754 research outputs found
PPARs and Tumor Microenvironment: The Emerging Roles of the Metabolic Master Regulators in Tumor Stromal-Epithelial Crosstalk and Carcinogenesis.
Peroxisome proliferator-activated receptors (PPARs) have been extensively studied for more than three decades. Consisting of three isotypes, PPARα, γ, and β/δ, these nuclear receptors are regarded as the master metabolic regulators which govern many aspects of the body energy homeostasis and cell fate. Their roles in malignancy are also increasingly recognized. With the growing interest in crosstalk between tumor stroma and epithelium, this review aims to highlight the current knowledge on the implications of PPARs in the tumor microenvironment. PPARγ plays a crucial role in the metabolic reprogramming of cancer-associated fibroblasts and adipocytes, coercing the two stromal cells to become substrate donors for cancer growth. Fibroblast PPARβ/δ can modify the risk of tumor initiation and cancer susceptibility. In endothelial cells, PPARβ/δ and PPARα are pro- and anti-angiogenic, respectively. Although the angiogenic role of PPARγ remains ambiguous, it is a crucial regulator in autocrine and paracrine signaling of cancer-associated fibroblasts and tumor-associated macrophages/immune cells. Of note, angiopoietin-like 4 (ANGPTL4), a secretory protein encoded by a target gene of PPARs, triggers critical oncogenic processes such as inflammatory signaling, extracellular matrix derangement, anoikis resistance and metastasis, making it a potential drug target for cancer treatment. To conclude, PPARs in the tumor microenvironment exhibit oncogenic activities which are highly controversial and dependent on many factors such as stromal cell types, cancer types, and oncogenesis stages. Thus, the success of PPAR-based anticancer treatment potentially relies on innovative strategies to modulate PPAR activity in a cell type-specific manner
Exploiting vulnerabilities of cancer by targeting nuclear receptors of stromal cells in tumor microenvironment.
The tumor microenvironment is a complex and dynamic cellular community comprising the tumor epithelium and various tumor-supporting cells such as immune cells, fibroblasts, immunosuppressive cells, adipose cells, endothelial cells, and pericytes. The interplay between the tumor microenvironment and tumor cells represents a key contributor to immune evasiveness, physiological hardiness and the local and systemic invasiveness of malignant cells. Nuclear receptors are master regulators of physiological processes and are known to play pro-/anti-oncogenic activities in tumor cells. However, the actions of nuclear receptors in tumor-supporting cells have not been widely studied. Given the excellent druggability and extensive regulatory effects of nuclear receptors, understanding their biological functionality in the tumor microenvironment is of utmost importance. Therefore, the present review aims to summarize recent evidence about the roles of nuclear receptors in tumor-supporting cells and their implications for malignant processes such as tumor proliferation, evasion of immune surveillance, angiogenesis, chemotherapeutic resistance, and metastasis. Based on findings derived mostly from cell culture studies and a few in vivo animal cancer models, the functions of VDR, PPARs, AR, ER and GR in tumor-supporting cells are relatively well-characterized. Evidence for other receptors, such as RARβ, RORγ, and FXR, is limited yet promising. Hence, the nuclear receptor signature in the tumor microenvironment may harbor prognostic value. The clinical prospects of a tumor microenvironment-oriented cancer therapy exploiting the nuclear receptors in different tumor-supporting cells are also encouraging. The major challenge, however, lies in the ability to develop a highly specific drug delivery system to facilitate precision medicine in cancer therapy
ActivityNET: Neural networks to predict public transport trip purposes from individual smart card data and POIs
Predicting trip purpose from comprehensive and continuous smart card data is beneficial for transport and city planners in investigating travel behaviors and urban mobility. Here, we propose a framework, ActivityNET, using Machine Learning (ML) algorithms to predict passengers’ trip purpose from Smart Card (SC) data and Points-of-Interest (POIs) data. The feasibility of the framework is demonstrated in two phases. Phase I focuses on extracting activities from individuals’ daily travel patterns from smart card data and combining them with POIs using the proposed “activity-POIs consolidation algorithm”. Phase II feeds the extracted features into an Artificial Neural Network (ANN) with multiple scenarios and predicts trip purpose under primary activities (home and work) and secondary activities (entertainment, eating, shopping, child drop-offs/pick-ups and part-time work) with high accuracy. As a case study, the proposed ActivityNET framework is applied in Greater London and illustrates a robust competence to predict trip purpose. The promising outcomes demonstrate that the cost-effective framework offers high predictive accuracy and valuable insights into transport planning
Strominger--Yau--Zaslow geometry, Affine Spheres and Painlev\'e III
We give a gauge invariant characterisation of the elliptic affine sphere
equation and the closely related Tzitz\'eica equation as reductions of real
forms of SL(3, \C) anti--self--dual Yang--Mills equations by two
translations, or equivalently as a special case of the Hitchin equation.
We use the Loftin--Yau--Zaslow construction to give an explicit expression
for a six--real dimensional semi--flat Calabi--Yau metric in terms of a
solution to the affine-sphere equation and show how a subclass of such metrics
arises from 3rd Painlev\'e transcendents.Comment: 38 pages. Final version. To appear in Communications in Mathematical
Physic
QGP flow fluctuations and the characteristics of higher moments
The dynamical development of expanding Quark-gluon Plasma (QGP) flow is
studied in a 3+1D fluid dynamical model with a globally symmetric, initial
condition. We minimize fluctuations arising from complex dynamical processes at
finite impact parameters and from fluctuating random initial conditions to have
a conservative fluid dynamical background estimate for the statistical
distributions of the thermodynamical parameters. We also avoid a phase
transition in the equation of state, and we let the matter supercool during the
expansion.
Then central Pb+Pb collisions at TeV are studied in an
almost perfect fluid dynamical model, with azimuthally symmetric initial state
generated in a dynamical flux-tube model. The general development of
thermodynamical extensives are also shown for lower energies.
We observe considerable deviations from a thermal equilibrium source as a
consequence of the fluid dynamical expansion arising from a least fluctuating
initial state
The Iterative Signature Algorithm for the analysis of large scale gene expression data
We present a new approach for the analysis of genome-wide expression data.
Our method is designed to overcome the limitations of traditional techniques,
when applied to large-scale data. Rather than alloting each gene to a single
cluster, we assign both genes and conditions to context-dependent and
potentially overlapping transcription modules. We provide a rigorous definition
of a transcription module as the object to be retrieved from the expression
data. An efficient algorithm, that searches for the modules encoded in the data
by iteratively refining sets of genes and conditions until they match this
definition, is established. Each iteration involves a linear map, induced by
the normalized expression matrix, followed by the application of a threshold
function. We argue that our method is in fact a generalization of Singular
Value Decomposition, which corresponds to the special case where no threshold
is applied. We show analytically that for noisy expression data our approach
leads to better classification due to the implementation of the threshold. This
result is confirmed by numerical analyses based on in-silico expression data.
We discuss briefly results obtained by applying our algorithm to expression
data from the yeast S. cerevisiae.Comment: Latex, 36 pages, 8 figure
Petroleum hydrocarbon rhizoremediation and soil microbial activity improvement via cluster root formation by wild proteaceae plant species
Rhizoremediation potential of different wild plant species for total (aliphatic) petroleum hydrocarbon (TPH)-contaminated soils was investigated. Three-week-old seedlings of Acacia inaequilatera, Acacia pyrifolia, Acacia stellaticeps, Banksia seminuda, Chloris truncata, Hakea prostrata, Hardenbergia violacea, and Triodia wiseana were transplanted in a soil contaminated with diesel and engine oil as TPH at pollution levels of 4,370 (TPH1) and 7,500 (TPH2) mg kg−1, and an uncontaminated control (TPH0). After 150 days, the presence of TPH negatively affected the plant growth, but the growth inhibition effect varied between the plant species. Plant growth and associated root biomass influenced the activity of rhizo-microbiome. The presence of B. seminuda, C. truncata, and H. prostrata significantly increased the TPH removal rate (up to 30% compared to the unplanted treatment) due to the stimulation of rhizosphere microorganisms. No significant difference was observed between TPH1 and TPH2 regarding the plant tolerance and rhizoremediation potentials of the three plant species. The presence of TPH stimulated cluster root formation in B. seminuda and H. prostrata which was associated with enhanced TPH remediation of these two members of Proteaceae family. These results indicated that B. seminuda, C. truncata, and H. prostrata wild plant species could be suitable candidates for the rhizoremediation of TPH-contaminated soil
HIV Linkage to Care and Retention in Care Rate Among MSM in Guangzhou, China
Quantifying HIV service provision along the HIV care continuum is increasingly important for monitoring and evaluating HIV interventions. We examined factors associated with linkage and retention in care longitudinally among MSM (n = 1974, 4933 person-years) diagnosed and living in Guangzhou, China, in 2008–2014. We measured longitudinal change of retention in care (≥2 CD4 tests per year) from linkage and antiretroviral therapy initiation (ART). We examined factors associated with linkage using logistic regression and with retention using generalized estimating equations. The rate of linkage to care was 89% in 2014. ART retention rate dropped from 71% (year 1) to 46% (year 2), suggesting that first-year retention measures likely overestimate retention over longer periods. Lower CD4 levels and older age predicted retention in ART care. These data can inform interventions to improve retention about some subgroups
Methods of measuring residual stresses in components
Residual stresses occur in many manufactured structures and components. Large number of investigations have been carried out to study this phenomenon and its effect on the mechanical characteristics of these components.
Over the years, different methods have been developed to measure residual stress for different types of components in order to obtain reliable assessment. The various specific methods have evolved over several decades and their practical applications have greatly benefited from the development of complementary technologies, notably in material cutting, full-field deformation measurement techniques, numerical methods and computing power. These complementary technologies have stimulated advances not only in measurement accuracy and reliability, but also in range of application; much greater detail in residual stresses measurement is now available. This paper aims to classify the different residual stresses measurement methods and to provide an overview of some of the recent advances in this area to help researchers on selecting their techniques among destructive, semi destructive and non destructive techniques depends on their application and the availabilities of those techniques. For each method scope, physical limitation, advantages and disadvantages are summarized. In the end this paper indicates some promising directions for future developments
The Effect of Valproic Acid on Mesenchymal Pluripotent Cell Proliferation and Differentiation in Extracellular Matrices
Valproic acid (2-n-propylpentanoic acid, VPA) is a widely used antiepileptic and anticonvulsant drug. Previous studies have reported that VPA effects osteogenesis in vivo and in vitro, yet it remains unclear whether VPA promotes cell differentiation of osteoblasts derived from mesenchymal cells. The purpose of this study was to clarify the effect of VPA on undifferentiated pluripotent mesenchymal cell proliferation and differentiation into osteoblasts while analyzing the impact of the absence or presence of extracellular matrices (ECMs). Mouse mesenchymal cells were cultured on non-coated plastic, type I collagen-coated, and fibronectin-coated plates in the absence or presence of VPA. A cell proliferation assay was performed in which modified formazan dye content was analyzed and proliferation nuclear antigen (PCNA)-positive cells were counted at various concentrations of VPA. A high concentration of VPA did not clearly alter cell morphology, but large numbers of stress fibers were observed in these cells and the cell proliferation ratio was decreased with positive PCNA counts. In the presence of matrices, the cell proliferation ratio decreased at low VPA concentrations compared with the ratio obtained in the absence of these ECMs. On the other hand, VPA promoted osteoblastic differentiation in the presence of type I collagen. These findings indicate that for undifferentiated mesenchymal cells, VPA promotes a decrease in the cell proliferation rate in the presence of ECMs and promotes osteoblastic differentiation, both of which could provide insight into additional mechanisms of osteoblastic cell differentiation caused by VPA
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