A Novel Role of Three Dimensional Graphene Foam to Prevent Heater Failure during Boiling

We report a novel boiling heat transfer (NBHT) in reduced graphene oxide (RGO) suspended in water (RGO colloid) near critical heat flux (CHF), which is traditionally the dangerous limitation of nucleate boiling heat transfer because of heater failure. When the heat flux reaches the maximum value (CHF) in RGO colloid pool boiling, the wall temperature increases gradually and slowly with an almost constant heat flux, contrary to the rapid wall temperature increase found during water pool boiling. The gained time by NBHT would provide the safer margin of the heat transfer and the amazing impact on the thermal system as the first report of graphene application. In addition, the CHF and boiling heat transfer performance also increase. This novel boiling phenomenon can effectively prevent heater failure because of the role played by the self-assembled three-dimensional foam-like graphene network (SFG).open2

HBV-Related Hepatocellular Carcinoma Susceptibility Gene KIF1B Is Not Associated with Development of Chronic Hepatitis B

A recent genome-wide association study has identified a new susceptibility locus, kinesin family member 1B gene (KIF1B), strongly associated with progression from chronic hepatitis B (CHB) to hepatitis B virus-related hepatocellular carcinoma (HCC) in Chinese population, this study was carried out to explore the role of the genetic variants in KIF1B in the development of chronic hepatitis B.Three KIF1B polymorphisms (rs8019, rs17401924, and rs17401966) were selected and genotyped in 473 CHB patients and 580 controls with no history of CHB. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated by logistic regression model. None of these three SNPs showed association with CHBs after adjusting for age and gender. Equivalence-based method analysis confirmed the absence of association. In the further haplotype analysis, three common haplotypes were observed in this study population, but no significant effect was also found for haplotypes in the progression to CHB.This study showed the new locus identified for HCC, KIF1B, was not associated with progression to CHB, implying distinct genetic susceptibility factor contributes to the progression from hepatitis B virus infection to HCC. Nevertheless, further comprehensive analyses are warranted to dissect the mechanism

Combined artificial bee colony algorithm and machine learning techniques for prediction of online consumer repurchase intention

A novel paradigm in the service sector i.e. services through the web is a progressive mechanism for rendering offerings over diverse environments. Internet provides huge opportunities for companies to provide personalized online services to their customers. But prompt novel web services introduction may unfavorably affect the quality and user gratification. Subsequently, prediction of the consumer intention is of supreme importance in selecting the web services for an application. The aim of study is to predict online consumer repurchase intention and to achieve this objective a hybrid approach which a combination of machine learning techniques and Artificial Bee Colony (ABC) algorithm has been used. The study is divided into three phases. Initially, shopping mall and consumer characteristic’s for repurchase intention has been identified through extensive literature review. Secondly, ABC has been used to determine the feature selection of consumers’ characteristics and shopping malls’ attributes (with > 0.1 threshold value) for the prediction model. Finally, validation using K-fold cross has been employed to measure the best classification model robustness. The classification models viz., Decision Trees (C5.0), AdaBoost, Random Forest (RF), Support Vector Machine (SVM) and Neural Network (NN), are utilized for prediction of consumer purchase intention. Performance evaluation of identified models on training-testing partitions (70-30%) of the data set, shows that AdaBoost method outperforms other classification models with sensitivity and accuracy of 0.95 and 97.58% respectively, on testing data set. This study is a revolutionary attempt that considers both, shopping mall and consumer characteristics in examine the consumer purchase intention.N/

Microbial regulation of the soil carbon cycle: evidence from gene-enzyme relationships.

A lack of empirical evidence for the microbial regulation of ecosystem processes, including carbon (C) degradation, hinders our ability to develop a framework to directly incorporate the genetic composition of microbial communities in the enzyme-driven Earth system models. Herein we evaluated the linkage between microbial functional genes and extracellular enzyme activity in soil samples collected across three geographical regions of Australia. We found a strong relationship between different functional genes and their corresponding enzyme activities. This relationship was maintained after considering microbial community structure, total C and soil pH using structural equation modelling. Results showed that the variations in the activity of enzymes involved in C degradation were predicted by the functional gene abundance of the soil microbial community (R2>0.90 in all cases). Our findings provide a strong framework for improved predictions on soil C dynamics that could be achieved by adopting a gene-centric approach incorporating the abundance of functional genes into process models

Machine Learning Approach for Prescriptive Plant Breeding

We explored the capability of fusing high dimensional phenotypic trait (phenomic) data with a machine learning (ML) approach to provide plant breeders the tools to do both in-season seed yield (SY) prediction and prescriptive cultivar development for targeted agro-management practices (e.g., row spacing and seeding density). We phenotyped 32 SoyNAM parent genotypes in two independent studies each with contrasting agro-management treatments (two row spacing, three seeding densities). Phenotypic trait data (canopy temperature, chlorophyll content, hyperspectral reflectance, leaf area index, and light interception) were generated using an array of sensors at three growth stages during the growing season and seed yield (SY) determined by machine harvest. Random forest (RF) was used to train models for SY prediction using phenotypic traits (predictor variables) to identify the optimal temporal combination of variables to maximize accuracy and resource allocation. RF models were trained using data from both experiments and individually for each agro-management treatment. We report the most important traits agnostic of agro-management practices. Several predictor variables showed conditional importance dependent on the agro-management system. We assembled predictive models to enable in-season SY prediction, enabling the development of a framework to integrate phenomics information with powerful ML for prediction enabled prescriptive plant breeding

Sex- and age-dependent association of SLC11A1 polymorphisms with tuberculosis in Chinese: a case control study

BACKGROUND: Host genetic factors are important determinants in tuberculosis (TB). The SLC11A1 (or NRAMP1) gene has been studied extensively for genetic association with TB, but with inconsistent findings. In addition, no study has yet looked into the effect of sex and age on the relationship between SLC11A1 polymorphisms and TB. METHODS: A case-control study was conducted. In total, 278 pulmonary TB patients and 282 sex- and age-matched controls without TB were recruited. All subjects were ethnic Chinese. On the basis of linkage disequilibrium pattern, three genetic markers from SLC11A1 and one from the nearby IL8RB locus were selected and examined for association with TB susceptibility. These markers were genotyped using single strand conformation polymorphism analysis or fragment analysis of amplified products. RESULTS: Statistically significant differences in allele (P = 0.0165, OR = 1.51) and genotype (P = 0.0163, OR = 1.59) frequencies of the linked markers SLC6a/b (classically called D543N and 3'UTR) of the SLC11A1 locus were found between patients and controls. With stratification by sex, positive associations were identified in the female group for both allele (P = 0.0049, OR = 2.54) and genotype (P = 0.0075, OR = 2.74) frequencies. With stratification by age, positive associations were demonstrated in the young age group (age ≤65 years) for both allele (P = 0.0047, OR = 2.52) and genotype (P = 0.0031, OR = 2.92) frequencies. All positive findings remained significant even after correction for multiple comparisons. No significant differences were noted in either the male group or the older age group. No significant differences were found for the other markers (one SLC11A1 marker and one IL8RB marker) either. CONCLUSION: This study confirmed the association between SLC11A1 and TB susceptibility and demonstrated for the first time that the association was restricted to females and the young age group

Metabolism before, during and after anaesthesia in colic and healthy horses

<p>Abstract</p> <p>Background</p> <p>Many colic horses are compromised due to the disease state and from hours of starvation and sometimes long trailer rides. This could influence their muscle energy reserves and affect the horses' ability to recover. The principal aim was to follow metabolic parameter before, during, and up to 7 days after anaesthesia in healthy horses and in horses undergoing abdominal surgery due to colic.</p> <p>Methods</p> <p>20 healthy horses given anaesthesia alone and 20 colic horses subjected to emergency abdominal surgery were anaesthetised for a mean of 228 minutes and 183 minutes respectively. Blood for analysis of haematology, electrolytes, cortisol, creatine kinase (CK), free fatty acids (FFA), glycerol, glucose and lactate was sampled before, during, and up to 7 days after anaesthesia. Arterial and venous blood gases were obtained before, during and up to 8 hours after recovery. Gluteal muscle biopsy specimens for biochemical analysis of muscle metabolites were obtained at start and end of anaesthesia and 1 h and 1 day after recovery.</p> <p>Results</p> <p>Plasma cortisol, FFA, glycerol, glucose, lactate and CK were elevated and serum phosphate and potassium were lower in colic horses before anaesthesia. Muscle adenosine triphosphate (ATP) content was low in several colic horses. Anaesthesia and surgery resulted in a decrease in plasma FFA and glycerol in colic horses whereas levels increased in healthy horses. During anaesthesia muscle and plasma lactate and plasma phosphate increased in both groups. In the colic horses plasma lactate increased further after recovery. Plasma FFA and glycerol increased 8 h after standing in the colic horses. In both groups, plasma concentrations of CK increased and serum phosphate decreased post-anaesthesia. On Day 7 most parameters were not different between groups. Colic horses lost on average 8% of their initial weight. Eleven colic horses completed the study.</p> <p>Conclusion</p> <p>Colic horses entered anaesthesia with altered metabolism and in a negative oxygen balance. Muscle oxygenation was insufficient during anaesthesia in both groups, although to a lesser extent in the healthy horses. The post-anaesthetic period was associated with increased lipolysis and weight loss in the colic horses, indicating a negative energy balance during the first week post-operatively.</p

Anticancer drugs for the modulation of endoplasmic reticulum stress and oxidative stress

Prior research has demonstrated how the endoplasmic reticulum (ER) functions as a multifunctional organelle and as a well-orchestrated protein-folding unit. It consists of sensors which detect stress-induced unfolded/misfolded proteins and it is the place where protein folding is catalyzed with chaperones. During this folding process, an immaculate disulfide bond formation requires an oxidized environment provided by the ER. Protein folding and the generation of reactive oxygen species (ROS) as a protein oxidative byproduct in ER are crosslinked. An ER stress-induced response also mediates the expression of the apoptosis-associated gene C/EBP-homologous protein (CHOP) and death receptor 5 (DR5). ER stress induces the upregulation of tumor necrosis factor-related apoptosis inducing ligand (TRAIL) receptor and opening new horizons for therapeutic research. These findings can be used to maximize TRAIL-induced apoptosis in xenografted mice. This review summarizes the current understanding of the interplay between ER stress and ROS. We also discuss how damage-associated molecular patterns (DAMPs) function as modulators of immunogenic cell death and how natural products and drugs have shown potential in regulating ER stress and ROS in different cancer cell lines. Drugs as inducers and inhibitors of ROS modulation may respectively exert inducible and inhibitory effects on ER stress and unfolded protein response (UPR). Reconceptualization of the molecular crosstalk among ROS modulating effectors, ER stress, and DAMPs will lead to advances in anticancer therapy

The predictability of ecological stability in a noisy world

Random environmental variation, or stochasticity, is a key determinant of ecological dynamics. While we have some appreciation of how environmental stochasticity can moderate the variability and persistence of communities, we know little about its implications for the nature and predictability of ecological responses to large perturbations. Here, we show that shifts in the temporal autocorrelation (colour) of environmental noise provoke trade-offs in ecological stability across a wide range of different food-web structures by stabilizing dynamics in some dimensions, while simultaneously destabilizing them in others. Specifically, increasingly positive autocorrelation (reddening) of environmental noise increases resilience by hastening the recovery of food webs following a large perturbation, but reduces their resistance to perturbation and increases their temporal variability (reduces biomass stability). In contrast, all stability dimensions become less predictable, showing increased variability around the mean response, as environmental noise reddens. Moreover, we found environmental reddening to be a considerably more important determinant of stability than intrinsic food-web characteristics. These findings reveal the fundamental and dominant role played by environmental stochasticity in determining the dynamics and stability of ecosystems, and extend our understanding of how the multiple dimensions of stability relate to each other beyond simple white noise environments