CARE1, a TY3-gypsy long terminal repeat retrotransposon in the food legume chickpea (Cicer arietinum L)

We report a novel Ty3-gypsy long terminal repeat retrotransposon CARE1 (_Cicer arietinum_ retro-element 1) in chickpea. This 5920-bp AT-rich (63%) element carries 723-bp 5' and 897-bp 3' LTRs respectively flanking an internal region of 4300-bp. The LTRs of CARE1 show 93.9% nucleotide identity to each other and have 4-bp (ACTA) terminal inverted repeats. A 17-bp potential tRNAmet primer binding site downstream to 5' LTR and a 13-bp polypurine tract upstream to 3' LTR have been identified. The order of domains (Gag-proteinase-reverse transcriptase-RNaseH-integrase) in the deduced amino acid sequence and phylogenetic tree constructed using reverse transcriptase sequences places CARE1 in the gypsy group of retrotransposons. Homologues of a number of _cis_-elements including CCAAT, TATA and GT-1 have been detected in the regulatory region or the 5' LTR of CARE1. Transgenic tobacco plants containing 5' LTR:GUS construct show that its 5'-LTR is inactive in a heterologous system under normal as well as tissue culture conditions. Genomic Southern blot experiments using 5’LTR of the element as a probe show that CARE1 or its related elements are present in the genomes of various chickpea accessions from various geographic regions

Clustering Based Classification and Analysis of Data

This paper presents Clustering Based Document classification and analysis of data. The proposed Clustering Based classification and analysis of data approach is based on Unsupervised and Supervised Document Classification. In this paper Unsupervised Document and Supervised Document Classification are used. In this approach Document collection, Text Preprocessing, Feature Selection, Indexing, Clustering Process and Results Analysis steps are used. Twenty News group data sets [20] are used in the Experiments. For experimental results analysis evaluated using the Analytical SAS 9.0 Software is used. The Experimental Results show the proposed approach out performs

Analysis of Social Networking Sites Using K- Mean Clustering Algorithm

Clustering is one of the very important technique used for classification of large dataset and widely applied to many applications including analysis of social networking sites, aircraft accidental, company performance etc. In recent days, Communication, advertising through social networking sites are most popular and interactive strategy among the users. This research attempts to find the large scale measurement study and analysis, effectiveness of communication strategy, analyzing the information about the usage, people’s interest in social network sites in promoting and advertising their brand in social networking sites. The significance of the proposed work is determined with the help of various surveys, and from people who use these sites. Further a more specific pre-processing method is applied to clean data and perform the clustering method to generate patterns that will be work as heuristics for designing more effective social networking sites

Origin of Ferroelectricity in Orthorhombic LuFeO3_3

We demonstrate that small but finite ferroelectric polarization ($\sim$0.01 $\mu$C/cm$^2$) emerges in orthorhombic LuFeO$_3$ ($Pnma$) at $T_N$ ($\sim$600 K) because of commensurate (k = 0) and collinear magnetic structure. The synchrotron x-ray and neutron diffraction data suggest that the polarization could originate from enhanced bond covalency together with subtle contribution from lattice. The theoretical calculations indicate enhancement of bond covalency as well as the possibility of structural transition to the polar $Pna2_1$ phase below $T_N$. The $Pna2_1$ phase, in fact, is found to be energetically favorable below $T_N$ in orthorhombic LuFeO$_3$ ($albeit$ with very small energy difference) than in isostructural and nonferroelectric LaFeO$_3$ or NdFeO$_3$. Application of electric field induces finite piezostriction in LuFeO$_3$ via electrostriction resulting in clear domain contrast images in piezoresponse force microscopy.Comment: 12 pages, 8 figure

Combining ability in fennel (Foeniculum vulgare Mill.) for yield and quality

Twelve genetically diverse varieties of fennel (Foeniculum vulgare) were evaluated at Jobner (Rajasthan) following diallel mating design for determining their utility as parents in the development of hybrids and/or high yielding composites. The analysis of variance indicated that varieties and heterosis were significant for most of the characters studied, indicating complex type of inheritance involving additive, dominance and epistatic components. The heterosis sum of squares accounted for more than 75% of the entries sum of squares. Partitioning of overall heterosis variation indicated that contribution of specific heterosis was the highest (>50%) among the three components. Estimates of genetic constants indicated that varietal heterosis effects were significa nt for all the tra its except days to 50% flowering and test weight, while specific heterosis effects were significant for most of the characters. The cross RF-101 x JF-25 showed the highest positive specific heterosis effect for seed yield plant·, along with high specific heterosis effects for umbels and biological yield plan 1''. These two parents represented a good choice to initiate inter-population improvement. &nbsp

Essential oil composition of petiole of Cinnamomum verum Bercht. & Presl.

Essential oil isolated from the petiole of  Cinnamomum verum was analysed by gaschromatography and gas chromatography-mass spectrometry. Twenty five compoundsaccounting for 87.31% of the total essential oil were identified. (E)-Cinnamaldehyde (33.04%)followed by eugenol (17.32%), linalool (16.85%) and (E)-cinnamyl acetate (11.78%) were themain components of the essential oil. This is the first report on the composition of essentialoil of petiole of C. verum. &nbsp

Analysis of Dimensionality Reduction Techniques on Big Data

Due to digitization, a huge volume of data is being generated across several sectors such as healthcare, production, sales, IoT devices, Web, organizations. Machine learning algorithms are used to uncover patterns among the attributes of this data. Hence, they can be used to make predictions that can be used by medical practitioners and people at managerial level to make executive decisions. Not all the attributes in the datasets generated are important for training the machine learning algorithms. Some attributes might be irrelevant and some might not affect the outcome of the prediction. Ignoring or removing these irrelevant or less important attributes reduces the burden on machine learning algorithms. In this work two of the prominent dimensionality reduction techniques, Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) are investigated on four popular Machine Learning (ML) algorithms, Decision Tree Induction, Support Vector Machine (SVM), Naive Bayes Classifier and Random Forest Classifier using publicly available Cardiotocography (CTG) dataset from University of California and Irvine Machine Learning Repository. The experimentation results prove that PCA outperforms LDA in all the measures. Also, the performance of the classifiers, Decision Tree, Random Forest examined is not affected much by using PCA and LDA.To further analyze the performance of PCA and LDA the eperimentation is carried out on Diabetic Retinopathy (DR) and Intrusion Detection System (IDS) datasets. Experimentation results prove that ML algorithms with PCA produce better results when dimensionality of the datasets is high. When dimensionality of datasets is low it is observed that the ML algorithms without dimensionality reduction yields better results

Triaging Interventional Pain Procedures During COVID-19 or Related Elective Surgery Restrictions: Evidence-Informed Guidance from the American Society of Interventional Pain Physicians (ASIPP)

BACKGROUND: The COVID-19 pandemic has worsened the pain and suffering of chronic pain patients due to stoppage of elective interventional pain management and office visits across the United States. The reopening of America and restarting of interventional techniques and elective surgical procedures has started. Unfortunately, with resurgence in some states, restrictions are once again being imposed. In addition, even during the Phase II and III of reopening, chronic pain patients and interventional pain physicians have faced difficulties because of the priority selection of elective surgical procedures.Chronic pain patients require high intensity care, specifically during a pandemic such as COVID-19. Consequently, it has become necessary to provide guidance for triaging interventional pain procedures, or related elective surgery restrictions during a pandemic. OBJECTIVES: The aim of these guidelines is to provide education and guidance for physicians, healthcare administrators, the public and patients during the COVID-19 pandemic. Our goal is to restore the opportunity to receive appropriate care for our patients who may benefit from interventional techniques. METHODS: The American Society of Interventional Pain Physicians (ASIPP) has created the COVID-19 Task Force in order to provide guidance for triaging interventional pain procedures or related elective surgery restrictions to provide appropriate access to interventional pain management (IPM) procedures in par with other elective surgical procedures. In developing the guidance, trustworthy standards and appropriate disclosures of conflicts of interest were applied with a section of a panel of experts from various regions, specialties, types of practices (private practice, community hospital and academic institutes) and groups. The literature pertaining to all aspects of COVID-19, specifically related to epidemiology, risk factors, complications, morbidity and mortality, and literature related to risk mitigation and stratification was reviewed. The evidence -- informed with the incorporation of the best available research and practice knowledge was utilized, instead of a simplified evidence-based approach. Consequently, these guidelines are considered evidence-informed with the incorporation of the best available research and practice knowledge. RESULTS: The Task Force defined the medical urgency of a case and developed an IPM acuity scale for elective IPM procedures with 3 tiers. These included emergent, urgent, and elective procedures. Examples of emergent and urgent procedures included new onset or exacerbation of complex regional pain syndrome (CRPS), acute trauma or acute exacerbation of degenerative or neurological disease resulting in impaired mobility and inability to perform activities of daily living. Examples include painful rib fractures affecting oxygenation and post-dural puncture headaches limiting the ability to sit upright, stand and walk. In addition, urgent procedures include procedures to treat any severe or debilitating disease that prevents the patient from carrying out activities of daily living. Elective procedures were considered as any condition that is stable and can be safely managed with alternatives. LIMITATIONS: COVID-19 continues to be an ongoing pandemic. When these recommendations were developed, different stages of reopening based on geographical regulations were in process. The pandemic continues to be dynamic creating every changing evidence-based guidance. Consequently, we provided evidence-informed guidance. CONCLUSION: The COVID-19 pandemic has created unprecedented challenges in IPM creating needless suffering for pain patients. Many IPM procedures cannot be indefinitely postponed without adverse consequences. Chronic pain exacerbations are associated with marked functional declines and risks with alternative treatment modalities. They must be treated with the concern that they deserve. Clinicians must assess patients, local healthcare resources, and weigh the risks and benefits of a procedure against the risks of suffering from disabling pain and exposure to the COVID-19 virus