Tianshengyuan-1 (TSY-1) regulates cellular Telomerase activity by methylation of TERT promoter.

Telomere and Telomerase have recently been explored as anti-aging and anti-cancer drug targets with only limited success. Previously we showed that the Chinese herbal medicine Tianshengyuan-1 (TSY-1), an agent used to treat bone marrow deficiency, has a profound effect on stimulating Telomerase activity in hematopoietic cells. Here, the mechanism of TSY-1 on cellular Telomerase activity was further investigated using HL60, a promyelocytic leukemia cell line, normal peripheral blood mononuclear cells, and CD34+ hematopoietic stem cells derived from umbilical cord blood. TSY-1 increases Telomerase activity in normal peripheral blood mononuclear cells and CD34+ hematopoietic stem cells with innately low Telomerase activity but decreases Telomerase activity in HL60 cells with high intrinsic Telomerase activity, both in a dose-response manner. Gene profiling analysis identified Telomerase reverse transcriptase (TERT) as the potential target gene associated with the TSY-1 effect, which was verified by both RT-PCR and western blot analysis. The β-galactosidase reporter staining assay showed that the effect of TSY-1 on Telomerase activity correlates with cell senescence. TSY-1 induced hypomethylation within TERT core promoter in HL60 cells but induced hypermethylation within TERT core promoter in normal peripheral blood mononuclear cells and CD34+ hematopoietic stem cells. TSY-1 appears to affect the Telomerase activity in different cell lines differently and the effect is associated with TERT expression, possibly via the methylation of TERT promoter

QCR7 affects the virulence of Candida albicans and the uptake of multiple carbon sources present in different host niches

BackgroundCandida albicans is a commensal yeast that may cause life-threatening infections. Studies have shown that the cytochrome b-c1 complex subunit 7 gene (QCR7) of C. albicans encodes a protein that forms a component of the mitochondrial electron transport chain complex III, making it an important target for studying the virulence of this yeast. However, to the best of our knowledge, the functions of QCR7 have not yet been characterized.MethodsA QCR7 knockout strain was constructed using SN152, and BALb/c mice were used as model animals to determine the role of QCR7 in the virulence of C. albicans. Subsequently, the effects of QCR7 on mitochondrial functions and use of carbon sources were investigated. Next, its mutant biofilm formation and hyphal growth maintenance were compared with those of the wild type. Furthermore, the transcriptome of the qcr7Δ/Δ mutant was compared with that of the WT strain to explore pathogenic mechanisms.ResultsDefective QCR7 reduced recruitment of inflammatory cells and attenuated the virulence of C. albicans infection in vivo. Furthermore, the mutant influenced the use of multiple alternative carbon sources that exist in several host niches (GlcNAc, lactic acid, and amino acid, etc.). Moreover, it led to mitochondrial dysfunction. Furthermore, the QCR7 knockout strain showed defects in biofilm formation or the maintenance of filamentous growth. The overexpression of cell-surface-associated genes (HWP1, YWP1, XOG1, and SAP6) can restore defective virulence phenotypes and the carbon-source utilization of qcr7Δ/Δ.ConclusionThis study provides new insights into the mitochondria-based metabolism of C. albicans, accounting for its virulence and the use of variable carbon sources that promote C. albicans to colonize host niches

Differentiable Genetic Programming for High-dimensional Symbolic Regression

Symbolic regression (SR) is the process of discovering hidden relationships from data with mathematical expressions, which is considered an effective way to reach interpretable machine learning (ML). Genetic programming (GP) has been the dominator in solving SR problems. However, as the scale of SR problems increases, GP often poorly demonstrates and cannot effectively address the real-world high-dimensional problems. This limitation is mainly caused by the stochastic evolutionary nature of traditional GP in constructing the trees. In this paper, we propose a differentiable approach named DGP to construct GP trees towards high-dimensional SR for the first time. Specifically, a new data structure called differentiable symbolic tree is proposed to relax the discrete structure to be continuous, thus a gradient-based optimizer can be presented for the efficient optimization. In addition, a sampling method is proposed to eliminate the discrepancy caused by the above relaxation for valid symbolic expressions. Furthermore, a diversification mechanism is introduced to promote the optimizer escaping from local optima for globally better solutions. With these designs, the proposed DGP method can efficiently search for the GP trees with higher performance, thus being capable of dealing with high-dimensional SR. To demonstrate the effectiveness of DGP, we conducted various experiments against the state of the arts based on both GP and deep neural networks. The experiment results reveal that DGP can outperform these chosen peer competitors on high-dimensional regression benchmarks with dimensions varying from tens to thousands. In addition, on the synthetic SR problems, the proposed DGP method can also achieve the best recovery rate even with different noisy levels. It is believed this work can facilitate SR being a powerful alternative to interpretable ML for a broader range of real-world problems

Global variations in diabetes mellitus based on fasting glucose and haemogloblin A1c

Fasting plasma glucose (FPG) and haemoglobin A1c (HbA1c) are both used to diagnose diabetes, but may identify different people as having diabetes. We used data from 117 population-based studies and quantified, in different world regions, the prevalence of diagnosed diabetes, and whether those who were previously undiagnosed and detected as having diabetes in survey screening had elevated FPG, HbA1c, or both. We developed prediction equations for estimating the probability that a person without previously diagnosed diabetes, and at a specific level of FPG, had elevated HbA1c, and vice versa. The age-standardised proportion of diabetes that was previously undiagnosed, and detected in survey screening, ranged from 30% in the high-income western region to 66% in south Asia. Among those with screen-detected diabetes with either test, the agestandardised proportion who had elevated levels of both FPG and HbA1c was 29-39% across regions; the remainder had discordant elevation of FPG or HbA1c. In most low- and middle-income regions, isolated elevated HbA1c more common than isolated elevated FPG. In these regions, the use of FPG alone may delay diabetes diagnosis and underestimate diabetes prevalence. Our prediction equations help allocate finite resources for measuring HbA1c to reduce the global gap in diabetes diagnosis and surveillance.peer-reviewe

3D face matching and registration based on hyperbolic ricci flow

3D surface matching is fundamental for shape analysis. As a powerful method in geometric analysis, Ricci flow can flexibly design metrics by prescribed target curvature. In this paper we describe a novel approach for matching surfaces with complicated topologies based on hyperbolic Ricci flow. For surfaces with negative Euler characteristics, such as a human face with holes (eye contours), the canonical hyperbolic metric is conformal to the original and can be efficiently computed. Then the surface can be canonically decomposed to hyperbolic hexagons. By matching the corresponding hyperbolic hexagons, the matching between surfaces can be easily established. Compared to existing methods, hyperbolic Ricci flow induces diffeomorphisms between surfaces with complicated topologies with negative Euler characteristics, while avoiding singularities. Furthermore, all the boundaries are intrinsically mapped to hyperbolic lines as alignment constraints. Finally, we demonstrate the applicability of this intrinsic shape representation for 3D face matching and registration. local isometric mapping [28], summation invariants [21], landmark-sliding [7], physics-based deformable models [30], Free-Form Deformation (FFD) [14], and Level-Set based methods [23]. However, many surface representations that use local geometric invariants can not guarantee a global convergence and might suffer from local minima in the presence of non-rigid deformations. To address this issue, many global parameterization methods have been developed recently based on conformal geometric map

UGV direction control by human arm gesture recognition via deterministic learning

In this paper, we present a novel method for controlling an unmanned ground vehicle (UGV) by using a new machine learning technique, called deterministic learning [1], to learn and recognize four specifically designed body languages, which represent four corresponding moving directions (i.e., left, right, up, and down) of the controlled UGV. The Microsoft Kinect sensor is employed to collect the human body skeleton data, from which four specifically-designed features are extracted for neural network training. The discrete-time deterministic learning algorithm is then utilized to train the radial basis function neural networks (RBFNNs). The dynamics of the human arm waving motion is guaranteed to be accurately identified, represented, and stored as a RBF NN model with converged constant NN weights. In the testing phase, a set of estimators are built based on the database established in the learning phase, so as to conduct real-time rapid recognition of new in-coming gesture commands. The smallest error principle is used to decode the human intention, the decoded result will then be sent to the UGV through TCP/IP to control the UGV\u27s moving directions. A full-integrated graphical user interface (GUI) has been developed based on Matlab to demonstrate effectiveness of the proposed approach and visualize the experimental results