Jerniganandersen4652

Huntington's Disease (HD) is a progressive, fatal neurodegenerative condition. While generally considered for its devastating neurological phenotype, disturbances in other organ systems and metabolic pathways outside the brain have attracted attention for possible relevance to HD pathology, potential as therapeutic targets, or use as biomarkers of progression. In addition, it is not established how metabolic changes in the HD brain correlate to progression across the full spectrum of early to late-stage disease. In this pilot study, we sought to explore the metabolic profile across manifest HD from early to advanced clinical staging through metabolomic analysis by mass spectrometry in plasma and cerebrospinal fluid (CSF). With disease progression, we observed nominally significant increases in plasma arginine, citrulline, and glycine, with decreases in total and D-serine, cholesterol esters, diacylglycerides, triacylglycerides, phosphatidylcholines, phosphatidylethanolamines, and sphingomyelins. In CSF, worsening disease was associated with nominally significant increases in NAD+, arginine, saturated long chain free fatty acids, diacylglycerides, triacylglycerides, and sphingomyelins. Notably, diacylglycerides and triacylglyceride species associated with clinical progression were different between plasma and CSF, suggesting different metabolic preferences for these compartments. Increasing NAD+ levels strongly correlating with disease progression was an unexpected finding. Our data suggest that defects in the urea cycle, glycine, and serine metabolism may be underrecognized in the progression HD pathology, and merit further study for possible therapeutic relevance.To overcome the scarcity of primary human alveolar epithelial cells for lung research, and the limitations of current cell lines to recapitulate the phenotype, functional and molecular characteristics of the healthy human alveolar epithelium, we have developed a new method to immortalise primary human alveolar epithelial lung cells using a non-viral vector to transfect the telomerase catalytic subunit (hTERT) and the simian virus 40 large-tumour antigen (SV40). Twelve strains of immortalised cells (ICs) were generated and characterised using molecular, immunochemical and morphological techniques. Cell proliferation and sensitivity to polystyrene nanoparticles (PS) were evaluated. ICs expressed caveolin-1, podoplanin and receptor for advanced glycation end-products (RAGE), and most cells were negative for alkaline phosphatase staining, indicating characteristics of AT1-like cells. However, most strains also contained some cells that expressed pro-surfactant protein C, classically described to be expressed only by AT2 cells. Thus, the ICs mimic the cellular heterogeneity in the human alveolar epithelium. These ICs can be passaged, replicate rapidly and remain confluent beyond 15 days. ICs showed differential sensitivity to positive and negatively charged PS nanoparticles, illustrating their potential value as an in vitro model to study respiratory bioreactivity. These novel ICs offer a unique resource to study human alveolar epithelial biology.A low intensity light beam emerges from a graded-index, highly multimode optical fibre with a speckled shape, while at higher intensity the Kerr nonlinearity may induce a spontaneous spatial self-cleaning of the beam. Here, we reveal that we can generate two self-cleaned beams with a mutual coherence large enough to produce a clear stable fringe pattern at the output of a nonlinear interferometer. The two beams are pumped by the same input laser, yet are self-cleaned into independent multimode fibres. We thus prove that the self-cleaning mechanism preserves the beams' mutual coherence via a noise-free parametric process. While directly related to the initial pump coherence, the emergence of nonlinear spatial coherence is achieved without additional noise, even for self-cleaning obtained on different modes, and in spite of the fibre structural disorder originating from intrinsic imperfections or external perturbations. Our discovery may impact theoretical approaches on wave condensation, and open new opportunities for coherent beam combining.Using mass cytometry, we investigated the expression of 28 markers on CD8+ and CD4+ T cells from HIV-1 infected patients with a variable size of HIV-1 reservoir defined as high (HR) and low (LR) reservoir; we aimed at identifying phenotypic associations of T cells with size of HIV-1 reservoir. We showed that the frequency of CD45+ CD8+ and CD4+ T cells was directly proportional to the size of HIV-1 reservoir; HR patients had a significantly larger frequency of blood CD45high T cells and higher CD45 expression on both CD8+ and CD4+ T cells. CD45 is a receptor-type protein tyrosine phosphatase essential in TCR signaling. Functional and phenotypical analysis of CD45high cells revealed that they express activation and proliferation markers (CD38 + HLA-DR + and Ki-67) and produce cytokines upon in vitro activation. CD45high T cells also expressed high levels of immune check-point PD-1. Our results link CD45 expression on T cells to HIV-1 reservoir; PD-1 expression on CD45high T cells may contribute to their exhaustion.In social species, the presence of several reproductive individuals can generate conflict. In social insects, as queen number increases, individual oviposition rate may decrease because of direct and indirect behavioural and/or chemical interactions. Understanding the factors that mediate differences in queen fecundity should provide insight into the regulation and maintenance of highly polygynous insect societies, such as those of the invasive Argentine ant (Linepithema humile). In this study, we investigated (1) whether differences in the oviposition rates of Argentine ant queens exposed to polygynous conditions could result from interactions among them; (2) whether such differences in fecundity stemmed from differences in worker attention; and (3) whether polygynous conditions affected the cuticular hydrocarbon profiles of queens (CHCs). We found that differences in queen fecundity and CHC profiles observed under polygynous conditions disappeared when queens were exposed to monogynous conditions, suggesting some form of reproductive inhibition may exist when queens cohabit. These differences did not seem to arise from variation in worker attention because more fecund queens were not more attractive to workers. Levels of some CHCs were higher in more fecund queens. These CHCs are associated with greater queen productivity and survival. Our findings indicate that such compounds could be multifunctional queen pheromones.Cancer-associated fibroblast (CAF) secretes extracellular vesicle (EV)-encapsulated microRNAs (miRNAs) which have been underlined great promise for therapeutic target for diseases and cancers. Our study aimed to explore the role of EV-encapsulated miR-10a-5p from CAFs in angiogenesis in cervical cancer. Expression of miR-10a-5p in clinical samples of cervical cancer and cancer cells was detected by in situ hybridization and RT-qPCR. Results demonstrated that miR-10a-5p expression was upregulated in both cancer tissues and cells. CAFs and normal fibroblasts (NFs) from cervical cancer patient tissues were characterized under transmission electron microscopy, followed by EV isolation from CAFs. The EVs labeled with PKH67 were cultured with cervical squamous cell carcinoma (CSCC) cell line (SiHa) and HUVECs. Data indicated that CAF-EVs were internalized by cancer cells and promoted cell proliferation and tube formation. CAF-EVs then were transfected with miR-10a-5p inhibitor and then injected into nude mice. While injection of CAF-EVs promoted tumor growth and increased VEGFR and CD31 expression level, miR-10a-5p inhibitor-treated CAF-EVs resulted in decreased tumor volume and amount of vessel around tumor. Of note, dual-luciferase reporter gene assay and bioinformatic analysis indicated TBX5 as a target gene of miR-10a-5p. Moreover, EV-derived miR-10a-5p promoted angiogenesis in vivo and in vitro through activation of Hedgehog signaling via downregulation of TBX5. Taken altogether, miR-10a-5p in CAF-EVs promoted CSCC cell angiogenesis and tumorigenicity via activation of Hh signaling by inhibition of TBX5, providing insight into novel treatment based on miR-10a-5p against CSCC.Phytophthora blight is one of the most serious diseases affecting melon (Cucumis melo) production. Due to the lack of highly resistant germplasms, the progress on disease-resistant research is slow. this website To understand the genetics of melon resistance to Phytophthora capsici, an F2 population containing 498 individuals was developed by crossing susceptible line E31 to highly resistant line ZQK9. Genetic analysis indicated that the resistance in ZQK9 was controlled by a dominant gene, tentatively named MePhyto. Through bulked-segregant analysis (BSA-Seq) and chromosome walking techniques, the MePhyto gene was mapped to a 52.44 kb interval on chromosome 12. In this region, there were eight genes and their expression patterns were validated by qRT-PCR. Among them, one wall-associated receptor kinase (WAK) gene MELO3C002430 was significantly induced in ZQK9 after P. capsici inoculation, but not in E31. Based on the non-synonymous mutation site in MELO3C002430, a cleaved amplified polymorphic sequence (CAPS) marker, CAPS2430, was developed and this maker was co-segregated with MePhyto in both F2 population and a collection of 36 melon accessions. Thus MELO3C002430 was considered as the candidate gene and CAPS2430 was a promising marker for marker-assisted selection (MAS) in breeding. These results lay a foundation for revealing the resistance mechanism of melon to P. capsici.Land susceptibility to wind erosion hazard in Isfahan province, Iran, was mapped by testing 16 advanced regression-based machine learning methods Robust linear regression (RLR), Cforest, Non-convex penalized quantile regression (NCPQR), Neural network with feature extraction (NNFE), Monotone multi-layer perception neural network (MMLPNN), Ridge regression (RR), Boosting generalized linear model (BGLM), Negative binomial generalized linear model (NBGLM), Boosting generalized additive model (BGAM), Spline generalized additive model (SGAM), Spike and slab regression (SSR), Stochastic gradient boosting (SGB), support vector machine (SVM), Relevance vector machine (RVM) and the Cubist and Adaptive network-based fuzzy inference system (ANFIS). Thirteen factors controlling wind erosion were mapped, and multicollinearity among these factors was quantified using the tolerance coefficient (TC) and variance inflation factor (VIF). Model performance was assessed by RMSE, MAE, MBE, and a Taylor diagram using both training and validation datasets. The result showed that five models (MMLPNN, SGAM, Cforest, BGAM and SGB) are capable of delivering a high prediction accuracy for land susceptibility to wind erosion hazard. DEM, precipitation, and vegetation (NDVI) are the most critical factors controlling wind erosion in the study area. Overall, regression-based machine learning models are efficient techniques for mapping land susceptibility to wind erosion hazards.Sheep farming has been fundamental to many civilizations in the world and is practiced in India since antiquity. Several thousand years of adaptation to local environmental conditions and selective breeding have evolved 44 sheep breeds in India. They are paramount in terms of economic, scientific, and cultural heritage. Genetic characterization information is imperative for sustainable utilization and conservation of ovine heritage. In this study, the genetic diversity, differentiation, and structure of 11 indigenous sheep breeds from three different agro-ecological zones of India were explored with genomic microsatellite loci and mitochondrial DNA (D loop). The estimated diversity parameters indicated that populations retained high levels of genetic diversity (Na = 8.27 ± 0.17; Ho = 0.65 ± 0.01), which provides an optimistic viewpoint for their survival. However, significant inbreeding was also observed in the nine populations. Moderate genetic differentiation existed among the groups (FST = 0.129 ± 0.012), and most likely clusters existing in the dataset are seven.