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Coumarin-based urea and urea–amide scaffolds 1 – 3 have been designed and synthesized for the selective and naked eye detection of cyanide ion. Of the three, compound 3 exhibits ratiometric fluorescence change selectively in the presence of CN − and validates the rationality in designing anion receptor. Upon interaction with CN − , the color of the solution of 3 in CH 3 CN under UV exposure becomes bright yellow, which is beneficial for its naked eye detection. Addition of CN − of ∼10 −4 M brings nice color change from colourless to yellow in ordinary light. The sensing event is supposed to be due to nucleophilic addition of CN − to the coumarin unit enabling intramolecular charge transfer (ICT) mechanism. [ABSTRACT FROM AUTHOR]

• Adopting CRISPR-Cas technology for target site identification. • Using k -mer technique to find PAM. • Using PAM to identify corresponding palindromes in reference sequences of viruses. • Designing primers complementary to palindromes for virus identification. • Finding population coverage of palindrome-PAM combinations for the virus sequences. Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) associated Cas protein (CRISPR-Cas) has turned out to be a very important tool for the rapid detection of viruses. This can be used for the identification of the target site in a virus by identifying a 3–6 nt length Protospacer Adjacent Motif (PAM) adjacent to the potential target site, thus motivating us to adopt CRISPR-Cas technique to identify SARS-CoV-2 as well as other members of Coronaviridae family. In this regard, we have developed a fast and effective method using k -mer technique in order to identify the PAM by scanning the whole genome of the respective virus. Subsequently, palindromic sequences adjacent to the PAM locations are identified as the potential target sites. Palindromes are considered in this work as they are known to identify viruses. Once all the palindrome-PAM combinations are identified, PAMs specific for the RNA-guided DNA Cas9/Cas12 endonuclease are identified to bind and cut the target sites. In this regard, PAMs such as 5'-TGG-3' and 5'-TTTA-3' in NSP3 and Exon for SARS-CoV-2, 5'-GGG-3' and 5'-TGG-3' in Exon and NSP2 for MERS-CoV and 5'-AGG-3' and 5'-TTTG-3' in Helicase and NSP3 respectively for SARS-CoV-1 are identified corresponding to SpCas9 and FnCas12a endonucleases. Finally, to recognise the target sites of Coronaviridae family as cleaved by SpCas9 and FnCas12a, complements of the palindromic target regions are designed as primers or guide RNA (gRNA). Therefore, such complementary gRNAs along with respective Cas proteins can be considered in assays for the identification of SARS-CoV-2, MERS-CoV and SARS-CoV-1. [ABSTRACT FROM AUTHOR]

Abstract: Differential evolution (DE) is a popular, simple, fast, efficient and stochastic optimization technique which shows a solid performance for diverse continuous optimization problems. However, the control parameters used in DE are fixed. This fact motivated many researchers in past few years to present improved variants of DE. It has been done by (1) modifying the self structure of DE, (2) integrating additional components within the structure of DE. For each category, several algorithms have been reported in the literature. Among of them, in this article, we have been selected four self structural modified adaptive variants of DE, called DE/best/1, DE with random scale factor, DE with time varying scale factor, modified differential evolution, after studying their working principles. Performance comparisons of all these algorithms are provided against the classical DE for ten numerical benchmark functions with the following performance measures: solution quality, number of generations required to find the optimal solution and frequency of finding the optimal solution. From the simulation results, it has been observed that the convergence speed of the recently proposed modified differential evolution is significantly better than others. Also statistical significance test has been carried out to establish the statistical significance of the results. [Copyright &y& Elsevier]

Abstract: In recent year, the problem of clustering in microarray data has been gaining significant attention. However most of the clustering methods attempt to find the group of genes where the number of cluster is known a priori. This fact motivated us to develop a new real-coded improved differential evolution based automatic fuzzy clustering algorithm which automatically evolves the number of clusters as well as the proper partitioning of a gene expression data set. To improve the result further, the clustering method is integrated with a support vector machine, a well-known technique for supervised learning. A fraction of the gene expression data points selected from different clusters based on their proximity to the respective centers, is used for training the SVM. The clustering assignments of the remaining gene expression data points are thereafter determined using the trained classifier. The performance of the proposed clustering technique has been demonstrated on five gene expression data sets by comparing it with the differential evolution based automatic fuzzy clustering, variable length genetic algorithm based fuzzy clustering and well known Fuzzy C-Means algorithm. Statistical significance test has been carried out to establish the statistical superiority of the proposed clustering approach. Biological significance test has also been carried out using a web based gene annotation tool to show that the proposed method is able to produce biologically relevant clusters of genes. The processed data sets and the matlab version of the software are available at http://bio.icm.edu.pl/∼darman/IDEAFC-SVM/. [Copyright &y& Elsevier]

Abstract: A new and an easy-to-make simple tripodal shaped chemosensor 1, comprising an anthracene moiety as a fluorophore and amide, alcohol functionalities as ligating groups has been designed and synthesized for Zn(II). In CH3CN containing 0.1% DMSO, upon excitation at 370nm, the chemosensor 1 exhibited an emission at 412nm, which increased to a large extent upon complexation of Zn(II). Among the other metal ions examined in the study, Cd2+ moderately perturbed the emission of 1 under similar conditions. [ABSTRACT FROM AUTHOR]

Abstract: A new triphenylamine-based receptor 1 has been designed and synthesized for the recognition of aliphatic dicarboxylates of various chain lengths. This receptor has been designed to utilize an amide–urea conjugate for binding dicarboxylates. The receptor 1 is found to bind the dicarboxylates with moderate binding strength under a semi rigid, propeller shaped, fluorescent triphenylamine spacer. The binding behavior was studied in CH3CN using 1H NMR, fluorescence and UV–vis spectroscopic methods. The conformational behavior of 1 and its complexation modes have been investigated using classical and quantum mechanical theoretical methods. The receptor is found to be selective for long chain suberate. [Copyright &y& Elsevier]

Abstract: The problem of classifying an image into different homogeneous regions is viewed as the task of clustering the pixels in the intensity space. In particular, satellite images contain landcover types some of which cover significantly large areas, while some (e.g., bridges and roads) occupy relatively much smaller regions. Detecting regions or clusters of such widely varying sizes presents a challenging task. A modified differential evolution based fuzzy clustering technique, is proposed in this article. Real-coded encoding of the cluster centres is used for this purpose. Results demonstrating the effectiveness of the proposed technique are provided for several synthetic and real life data sets as well as for some benchmark functions. Different landcover regions in remote sensing imagery have also been classified using the proposed technique to establish its efficiency. Statistical significance tests have been performed to establish the superiority of the proposed algorithm. [Copyright &y& Elsevier]

Abstract: A new ortho-phenylenediamine-based fluorescent cleft 1 has been designed and synthesized. The open cleft of 1 selectively recognizes tetrabutylammonium dihydrogen phosphate in CH3CN by exhibiting a significant decrease in the emission of anthracene. The interactions of 1 are also investigated in aq CH3OH where no measurable change in the emission is observed for tetrabutylammonium dihydrogen phosphate, although sodium salts of phosphate, hydrogen phosphate, and dihydrogen phosphate exhibit moderate changes. Tetrabutylammonium hydrogen sulfate is sensed effectively in aq CH3OH. The anion binding properties of 1 were evaluated by 1H NMR, UV–vis, and fluorescence spectroscopic methods. [Copyright &y& Elsevier]

Abstract: Two ortho-phenylenediamine-based new receptors 1 and 2 with an anthracene-coupled benzimidazolium motif have been designed and synthesized. The directed hydrogen bonds (both conventional and unconventional) and charge–charge interactions allowed the open clefts of both 1 and 2 to bind carboxylate, fluoride and dihydrogenphosphate anions with moderate binding constant values. The selectivity and sensitivity were ascertained by 1H NMR, UV–vis and fluorescence spectroscopic methods. The binding cleft of 2 is found to be more effective than that of 1. [Copyright &y& Elsevier]

• Genome-wide analysis is performed on 10664 SARS-CoV-2 genomes. • 107 genetic mutations are found as SNP in Coding and Non-Coding regions. • Five major virus strains are identified by clustering of SNP data. • Signature SNPs are identified in each cluster. • Virus genomes are collected from GISAID database. Since the onslaught of SARS-CoV-2, the research community has been searching for a vaccine to fight against this virus. However, during this period, the virus has mutated to adapt to the different environmental conditions in the world and made the task of vaccine design more challenging. In this situation, the identification of virus strains is very much timely and important task. We have performed genome-wide analysis of 10664 SARS-CoV-2 genomes of 73 countries to identify and prepare a Single Nucleotide Polymorphism (SNP) dataset of SARS-CoV-2. Thereafter, with the use of this SNP data, the advantage of hierarchical clustering is taken care of in such a way so that Average Linkage and Complete Linkage with Jaccard and Hamming distance functions are applied separately in order to identify the virus strains as clusters present in the SNP data. In this regard, the consensus of both the clustering results are also considered while Silhouette index is used as a cluster validity index to measure the goodness of the clusters as well to determine the number of clusters or virus strains. As a result, we have identified five major clusters or virus strains present worldwide. Apart from quantitative measures, these clusters are also visualized using Visual Assessment of Tendency (VAT) plot. The evolution of these clusters are also shown. Furthermore, top 10 signature SNPs are identified in each cluster and the non-synonymous signature SNPs are visualised in the respective protein structures. Also, the sequence and structural homology-based prediction along with the protein structural stability of these non-synonymous signature SNPs are reported in order to judge the characteristics of the identified clusters. As a consequence, T85I, Q57H and R203M in NSP2, ORF3a and Nucleocapsid respectively are found to be responsible for Cluster 1 as they are damaging and unstable non-synonymous signature SNPs. Similarly, F506L and S507C in Exon are responsible for both Clusters 3 and 4 while Clusters 2 and 5 do not exhibit such behaviour due to the absence of any non-synonymous signature SNPs. In addition to all these, the code, SNP dataset, 10664 labelled SARS-CoV-2 strains and additional results as supplementary are provided through our website for further use. [ABSTRACT FROM AUTHOR]

The pandemic due to novel coronavirus, SARS-CoV-2 is a serious global concern now. More than thousand new COVID-19 infections are getting reported daily for this virus across the globe. Thus, the medical research communities are trying to find the remedy to restrict the spreading of this virus, while the vaccine development work is still under research in parallel. In such critical situation, not only the medical research community, but also the scientists in different fields like microbiology, pharmacy, bioinformatics and data science are also sharing effort to accelerate the process of vaccine development, virus prediction, forecasting the transmissible probability and reproduction cases of virus for social awareness. With the similar context, in this article, we have studied sequence variability of the virus primarily focusing on three aspects: (a) sequence variability among SARS-CoV-1, MERS-CoV and SARS-CoV-2 in human host, which are in the same coronavirus family, (b) sequence variability of SARS-CoV-2 in human host for 54 different countries and (c) sequence variability between coronavirus family and country specific SARS-CoV-2 sequences in human host. For this purpose, as a case study, we have performed topological analysis of 2391 global genomic sequences of SARS-CoV-2 in association with SARS-CoV-1 and MERS-CoV using an integrated semi-alignment based computational technique. The results of the semi-alignment based technique are experimentally and statistically found similar to alignment based technique and computationally faster. Moreover, the outcome of this analysis can help to identify the nations with homogeneous SARS-CoV-2 sequences, so that same vaccine can be applied to their heterogeneous human population. • Topological analysis of more than 2K SARS-CoV-2 Sequences • Sequence variability among SARS-CoV-1, MERS-CoV and SARS-CoV-2 • Sequence variability of SARS-CoV-2 in 54 countries • Development of Semi-Alignment based technique • Sequence similarity can help in decision making to apply vaccine [ABSTRACT FROM AUTHOR]

Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) is a threat to the human population and has created a worldwide pandemic. Daily thousands of people are getting affected by the SARS-CoV-2 virus; India being no exception. In this situation, there is no doubt that vaccine is the primary prevention strategy to contain the wave of COVID-19 pandemic. In this regard, genome-wide analysis of SARS-CoV-2 is important to understand its genetic variability. This has motivated us to analyse 566 Indian SARS-CoV-2 sequences using multiple sequence alignment techniques viz. ClustalW, MUSCLE, ClustalO and MAFFT to align and subsequently identify the lists of mutations as substitution, deletion, insertion and SNP. Thereafter, a consensus of these results, called as Consensus Multiple Sequence Alignment (CMSA), is prepared to have the final list of mutations so that the advantages of all four alignment techniques can be preserved. The analysis shows 767, 2025 and 54 unique substitutions, deletions and SNPs in Indian SARS-CoV-2 genomes. More precisely, out of 54 SNPs, 4 SNPs are present close to the 60% of the virus population. The results of this experiment can be useful for virus classification, designing and defining the dose of vaccine for the Indian population. [ABSTRACT FROM AUTHOR]

The wave of COVID-19 is a big threat to the human population. Presently, the world is going through different phases of lock down in order to stop this wave of pandemic; India being no exception. We have also started the lock down on 23rd March 2020. In this current situation, apart from social distancing only a vaccine can be the proper solution to serve the population of human being. Thus it is important for all the nations to perform the genome-wide analysis in order to identify the genetic variation in Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) so that proper vaccine can be designed. This fast motivated us to analyze publicly available 566 Indian complete or near complete SARS-CoV-2 genomes to find the mutation points as substitution, deletion and insertion. In this regard, we have performed the multiple sequence alignment in presence of reference sequence from NCBI. After the alignment, a consensus sequence is built to analyze each genome in order to identify the mutation points. As a consequence, we have found 933 substitutions, 2449 deletions and 2 insertions, in total 3384 unique mutation points, in 566 genomes across 29.9 K bp. Further, it has been classified into three groups as 100 clusters of mutations (mostly deletions), 1609 point mutations as substitution, deletion and insertion and 64 SNPs. These outcomes are visualized using BioCircos and bar plots as well as plotting entropy value of each genomic location. Moreover, phylogenetic analysis has also been performed to see the evolution of SARS-CoV-2 virus in India. It also shows the wide variation in tree which indeed vivid in genomic analysis. Finally, these SNPs can be the useful target for virus classification, designing and defining the effective dose of vaccine for the heterogeneous population. Unlabelled Image • Genome-wide analysis of 566 Indian SARS-CoV-2 genomes • Genetic mutation has been found as substitution, deletion, insertion and SNP. • Virus genomes are collected from GISAID database. • 57 out of 64 SNPs have been found in 6 coding regions of SARS-CoV-2 genomes. • Phylogenetic analysis shows wide variation in virus strain as well. [ABSTRACT FROM AUTHOR]

The pandemic of COVID-19 has been haunting us for almost the past two years. Although, the vaccination drive is in full swing throughout the world, different mutations of the SARS-CoV-2 virus are making it very difficult to put an end to the pandemic. The second wave in India, one of the worst sufferers of this pandemic, can be mainly attributed to the Delta variant i.e. B.1.617.2. Thus, it is very important to analyse and understand the mutational trajectory of SARS-CoV-2 through the study of the 26 virus proteins. In this regard, more than 17,000 protein sequences of Indian SARS-CoV-2 genomes are analysed using entropy-based approach in order to find the monthly mutational trajectory. Furthermore, Hellinger distance is also used to show the difference of the mutation events between the consecutive months for each of the 26 SARS-CoV-2 protein. The results show that the mutation rates and the mutation events of the viral proteins though changing in the initial months, start stabilizing later on for mainly the four structural proteins while the non-structural proteins mostly exhibit a more constant trend. As a consequence, it can be inferred that the evolution of the new mutative configurations will eventually reduce. • Analysis of mutational trajectory of SARS-CoV-2 considering its 26 proteins. • The analysis is performed by entropy-based approach. • Hellinger distance is used to show difference of mutation events between two months. • Mutation rates and mutation events eventually stabilises for four structural proteins. • It can be inferred that evolution of new mutative configurations will reduce. [ABSTRACT FROM AUTHOR]

Abstract: The interactions between xenon and DMPC/DHPC bicelles (q =3.5%,7.5%w/v) were studied via 2H, 129Xe, 131Xe, and optically enhanced 129Xe NMR. The chemical shifts, linewidths, and quadrupolar couplings of the xenon/bicelle NMR signals were correlated with different regions of the bicellar phase diagram. The addition of xenon (<70mM) was observed to reduce the temperature-onset of bicelle alignment by several degrees, in quantitative agreement with effects previously observed with chloroform; however, the stable liquid-crystalline range was not significantly reduced. Preliminary laser-polarized xenon/bicelle studies yielded 129Xe T 1 values of ∼120s, long enough to permit a variety of planned experiments. [Copyright &y& Elsevier]

The surge of SARS-CoV-2 has created a wave of pandemic around the globe due to its high transmission rate. To contain this virus, researchers are working around the clock for a solution in the form of vaccine. Due to the impact of this pandemic, the economy and healthcare have immensely suffered around the globe. Thus, an efficient vaccine design is the need of the hour. Moreover, to have a generalised vaccine for heterogeneous human population, the virus genomes from different countries should be considered. Thus, in this work, we have performed genome-wide analysis of 10,664 SARS-CoV-2 genomes of 73 countries around the globe in order to identify the potential conserved regions for the development of peptide based synthetic vaccine viz. epitopes with high immunogenic and antigenic scores. In this regard, multiple sequence alignment technique viz. Clustal Omega is used to align the 10,664 SARS-CoV-2 virus genomes. Thereafter, entropy is computed for each genomic coordinate of the aligned genomes. The entropy values are then used to find the conserved regions. These conserved regions are refined based on the criteria that their lengths should be greater than or equal to 60 nt and their corresponding protein sequences are without any stop codons. Furthermore, Nucleotide BLAST is used to verify the specificity of the conserved regions. As a result, we have obtained 17 conserved regions that belong to NSP3, NSP4, NSP6, NSP8, RdRp, Helicase, endoRNAse, 2'-O-RMT, Spike glycoprotein, ORF3a protein, Membrane glycoprotein and Nucleocapsid protein. Finally, these conserved regions are used to identify the T-cell and B-cell epitopes with their corresponding immunogenic and antigenic scores. Based on these scores, the most immunogenic and antigenic epitopes are then selected for each of these 17 conserved regions. Hence, we have obtained 30 MHC-I and 24 MHC-II restricted T-cell epitopes with 14 and 13 unique HLA alleles and 21 B-cell epitopes for the 17 conserved regions. Moreover, for validating the relevance of these epitopes, the binding conformation of the MHC-I and MHC-II restricted T-cell epitopes are shown with respect to HLA alleles. Also, the physico-chemical properties of the epitopes are reported along with Ramchandran plots and Z -Scores and the population coverage is shown as well. Overall, the analysis shows that the identified epitopes can be considered as potential candidates for vaccine design. • Genome-wide analysis of 10,664 SARS-CoV-2 genomes of 73 countries. • Identification of 17 potential conserved regions. • Identification of 30, 24 and 21 best immunogenic and antigenic MHC-I and MHC-II T-cell and B-cell epitopes. • Physico-chemical properties of the epitopes are studied. • Docking with Ramachandran Plot and Z-score are performed. [ABSTRACT FROM AUTHOR]

• Genome-wide analysis of 566 Indian SARS-CoV-2 genomes. • Finding potential 23 consensus conserved regions. • 34, 37 and 29 best immunogenic and antigenic MHC-I, MHC-II T-cell and B-cell epitopes. • Physico-chemical properties of the epitopes are studied. • Docking with Ramachandran Plot and Z-score are performed. SARS-CoV-2 has a high transmission rate and shows frequent mutations, thus making vaccine development an arduous task. However, researchers around the globe are working hard to find a solution e.g. synthetic vaccine. Here, we have performed genome-wide analysis of 566 Indian SARS-CoV-2 genomes to extract the potential conserved regions for identifying peptide based synthetic vaccines, viz. epitopes with high immunogenicity and antigenicity. In this regard, different multiple sequence alignment techniques are used to align the SARS-CoV-2 genomes separately. Subsequently, consensus conserved regions are identified after finding the conserved regions from each aligned result of alignment techniques. Further, the consensus conserved regions are refined considering that their lengths are greater than or equal to 60nt and their corresponding proteins are devoid of any stop codons. Subsequently, their specificity as query coverage are verified using Nucleotide BLAST. Finally, with these consensus conserved regions, T-cell and B-cell epitopes are identified based on their immunogenic and antigenic scores which are then used to rank the conserved regions. As a result, we have ranked 23 consensus conserved regions that are associated with different proteins. This ranking also resulted in 34 MHC-I and 37 MHC-II restricted T-cell epitopes with 16 and 19 unique HLA alleles and 29 B-cell epitopes. After ranking, the consensus conserved region from NSP3 gene is obtained that is highly immunogenic and antigenic. In order to judge the relevance of the identified epitopes, the physico-chemical properties and binding conformation of the MHC-I and MHC-II restricted T-cell epitopes are shown with respect to HLA alleles. [ABSTRACT FROM AUTHOR]

Purpose Aim of this retrospective study was to compare diagnostic accuracy of proposed automatic normalization method to quantify the relative cerebral blood volume (rCBV) with existing contra-lateral region of interest (ROI) based CBV normalization method for glioma grading using T1-weighted dynamic contrast enhanced MRI (DCE-MRI). Material and methods Sixty patients with histologically confirmed gliomas were included in this study retrospectively. CBV maps were generated using T1-weighted DCE-MRI and are normalized by contralateral ROI based method (rCBV_contra), unaffected white matter (rCBV_WM) and unaffected gray matter (rCBV_GM), the latter two of these were generated automatically. An expert radiologist with > 10 years of experience in DCE-MRI and a non-expert with one year experience were used independently to measure rCBVs. Cutoff values for glioma grading were decided from ROC analysis. Agreement of histology with rCBV_WM, rCBV_GM and rCBV_contra respectively was studied using Kappa statistics and intra-class correlation coefficient (ICC). Result The diagnostic accuracy of glioma grading using the measured rCBV_contra by expert radiologist was found to be high (sensitivity = 1.00, specificity = 0.96, p < 0.001) compared to the non-expert user (sensitivity = 0.65, specificity = 0.78, p < 0.001). On the other hand, both the expert and non-expert user showed similar diagnostic accuracy for automatic rCBV_WM (sensitivity = 0.89, specificity = 0.87, p = 0.001) and rCBV_GM (sensitivity = 0.81, specificity = 0.78, p = 0.001) measures. Further, it was also observed that, contralateral based method by expert user showed highest agreement with histological grading of tumor (kappa = 0.96, agreement 98.33%, p < 0.001), however; automatic normalization method showed same percentage of agreement for both expert and non-expert user. rCBV_WM showed an agreement of 88.33% (kappa = 0.76,p < 0.001) with histopathological grading. Conclusion It was inferred from this study that, in the absence of expert user, automated normalization of CBV using the proposed method could provide better diagnostic accuracy compared to the manual contralateral based approach. [ABSTRACT FROM AUTHOR]

Purpose Dynamic contrast enhanced (DCE) MRI is used to grade and to monitor the progression of glioma while on treatment. Usually, a fixed hematocrit (Hct) value for adults is assumed to be ~ 45%; however, it is actually known for individual variations. Purpose of this study was to investigate the effect of measured Hct values in glioma grading using DCE-MRI. Materials and methods Fifty glioma patients were included in this study. Kinetic and hemodynamic parameters were estimated for each patient using assumed as well as measured Hct values. To look the changes in Hct value over time, Hct was measured multiple times from 10 of these glioma patients who were on treatment. Simulation was done to look for the effect of extreme variations of Hct values on perfusion metrics. The data was compared to look for significant differences in the perfusion metrics derived from assumed and measured Hct values. Results The measured Hct value in patients was found to be (40.4 ± 4.28)%. The sensitivity and specificity of DCE-MRI parameters in glioma grading were not significantly influenced by using measured vis-a-vis assumed Hct values. The serial Hct values from 10 patients who were on treatment showed a fluctuation of 15–20% over time. The simulated data showed linear influence of Hct values on kinetic parameters. The tumor grading was altered on altering the Hct values in borderline cases. Conclusion Hct values influence the hemodynamic and kinetic metrics linearly and may affect glioma grading. However, perfusion metrics values might change significantly with large change in Hct values, especially in patients who are on chemotherapy necessitating its use in the DCE model. [ABSTRACT FROM AUTHOR]

Introduction The resting state functional MRI (rsfMRI) approach is useful to explore the brain's functional organization in health and disease conditions. In this study, using rsfMRI the alteration in brain due to vitamin B12 deficiency and reversibility of these alterations following therapy was studied. Methods Thirteen patients with clinical and biochemical evidence of vitamin B12 deficiency were recruited in this study. Fifteen age and sex matched healthy controls were also included. Patients and controls were clinically evaluated using neuropsychological test (NPT). The analysis was carried out using regional homogeneity (ReHo) and low frequency oscillations (LFO) of BOLD signals in resting state. Six patients were also evaluated with rsfMRI and NPT after 6 weeks replacement therapy. Results ReHo values in patients with vitamin B12 deficiency were significantly lower than controls in the entire cerebrum and the brain networks associated with cognition control i.e. default mode, cingulo-opercular and fronto-parietal network. There was no significant difference using LFO and it did not show significant correlations with NPT scores. ReHo showed significant correlation with NPT scores. All the 6 patients showed increase in ReHo after replacement therapy. Conclusion We conclude that brain networks associated with cognition control are altered in patients with vitamin B12 deficiency, which partially recover following six weeks of replacement therapy. This is the first study to evaluate the rsfMRI in the light of clinical neuropsychological evaluation in patients. rsfMRI may be used as functional biomarker to assess therapeutic response in vitamin B12 deficiency patients. [ABSTRACT FROM AUTHOR]

• Automation of diabetes detection using Convolutional LSTM. • Diabetes can be detected accurately from clinical data. • Convolutional LSTM based robust model achieved 97.26 % classification accuracy. • Feature selection affects performance of the developed model. Diabetes is a chronic disease that occurs when the pancreas does not generate sufficient insulin or the body cannot effectively utilize the produced insulin. If it remains unidentified and untreated, then it could be very deadliest. One can lead a healthy life with proper treatment if the presence of diabetes can be detected at an early stage. When the conventional process of detecting diabetes is tedious, there is a need of an automated system for identifying diabetes from the clinical and physical data. In this study, we developed a novel diabetes classifying model based on Convolutional Long Short-term Memory (Conv-LSTM) that was not applied yet in this regard. We applied another three popular models such as Convolutional Neural Network (CNN), Traditional LSTM (T-LSTM), and CNN-LSTM and compared the performance with our developed model over the Pima Indians Diabetes Database (PIDD). Significant features were extracted from the dataset using Boruta algorithm that returned glucose, BMI, insulin, blood pressure, and age as important features for classifying diabetes patients more accurately. We performed hyperparameter optimization using Grid Search algorithm in order to find the optimal parameters for the applied models. Initial experiment by splitting the dataset into separate training and testing sets, the Conv-LSTM-based model classified the diabetes patients with the highest accuracy of 91.38 %. In later, using cross-validation technique the Conv-LSTM model achieved the highest accuracy of 97.26 % and outperformed the other three models along with the state-of-the-art models. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the efficacy of optimized T1-Perfusion MRI protocol (protocol-2) with whole brain coverage and improved spatial resolution using Compressed-SENSE (CSENSE) to differentiate high-grade-glioma (HGG) and low-grade-glioma (LGG) and to compare it with the conventional protocol (protocol-1) with partial brain coverage used in our center.Methods: This study included MRI data from 5 healthy volunteers, a phantom and 126 brain tumor patients. Current study had two parts: To analyze the effect of CSENSE on 3D-T1-weighted (W) fast-field-echo (FFE) images, T1-W, dual-PDT2-W turbo-spin-echo images and T1 maps, and to evaluate the performance of high resolution T1-Perfusion MRI protocol with whole brain coverage optimized using CSENSE. Coefficient-of-Variation (COV), Relative-Percentage-Error (RPE), Normalized-Mean-Squared-Error (NMSE) and qualitative scoring were used for the former study. Tracer-kinetic (Ktrans,ve,vp) and hemodynamic (rCBV,rCBF) parameters computed from both protocols were used to differentiate LGG and HGG.Results: The image quality of all structural images was found to be of diagnostic quality till R = 4. NMSE in healthy T1-W-FFE images and COV in phantom images increased with-respect-to R and images provided optimum quality till R = 4. Structural images and maps exhibited artefacts from R = 6. All parameters in tumor tissue and hemodynamic parameters in healthy gray matter tissue computed from both protocols were not significantly different. Parameters computed from protocol-2 performed better in terms of glioma grading. For both protocols, rCBF performed least (AUC = 0.759 and 0.851) and combination of all parameters performed best (AUC = 0.890 and 0.964).Conclusion: CSENSE (R = 4) can be used to improve the resolution and brain coverage for T1-Perfusion analysis used to differentiate gliomas. [ABSTRACT FROM AUTHOR]