. Demographic characteristics and the obstetric outcomes were noted. RESULTS During the study period of 2 years, 108 Caesarean sections were performed for abnormal presentation in the Department of OBG, JSS Hospital, Mysuru. Out of 108 patients, 15 (13.89%) patients were diagnosed with uterine anomalies and 93 (86.11%) patients had normal uterus. Majority of the patients with uterine anomalies who underwent caesarean section were primigravida (80%) and also majority of them belonged to the age group of 25 - 30 years (73.3%). The most commonly observed uterine anomaly during the study period was arcuate uterus. Though our study included cases only with abnormal presentation, 12 out of 15 (80%) had breech presentation and the rest 3 (20%) had transverse lie. History of miscarriage was found to be higher in patients with uterine anomalies. 53.3% patients with uterine anomaly had preterm delivery & the preterm delivery rate in patients with normal uterus was lesser i.e. 40.9%. CONCLUSIONS Congenital uterine anomalies can affect reproductive and obstetric outcomes and hence their accurate diagnosis can benefit several women from adverse outcomes. Caesarean section can be one of the diagnostic modalities for uterine malformations with no increase in the operative time or risk or cost for the patient.

The brain-computer-interfaces (BCI) can also be referred towards a mindmachine interface that can provide a non-muscular communication channel in between the computer device and human brain. To measure the brain activity, electroencephalography (EEG) has been widely utilized in the applications of BCI to work system in real-time. It has been analyzed that the identification probability performed with other methodologies do not provide optimal classification accuracy. Therefore, it is required to focus on the process of feature extraction to achieve maximum classification accuracy. In this paper, a novel process of data-driven spatial has been proposed to improve the detection of steady state visually evoked potentials (SSVEPs) at BCI. Here, EACA has been proposed, which can develop the reproducibility of SSVEP across many trails. Further this can be utilized to improve the SSVEP from a noisy data signal by eliminating the activities of EEG background. In the simulation process, the SSVEP dataset recorded from given 11 subjects are considered. To validate the performance, the state-of-art method is considered to compare with the EDCA based proposed approach.

Silicon photonics circuits with ring and bus waveguides are of interesting topics. The modelling of the ring modulator is analyzed in this work. The modulators are very important unit of the communication system. Simulating these structures require more time and longer propagation lengths. The 2.5D finite difference time domain also called variational FDTD is used to simulate the ring modulator. Ring modulator modal response is analyzed and the wavelength used is in the Conventional band. The Conventional band wavelength is from 1530 nm to 1565 nm band in optical communication systems. The micro ring modulator structure has effective index of 1.244672 and group index of 1.557. Transfer electric mode is used for the analysis.

Background: Chicken is one of the important meat sources throughout the globe. Muscle development and egg production are important genetic traits in commercially raising chickens. However, not much information is available in the fast and slow growth of chicken to determine the expression of genes involved in muscle development and egg production in embryo initiation and developmental stages. This study was designed to investigate why improved Aseel (PD4) growing slowly compared with the control broiler (CB), microarray was conducted with the 7th-day embryo and 18th-day thigh muscle of improved Aseel (PD4) and control broiler (CL), respectively.Results: In the differential transcripts screening, all the transcripts obtained by microarray of slow and fast growth groups were screened by fold change ≥1 and false discovery rate (FDR) th-day embryo and 18th-day thigh muscle of improved Aseel compared to the control broiler. Further analysis showed that a high number of transcripts are differentially regulated in the 7th-day improved Aseel embryo (15382) and fewer transcripts were differentially regulated (3640) in the 18th-day thigh muscle of improved Aseel compared to control broiler. In the 7th and 18th-day improved Aseel embryo, 10127, 2102, 5255, and 1538 transcripts were up and down-regulated, respectively. The commonly up and down-regulated transcripts are 545 and 381 between the 7th and 18th-day of embryos. In this study, we have selected 18 Gallus gallus candidate reference genes from NCBI and total RNA was isolated from control broiler, improved Aseel embryo tissues, and studied their expression profiles by real-time quantitative PCR (qPCR). The best housekeeping gene was identified by using geNorm, NormFinder, BestKeeper, Delta CT, and RefFinder analytical software. The result showed that the TFRC gene is the most stable and further it is used for qPCR data normalization. Further, to validate the differentially expressed genes (DEGs) related to muscle growth, myostatin signaling and development, fatty acid metabolism genes in improved Aseel (PD4) and control broiler embryo tissues by qPCR. Conclusion: Our study identified DEGs that regulate myostatin signaling and differentiation pathway, glycolysis and gluconeogenesis, fatty acid metabolism, Jak-STAT, mTOR, and TGF-β signaling pathways, tryptophan metabolism, PI3K-Akt signaling pathways in improved Aseel. The results revealed that the gene expression architecture is present in the improved Aseel exhibiting embryo growth that will help to improve muscle development, differentiation, egg production, as well as protein synthesis in improved Aseel native chicken. Our findings may be used as a model for improving the growth in improved Aseel as well as optimizing the growth in the control broiler.

Recent progress in technologies and medical science have given momentum to Brain-computer interfaces (BCIs) system in generalizing human-computer communication, specifically for the people struggling with neuro-muscular disabilities. Currently, BCI systems heavily rely upon efficient electroencephalograms (EEG) signal detection from the current available solutions due to their noninvasiveness. However, the existing BCI systems works efficiently for less number of user instructions. Therefore, in this study, Adaptive Correlation based Component Analysis (ACCA) method is proposed to establish a proper Brain-computer interfaces (BCIs) system between user and computer. Here, correlation analysis is performed to identify spatial probability for a particular frequency using Adaptive Correlation based Component Analysis (ACCA) method. Maximum correlation between EEG signal phases triggered by both single and multiple flickers are generated using ACCA method to efficiently extract SSVEP component. The performance of proposed ACCA method is analysed against traditional SSVEP extraction methods in terms of classification accuracy, time consumption and Information Transfer Rate (ITR) for different subjects.

The proposed paper incorporates the modeling and simulation of the highly sensitive photonic crystal-based sensor for breast and cervical cancer infected cell detection. Two structures, regular hexagon, and 180° phase mismatched hexagon are used. For the detection of cancer infected cells, the change in refractive index is considered as the basis and corresponding change in properties of light rays is observed. A comparative study has been done for the sensitivity of the designed sensor. The results are observed with the wavelength shift in the transmitted spectrum. The sensitivity is calculated for both the structures and found that 180° phase mismatch is better than hexagonal shape as regards to cervical cancer detection, hexagonal structure fares better than 1800 phase mismatch for breast cancer detection with regards to performance in sensitivity.

Background: Recurrent herpes labialis (RHL) is a disorder with serious health and social consequences and which affects most of the adults. However, high degree of research paucity pertaining to its prevalence was observed, especially from India. Objective: The objective of this study was to assess the prevalence of RHL in western Maharashtra. Materials and Methods: In this prospective study, 1368 patients of either sex, satisfying the inclusion criteria, were included in the study after screening 34,560 patients for RHL lesions. Demographic data such as age, gender and risk factors, namely stress, menstruation and common cold, were recorded. Further, data regarding history of itching, fever, malaise and burning on lips followed by vesicles and frequency of lesions and duration were also recorded. After clinical examination site, size and nature of lesion were noted on a clinical pro forma. Results were compared statistically, and P < 0.05 was considered statistically significant. Results: The majority of the patients were in the age group of 30–39 years, with a female predominance (63.89%) (male:female = 0.33:0.59). Stress (43%) was the most common risk factor in the occurrence of RHL, followed by disturbed menstruation cycle (21%). Most of the patients had two episodes of RHL (42.4%), whereas some had just one episode of recurrence (25.4%) in the past 1 year. The most commonly occurring location for RHL was upper lip (47%), especially the left side (19.1%) and right side (18.2%) of the upper lip. The overall prevalence rate of RHL in our study was 3.9%. Conclusion: An established prevalence (3.9%) of RHL occurs among patients in western Maharashtra.