Project description:Comparative RAD sequencing for detecting SNPs with differential allele frequency between potato genotypes with high and low tuber yield, starch content and starch yield.
Project description:A search for magnetised quark nuggets (MQN) is reported using acoustic signals from hydrophones placed in the Great Salt Lake (GSL) in the USA. No events satisfying the expected signature were seen. This observation allows limits to be set on the flux of MQNs penetrating the Earth's atmosphere and depositing energy in the GSL. The expected signature of the events was derived from pressure pulses caused by high-explosive cords between the lake surface and bottom at various locations in the GSL. The limits obtained from this search are compared with those obtained from previous searches and are compared to models for the formation of MQNs.
Project description:It's increasingly important but difficult to determine potential biomarkers of schizophrenia (SCZ) disease, owing to the complex pathophysiology of this disease. In this study, a network-fusion based framework was proposed to identify genetic biomarkers of the SCZ disease. A three-step feature selection was applied to single nucleotide polymorphisms (SNPs), DNA methylation, and functional magnetic resonance imaging (fMRI) data to select important features, which were then used to construct two gene networks in different states for the SNPs and DNA methylation data, respectively. Two health networks (one is for SNP data and the other is for DNA methylation data) were combined into one health network from which health minimum spanning trees (MSTs) were extracted. Two disease networks also followed the same procedures. Those genes with significant changes were determined as SCZ biomarkers by comparing MSTs in two different states and they were finally validated from five aspects. The effectiveness of the proposed discovery framework was also demonstrated by comparing with other network-based discovery methods. In summary, our approach provides a general framework for discovering gene biomarkers of the complex diseases by integrating imaging genomic data, which can be applied to the diagnosis of the complex diseases in the future.
Project description:ObjectiveThe researchers assessed prevalence in the clinical case report literature of multiple reports independently reporting the same (or nearly the same) main finding.MethodsResults from forty-five PubMed queries were examined for incidence and features of main findings ("nuggets") shared in at least four case reports.ResultsThe authors found that nuggets are surprisingly prevalent and large in the case report literature, the largest found so far was reported in seventeen articles. In most cases, the main findings of case reports were evident from examining titles alone.ConclusionsOur curated examples should serve as gold standards for developing specific automated methods for finding nuggets. Nuggets potentially enable finding-based (instead of topic-based) information retrieval.
Project description:The development of genomic technology for smart diagnosis and therapies for various diseases has lately been the most demanding area for computer-aided diagnostic and treatment research. Exponential breakthroughs in artificial intelligence and machine intelligence technologies could pave the way for identifying challenges afflicting the healthcare industry. Genomics is paving the way for predicting future illnesses, including cancer, Alzheimer's disease, and diabetes. Machine learning advancements have expedited the pace of biomedical informatics research and inspired new branches of computational biology. Furthermore, knowing gene relationships has resulted in developing more accurate models that can effectively detect patterns in vast volumes of data, making classification models important in various domains. Recurrent Neural Network models have a memory that allows them to quickly remember knowledge from previous cycles and process genetic data. The present work focuses on type 2 diabetes prediction using gene sequences derived from genomic DNA fragments through automated feature selection and feature extraction procedures for matching gene patterns with training data. The suggested model was tested using tabular data to predict type 2 diabetes based on several parameters. The performance of neural networks incorporating Recurrent Neural Network (RNN) components, Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU) was tested in this research. The model's efficiency is assessed using the evaluation metrics such as Sensitivity, Specificity, Accuracy, F1-Score, and Mathews Correlation Coefficient (MCC). The suggested technique predicted future illnesses with fair Accuracy. Furthermore, our research showed that the suggested model could be used in real-world scenarios and that input risk variables from an end-user Android application could be kept and evaluated on a secure remote server.
Project description:Voltage-gated ion channels underlie electrical activity of neurons and are dynamically regulated by diverse cell signaling pathways that alter their phosphorylation state. Recent global mass spectrometric-based analyses of the mouse brain phosphoproteome have yielded a treasure trove of new data as to the extent and nature of phosphorylation of numerous ion channel principal or α subunits in mammalian brain. Here we compile and review data on 347 phosphorylation sites (261 unique) on 42 different voltage-gated ion channel α subunits that were identified in these recent studies. Researchers in the ion channel field can now begin to explore the role of these novel in vivo phosphorylation sites in the dynamic regulation of the localization, activity, and expression of brain ion channels through multisite phosphorylation of their principal subunits.
Project description:Banana peel powder is considered one of the most nutritive and effective waste product to be utilized as a functional additive in the food industry. This study aimed to determine the impact of banana peel powder at concentrations of 2%, 4%, and 6% on the nutritional composition, physicochemical parameters, antioxidant potential, cooking properties, microbial count, and organoleptic properties of functional nuggets during storage at refrigeration temperature for 21 days. Results showed a significant increase in nutritional content including ash and crude fiber ranging from 2.52 ± 0.017% to 6.45 ± 0.01% and 0.51 ± 0.01% to 2.13 ± 0.01%, respectively, whereas a significant decrease was observed in crude protein and crude fat ranging from 13.71 ± 0.02% to 8.92 ± 0.02% and 9.25 ± 0.02% to 4.51 ± 0.01%, respectively. The incorporation of banana peel powder significantly improved the Water Holding Capacity from 5.17% to 8.37%, cooking yield from 83.20 ± 0.20% to 87.73 ± 0.16% and cooking loss from 20.19 ± 0.290% to 13.98 ± 0.15%. Antioxidant potential was significantly improved as TPC of functional nuggets increased ranging from 3.73 ± 0.02 mg GAE/g to 8.53 ± 0.02 mg GAE/g while a decrease in TBARS (0.18 ± 0.02 mg malonaldehyde/kg to 0.14 ± 0.02 mg malonaldehyde/kg) was observed. Furthermore, functional broiler nuggets depicted a significantly reduced total plate count (3.06-4.20 × 105 CFU/g) than control, which is likely due to high amounts of phenolic compounds in BPP. Broiler nuggets supplemented with 2% BPP (T1) received the greatest sensory scores in terms of flavour, tenderness, and juiciness. Results of current study revealed the potential of BPP to be utilized as an effective natural source of fibre supplementation in food products along with enhanced antioxidant and anti-microbial properties.
Project description:BackgroundMathematical models are needed for the design of breeding programs using genomic prediction. While deterministic models for selection on pedigree-based estimates of breeding values (PEBV) are available, these have not been fully developed for genomic selection, with a key missing component being the accuracy of genomic EBV (GEBV) of selection candidates. Here, a deterministic method was developed to predict this accuracy within a closed breeding population based on the accuracy of GEBV and PEBV in the reference population and the distance of selection candidates from their closest ancestors in the reference population.MethodsThe accuracy of GEBV was modeled as a combination of the accuracy of PEBV and of EBV based on genomic relationships deviated from pedigree (DEBV). Loss of the accuracy of DEBV from the reference to the target population was modeled based on the effective number of independent chromosome segments in the reference population (Me). Measures of Me derived from the inverse of the variance of relationships and from the accuracies of GEBV and PEBV in the reference population, derived using either a Fisher information or a selection index approach, were compared by simulation.ResultsUsing simulation, both the Fisher and the selection index approach correctly predicted accuracy in the target population over time, both with and without selection. The index approach, however, resulted in estimates of Me that were less affected by heritability, reference size, and selection, and which are, therefore, more appropriate as a population parameter. The variance of relationships underpredicted Me and was greatly affected by selection. A leave-one-out cross-validation approach was proposed to estimate required accuracies of EBV in the reference population. Aspects of the methods were validated using real data.ConclusionsA deterministic method was developed to predict the accuracy of GEBV in selection candidates in a closed breeding population. The population parameter Me that is required for these predictions can be derived from an available reference data set, and applied to other reference data sets and traits for that population. This method can be used to evaluate the benefit of genomic prediction and to optimize genomic selection breeding programs.
Project description:Human insulin (HI) is a well-characterized natural hormone which regulates glycose levels into the blood-stream and is widely used for diabetes treatment. Numerous studies have manifested that despite significant efforts devoted to structural characterization of this molecule and its complexes with organic compounds (ligands), there is still a rich diagram of phase transitions and novel crystalline forms to be discovered. Towards the improvement of drug delivery, identification of new insulin polymorphs from polycrystalline samples, simulating the commercially available drugs, is feasible today via macromolecular X-ray powder diffraction (XRPD). This approach has been developed, and is considered as a respectable method, which can be employed in biosciences for various purposes, such as observing phase transitions and characterizing bulk pharmaceuticals. An overview of the structural studies on human insulin complexes performed over the past decade employing both synchrotron and laboratory sources for XRPD measurements, is reported herein. This review aims to assemble all of the recent advances in the diabetes treatment field in terms of drug formulation, verifying in parallel the efficiency and applicability of protein XRPD for quick and accurate preliminary structural characterization in the large scale.
Project description:Quark nuggets are theoretical objects composed of approximately equal numbers of up, down, and strange quarks. They are also called strangelets, nuclearites, AQNs, slets, Macros, and MQNs. Quark nuggets are a candidate for dark matter, which has been a mystery for decades despite constituting ~ 85% of the universe's mass. Most previous models of quark nuggets have assumed no intrinsic magnetic field; however, Tatsumi found that quark nuggets may exist in magnetars as a ferromagnetic liquid with a magnetic field BS = 1012±1 T. We apply that result to quark nuggets, a dark-matter candidate consistent with the Standard Model, and report results of analytic calculations and simulations that show they spin up and emit electromagnetic radiation at ~ 104 to ~ 109 Hz after passage through planetary environments. The results depend strongly on the value of Bo, which is a parameter to guide and interpret observations. A proposed sensor system with three satellites at 51,000 km altitude illustrates the feasibility of using radio-frequency emissions to detect 0.003 to 1,600 MQNs, depending on Bo, during a 5 year mission.