Corn starch nanoparticles: Preparation, characterization, and utilization as a fat replacer in salad dressing

Corn starch nanoparticles (SNPs) were prepared by ultrasonication of starch for 40 min at an amplitude of 40%, which modified some properties of starch so that it could be used as a functional ingredient in food products. The cereal SNPs were characterized on the basis of their morphology, particle size, crystallinity, pasting and functional properties. The SNPs were globular in shape as revealed by TEM. There was a decrease in the crystallanity of SNPs, whereas the functional properties were enhanced in SNPs as compared to raw starch. SNPs had a less paste viscosity as compared to raw starch. Salad dressing was prepared by using SNPs as a fat replacer. The prepared salad dressing was examined for colour, viscosity, and sensory attributes. Overall 60% of fat was replaced using corn SNPs without compromising its quality characteristics

Toward 959 nematode genomes

The sequencing of the complete genome of the nematode Caenorhabditis elegans was a landmark achievement and ushered in a new era of whole-organism, systems analyses of the biology of this powerful model organism. The success of the C. elegans genome sequencing project also inspired communities working on other organisms to approach genome sequencing of their species. The phylum Nematoda is rich and diverse and of interest to a wide range of research fields from basic biology through ecology and parasitic disease. For all these communities, it is now clear that access to genome scale data will be key to advancing understanding, and in the case of parasites, developing new ways to control or cure diseases. The advent of second-generation sequencing technologies, improvements in computing algorithms and infrastructure and growth in bioinformatics and genomics literacy is making the addition of genome sequencing to the research goals of any nematode research program a less daunting prospect. To inspire, promote and coordinate genomic sequencing across the diversity of the phylum, we have launched a community wiki and the 959 Nematode Genomes initiative (www.nematodegenomes.org/). Just as the deciphering of the developmental lineage of the 959 cells of the adult hermaphrodite C. elegans was the gateway to broad advances in biomedical science, we hope that a nematode phylogeny with (at least) 959 sequenced species will underpin further advances in understanding the origins of parasitism, the dynamics of genomic change and the adaptations that have made Nematoda one of the most successful animal phyla

Organic Fingerprint Powders Based on Fluorescent Phloxine B Dye.

Novel fingerpript powders based on organic dye (phJoxine B) have been prepared using insoluble salts as adhesive matrials in place of costly resinous polymers (which most conventional powders incorporate). Sharp and clear prints have been developed on a wide range of surfaces, such as paper, I plastic, glass, bakelite, enamelled metal and polished wood. The fluorescent nature of phloxine B assists in developing weak prints und.er ultraviolet light which can assist a forensic scientist for lifting  fingerprints frqm the scene of crime and also a defence scientist for establishing the identity of deceased native soldiers as well as of prisoners of war

Entropic Analysis of Votes Expressed in Italian Elections between 1948 and 2018

In Italy, the elections occur often, indeed almost every year the citizens are involved in a democratic choice for deciding leaders of different administrative entities. Sometimes the citizens are called to vote for filling more than one office in more than one administrative body. This phenomenon has occurred 35 times after 1948; it creates the peculiar condition of having the same sample of people expressing decisions on political bases at the same time. Therefore, the Italian contemporaneous ballots constitute the occasion to measure coherence and chaos in the way of expressing political opinion. In this paper, we address all the Italian elections that occurred between 1948 and 2018. We collect the number of votes per party at each administrative level and we treat each election as a manifestation of a complex system. Then, we use the Shannon entropy and the Gini Index to study the degree of disorder manifested during different types of elections at the municipality level. A particular focus is devoted to the contemporaneous elections. Such cases implicate different disorder dynamics in the contemporaneous ballots, when different administrative level are involved. Furthermore, some features that characterize different entropic regimes have emerged

IHWC: intelligent hidden web crawler for harvesting data in urban domains

Due to the massive size of the hidden web, searching, retrieving and mining rich and high-quality data can be a daunting task. Moreover, with the presence of forms, data cannot be accessed easily. Forms are dynamic, heterogeneous and spread over trillions of web pages. Significant efforts have addressed the problem of tapping into the hidden web to integrate and mine rich data. Effective techniques, as well as application in special cases, are required to be explored to achieve an effective harvest rate. One such special area is atmospheric science, where hidden web crawling is least implemented, and crawler is required to crawl through the huge web to narrow down the search to specific data. In this study, an intelligent hidden web crawler for harvesting data in urban domains (IHWC) is implemented to address the relative problems such as classification of domains, prevention of exhaustive searching, and prioritizing the URLs. The crawler also performs well in curating pollution-related data. The crawler targets the relevant web pages and discards the irrelevant by implementing rejection rules. To achieve more accurate results for a focused crawl, ICHW crawls the websites on priority for a given topic. The crawler has fulfilled the dual objective of developing an effective hidden web crawler that can focus on diverse domains and to check its integration in searching pollution data in smart cities. One of the objectives of smart cities is to reduce pollution. Resultant crawled data can be used for finding the reason for pollution. The crawler can help the user to search the level of pollution in a specific area. The harvest rate of the crawler is compared with pioneer existing work. With an increase in the size of a dataset, the presented crawler can add significant value to emission accuracy. Our results are demonstrating the accuracy and harvest rate of the proposed framework, and it efficiently collect hidden web interfaces from large-scale sites and achieve higher rates than other crawlers

ORIGINAL ARTICLE: Status of Methicillin Resistant Staphylococcus aureus Infections and Evaluation of PVL Producing Strains in Belgaum, South India

Background: Panton Valentine Leukocidin (PVL) toxin, responsible for increased virulence and more severe infections can be found in both Methicillin-sensitive and Methicillin-resistant strains of Staphylococcus aureus (MSSA and MRSA). Aims & Objectives: To generate baseline data on the extent of MRSA infections and to estimate the frequency of PVL-positive S.aureus in Belgaum, South India. Material & Methods: 70 clinical isolates of S.aureus were obtained from various laboratories in Belgaum city. Theseisolates were identified, phenotypically characterized as MRSA/MSSA by disc diffusion method using oxacillin discs (1 µg) and genetically by multiplex PCR for mecA and fem B genes. PCR was subsequently carried out on all isolates to detect LukS-PV and LukF-PV genes, the markers for potential producers of PVL toxin. Results: 27 out of 70 isolates (38.6%) were confirmed as MRSA by PCR formecA. The prevalence of PVL gene was 85.1% and 48.8% in MRSA and MSSA respectively. The overall prevalence of PVL positive S.aureuswas 62.85%. Conclusion: Our study showed high percentage of PVL positive MRSA and MSSA, higher than the most reports worldwide. In the backdrop of bacterial strains gaining multiple drug resistance, our study warrants further epidemiological studies in hospitals and community levels in the region

Impacts of Oxidative Stress and Antioxidants on Semen Functions

Oxidative stress (OS) has been considered a major contributory factor to the infertility. Oxidative stress is the result of imbalance between the reactive oxygen species (ROS) and antioxidants in the body which can lead to sperm damage, deformity, and eventually male infertility. Although high concentrations of the ROS cause sperm pathology (ATP depletion) leading to insufficient axonemal phosphorylation, lipid peroxidation, and loss of motility and viability but, many evidences demonstrate that low and controlled concentrations of these ROS play an important role in sperm physiological processes such as capacitation, acrosome reaction, and signaling processes to ensure fertilization. The supplementation of a cryopreservation extender with antioxidant has been shown to provide a cryoprotective effect on mammalian sperm quality. This paper reviews the impacts of oxidative stress and reactive oxygen species on spermatozoa functions, causes of ROS generation, and antioxidative strategies to reduce OS. In addition, we also highlight the emerging concept of utilizing OS as a tool of contraception

A neural-network based method for prediction of γ-turns in proteins from multiple sequence alignment

In the present study, an attempt has been made to develop a method for predicting γ-turns in proteins. First, we have implemented the commonly used statistical and machine-learning techniques in the field of protein structure prediction, for the prediction of γ-turns. All the methods have been trained and tested on a set of 320 nonhomologous protein chains by a fivefold cross-validation technique. It has been observed that the performance of all methods is very poor, having a Matthew's Correlation Coefficient (MCC)≤0.06. Second, predicted secondary structure obtained from PSIPRED is used in γ-turn prediction. It has been found that machine-learning methods outperform statistical methods and achieve an MCC of 0.11 when secondary structure information is used. The performance of γ-turn prediction is further improved when multiple sequence alignment is used as the input instead of a single sequence. Based on this study, we have developed a method, GammaPred, for γ-turn prediction (MCC=0.17). The GammaPred is a neural-network-based method, which predicts γ-turns in two steps. In the first step, a sequence-to-structure network is used to predict the γ-turns from multiple alignment of protein sequence. In the second step, it uses a structure-to-structure network in which input consists of predicted γ-turns obtained from the first step and predicted secondary structure obtained from PSIPRED. (A Web server based on GammaPred is available at http://www.imtech.res.in/raghava/gammapred/)

A neural network method for prediction of β-turn types in proteins using evolutionary information

Motivation: The prediction of β-turns is an important element of protein secondary structure prediction. Recently, a highly accurate neural network based method Betatpred2 has been developed for predicting β-turns in proteins using position-specific scoring matrices (PSSM) generated by PSI-BLAST and secondary structure information predicted by PSIPRED. However, the major limitation of Betatpred2 is that it predicts only β-turn and non-β-turn residues and does not provide any information of different β-turn types. Thus, there is a need to predict β-turn types using an approach based on multiple sequence alignment, which will be useful in overall tertiary structure prediction. Results: In the present work, a method has been developed for the prediction of β-turn types I, II, IV and VIII. For each turn type, two consecutive feed-forward back-propagation networks with a single hidden layer have been used where the first sequence-to-structure network has been trained on single sequences as well as on PSI-BLAST PSSM. The output from the first network along with PSIPRED predicted secondary structure has been used as input for the second-level structure-to-structure network. The networks have been trained and tested on a non-homologous dataset of 426 proteins chains by 7-fold cross-validation. It has been observed that the prediction performance for each turn type is improved significantly by using multiple sequence alignment. The performance has been further improved by using a second level structure-to-structure network and PSIPRED predicted secondary structure information. It has been observed that Type I and II β-turns have better prediction performance than Type IV and VIII β-turns. The final network yields an overall accuracy of 74.5, 93.5, 67.9 and 96.5% with MCC values of 0.29, 0.29, 0.23 and 0.02 for Type I, II, IV and VIII β-turns, respectively, and is better than random prediction. Availability: A web server for prediction of β-turn types I, II, IV and VIII based on above approach is available at http://www.imtech.res.in/raghava/betaturns/ and http://bioinformatics.uams.edu/mirror/betaturns/ (mirror site)