Hypocholesterolemic Effect of Potent Peptide and Bioactive Fraction from Pigeon Pea By-Products in Wistar Rats

International Journal of Peptide Research and Therapeutics - Elevation high plasma cholesterol plays a significant role in promoting the incidence of atherosclerosis and coronary heart disease. In...     

Black Spot: a platform for automated and rapid estimation of leaf area from scanned images

Leaf area and its derivatives (e.g. specific leaf area) are widely used in ecological assessments, especially in the fields of plant–animal interactions, plant community assembly, ecosystem functioning and global change. Estimating leaf area is highly time-consuming, even when using specialized software to process scanned leaf images, because manual inputs are invariably required for scale detection and leaf surface digitisation. We introduce Black Spot Leaf Area Calculator (hereafter, Black Spot), a technique and stand-alone software package for rapid and automated leaf area assessment from images of leaves taken with standard flatbed scanners. Black Spot operates on comprehensive rule-sets for colour band ratios to carry out pixel-based classification which isolates leaf surfaces from the image background. Importantly, the software extracts information from associated image meta-data to detect image scale, thereby eliminating the need for time-consuming manual scale calibration. Black Spot’s output provides the user with estimates of leaf area as well as classified images for error checking. We tested this method and software combination on a set of 100 leaves of 51 different plant species collected from the field. Leaf area estimates generated using Black Spot and by manual processing of the images using an image editing software generated statistically identical results. Mean error rate in leaf area estimates from Black Spot relative to manual processing was ?0.4 % (SD = 0.76). The key advantage of Black Spot is the ability to rapidly batch process multi-species datasets with minimal user effort and at low cost, thus making it a valuable tool for field ecologists.     

Resveratrol depletes mitochondrial DNA and inhibition of autophagy enhances resveratrol-induced caspase activation

We recently demonstrated that resveratrol induces caspase-dependent apoptosis in multiple cancer cell types. Whether apoptosis is also regulated by other cell death mechanisms such as autophagy is not clearly defined. Here we show that inhibition of autophagy enhanced resveratrol-induced caspase activation and apoptosis. Resveratrol inhibited colony formation and cell proliferation in multiple cancer cell types. Resveratrol treatment induced accumulation of LC3-II, which is a key marker for autophagy. Pretreatment with 3-methyladenine (3-MA), an autophagy inhibitor, increased resveratrol-mediated caspase activation and cell death in breast and colon cancer cells. Inhibition of autophagy by silencing key autophagy regulators such as ATG5 and Beclin-1 enhanced resveratrol-induced caspase activation. Mechanistic analysis revealed that Beclin-1 did not interact with proapoptotic proteins Bax and Bak; however, Beclin-1 was found to interact with p53 in the cytosol and mitochondria upon resveratrol treatment. Importantly, resveratrol depleted ATPase 8 gene, and thus, reduced mitochondrial DNA (mtDNA) content, suggesting that resveratrol induces damage to mtDNA causing accumulation of dysfunctional mitochondria triggering autophagy induction. Together, our findings indicate that induction of autophagy during resveratrol-induced apoptosis is an adaptive response.     

Concentration Sensing by the Moving Nucleus in Cell Fate Determination: A Computational Analysis

During development of the vertebrate neuroepithelium, the nucleus in neural progenitor cells (NPCs) moves from the apex toward the base and returns to the apex (called interkinetic nuclear migration) at which point the cell divides. The fate of the resulting daughter cells is thought to depend on the sampling by the moving nucleus of a spatial concentration profile of the cytoplasmic Notch intracellular domain (NICD). However, the nucleus executes complex stochastic motions including random waiting and back and forth motions, which can expose the nucleus to randomly varying levels of cytoplasmic NICD. How nuclear position can determine daughter cell fate despite the stochastic nature of nuclear migration is not clear. Here we derived a mathematical model for reaction, diffusion, and nuclear accumulation of NICD in NPCs during interkinetic nuclear migration (INM). Using experimentally measured trajectory-dependent probabilities of nuclear turning, nuclear waiting times and average nuclear speeds in NPCs in the developing zebrafish retina, we performed stochastic simulations to compute the nuclear trajectory-dependent probabilities of NPC differentiation. Comparison with experimentally measured nuclear NICD concentrations and trajectory-dependent probabilities of differentiation allowed estimation of the NICD cytoplasmic gradient. Spatially polarized production of NICD, rapid NICD cytoplasmic consumption and the time-averaging effect of nuclear import/export kinetics are sufficient to explain the experimentally observed differentiation probabilities. Our computational studies lend quantitative support to the feasibility of the nuclear concentration-sensing mechanism for NPC fate determination in zebrafish retina.     

Biomarkers of neurodegenerative disorders： How good are they？

Biomarkers are very important indicators of normal and abnormal biological processes. Specific changes in pathologies,biochemistries and genetics can give us comprehensive information regarding the nature of any particular disease. A good biomarker should be precise and reliable, distinguishable between normal and interested disease, and differentiable between different diseases. It is believed that biomarkers have great potential in predicting chances for diseases, aiding in early diagnosis, and setting standards for the development of new remedies to treat diseases. New technologies have enabled scientists to identify biomarkers of several different neurodegenerative diseases. The followings, for instance,are only a few of the many new biomarkers that have been recently identified: the phosphorylated tau protein and aggregated β-amyloid peptide for Alzheimer‘s disease (AD), α-synuclein contained Lewy bodies and altered dopamine transporter (DAT) imaging for Parkinson‘s disease (PD), SOD mutations for familial amyotrophic lateral sclerosis (ALS), and CAG repeats resulted from Huntington‘s gene mutations in Huntington‘s disease (HD). This article will focus on the most-recent findings of biomarkers belonging to the four mentioned neurodegenerative diseases.