Hematopoietic Jagged1 is a fetal liver niche factor required for functional maturation and engraftment of fetal hematopoietic stem cells

Notch signaling is essential for the emergence of definitive hematopoietic stem cells (HSCs) in the embryo and their development in the fetal liver niche. However, how Notch signaling is activated and which fetal liver cell type provides the ligand for receptor activation in HSCs is unknown. Here we provide evidence that endothelial Jagged1 (Jag1) has a critical early role in fetal liver vascular development but is not required for hematopoietic function during fetal HSC expansion. We demonstrate that Jag1 is expressed in many hematopoietic cells in the fetal liver, including HSCs, and that its expression is lost in adult bone marrow HSCs. Deletion of hematopoietic Jag1 does not affect fetal liver development; however, Jag1-deficient fetal liver HSCs exhibit a significant transplantation defect. Bulk and single-cell transcriptomic analysis of HSCs during peak expansion in the fetal liver indicates that loss of hematopoietic Jag1 leads to the downregulation of critical hematopoietic factors such as GATA2, Mllt3, and HoxA7, but does not perturb Notch receptor expression. Ex vivo activation of Notch signaling in Jag1-deficient fetal HSCs partially rescues the functional defect in a transplant setting. These findings indicate a new fetal-specific niche that is based on juxtracrine hematopoietic Notch signaling and reveal Jag1 as a fetal-specific niche factor essential for HSC function

Anticancer potential of Thevetia peruviana fruit methanolic extract

Abstract Background: Thevetia peruviana (Pers.) K. Schum or Cascabela peruviana (L.) Lippold (commonly known as ayoyote, codo de fraile, lucky nut, or yellow oleander), native to Mexico and Central America, is a medicinal plant used traditionally to cure diseases like ulcers, scabies, hemorrhoids and dissolve tumors. The purpose of this study was to evaluate the cytotoxic, antiproliferative and apoptotic activity of methanolic extract of T. peruviana fruits on human cancer cell lines. Methods: The cytotoxic activity of T. peruviana methanolic extract was carried out on human breast, colorectal, prostate and lung cancer cell lines and non-tumorigenic control cells (fibroblast and Vero), using the MTT assay. For proliferation and motility, clonogenic and wound-healing assays were performed. Morphological alterations were monitored by trypan blue exclusion, as well as DNA fragmentation and AO/EB double staining was performed to evaluate apoptosis. The extract was separated using flash chromatography, and the resulting fractions were evaluated on colorectal cancer cells for their cytotoxic activity. The active fractions were further analyzed through mass spectrometry. Results: The T. peruviana methanolic extract exhibited cytotoxic activity on four human cancer cell lines: prostate, breast, colorectal and lung, with values of IC50 1.91 ± 0.76, 5.78 ± 2.12, 6.30 ± 4.45 and 12.04 ± 3.43 μg/mL, respectively. The extract caused a significant reduction of cell motility and colony formation on all evaluated cancer cell lines. In addition, morphological examination displayed cell size reduction, membrane blebbing and detachment of cells, compared to non-treated cancer cell lines. The T. peruviana extract induced apoptotic cell death, which was confirmed by DNA fragmentation and AO/EB double staining. Fractions 4 and 5 showed the most effective cytotoxic activity and their MS analysis revealed the presence of the secondary metabolites: thevetiaflavone and cardiac glycosides. Conclusion: T. peruviana extract has potential as natural anti-cancer product with critical effects in the proliferation, motility, and adhesion of human breast and colorectal cancer cells, and apoptosis induction in human prostate and lung cancer cell lines, with minimal effects on non-tumorigenic cell lines. Keywords: Cytotoxic activity, Anti-proliferative activity, Motility, Apoptosis, Human cancer cells, Flavonoid, Cardiac glycoside

A constraint-based genetic algorithm for optimizing neural network architectures for detection of loss of coolant accidents of nuclear power plants

© 2018 Elsevier B.V. The loss of coolant accident (LOCA) of a nuclear power plant (NPP) is a severe accident in the nuclear energy industry. Nowadays, neural networks have been trained on nuclear simulation transient datasets to detect LOCA. This paper proposes a constraint-based genetic algorithm (GA) to find optimised 2-hidden layer network architectures for detecting LOCA of a NPP. The GA uses a proposed constraint satisfaction algorithm called random walk heuristic to create an initial population of neural network architectures of high performance. At each generation, the GA population is split into a sub-population of feature subsets and a sub-population of 2-hidden layer architectures to breed offspring from each sub-population independently in order to generate a wide variety of network architectures. During breeding 2-hidden layer architectures, a constraint-based nearest neighbor search algorithm is proposed to find the nearest neighbors of the offspring population generated by mutation. The results showed that for LOCA detection, the GA-optimised network outperformed a random search, an exhaustive search and a RBF kernel support vector regression (SVR) in terms of generalization performance. For the skillcraft dataset of the UCI machine learning repository, the GA-optimised network has a similar performance to the RBF kernel SVR and outperformed the other approaches

Host-Adaptation of Francisella tularensis Alters the Bacterium's Surface-Carbohydrates to Hinder Effectors of Innate and Adaptive Immunity

The gram-negative bacterium Francisella tularensis survives in arthropods, fresh water amoeba, and mammals with both intracellular and extracellular phases and could reasonably be expected to express distinct phenotypes in these environments. The presence of a capsule on this bacterium has been controversial with some groups finding such a structure while other groups report that no capsule could be identified. Previously we reported in vitro culture conditions for this bacterium which, in contrast to typical methods, yielded a bacterial phenotype that mimics that of the bacterium's mammalian, extracellular phase.SDS-PAGE and carbohydrate analysis of differentially-cultivated F. tularensis LVS revealed that bacteria displaying the host-adapted phenotype produce both longer polymers of LPS O-antigen (OAg) and additional HMW carbohydrates/glycoproteins that are reduced/absent in non-host-adapted bacteria. Analysis of wildtype and OAg-mutant bacteria indicated that the induced changes in surface carbohydrates involved both OAg and non-OAg species. To assess the impact of these HMW carbohydrates on the access of outer membrane constituents to antibody we used differentially-cultivated bacteria in vitro to immunoprecipitate antibodies directed against outer membrane moieties. We observed that the surface-carbohydrates induced during host-adaptation shield many outer membrane antigens from binding by antibody. Similar assays with normal mouse serum indicate that the induced HMW carbohydrates also impede complement deposition. Using an in vitro macrophage infection assay, we find that the bacterial HMW carbohydrate impedes TLR2-dependent, pro-inflammatory cytokine production by macrophages. Lastly we show that upon host-adaptation, the human-virulent strain, F. tularensis SchuS4 also induces capsule production with the effect of reducing macrophage-activation and accelerating tularemia pathogenesis in mice.F. tularensis undergoes host-adaptation which includes production of multiple capsular materials. These capsules impede recognition of bacterial outer membrane constituents by antibody, complement, and Toll-Like Receptor 2. These changes in the host-pathogen interface have profound implications for pathogenesis and vaccine development

Molecular control of nitric oxide synthesis through eNOS and caveolin-1 interaction regulates osteogenic differentiation of adipose-derived stem cells by modulation of Wnt/β-catenin signaling

BACKGROUND: Nitric oxide (NO) plays a role in a number of physiological processes including stem cell differentiation and osteogenesis. Endothelial nitric oxide synthase (eNOS), one of three NO-producing enzymes, is located in a close conformation with the caveolin-1 (CAV-1(WT)) membrane protein which is inhibitory to NO production. Modification of this interaction through mutation of the caveolin scaffold domain can increase NO release. In this study, we genetically modified equine adipose-derived stem cells (eASCs) with eNOS, CAV-1(WT), and a CAV-1(F92A) (CAV-1(WT) mutant) and assessed NO-mediated osteogenic differentiation and the relationship with the Wnt signaling pathway. METHODS: NO production was enhanced by lentiviral vector co-delivery of eNOS and CAV-1(F92A) to eASCs, and osteogenesis and Wnt signaling was assessed by gene expression analysis and activity of a novel Runx2-GFP reporter. Cells were also exposed to a NO donor (NONOate) and the eNOS inhibitor, l-NAME. RESULTS: NO production as measured by nitrite was significantly increased in eNOS and CAV-1(F92A) transduced eASCs +(5.59 ± 0.22 μM) compared to eNOS alone (4.81 ± 0.59 μM) and un-transduced control cells (0.91 ± 0.23 μM) (p < 0.05). During osteogenic differentiation, higher NO correlated with increased calcium deposition, Runx2, and alkaline phosphatase (ALP) gene expression and the activity of a Runx2-eGFP reporter. Co-expression of eNOS and CAV-1(WT) transgenes resulted in lower NO production. Canonical Wnt signaling pathway-associated Wnt3a and Wnt8a gene expressions were increased in eNOS-CAV-1(F92A) cells undergoing osteogenesis whilst non-canonical Wnt5a was decreased and similar results were seen with NONOate treatment. Treatment of osteogenic cultures with 2 mM l-NAME resulted in reduced Runx2, ALP, and Wnt3a expressions, whilst Wnt5a expression was increased in eNOS-delivered cells. Co-transduction of eASCs with a Wnt pathway responsive lenti-TCF/LEF-dGFP reporter only showed activity in osteogenic cultures co-transduced with a doxycycline inducible eNOS. Lentiviral vector expression of canonical Wnt3a and non-canonical Wnt5a in eASCs was associated with induced and suppressed osteogenic differentiation, respectively, whilst treatment of eNOS-osteogenic cells with the Wnt inhibitor Dkk-1 significantly reduced expressions of Runx2 and ALP. CONCLUSIONS: This study identifies NO as a regulator of canonical Wnt/β-catenin signaling to promote osteogenesis in eASCs which may contribute to novel bone regeneration strategies. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13287-016-0442-9) contains supplementary material, which is available to authorized users

Invariant properties of a locally salient dither pattern with a spatial-chromatic histogram

Compacted Dither Pattern Code (CDPC) is a recently found feature which is successful in irregular shapes based visual depiction. Locally salient dither pattern feature is an attempt to expand the capability of CDPC for both regular and irregular shape based visual depiction. This paper presents an analysis of rotational and scale invariance property of locally salient dither pattern feature with a two dimensional spatial-chromatic histogram, which expands the applicability of the visual feature. Experiments were conducted to exhibit rotational and scale invariance of the feature. These experiments were conducted by combining linear Support Vector Machine (SVM) classifier to the new feature. The experimental results revealed that the locally salient dither pattern feature with the spatial-chromatic histogram is rotationally and scale invariant

A Feature clustering approach based on histogram of oriented optical flow and superpixels

Visual feature clustering is one of the cost-effective approaches to segment objects in videos. However, the assumptions made for developing the existing algorithms prevent them from being used in situations like segmenting an unknown number of static and moving objects under heavy camera movements. This paper addresses the problem by introducing a clustering approach based on superpixels and short-term Histogram of Oriented Optical Flow (HOOF). Salient Dither Pattern Feature (SDPF) is used as the visual feature to track the flow and Simple Linear Iterative Clustering (SLlC) is used for obtaining the superpixels. This new clustering approach is based on merging superpixels by comparing short term local HOOF and a color cue to form high-level semantic segments. The new approach was compared with one of the latest feature clustering approaches based on K-Means in eight-dimensional space and the results revealed that the new approach is better by means of consistency, completeness, and spatial accuracy. Further, the new approach completely solved the problem of not knowing the number of objects in a scene

Real-Time Monitoring and Driver Feedback to Promote Fuel Efficient Driving

Improving the fuel efficiency of vehicles is imperative to reduce costs andprotect the environment. While the efficient engine and vehicle designs, aswell as intelligent route planning, are well-known solutions to enhance thefuel efficiency, research has also demonstrated that the adoption offuel-efficient driving behaviors could lead to further savings. In this work,we propose a novel framework to promote fuel-efficient driving behaviorsthrough real-time automatic monitoring and driver feedback. In this framework,a random-forest based classification model developed using historical data toidentifies fuel-inefficient driving behaviors. The classifier considersdriver-dependent parameters such as speed and acceleration/decelerationpattern, as well as environmental parameters such as traffic, road topography,and weather to evaluate the fuel efficiency of one-minute driving events. Whenan inefficient driving action is detected, a fuzzy logic inference system isused to determine what the driver should do to maintain fuel-efficient drivingbehavior. The decided action is then conveyed to the driver via a smartphone ina non-intrusive manner. Using a dataset from a long-distance bus, wedemonstrate that the proposed classification model yields an accuracy of 85.2%while increasing the fuel efficiency up to 16.4%