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A crucial component of describing plants for tracking plant growth is plant phenotyping. In this research, an effective method for identifying healthy, damaged, or infected leaves utilising image processing and machine learning approaches is presented. Many illnesses deplete the chlorophyll of brown or black markings appear on the leaf area of the leaves. They can be found out utilising machine learning methods for classification, feature extraction, picture preprocessing, and image segmentation. Grey Level Co-occurrence Matrix (GLCM) is used for feature extraction. One of themachine learning techniques used for classification is called the Support Vector Machine (SVM). When compared to the SVM method, the Convolutional Neural Network (CNN) produced better recognition accuracy. Finding disease on crops is a crucial responsibility in agricultural techniques.

India is one of the worldwide tourist goals. The current study focused on it in the context of tourism policy and its industry, which is rapidly growing, particularly in Delhi and Mumbai. The rapid growth of India's tourism industry is affected by several factors, including the country's high economic growth rate over the past decade and the fact that it is now one of the more affordable destinations for foreign tourists. Additionally, several significant challenges are associated with developing the Indian tourism industry. To address these challenges and ensure that India's tourism industry continues to increase in the years to come, the government has introduced several new tourism policies aimed at improving the infrastructure available in the country and making it easier for tourists to come and visit. These policies include introducing a dedicated high-speed rail link between Delhi and Mumbai, developing a new visa policy for foreign visitors, and simplifying the process for acquiring permits for filming or photography in popular tourist attractions nationwide. Overall, these policies represent a significant step in developing the Indian tourism industry and will go a long way towards guaranteeing that the country remains a popular destination for tourists worldwide for many years; this study focused on it deeply based on earlier investigations.

We present MargNet, a deep learning-based classifier for identifying stars, quasars and compact galaxies using photometric parameters and images from the Sloan Digital Sky Survey (SDSS) Data Release 16 (DR16) catalogue. MargNet consists of a combination of Convolutional Neural Network (CNN) and Artificial Neural Network (ANN) architectures. Using a carefully curated dataset consisting of 240,000 compact objects and an additional 150,000 faint objects, the machine learns classification directly from the data, minimising the need for human intervention. MargNet is the first classifier focusing exclusively on compact galaxies and performs better than other methods to classify compact galaxies from stars and quasars, even at fainter magnitudes. This model and feature engineering in such deep learning architectures will provide greater success in identifying objects in the ongoing and upcoming surveys, such as Dark Energy Survey (DES) and images from the Vera C. Rubin Observatory., 14 pages, 10 figures, Accepted for publication in MNRAS

Purpose AMD genetic studies have revealed various genetic loci as causal to AMD pathology. We have described the genetic complexity of Indian AMD by describing the interaction of genotypes and subsequent changes in protein expression under the influence of environmental factors. This can be utilized to enhance the diagnostic and therapeutic efficacy in AMD patients. Design Genotype association was studied in 464 participants (AMD =277 & controls = 187) for eight genetic variants and their corresponding protein expression Methods SNP analysis and protein expression analysis was carried out in AMD and controls in tandem with longitudinal assessment of protein levels during the course of AMD pathology. ANCOVA and contrast analysis were used to examine the genotypic interactions and corresponding alterations in protein levels. In order to identify the important genetic variants Logistic Regression (LR) modeling was carried out and to authenticate the model Area under the Receiver Operating Characteristic curve (AUROC) were also computed. Results We have found genetic variants of rs5749482 (TIMP-3), rs11200638 (HTRA1), rs769449 (APOE) and rs6795735 (ADAMTS9) to be associated with AMD, concomitant with significant alterations of studied proteins levels. Analysis also revealed that the genetic interaction between APOE-HTRA1 genotypes and changes in LIPC levels (>6 pg/ug) by one unit change in SNP, play a crucial role in AMD. LR model suggested that the seven factors (including both genetic and environmental) can be utilized to predict the AMD cases with 88% efficacy and 95.6% AUROC. Conclusion Results suggest that diagnostic and therapeutic strategy for Indian AMD must include estimation of genetic interaction and concomitant changes in expression levels of proteins under influence of environmental factors.

Owing to the current and upcoming extensive surveys studying the stellar variability, accurate and quicker methods are required for the astronomers to automate the classification of variable stars. The traditional approach of classification requires the calculation of the period of the observed light curve and assigning different variability patterns of phase folded light curves to different classes. However, applying these methods becomes difficult if the light curves are sparse or contain temporal gaps. Also, period finding algorithms start slowing down and become redundant in such scenarios. In this work, we present a new automated method, 1D CNN-LSTM, for classifying variable stars using a hybrid neural network of one-dimensional CNN and LSTM network which employs the raw time-series data from the variable stars. We apply the network to classify the time-series data obtained from the OGLE and the CRTS survey. We report the best average accuracy of 85% and F1 score of 0.71 for classifying five classes from the OGLE survey. We simultaneously apply other existing classification methods to our dataset and compare the results.

Coronaviruses have led to three major outbreaks to date-Severe Acute Respiratory Syndrome (SARS; 2002), Middle East Respiratory Syndrome (MERS; 2012) and the ongoing pandemic, Coronavirus Disease (COVID-19; 2019). Coronavirus infections are usually mild in children. However, a few children with MERS had presented with a severe phenotype in the acute phase resulting in progressive pneumonic changes with increasing oxygen dependency and acute respiratory distress requiring ventilatory support. A subset of children with a history of SARS-CoV-2 infection develops a multisystem hyper-inflammatory phenotype known as Multisystem Inflammatory Syndrome in Children (MIS-C). This syndrome occurs 4-6 weeks after infection with SARS-CoV-2 and has been reported more often from areas with high community transmission. Children with MIS-C present with high fever and often have involvement of cardiovascular, gastrointestinal and hematologic systems leading to multiorgan failure. This is accompanied by elevation of pro-inflammatory cytokines such as IL-6 and IL-10. MIS-C has several similarities with Kawasaki disease (KD) considering children with both conditions present with fever, rash, conjunctival injection, mucosal symptoms and swelling of hands and feet. For reasons that are still not clear, both KD and MIS-C were not reported during the SARS-CoV and MERS-CoV outbreaks. As SARS-CoV-2 differs from SARS-CoV by 19.5% and MERS by 50% in terms of sequence identity, differences in genomic and proteomic profiles may explain the varied disease immunopathology and host responses. Left untreated, MIS-C may lead to severe abdominal pain, ventricular dysfunction and shock. Immunological investigations reveal reduced numbers of follicular B cells, increased numbers of terminally differentiated CD4+T lymphocytes, and decreased IL-17A. There is still ambiguity about the clinical and immunologic risk factors that predispose some children to development of MIS-C while sparing others. Host-pathogen interactions in SARS, MERS and COVID-19 are likely to play a crucial role in the clinical phenotypes that manifest. This narrative review focuses on the immunological basis for development of MIS-C syndrome in the ongoing SARS-CoV-2 pandemic. To the best of our knowledge, these aspects have not been reviewed before.

Kawasaki disease (KD) is a common febrile multisystemic inflammatory illness in children that preferentially affects coronary arteries. Children with KD who develop coronary artery aneurysms have a life-long risk of premature coronary artery disease. Hypothesis of inherent predisposition to KD is supported by epidemiological evidence that suggests increased risk of development of disease in certain ethnicities and in children with a previous history of KD in siblings or parents. However, occurrence of cases in clusters, seasonal variation, and very low risk of recurrence suggests an acquired trigger (such as infections) for the development of illness. Epigenetic mechanisms that modulate gene expression can plausibly explain the link between genetic and acquired predisposing factors in KD. Analysis of epigenetic factors can also be used to derive biomarkers for diagnosis and prognostication in KD. Moreover, epigenetic mechanisms can also help in pharmacogenomics with the development of targeted therapies. In this review, we analysed the available literature on epigenetic factors such as methylation, micro-RNAs, and long non-coding RNAs in KD and discuss how these mechanisms can help us better understand the disease pathogenesis and advance the development of new biomarkers in KD.

Degeneration of macular photoreceptors is a prominent characteristic of age-related macular degeneration (AMD) which leads to devastating and irreversible vision loss in the elderly population. In this exploratory study, the contribution of environmental factors on the progression of AMD pathology by probing the expression of candidate proteins was analyzed. Four hundred and sixty four participants were recruited in the study comprising of AMD (n = 277) and controls (n = 187). Genetics related data was analyzed to demonstrate the activities of daily living (ADL) by using regression analysis and statistical modeling, including contrast estimate, multinomial regression analysis in AMD progression. Regression analysis revealed contribution of smoking, alcohol, and sleeping hours on AMD by altered expression of IER-3, HTRA1, B3GALTL, LIPC and TIMP3 as compared to normal levels. Contrast estimate supports the gender polarization phenomenon in AMD by significant decreased expression of SLC16A8 and LIPC in control population which was found to be unaltered in AMD patients. The smoking, food habits and duration of night sleeping hours also contributed in AMD progression as evident from multinomial regression analysis. Predicted model (prediction estimate = 86.7%) also indicated the crucial role of night sleeping hours along with the decreased expression of TIMP-3, IER3 and SLC16A8. Results revealed an unambiguous role of environmental factors in AMD progression mediated by various regulatory proteins which might result in intermittent AMD phenotypes and possibly influence the outcome of anti-VEGF treatment.

Platelets have a very essential role in periodontal regeneration as they are pond of cytokines and growth factors which are the key source for accelerating hard and soft tissue healing. Autologous platelet concentrate i.e. Platelet-rich plasma (1st Generation platelet concentrate) and platelet-rich fibrin (2nd Generation platelet concentrate) are formulated from patient’s own venous blood. Present day studies are being concentrated on the evolution of alternatives which are non-toxic, easy to prepare, biocompatible and cost effective that lead in the local release of growth factors for regeneration and maturation of the bone and soft tissue. PRF is a natural autologous fibrin-based biomaterial derived from an anticoagulant-free venous blood, with no artificial biochemical modifier that ushers to procure fibrin membranes enriched with platelets & growth factors. During centrifugation, slow polymerization forms PRF i.e a fibrin-based structure healing biomaterial better than 1st Generation platelet concentrate and other fibrin adhesives. Keywords: Platelet rich fibrin, Platelet concentrate, Growth factors, Regeneration, Guided tissue regeneration.

Background Spinal cord injury (SCI) leads to serious complications involving primary trauma and progressive loss due to inflammation, local ischemia, or infection. Despite a worldwide annual incidence of 15 to 40 cases per million, methylprednisolone is the only treatment available to alleviate neurologic dysfunction; therefore, research is currently focused on identifying novel targets by biochemical and molecular studies. Purpose Here, we investigated the expression of various molecular markers at the messenger ribonucleic acid (mRNA) and protein level at day 0 and day 30 post-SCI. Methods Enzyme-linked immunosorbent assay (ELISA) was performed to determine the expression of CASPASE-3 and heat shock protein-27 (HSP-27) in serum samples. Real-time polymerase chain reaction (RT-PCR) was performed to determine the level of mRNA expression of vascular endothelial growth factor receptor-1 (VEGFR-1), VEGFR-2, HSP-27, monocyte chemoattractant protein-1 (MCP-1), and CASPASE-3. Results HSP-27 expression at day 30, as compared with day 0, showed significant downregulation. In contrast, there was elevated expression of MCP-1. ELISA analysis showed no significant change in the expression of CASPASE-3 or HSP-27. Conclusion There may be possible opposing role of HSP-27 and MCP-1 governing SCI. Their association can be studied by designing in vitro studies.

Background: Many factors including genetic and environmental are responsible for the incidence of Age-related Macular Degeneration (AMD). However, its pathogenesis has not been clearly elucidated yet. Objective: This study aimed to estimate the Age-Related Maculopathy Susceptibility 2 (ARMS2), Collagen type VIII Alpha 1 chain (COL8A1), Rad 51 paralog(RAD51B), and Vascular Endothelial Growth Factor (VEGF) protein levels in serum of AMD and control participants and to further investigate their correlation to understand AMD pathogenesis. Methods: For this case-control study, 31 healthy control and 57 AMD patients were recruited from Advanced Eye Centre, Post Graduate Institute of Medical Education and Research, Chandigarh, India. A blood sample was taken and serum was isolated from it. ELISA (enzyme-linked immunosorbent assay) was used for the estimation of proteins in the serum of patients. Results: ARMS2 and COL8A1 levels were significantly elevated in the AMD group than in the control group. The highest levels of ARMS2, COL8A1, and VEGF proteins were recorded for the wet AMD sub-group. The study results endorsed significant positive correlation between these following molecules; ARMS2 and COL8A1 (r = 0.933, p < 0.0001), ARMS2 and RAD51B (r = 0.704, p < 0.0001), ARMS2 and VEGF (r = 0.925, p < 0.0001), COL8A1 and RAD51B (r = 0.736, p < 0.0001), COL8A1 and VEGF (r = 0.879, p < 0.0001), and RAD51B and VEGF (r = 0.691, p < 0.0001). Conclusion: The ARMS2 and COL8A1 levels were significantly higher and RAD51B was significantly lower in the AMD group than controls. Also, a significant statistical correlation was detected between these molecules, indicating that their interaction may be involved in the pathogenesis of AMD.

Low Mass X-ray binaries (LMXBs) are binary systems where one of the components is either a black hole or a neutron star and the other is a less massive star. It is challenging to unambiguously determine whether a LMXB hosts a black hole or a neutron star. In the last few decades, multiple observational works have tried, with different levels of success, to address this problem. In this paper, we explore the use of machine learning to tackle this observational challenge. We train a random forest classifier to identify the type of compact object using the energy spectrum in the energy range 5-25 keV obtained from the Rossi X-ray Timing Explorer archive. We report an average accuracy of 87+/-13 in classifying the spectra of LMXB sources. We further use the trained model for predicting the classes for LMXB systems with unknown or ambiguous classification. With the ever-increasing volume of astronomical data in the X-ray domain from present and upcoming missions (e.g., SWIFT, XMM-Newton, XARM, ATHENA, NICER), such methods can be extremely useful for faster and robust classification of X-ray sources and can also be deployed as part of the data reduction pipeline., 16 pages, 10 figures, 7 tables, Accepted for publication in MNRAS main journal

BackgroundSevere Combined Immune Deficiency (SCID) is an inherited defect in lymphocyte development and function that results in life-threatening opportunistic infections in early infancy. Data on SCID from developing countries are scarce.ObjectiveTo describe clinical and laboratory features of SCID diagnosed at immunology centers across India.MethodsA detailed case proforma in an Excel format was prepared by one of the authors (PV) and was sent to centers in India that care for patients with primary immunodeficiency diseases. We collated clinical, laboratory, and molecular details of patients with clinical profile suggestive of SCID and their outcomes. Twelve (12) centers provided necessary details which were then compiled and analyzed. Diagnosis of SCID/combined immune deficiency (CID) was based on 2018 European Society for Immunodeficiencies working definition for SCID.ResultsWe obtained data on 277 children; 254 were categorized as SCID and 23 as CID. Male-female ratio was 196:81. Median (inter-quartile range) age of onset of clinical symptoms and diagnosis was 2.5 months (1, 5) and 5 months (3.5, 8), respectively. Molecular diagnosis was obtained in 162 patients - IL2RG (36), RAG1 (26), ADA (19), RAG2 (17), JAK3 (15), DCLRE1C (13), IL7RA (9), PNP (3), RFXAP (3), CIITA (2), RFXANK (2), NHEJ1 (2), CD3E (2), CD3D (2), RFX5 (2), ZAP70 (2), STK4 (1), CORO1A (1), STIM1 (1), PRKDC (1), AK2 (1), DOCK2 (1), and SP100 (1). Only 23 children (8.3%) received hematopoietic stem cell transplantation (HSCT). Of these, 11 are doing well post-HSCT. Mortality was recorded in 210 children (75.8%).ConclusionWe document an exponential rise in number of cases diagnosed to have SCID over the last 10 years, probably as a result of increasing awareness and improvement in diagnostic facilities at various centers in India. We suspect that these numbers are just the tip of the iceberg. Majority of patients with SCID in India are probably not being recognized and diagnosed at present. Newborn screening for SCID is the need of the hour. Easy access to pediatric HSCT services would ensure that these patients are offered HSCT at an early age.

Quality assurance (QA) is an indispensable part of manufacturing industries and corporate. Academics also require QA to avoid misconduct and conflicts. Adhering to Good Lab Practices (GLP) is definitely a prerequisite for quality research. Implementation of QA should be done at different stages of research like the inception of research idea, generation of data, and presentation of research data to avoid any future conflicts. There are numerous examples in the history of research and academics where considerable conflicts raised doubts about the credibility and quality of research. QA provides the reproducibility, back traceability, and reliability of the generated data. It not only strengthens and encourages the researchers to contribute in the respective field but also acts as a defensive shield against jealousy and negative competition-driven accusations. Protection of intellectual property is also vital in academics as it is a sensitive area often infested with conflicts. QA can help in this regard by safeguarding Intellectual Property. Stringent implementation and monitoring of the QA and timely amendment can provide the maximum benefits in academics and research fraternities. This chapter describes the concepts like implementation of QA in academics, history of scientific misconduct which supports the notion of the need for QA, and also lays a format for how QA can be implemented at various stages of research. Here we also discuss the role of QA personnel in ensuring Quality in a GLP laboratory. It basically outlines the importance of QA and how it can be implemented in academics to avoid conflicts and improve the quality standard of research.

The analysis of degenerative diseases has revealed genetic heterogeneity and this appears to contribute towards an increase in the complexity of disease with influences from environmental factors. Worldwide Genetic studies have identified various common and non-synonymous alleles with AMD across the geographically distributed population. With the advancement in technology, the biological contribution of rare alleles and “Junk DNA” in AMD has been suggested. The biological significance of intronic, rare alleles, and transposons in degenerative diseases can provide the necessary information to assess the evolution of the complexity of AMD pathology.

Eye disorders constitute several vision associated complications that may result in blindness, if not treated on time. Vision researchers have been trying to develop effective treatment strategies but without much success. The continuous failure or ineffectiveness of ocular drugs has led to a desire for alternative therapies. Various preclinical studies have shown that herbal extracts or stem cells can be used as alternative strategies. However, its human benefits can only be accepted after clinical trials. This review provides a new perspective on integrated therapies for eye disorders based on sound scientific evidences compiled from review of the literature. The review provides compelling arguments for much-needed paradigm shift in the face of the failure of current ocular drugs.

Abstarct Background Amyotrophic lateral sclerosis (ALS) is a rare motor neuron disease with progressive degeneration of motor neurons. Various molecules have been explored to provide the early diagnostic/prognostic tool for ALS without getting much success in the field and miscellaneous reports studied in various population. Objective The study was aimed to see the differential expression of proteins involved in angiogenesis (angiogenin [ANG], vascular endothelial growth factor [VEGF], vascular endothelial growth factor receptor 2 [VEGFR2], etc), proteinopathy (transactive response DNA binding protein-43 [TDP-43] and optineurin [OPTN]), and neuroinflammation (monocyte chemoattractant protein-1[MCP-1]) based on the characteristics of ALS pathology. Though, suitable panel based on protein expression profile can be designed to robust the ALS identification by enhancing the prognostic and diagnostic efficacy for ALS. Methods A total of 89 ALS patients and 98 nonneurological controls were analyzed for the protein expression. Expression of angiogenic (VEGF, VEGFR2, and ANG), neuroinflammation (MCP-1), and proteinopathy (TDP-43 and OPTN) markers were estimated in plasma of the participants. Proteins were normalized with respective value of total protein before employing statistical analysis. Results Analysis has exhibited significantly reduced expression of angiogenic, proteinopathy, and neuroinflammation biomarkers in ALS patients in comparison to controls. Spearman’s correlation analysis has showed the positive correlation to each protein. Conclusion Altered expression of these proteins is indicating the prominent function in ALS pathology which may be interdependent and may have a synergistic role. Hence, a panel of expression can be proposed to diagnose ALS patient which may also suggest the modulation of therapeutic strategy according to expression profile of patient.

Background Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative fatal disease that can affect the neurons of brain and spinal cord. ALS genetics has identified various genes to be associated with disease pathology. Oxidative stress induced bunina and lewy bodies formation can be regulated through the action of SOD1 protein. Hence, in the present study we aim to analyse the structural and functional annotation of various reported SOD1 variants throughout and their putative correlation with the location of mutation and degree of ALS severity by inferring the structural and functional alterations in different SOD1 variants. Methods We have retrieved around 69 SNPs of SOD1 gene from Genecards. Structural annotation of SOD1 variants were performed using SWISS Model, I-Mutant 2.0, Dynamut, ConSurf. Similarly, the functional annotation of same variants were done using SIFT, PHP-SNP, PolyPhen2, PROVEAN and RegulomeDB. Ramachandran plot was also obtained for six synonymous SNPs to compare the amino acid distribution of wild-type SOD1 (WT SOD1) protein. Frequency analysis, Chi square analysis, ANOVA and multiple regression analysis were performed to compare the structural and functional components among various groups. Results and conclusion Results showed the mutations in conserved domain of SOD1 protein are more deleterious and significantly distort the tertiary structure of protein by altering Gibb’s free energy and entropy. Moreover, significant changes in SIFT, PHP-SNP, PolyPhen2, PROVEAN and RegulomeDB scores were also observed in mutations located in conserved domain of SOD1 protein. Multiple regression results were also suggesting the significant alterations in free energy and entropy for conserved domain mutations which were concordant with structural changes of SOD1 protein. Results of the study are suggesting the biological importance of location of mutation(s) which may derive the different disease phenotypes and must be dealt accordingly to provide precise therapy for ALS patients.

Theoretical stellar spectra rely on model stellar atmospheres computed based on our understanding of the physical laws at play in the stellar interiors. These models, coupled with atomic and molecular line databases, are used to generate theoretical stellar spectral libraries (SSLs) comprising of stellar spectra over a regular grid of atmospheric parameters (temperature, surface gravity, abundances) at any desired resolution. Another class of SSLs is referred to as empirical spectral libraries; these contain observed spectra at limited resolution. SSLs play an essential role in deriving the properties of stars and stellar populations. Both theoretical and empirical libraries suffer from limited coverage over the parameter space. This limitation is overcome to some extent by generating spectra for specific sets of atmospheric parameters by interpolating within the grid of available parameter space. In this work, we present a method for spectral interpolation in the optical region using machine learning algorithms that are generic, easily adaptable for any SSL without much change in the model parameters, and computationally inexpensive. We use two machine learning techniques, Random Forest (RF) and Artificial Neural Networks (ANN), and train the models on the MILES library. We apply the trained models to spectra from the CFLIB for testing and show that the performance of the two models is comparable. We show that both the models achieve better accuracy than the existing methods of polynomial based interpolation and the Gaussian radial basis function (RBF) interpolation., Accepted for publication in MNRAS. 16 pages, 16 figures

Decrypting the interface residues of the protein complexes provides insight into the functions of the proteins and, hence, the overall cellular machinery. Computational methods have been devised in the past to predict the interface residues using amino acid sequence information, but all these methods have been majorly applied to predict for prokaryotic protein complexes. Since the composition and rate of evolution of the primary sequence is different between prokaryotes and eukaryotes, it is important to develop a method specifically for eukaryotic complexes. Here, we report a new hybrid pipeline for predicting the protein-protein interaction interfaces in a pairwise manner from the amino acid sequence information of the interacting proteins. It is based on the framework of Co-evolution, machine learning (Random Forest), and Network Analysis named CoRNeA trained specifically on eukaryotic protein complexes. We use Co-evolution, physicochemical properties, and contact potential as major group of features to train the Random Forest classifier. We also incorporate the intra-contact information of the individual proteins to eliminate false positives from the predictions keeping in mind that the amino acid sequence of a protein also holds information for its own folding and not only the interface propensities. Our prediction on example datasets shows that CoRNeA not only enhances the prediction of true interface residues but also reduces false positive rates significantly.

In India, the use of helmets is mandatory at construction sites as per the government rules. The Use of a helmet protects the labor head against the impact of the falling object from any height. There was a modification in the ordinary helmet into a smart helmet by adding the Work Mode (ON/OFF) system, GPS System, Smoke Sensor, Task Completion Button. In this, the helmets will automatically be updating the work mode ON, if someone wears it because a clamp turns the system ON and information goes to the supervisor. Same as this, there are two more buttons which indicate the task completion and about the emergency. Using all these buttons and GPS Systems helps the labor in many ways and manage the tasks and time also. For this, GPS and an IOT System are being used for the functioning of a helmet. Real-time signals will be sending constantly with the help of installed various sensors to monitor. The main objective of this study is to activate the installed components so that the various emergencies can be detected at the supervisor’s room or to find the location of the worker, to keep an eye on the various field tasks. It will give the actual location of the worker and in case of emergency; the labor can send his/her exact location to the command office of the construction site. For this, HC12 technology is chosen to give the information by sending pinpoint location, HC12 module which has a GPS module. These components are installed in the ordinary helmet and connect all these with a single computer so that all the activities are to be supervised in a single computer. By installing all the components in one construction helmet, the internet of things based smart helmet is assembled.

With the beaming concept of Construction 4.0, the disciplines of structural engineering and construction industry needs an advancement for the data collection, interpretation and analysis. Augmented Intelligence and its various disciplines find an impressive application in the field of civil engineering for data requisition, management and performance. This study focuses on how AI and its various principles can be blended with the emerging areas of structural engineering, and how it is shaping the construction industry by employing in the major areas of monitoring of structural health, assessment of damages and construction management.

As of late, structural deterioration of RC structures affected by corrosion is widely reported. A new evolution in maintenance of RC structures is by cathodic protection by the use of carbon fiber reinforced polymers (CFRP). Using steel bar as a cathode and wrapping of CFRP as anode, which prevents removal of concrete cover and blocks further corrosion of steel. This article presents the utilization of CFRP for the protection of the strand. Series of laboratory trials using cathodic protection systems were performed on pre-stressed concrete structures in order to set up reliable cathodic protection systems. To initiate initial corrosion in the specimens, pre-stressing tendon used as the cathode and CFRP used as the anode while the in the active protection, the impressed charge was passed through the strand. Then, CFRP sheets were bonded by using conductive adhesive to the block samples. Extensively corrosive environment was imposed on the samples for the particular period of time and it was observed that the active protection method is very effectual in slowing down the corrosion of the strand.

Background: India is fast becoming the diabetic capital in the world according to a recent report. Patients with diabetes are at increased risk of mortality due to diabetic complications, which has enormous implications for the health budget. Objectives: The main objective of this review is to provide an overview of the work carried out in the world, including modern and traditional approaches for the prevention and management of diabetes and reducing the chances of onset of further complications via cost-effective lifestyle interventions and integrative medicine. Material and Methods: We performed a literature search from various databases like PubMed, Scopus, Google scholar, etc., using the keywords diabetes, prediabetes, MCI and prediabetes, diabetes and yoga, diabetes. Results: Upon reviewing the published articles, it was noticed that one of the most neglected complications of diabetes, namely cognitive dysfunction, which is characterized by a pattern of vascular dementia and Alzheimer disease (AD), has been largely ignored, and there has been no large study investigating the role of yoga intervention in diabetes and/or associated cognitive impairment. Conclusion: The review article opens new paradigms for researchers to evaluate the connection between diabetes and AD through a yoga-based national campaign on diabetes. This paves the way towards the goal of integrative medicine.

AMD is a complex eye disease predominantly occurring in aged population. Till now about 53 genetic loci have been found to be associated with the AMD pathology. AMD pathogenesis is being increasingly known to progress through mechanisms independent of the CFH mediated pathway. Therefore, our aim for current study was to examine the genes by analyzing their expression levels in AMD. We recruited about 50 AMD and same number of age matched controls. We analyzed the CFH duplication and deletion by multiplex ligation probe amplification (MLPA) and found no duplication and deletion in CFH gene in AMD patients. We also estimated the IER-3, SLC16A8, LIPC, and TIMP-3 expression levels in both CFH-negative AMD cases (i.e. no duplication and deletion in CFH gene) besides examining these in AMD and controls. We found that the expression level of LIPC, SLC16A8, and TIMP-3 was significantly associated with AMD pathology in both groups (LIPC: P = 0.008, SLC16A8: P

Crystal structures of jacalin in complex with GlcNAc beta-(1,3) Gal-beta-OMe and Gal beta-(1,3) Gal-beta-OMe have been determined. The binding of the ligands to jacalin is similar to that of analogous -substituted disaccharides. However, the beta-substituted beta-(1,3) linked disaccharides get distorted at the anomeric center and the glycosidic linkage. The distortion results in higher internal energies of the ligands leading to lower affinity to the lectin. This confirms the possibility of using ligand distortion as a strategy for modulating binding affinity. Unlike in the case of alpha-substituted monosaccharides bound to jacalin, where a larger distortion at the anomeric center was observed, smaller distortions are distributed among two centers in the structures of the two beta-substituted beta-(1,3) linked disaccharides presented here. These disaccharides, like the unsubstituted and alpha-substituted counterparts, bind jacalin with the reducing Gal at the primary binding site, indicating that the lower binding affinity of beta-substituted disaccharides is not enough to overcome the intrinsic propensity of Gal -(1,3) Gal-based disaccharides to bind jacalin with the reducing sugar at the primary site. (C) 2017 IUBMB Life, 69(2): 72-78, 2017.

Mycobacterium abscessus is a rapid growing nontuberculous mycobacteria (NTM) and a clinically significant pathogen capable of causing variable infections in humans that are difficult to treat and may require months of therapy/surgical interventions. Like other NTMs, M. abscessus can be associated with outbreaks leading to complex investigations and treatment of affected cases. Typing schemes for bacterial pathogens provide numerous applications; including identifying chain of transmission and tracking genomic evolution, are lacking or limited for many NTMs including M. abscessus.We extended the existing scheme from PubMLST using whole-genome data for M. abscessus by extracting data for 15 genetic regions within the M. abscessus genome. A total of 168 whole genomes and 11 gene sequences were used to build this scheme (MAB-multilocus sequence typing [MLST]).All seven genes from the PubMLST scheme, namely argH, cya, gnd, murC, pta, purH, and rpoB, were expanded by 10, 14, 13, 10, 13, 10, and 9 alleles, respectively. Another eight novel genes were added including hsp 65, erm(41), arr, rrs, rrl, gyrA, gyrB, and recA with 16, 16, 25, 7, 32, 35, 29, and 15 alleles, respectively, with 85 unique sequence types identified among all isolates.MAB-MLST can provide identification of M. abscessus complex to the subspecies level based on three genes and can provide antimicrobial resistance susceptibility prediction based on results from seven genes. MAB-MLST generated a total of 85 STs, resulting in subtyping of 90 additional isolates that could not be genotyped using PubMLST and yielding results comparable to whole-genome sequencing (WGS). Implementation of a Galaxy-based data analysis tool, MAB-MLST, that simplifies the WGS data and yet maintains a high discriminatory index that can aid in deciphering an outbreak has vast applicability for routine diagnostics.

MotivationDecrypting the interface residues of the protein complexes provide insight into the functions of the proteins and hence the overall cellular machinery. Computational methods have been devised in the past to predict the interface residues using amino acid sequence information but all these methods have been majorly applied to predict for prokaryotic protein complexes. Since the composition and rate of evolution of the primary sequence is different between prokaryotes and eukaryotes, it is important to develop a method specifically for eukaryotic complexes.nnResultsHere we report a new hybrid pipeline for the prediction of protein-protein interaction interfaces from the amino acid sequence information which is based on the framework of co-evolution, machine learning (random forest) and network analysis named CoRNeA trained specifically on eukaryotic protein complexes. We use conservation, structural and contact potential as major group of features to train the random forest classifier. We also incorporate the intra contact information of the individual proteins to eliminate false positives from the predictions keeping in mind that the amino acid sequence also holds information for its own folding and not only the interface propensities. Our prediction on example datasets shows that CoRNeA not only enhances the prediction of true interface residues but also reduces false positive rates significantly.