Nature and role of root exudates: Efficacy in bioremediation

Root exudate is one of the ways for plant communication to the neighboring plant and adjoining of microorganisms present in the rhizosphere of the root. The chemicals ingredients of the root exudates are specific to a particular plant species and also depend on the nearby biotic and abiotic environment. The chemical ingredient exuded by plant roots include amino acids, sugars, organic acids, vitamins, nucleotides, various other secondary metabolites and many other high molecular weight substances as primarily mucilage and some unidentified substances. Through the exudation of a wide variety of compounds, roots may regulate the soil microbial community in their immediate vicinity, cope with herbivores, encourage beneficial symbioses, change the chemical and physical properties of the soil and inhibit the growth of competing plant species. Root exudates mediate various positive and negative interactions like plant-plant and plant-microbe interactions. The present review has been undertaken to examine the possible role of root exudates on the removal of the polluted matter and nourishing the neighboring microorganisms present in the rhizosphere of the root.Key words: Rhizosphere, root exudates, bioremediation, rhizoremediation

Bibliometric Study of Publications in Conference Proceedings of SRFLIS Summit during 2014-2019

The purpose of the study is to conduct bibliometric analysis of the research articles of SRFLIS Summit- International Conferences held during the year 2014-2019 and investigate the various dimensions of bibliometric study. The paper conducts bibliometric analysis of 220 articles which were published in the form of conference papers during the covered period. The study evaluates the various aspects of published conference articles of SRFLIS Summit. The study highlights the chronological distribution of papers, authorship pattern, geographical distribution, and affiliation of authors, citation pattern and length of articles. The results explore that the majority of the contributions by two authors. It is observed that a total of 2720 citations counted to the contributions and most cited documents are journal articles. The analysis of countries found that majority of contributions are from India and authors from New Delhi published maximum papers. The study evaluates the publication trends and has important implications for scholars and researcher

Robust Feature-Based Automated Multi-View Human Action Recognition System

© 2013 IEEE. Automated human action recognition has the potential to play an important role in public security, for example, in relation to the multiview surveillance videos taken in public places, such as train stations or airports. This paper compares three practical, reliable, and generic systems for multiview video-based human action recognition, namely, the nearest neighbor classifier, Gaussian mixture model classifier, and the nearest mean classifier. To describe the different actions performed in different views, view-invariant features are proposed to address multiview action recognition. These features are obtained by extracting the holistic features from different temporal scales which are modeled as points of interest which represent the global spatial-temporal distribution. Experiments and cross-data testing are conducted on the KTH, WEIZMANN, and MuHAVi datasets. The system does not need to be retrained when scenarios are changed which means the trained database can be applied in a wide variety of environments, such as view angle or background changes. The experiment results show that the proposed approach outperforms the existing methods on the KTH and WEIZMANN datasets

Do big athletes have big hearts? Impact of extreme anthropometry upon cardiac hypertrophy in professional male athletes.

AIM: Differentiating physiological cardiac hypertrophy from pathology is challenging when the athlete presents with extreme anthropometry. While upper normal limits exist for maximal left ventricular (LV) wall thickness (14 mm) and LV internal diameter in diastole (LVIDd, 65 mm), it is unknown if these limits are applicable to athletes with a body surface area (BSA) >2.3 m(2). PURPOSE: To investigate cardiac structure in professional male athletes with a BSA>2.3 m(2), and to assess the validity of established upper normal limits for physiological cardiac hypertrophy. METHODS: 836 asymptomatic athletes without a family history of sudden death underwent ECG and echocardiographic screening. Athletes were grouped according to BSA (Group 1, BSA>2.3 m(2), n=100; Group 2, 2-2.29 m(2), n=244; Group 3, 13 mm, but in combination with an abnormal ECG suspicious of an inherited cardiac disease. CONCLUSION: Regardless of extreme anthropometry, established upper limits for physiological cardiac hypertrophy of 14 mm for maximal wall thickness and 65 mm for LVIDd are clinically appropriate for all athletes. However, the abnormal ECG is key to diagnosis and guides follow-up, particularly when cardiac dimensions are within accepted limits

The impact of chronic endurance and resistance training upon the right ventricular phenotype in male athletes

Objectives The traditional view of differential left ventricular adaptation to training type has been questioned. Right ventricular (RV) data in athletes are emerging but whether training type mediates this is not clear. The primary aim of this study was to evaluate the RV phenotype in endurance- vs. resistance-trained male athletes. Secondary aims included comparison of RV function in all groups using myocardial speckle tracking, and the impact of allometric scaling on RV data interpretation. Methods A prospective cross-sectional design assessed RV structure and function in 19 endurance-trained (ET), 21 resistance-trained (RT) and 21 sedentary control subjects (CT). Standard 2D tissue Doppler imaging and speckle tracking echocardiography assessed RV structure and function. Indexing of RV structural parameters to body surface area (BSA) was undertaken using allometric scaling. Results A higher absolute RV diastolic area was observed in ET (mean ± SD: 27 ± 4 cm2) compared to CT (22 ± 4 cm2; P < 0.05) that was maintained after scaling. Whilst absolute RV longitudinal dimension was greater in ET (88 ± 9 mm) than CT (81 ± 10 mm; P < 0.05), this difference was removed after scaling. Wall thickness was not different between ET and RT and there were no between group differences in global or regional RV function. Conclusion We present some evidence of RV adaptation to chronic ET in male athletes but limited structural characteristics of an athletic heart were observed in RT. Global and regional RV functions were comparable between groups. Allometric scaling altered data interpretation in some variables

Cytokinesis in bloodstream stage Trypanosoma brucei requires a family of katanins and spastin

Microtubule severing enzymes regulate microtubule dynamics in a wide range of organisms and are implicated in important cell cycle processes such as mitotic spindle assembly and disassembly, chromosome movement and cytokinesis. Here we explore the function of several microtubule severing enzyme homologues, the katanins (KAT80, KAT60a, KAT60b and KAT60c), spastin (SPA) and fidgetin (FID) in the bloodstream stage of the African trypanosome parasite, Trypanosoma brucei. The trypanosome cytoskeleton is microtubule based and remains assembled throughout the cell cycle, necessitating its remodelling during cytokinesis. Using RNA interference to deplete individual proteins, we show that the trypanosome katanin and spastin homologues are non-redundant and essential for bloodstream form proliferation. Further, cell cycle analysis revealed that these proteins play essential but discrete roles in cytokinesis. The KAT60 proteins each appear to be important during the early stages of cytokinesis, while downregulation of KAT80 specifically inhibited furrow ingression and SPA depletion prevented completion of abscission. In contrast, RNA interference of FID did not result in any discernible effects. We propose that the stable microtubule cytoskeleton of T. brucei necessitates the coordinated action of a family of katanins and spastin to bring about the cytoskeletal remodelling necessary to complete cell divisio

Deep learning in classifying depth of anesthesia (DoA)

© Springer Nature Switzerland AG 2019. This present study is what we think is one of the first studies to apply Deep Learning to learn depth of anesthesia (DoA) levels based solely on the raw EEG signal from a single channel (electrode) originated from many subjects under full anesthesia. The application of Deep Neural Networks to detect levels of Anesthesia from Electroencephalogram (EEG) is relatively new field and has not been addressed extensively in current researches as done with other fields. The peculiarities of the study emerges from not using any type of pre-processing at all which is usually done to the EEG signal in order to filter it or have it in better shape, but rather accept the signal in its raw nature. This could make the study a peculiar, especially with using new development tool that seldom has been used in deep learning which is the DeepLEarning4J (DL4J), the java programming environment platform made easy and tailored for deep neural network learning purposes. Results up to 97% in detecting two levels of Anesthesia have been reported successfully

Rapid Diagnostic Algorithms as a Screening Tool for Tuberculosis: An Assessor Blinded Cross-Sectional Study

Background: A major obstacle to effectively treat and control tuberculosis is the absence of an accurate, rapid, and low-cost diagnostic tool. A new approach for the screening of patients for tuberculosis is the use of rapid diagnostic classification algorithms. Methods: We tested a previously published diagnostic algorithm based on four biomarkers as a screening tool for tuberculosis in a Central European patient population using an assessor-blinded cross-sectional study design. In addition, we developed an improved diagnostic classification algorithm based on a study population at a tertiary hospital in Vienna, Austria, by supervised computational statistics. Results: The diagnostic accuracy of the previously published diagnostic algorithm for our patient population consisting of 206 patients was 54% (CI: 47%–61%). An improved model was constructed using inflammation parameters and clinical information. A diagnostic accuracy of 86% (CI: 80%–90%) was demonstrated by 10-fold cross validation. An alternative model relying solely on clinical parameters exhibited a diagnostic accuracy of 85% (CI: 79%–89%). Conclusion: Here we show that a rapid diagnostic algorithm based on clinical parameters is only slightly improved by inclusion of inflammation markers in our cohort. Our results also emphasize the need for validation of new diagnostic algorithms in different settings and patient populations