SHANK3 genetic polymorphism and susceptibility to ASD: evidence from molecular,in silico,and meta-analysis approaches

Molecular Biology Reports - The SHANK3 gene encodes a master synaptic scaffolding protein at the excitatory synapse’s postsynaptic density, which is predominantly responsible for constructing...     

Spectral lights trigger biomass accumulation and production of antioxidant secondary metabolites in adventitious root cultures of Stevia rebaudiana (Bert.)

Stevia rebaudiana (S. rebaudiana) is the most important therapeutic plant species and has been accepted as such worldwide. It has a tendency to accumulate steviol glycosides, which are 300 times sweeter than marketable sugar. Recently, diabetic patients commonly use this plant as a sugar substitute for sweet taste. In the present study, the effects of different spectral lights were investigated on biomass accumulation and production of secondary metabolites in adventitious root cultures of S. rebaudiana. For callus development, leaf explants were excised from seed-derived plantlets and inoculated on a Murashige and Skoog (MS) medium containing the combination of 2,4-dichlorophenoxy acetic acid (2, 4-D, 2.0 mg/l) and 6-benzyladenine (BA, 2.0 mg/l), while 0.5 mg/l naphthalene acetic acid (NAA) was used for adventitious root culture. Adventitious root cultures were exposed to different spectral lights (blue, green, violet, red and yellow) for a 30-day period. White light was used as control. The growth kinetics was studied for 30 days with 3-day intervals. In this study, the violet light showed the maximum accumulation of fresh biomass (2.495 g/flask) as compared to control (1.63 g/flask), while red light showed growth inhibition (1.025 g/flask) as compared to control. The blue light enhanced the highest accumulation of phenolic content (TPC; 6.56 mg GAE/g DW), total phenolic production (TPP; 101 mg/flask) as compared to control (5.44 mg GAE/g DW; 82.2 mg GAE/g DW), and exhibited a strong correlation with dry biomass. Blue light also improved the accumulation of total flavonoid content (TFC; 4.33 mg RE/g DW) and total flavonoid production (TFP; 65 mg/flask) as compared to control. The violet light showed the highest DPPH inhibition (79.72%), while the lowest antioxidant activity was observed for control roots (73.81%). Hence, we concluded that the application of spectral lights is an auspicious strategy for the enhancement of the required antioxidant secondary metabolites in adventitious root cultures of S. rebaudiana and of other medicinal plants.     

A Space Efficient Flexible Pivot Selection Approach to Evaluate Determinant and Inverse of a Matrix

This paper presents new simple approaches for evaluating determinant and inverse of a matrix. The choice of pivot selection has been kept arbitrary thus they reduce the error while solving an ill conditioned system. Computation of determinant of a matrix has been made more efficient by saving unnecessary data storage and also by reducing the order of the matrix at each iteration, while dictionary notation [1] has been incorporated for computing the matrix inverse thereby saving unnecessary calculations. These algorithms are highly class room oriented, easy to use and implemented by students. By taking the advantage of flexibility in pivot selection, one may easily avoid development of the fractions by most. Unlike the matrix inversion method [2] and [3], the presented algorithms obviate the use of permutations and inverse permutations.     

Machine Learning in Healthcare

Recent advancements in Artificial Intelligence (AI) and Machine Learning (ML) technology have brought on substantial strides in predicting and identifying health emergencies, disease populations, and disease state and immune response, amongst a few. Although, skepticism remains regarding the practical application and interpretation of results from ML-based approaches in healthcare settings, the inclusion of these approaches is increasing at a rapid pace. Here we provide a brief overview of machine learning-based approaches and learning algorithms including supervised, unsupervised, and reinforcement learning along with examples. Second, we discuss the application of ML in several healthcare fields, including radiology, genetics, electronic health records, and neuroimaging. We also briefly discuss the risks and challenges of ML application to healthcare such as system privacy and ethical concerns and provide suggestions for future applications.     

Rapid and reliable protein structure determination via chemical shift threading

Protein structure determination using nuclear magnetic resonance (NMR) spectroscopy can be both time-consuming and labor intensive. Here we demonstrate how chemical shift threading can permit rapid, robust, and accurate protein structure determination using only chemical shift data. Threading is a relatively old bioinformatics technique that uses a combination of sequence information and predicted (or experimentally acquired) low-resolution structural data to generate high-resolution 3D protein structures. The key motivations behind using NMR chemical shifts for protein threading lie in the fact that they are easy to measure, they are available prior to 3D structure determination, and they contain vital structural information. The method we have developed uses not only sequence and chemical shift similarity but also chemical shift-derived secondary structure, shift-derived super-secondary structure, and shift-derived accessible surface area to generate a high quality protein structure regardless of the sequence similarity (or lack thereof) to a known structure already in the PDB. The method (called E-Thrifty) was found to be very fast (often?<?10 min/structure) and to significantly outperform other shift-based or threading-based structure determination methods (in terms of top template model accuracy)—with an average TM-score performance of 0.68 (vs. 0.50–0.62 for other methods). Coupled with recent developments in chemical shift refinement, these results suggest that protein structure determination, using only NMR chemical shifts, is becoming increasingly practical and reliable. E-Thrifty is available as a web server at http://ethrifty.ca.     

Dynamic role of single-celled fungi in ruminal microbial ecology and activities

In ruminants, high fermentation capacity is necessary to develop more efficient ruminant production systems. Greater level of production depends on the ability of the microbial ecosystem to convert organic matter into precursors of milk and meat. This has led to increased interest by animal nutritionists, biochemists and microbiologists in evaluating different strategies to manipulate the rumen biota to improve animal performance, production efficiency and animal health. One of such strategies is the use of natural feed additives such as single-celled fungi yeast. The main objectives of using yeasts as natural additives in ruminant diets include; (i) to prevent rumen microflora disorders, (ii) to improve and sustain higher production of milk and meat, (iii) to reduce rumen acidosis and bloat which adversely affect animal health and performance, (iv) to decrease the risk of ruminant-associated human pathogens and (v) to reduce the excretion of nitrogenous-based compounds, carbon dioxide and methane. Yeast, a natural feed additive, has the potential to enhance feed degradation by increasing the concentration of volatile fatty acids during fermentation processes. In addition, microbial growth in the rumen is enhanced in the presence of yeast leading to the delivery of a greater amount of microbial protein to the duodenum and high nitrogen retention. Single-celled fungi yeast has demonstrated its ability to increase fibre digestibility and lower faecal output of organic matter due to improved digestion of organic matter, which subsequently improves animal productivity. Yeast also has the ability to alter the fermentation process in the rumen in a way that reduces methane formation. Furthermore, yeast inclusion in ruminant diets has been reported to decrease toxins absorption such as mycotoxins and promote epithelial cell integrity. This review article provides information on the impact of single-celled fungi yeast as a feed supplement on ruminal microbiota and its function to improve the health and productive longevity of ruminants.     

Determination of antioxidant activity,total phenolics and fatty acids in essential oils and other extracts from callus culture,seeds and leaves of Argania spinosa (L.) Skeels

Plant Cell, Tissue and Organ Culture (PCTOC) - Argan (Argania spinosa (L.) Skeels) is an endangered and endemic agroforestry species of Morocco highly appreciated for its nutraceutical properties....     

RT-PCR Assays for Seven Serotypes of Epizootic Haemorrhagic Disease Virus & Their Use to Type Strains from the Mediterranean Region and North America

Epizootic haemorrhagic disease virus (EHDV) infects wild ruminants, causing a frequently fatal haemorrhagic disease. However, it can also cause bluetongue-like disease in cattle, involving significant levels of morbidity and mortality, highlighting a need for more rapid and reliable diagnostic assays. EHDV outer-capsid protein VP2 (encoded by genome-segment 2 [Seg-2]) is highly variable and represents the primary target for neutralising antibodies generated by the mammalian host. Consequently VP2 is also the primary determinant of virus “serotype”, as identified in virus neutralisation tests (VNT). Although previous reports have indicated eight to ten EHDV serotypes, recent serological comparisons and molecular analyses of Seg-2 indicate only seven EHDV “types”. Oligonucleotide primers were developed targeting Seg-2, for use in conventional RT-PCR assays to detect and identify these seven types. These assays, which are more rapid and sensitive, still show complete agreement with VNT and were used to identify recent EHDV isolates from the Mediterranean region and North America.