The effect of polyethylene glycol on shellac stability

The effect of polyethylene glycol (PEG) having amolecular weight of 1000 and 2000 on shellac stability has been investigated in this research. The shellac was shellac wax free, and the solvent was ethanol 96%. Shellac films were prepared by solventevaporationmethod. The stability of shellac was investigated using insoluble solid test, Fourier Transform Infra Red (FTIR), Thermogravimetry Analyzer (TGA), and Water Vapour Transmission Rate (WVTR). The results showed that stability of shellac decreased after heating at 125oC for 10,30,90,and 180 minutes, and storing for 1 month at 27 oC and 85 relative humidity (RH). PEG improved the stability, and the most stable effect was achieved through PEG1000

Radiographic parameters for diagnosing sand colic in horses

<p>Abstract</p> <p>Background</p> <p>Ingestion of sand can cause colic, diarrhoea and weight loss in horses, but these signs are unspecific and can have many other causes. The amount of sand that induces disease may vary between individuals. To avoid over-diagnosing, it is important to determine the amount of sand that can be found in horses without clinical signs of gastrointestinal disease. The aim of this study was to use previously suggested parameters for establishing a radiographic diagnosis of sand colic, and compare these findings between a sand colic group and a control group.</p> <p>Methods</p> <p>Abdominal radiographs were obtained in 30 horses with a complaint unrelated to the gastrointestinal tract. In addition, archived abdominal radiographs of 37 clinical cases diagnosed with sand impaction were investigated. The size of the mineral opacity indicative of sand in the abdomen was measured and graded according to a previously published protocol based on height and length. Location, homogeneity, opacity and number of sand accumulations were also recorded.</p> <p>Results</p> <p>Twenty out of 30 control horses (66%) had one or more sand accumulations. In the present study; height, length and homogeneity of the accumulations were useful parameters for establishing a diagnosis of sand colic. Radiographically defined intestinal sand accumulation grades of up to 2 was a common finding in horses with no clinical signs from the gastrointestinal tract whereas most of the clinical cases had much larger grades, indicating larger sand accumulations.</p> <p>Conclusion</p> <p>Further work to establish a reliable grading system for intestinal sand content is warranted, but a previously proposed grading system based on measurements of height and length may be an alternative for easy assessment of sand accumulations in the meantime. The present study indicates that a grade 1 – 2 sand accumulation in the intestine is a frequent finding in horses. When working up a case with clinical signs from the gastrointestinal tract, one or more accumulations of this grade should not be considered the cause until other possibilities have been ruled out.</p

Machine Learning Approach for Prescriptive Plant Breeding

We explored the capability of fusing high dimensional phenotypic trait (phenomic) data with a machine learning (ML) approach to provide plant breeders the tools to do both in-season seed yield (SY) prediction and prescriptive cultivar development for targeted agro-management practices (e.g., row spacing and seeding density). We phenotyped 32 SoyNAM parent genotypes in two independent studies each with contrasting agro-management treatments (two row spacing, three seeding densities). Phenotypic trait data (canopy temperature, chlorophyll content, hyperspectral reflectance, leaf area index, and light interception) were generated using an array of sensors at three growth stages during the growing season and seed yield (SY) determined by machine harvest. Random forest (RF) was used to train models for SY prediction using phenotypic traits (predictor variables) to identify the optimal temporal combination of variables to maximize accuracy and resource allocation. RF models were trained using data from both experiments and individually for each agro-management treatment. We report the most important traits agnostic of agro-management practices. Several predictor variables showed conditional importance dependent on the agro-management system. We assembled predictive models to enable in-season SY prediction, enabling the development of a framework to integrate phenomics information with powerful ML for prediction enabled prescriptive plant breeding

Nr4a1-eGFP Is a Marker of Striosome-Matrix Architecture, Development and Activity in the Extended Striatum

Transgenic mice expressing eGFP under population specific promoters are widely used in neuroscience to identify specific subsets of neurons in situ and as sensors of neuronal activity in vivo. Mice expressing eGFP from a bacterial artificial chromosome under the Nr4a1 promoter have high expression within the basal ganglia, particularly within the striosome compartments and striatal-like regions of the extended amygdala (bed nucleus of the stria terminalis, striatal fundus, central amygdaloid nucleus and intercalated cells). Grossly, eGFP expression is inverse to the matrix marker calbindin 28K and overlaps with mu-opioid receptor immunoreactivity in the striatum. This pattern of expression is similar to Drd1, but not Drd2, dopamine receptor driven eGFP expression in structures targeted by medium spiny neuron afferents. Striosomal expression is strong developmentally where Nr4a1-eGFP expression overlaps with Drd1, TrkB, tyrosine hydroxylase and phospho-ERK, but not phospho-CREB, immunoreactivity in “dopamine islands”. Exposure of adolescent mice to methylphenidate resulted in an increase in eGFP in both compartments in the dorsolateral striatum but eGFP expression remained brighter in the striosomes. To address the role of activity in Nr4a1-eGFP expression, primary striatal cultures were prepared from neonatal mice and treated with forskolin, BDNF, SKF-83822 or high extracellular potassium and eGFP was measured fluorometrically in lysates. eGFP was induced in both neurons and contaminating glia in response to forskolin but SKF-83822, brain derived neurotrophic factor and depolarization increased eGFP in neuronal-like cells selectively. High levels of eGFP were primarily associated with Drd1+ neurons in vitro detected by immunofluorescence; however ∼15% of the brightly expressing cells contained punctate met-enkephalin immunoreactivity. The Nr4a1-GFP mouse strain will be a useful model for examining the connectivity, physiology, activity and development of the striosome system

HLA-C and HIV-1: friends or foes?

The major histocompatibility complex class I protein HLA-C plays a crucial role as a molecule capable of sending inhibitory signals to both natural killer (NK) cells and cytotoxic T lymphocytes (CTL) via binding to killer cell Ig-like receptors (KIR). Recently HLA-C has been recognized as a key molecule in the immune control of HIV-1. Expression of HLA-C is modulated by a microRNA binding site. HLA-C alleles that bear substitutions in the microRNA binding site are more expressed at the cell surface and associated with the control of HIV-1 viral load, suggesting a role of HLA-C in the presentation of antigenic peptides to CTLs. This review highlights the role of HLA-C in association with HIV-1 viral load, but also addresses the contradiction of the association between high cell surface expression of an inhibitory molecule and strong cell-mediated immunity. To explore additional mechanisms of control of HIV-1 replication by HLA-C, we address specific features of the molecule, like its tendency to be expressed as open conformer upon cell activation, which endows it with a unique capacity to associate with other cell surface molecules as well as with HIV-1 proteins

Scientific, sustainability and regulatory challenges of cultured meat

Producing meat without the drawbacks of conventional animal agriculture would greatly contribute to future food and nutrition security. This Review Article covers biological, technological, regulatory and consumer acceptance challenges in this developing field of biotechnology. Cellular agriculture is an emerging branch of biotechnology that aims to address issues associated with the environmental impact, animal welfare and sustainability challenges of conventional animal farming for meat production. Cultured meat can be produced by applying current cell culture practices and biomanufacturing methods and utilizing mammalian cell lines and cell and gene therapy products to generate tissue or nutritional proteins for human consumption. However, significant improvements and modifications are needed for the process to be cost efficient and robust enough to be brought to production at scale for food supply. Here, we review the scientific and social challenges in transforming cultured meat into a viable commercial option, covering aspects from cell selection and medium optimization to biomaterials, tissue engineering, regulation and consumer acceptance

A novel single-cell based method for breast cancer prognosis

Breast cancer prognosis is challenging due to the heterogeneity of the disease. Various computational methods using bulk RNA-seq data have been proposed for breast cancer prognosis. However, these methods suffer from limited performances or ambiguous biological relevance, as a result of the neglect of intra-tumor heterogeneity. Recently, single cell RNA-sequencing (scRNA-seq) has emerged for studying tumor heterogeneity at cellular levels. In this paper, we propose a novel method, scPrognosis, to improve breast cancer prognosis with scRNA-seq data. scPrognosis uses the scRNA-seq data of the biological process Epithelial-to-Mesenchymal Transition (EMT). It firstly infers the EMT pseudotime and a dynamic gene co-expression network, then uses an integrative model to select genes important in EMT based on their expression variation and differentiation in different stages of EMT, and their roles in the dynamic gene co-expression network. To validate and apply the selected signatures to breast cancer prognosis, we use them as the features to build a prediction model with bulk RNA-seq data. The experimental results show that scPrognosis outperforms other benchmark breast cancer prognosis methods that use bulk RNA-seq data. Moreover, the dynamic changes in the expression of the selected signature genes in EMT may provide clues to the link between EMT and clinical outcomes of breast cancer. scPrognosis will also be useful when applied to scRNA-seq datasets of different biological processes other than EMT.Xiaomei Li, Lin Liu, Gregory J. Goodall, Andreas Schreiber, Taosheng Xu, Jiuyong Li, Thuc D. L

An integrative approach for building personalized gene regulatory networks for precision medicine

Only a small fraction of patients respond to the drug prescribed to treat their disease, which means that most are at risk of unnecessary exposure to side effects through ineffective drugs. This inter-individual variation in drug response is driven by differences in gene interactions caused by each patient's genetic background, environmental exposures, and the proportions of specific cell types involved in disease. These gene interactions can now be captured by building gene regulatory networks, by taking advantage of RNA velocity (the time derivative of the gene expression state), the ability to study hundreds of thousands of cells simultaneously, and the falling price of single-cell sequencing. Here, we propose an integrative approach that leverages these recent advances in single-cell data with the sensitivity of bulk data to enable the reconstruction of personalized, cell-type- and context-specific gene regulatory networks. We expect this approach will allow the prioritization of key driver genes for specific diseases and will provide knowledge that opens new avenues towards improved personalized healthcare