Supercapacitor application for PV power smoothing

© 2018 IEEE. The penetration of renewable energy technologies causes grid stability problems and voltage flickering due to fluctuations of the weather conditions, which affect the produced renewable power. The system is first analyzed in order to define the operating point and the controlling parameters, in which the produced power is shared correctly between the load and the grid at a power factor close to unity. The operating point is set by the duty cycle (D) of the DC-DC converter, the depth of modulation (Dm) of the inverter, and the phase angle (δ) between the output voltage of the inverter and the grid voltage. The simulated system is operated in an open-loop mode in order to investigate the effect of each parameter without the corrective action of the closed-loops of the MPPT and the load angle. After that, a preliminary investigation of a supercapacitor controlled storage application is performed in terms of the power flow. All calculated theoretical results are verified by the simulation results. This research forms the basis for an in-depth investigation of supercapacitor and battery storage in grid-connected systems

PocketMatch: A new algorithm to compare binding sites in protein structures

Background: Recognizing similarities and deriving relationships among protein molecules is a fundamental
requirement in present-day biology. Similarities can be present at various levels which can be detected through comparison of protein sequences or their structural folds. In some cases similarities obscure at these levels could be present merely in the substructures at their binding sites. Inferring functional similarities between protein molecules by comparing their binding sites is still largely exploratory and not as yet a routine protocol. One of
the main reasons for this is the limitation in the choice of appropriate analytical tools that can compare binding sites with high sensitivity. To benefit from the enormous amount of structural data that is being rapidly accumulated, it is essential to have high throughput tools that enable large scale binding site comparison.

Results: Here we present a new algorithm PocketMatch for comparison of binding sites in a frame invariant
manner. Each binding site is represented by 90 lists of sorted distances capturing shape and chemical nature of the site. The sorted arrays are then aligned using an incremental alignment method and scored to obtain PMScores for pairs of sites. A comprehensive sensitivity analysis and an extensive validation of the algorithm have been carried out. Perturbation studies where the geometry of a given site was retained but the residue types were changed randomly, indicated that chance similarities were virtually non-existent. Our analysis also demonstrates that shape information alone is insufficient to discriminate between diverse binding sites, unless
combined with chemical nature of amino acids.

Conclusions: A new algorithm has been developed to compare binding sites in accurate, efficient and
high-throughput manner. Though the representation used is conceptually simplistic, we demonstrate that along
with the new alignment strategy used, it is sufficient to enable binding comparison with high sensitivity. Novel methodology has also been presented for validating the algorithm for accuracy and sensitivity with respect to geometry and chemical nature of the site. The method is also fast and takes about 1/250th second for one comparison on a single processor. A parallel version on BlueGene has also been implemented

High-throughput Screening and Sensitized Bacteria Identify an M. tuberculosis Dihydrofolate Reductase Inhibitor with Whole Cell Activity

Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis, is a bacterial pathogen that claims roughly 1.4 million lives every year. Current drug regimens are inefficient at clearing infection, requiring at least 6 months of chemotherapy, and resistance to existing agents is rising. There is an urgent need for new drugs that are more effective and faster acting. The folate pathway has been successfully targeted in other pathogens and diseases, but has not yielded a lead drug against tuberculosis. We developed a high-throughput screening assay against Mtb dihydrofolate reductase (DHFR), a critical enzyme in the folate pathway, and screened a library consisting of 32,000 synthetic and natural product-derived compounds. One potent inhibitor containing a quinazoline ring was identified. This compound was active against the wild-type laboratory strain H37Rv (MIC99 = 207 µM). In addition, an Mtb strain with artificially lowered DHFR levels showed increased sensitivity to this compound (MIC99 = 70.7 µM), supporting that the inhibition was target-specific. Our results demonstrate the potential to identify Mtb DHFR inhibitors with activity against whole cells, and indicate the power of using a recombinant strain of Mtb expressing lower levels of DHFR to facilitate the discovery of antimycobacterial agents. With these new tools, we highlight the folate pathway as a potential target for new drugs to combat the tuberculosis epidemic

Comparative and Functional Genomics of Rhodococcus opacus PD630 for Biofuels Development

The Actinomycetales bacteria Rhodococcus opacus PD630 and Rhodococcus jostii RHA1 bioconvert a diverse range of organic substrates through lipid biosynthesis into large quantities of energy-rich triacylglycerols (TAGs). To describe the genetic basis of the Rhodococcus oleaginous metabolism, we sequenced and performed comparative analysis of the 9.27 Mb R. opacus PD630 genome. Metabolic-reconstruction assigned 2017 enzymatic reactions to the 8632 R. opacus PD630 genes we identified. Of these, 261 genes were implicated in the R. opacus PD630 TAGs cycle by metabolic reconstruction and gene family analysis. Rhodococcus synthesizes uncommon straight-chain odd-carbon fatty acids in high abundance and stores them as TAGs. We have identified these to be pentadecanoic, heptadecanoic, and cis-heptadecenoic acids. To identify bioconversion pathways, we screened R. opacus PD630, R. jostii RHA1, Ralstonia eutropha H16, and C. glutamicum 13032 for growth on 190 compounds. The results of the catabolic screen, phylogenetic analysis of the TAGs cycle enzymes, and metabolic product characterizations were integrated into a working model of prokaryotic oleaginy.Cambridge-MIT InstituteMassachusetts Institute of Technology. (Seed Grant program)Shell Oil CompanyNational Institute of Allergy and Infectious Diseases (U.S.)United States. National Institutes of HealthNational Institutes of Health. Department of Health and Human Services (Contract No. HHSN272200900006C

Computational Comparative Study of Tuberculosis Proteomes Using a Model Learned from Signal Peptide Structures

Secretome analysis is important in pathogen studies. A fundamental and convenient way to identify secreted proteins is to first predict signal peptides, which are essential for protein secretion. However, signal peptides are highly complex functional sequences that are easily confused with transmembrane domains. Such confusion would obviously affect the discovery of secreted proteins. Transmembrane proteins are important drug targets, but very few transmembrane protein structures have been determined experimentally; hence, prediction of the structures is essential. In the field of structure prediction, researchers do not make assumptions about organisms, so there is a need for a general signal peptide predictor

Determinants of stigma in a cohort of hellenic patients suffering from multiple sclerosis: A cross-sectional study

Background: Patients suffering from several neurologic disorders may bear the "stigma" of their disease, being disqualified from full social acceptance. Although stigma is considered to be present in Multiple Sclerosis (MS), the factors that influence its levels are ambiguous. Aim of our study was to examine, for the first time in the literature, the basic determinants of stigma in a Hellenic MS-patients cohort, as well as how stigma affects their Quality-of-Life (QoL) profiles. Methods: Three hundred forty two patients were recruited in this study. Data collected concerned sociodemographic and disease-related variables, mental illness assessment, Multiple-Sclerosis-QoL-54 (MSQoL-54) and Stigma-Scale-for-Chronic-Illness-24 (SSCI-24) questionnaires. Potential determinants were evaluated with univariate statistical analyses for their contribution to total, internalized (inner-self derived) and externalized (society derived) stigma. Important findings were further evaluated on hierarchical regression models. Results: Disability levels were found to be the most powerful predictor in all stigma categories, followed by the presence of mental illness. Working and caregiving status were also ascertained as determinants of internalized stigma. Stigma levels displayed strong negative correlation with all composites of MSQoL-54. Conclusions: Stigma is present in the social environment of MS patients and was confirmed as a barrier (according to the International Classification of Functioning, Disability and Health), with detrimental effects on their QoL levels and functioning performances. Disability and mental illness were shown as the principal determinants of stigma, while financial characteristics were not as equally involved. Further validation of these results in other MS populations may provide safer conclusions, towards more efficacious patient-centered care outcomes. © 2016 The Author(s)

Invasion of Caulerpa racemosa var. cilindrica (Caulerpales, Chlorophyta) in the Mediterranean Sea: an assessment of the spread

Peer reviewe