Self-assembled natural rubber/multi-walled carbon nanotube composites using latex compounding techniques

Functionalization of multi-walled carbon nanotubes (MWCNTs) plays an important role in eliminating nanotube aggregation for reinforcing polymeric materials. We prepared a new class of natural rubber (NR)/MWCNT composites by using latex compounding and self-assembly technique. The MWCNTs were functionalized with mixed acids (H2SO₄/HNO₃ = 3:1, volume ratio) and then assembled with poly (diallyldimethylammonium chloride) and latex particles. The Fourier transform infrared spectroscopy, transmission electron microscopy, and scanning electron microscopy were used to investigate the assembling mechanism between latex particles and MWCNTs. It is found that MWCNTs are homogenously dispersed in the natural rubber (NR) latex as individual nanotubes since strong self-aggregation of MWCNTs has been greatly depressed with their surface functionalization. The well-dispersed MWCNTs produce a remarkable increase in the tensile strength of NR even when the amount of MWCNTs is only 1 wt.%. Dynamic mechanical analysis shows that the glass transition temperature of composites is higher and the inner-thermogenesis and thermal stability of NR/MWCNT composites are better, when compared to those of the pure NR. The marked improvement in these properties is largely due to the strong interfacial adhesion between the NR phase and MWCNTs. Functionalization of MWCNTs represents a potentially powerful technology for significant reinforcement of natural rubber materials.

Akt, AS160, metabolic risk factors and aerobic fitness in middle-aged women

This study investigated the association between the basal (rest) insulin-signaling proteins, Akt, and the Akt substrate AS160, metabolic risk factors, inflammatory markers and aerobic fitness, in middle-aged women with varying numbers of metabolic risk factors for type 2 diabetes. Methods: Sixteen women (n=16) aged 51.3±5.1 (mean ±SD) years provided muscle biopsies and blood samples at rest. In addition, anthropometric characteristics and aerobic power were assessed and the number of metabolic risk factors for each participant was determined (IDF criteria). Results: The mean number of metabolic risk factors was 1.6±1.2. Total Akt was negatively correlated with IL-1β (r = -0.45, p = 0.046), IL-6 (r = -0.44, p = 0.052) and TNF-α (r = -0.51, p = 0.025). Phosphorylated AS160 was positively correlated with HDL (r = 0.58, p= 0.024) and aerobic fitness (r = 0.51, p=0.047). Furthermore, a multiple regression analysis revealed that both HDL (t=2.5, p=0.032) and VO_2peak (t=2.4, p=0.037) were better predictor for phosphorylated AS160 than TNF-α or IL-6 (p>0.05). Conclusions: Elevated inflammatory markers and increased metabolic risk factors may inhibit insulin-signaling protein phosphorylation in middle-aged women, thereby increasing insulin resistance under basal conditions. Furthermore, higher HDL and fitness levels are associated with an increase AS160 phosphorylation, which may in turn reduce insulin resistance.

N-Acetyl cysteine as a glutathione precursor for schizophrenia : a double blind, randomized, placebo-controlled trial

Background: Brain glutathione levels are decreased in schizophrenia, a disorder that often is chronic and refractory to treatment. N-acetyl cysteine (NAC) increases brain glutathione in rodents. This study was conducted to evaluate the safety and effectiveness of oral NAC (1 g orally twice daily [b.i.d.]) as an add-on to maintenance medication for the treatment of chronic schizophrenia over a 24-week period.

Methods: A randomized, multicenter, double-blind, placebo-controlled study. The primary readout was change from baseline on the Positive and Negative Symptoms Scale (PANSS) and its components. Secondary readouts included the Clinical Global Impression (CGI) Severity and Improvement scales, as well as general functioning and extrapyramidal rating scales. Changes following a 4-week treatment discontinuation were evaluated. One hundred forty people with chronic schizophrenia on maintenance antipsychotic medication were randomized; 84 completed treatment.

Results: Intent-to-treat analysis revealed that subjects treated with NAC improved more than placebo-treated subjects over the study period in PANSS total [5.97 (10.44, 1.51), p .009], PANSS negative [mean difference 1.83 (95% confidence interval: 3.33, .32), p .018], and PANSS general [2.79 (5.38, .20), p .035], CGI-Severity (CGI-S) [.26 (.44,.08), p .004], and CGI-Improvement (CGI-I) [.22 (.41, .03), p .025] scores. No significant change on the PANSS positive subscale was seen. N-acetyl cysteine treatment also was associated with an improvement in akathisia (p .022). Effect sizes at end point were consistent with moderate benefits.

Conclusions: These data suggest that adjunctive NAC has potential as a safe and moderately effective augmentation strategy for chronic schizophrenia.

In-situ cadmium phytoremediation using solanum nigrum L. : the bio-accumulation characteristics trail

In this study, Solanum nigrum L. was used in-situ for Cd phytoremediation in Cd polluted soil on Shenyang Zhangshi Irrigation area (SZIA) in 2008. The performance of the plant over the whole growth stage was assessed. Results showed, during the whole experimental stage, the aboveground biomass of single Solanum nigrum L. grew by a factor of 190, from 1.6 ± 0.4 g to 300.3 ± 30.2 g with 141.2 times extracted Cd increase from 0.025 ± 0.001 to 3.53 ± 0.16 mg. Both the distribution of biomass and amount of extracted Cd in the aboveground part of the plant changed according to the growth of the plant. Particularly, the percentage of biomass and extracted Cd in the stem increased from 20% to 80% and from 11% to 69%, respectively. The bioconcentration factor and transfer factor both varied significantly during the growth of the plant and the lowest values were measured at the flowering stage (0.94 ± 0.31 and 3.48 ± 1.14 respectively). The results in this paper provide reference values for the future research on the application of Solanum nigrum L. in phytoremediation and on chemical or/and agricultural strategies for phytoextraction efficiency enhancement.

β-blockers reduce bone resorption marker in early postmenopausal women

Background: There is evidence to suggest that β-blockers used in the management of cardiovascular disease may also modulate bone metabolism and reduce bone fragility.

Aim: The study aimed to determine the association between β-blocker use, serum markers of bone turnover and bone loss in early postmenopausal women.

Subjects and methods: In this observational study, we evaluated β-blocker exposure in association with serum levels of C-telopeptide and bone-specific alkaline phosphatase, and rates of bone loss. β-blocker use, concomitant therapy and lifestyle were documented for 197 women (50–59 years), 175 of whom had changes in whole body bone mineral density monitored over a 2–year period.

Results: Twenty-four β-blocker users were identified at baseline. After controlling for concomitant use of hormone therapy, C-telopeptide levels were 6.7% lower among β-blocker users (p = 0.02). No association was detected between bone-specific alkaline phosphatase and β-blocker use. Analysis of 15 β-blocker users and 152 non-users identified 2 years post-baseline showed that levels of C-telopeptide but not bone-specific alkaline phosphatase were predictors of adjusted rates of bone loss (p = 0.008 and p>0.05, respectively). Adjusted rates of bone loss were −0.001 ± 0.026 g cm−2 over 2 years for the users and −0.004 ± 0.025 g cm−2 over 2 years for non-users, but this difference was not significant.

Conclusion: β-blockers might suppress bone resorption with relative preservation of bone formation. A study with greater power is required to determine whether β-blocker use is associated with lower rates of bone loss.

Genetic changes, intra- and inter-specific introgression in farmed Nile tilapia (Oreochromis niloticus) in Thailand

Fourteen microsatellite loci were used to examine genetic changes of four strains in Nile tilapia (Oreochromis niloticus) derived from genetically improved farmed tilapia (GIFT) and two strains derived from a local Chitralada strain of Nile tilapia in Thailand. Reference populations, including the ninth generation of GIFT strain, the original Chitralada strain, two conspecific reference populations from Ivory Coast and Uganda, and one population each of Oreochromis mossambicus and Oreochromis aureus, were also examined. Despite minor genetic changes, three of the four GIFT-derived populations retained their purity as GIFT while genetic variation did not decline. One of the GIFT-derived populations showed high levels of introgression from the Chitralada strain. Likewise, introgression from GIFT to the Chitralada-derived populations was seen. Inter-specific introgression from O. mossambicus was observed in the GIFT reference population and one of the Chitralada-derived strains. Introgression from O. aureus was detected in one of the GIFT-derived populations with a history of intensive inter-strain crossing. However, the introgression resulted in elevated genetic variation relative to the Chitralada original strains.

Zero-day malware detection based on supervised learning algorithms of API call signatures

Zero-day or unknown malware are created using code obfuscation techniques that can modify the parent code to produce offspring copies which have the same functionality but with different signatures. Current techniques reported in literature lack the capability of detecting zero-day malware with the required accuracy and efficiency. In this paper, we have proposed and evaluated a novel method of employing several data mining techniques to detect and classify zero-day malware with high levels of accuracy and efficiency based on the frequency of Windows API calls. This paper describes the methodology employed for the collection of large data sets to train the classifiers, and analyses the performance results of the various data mining algorithms adopted for the study using a fully automated tool developed in this research to conduct the various experimental investigations and evaluation. Through the performance results of these algorithms from our experimental analysis, we are able to evaluate and discuss the advantages of one data mining algorithm over the other for accurately detecting zero-day malware successfully. The data mining framework employed in this research learns through analysing the behavior of existing malicious and benign codes in large datasets. We have employed robust classifiers, namely Naïve Bayes (NB) Algorithm, k−Nearest Neighbor (kNN) Algorithm, Sequential Minimal Optimization (SMO) Algorithm with 4 differents kernels (SMO - Normalized PolyKernel, SMO – PolyKernel, SMO – Puk, and SMO- Radial Basis Function (RBF)), Backpropagation Neural Networks Algorithm, and J48 decision tree and have evaluated their performance. Overall, the automated data mining system implemented for this study has achieved high true positive (TP) rate of more than 98.5%, and low false positive (FP) rate of less than 0.025, which has not been achieved in literature so far. This is much higher than the required commercial acceptance level indicating that our novel technique is a major leap forward in detecting zero-day malware. This paper also offers future directions for researchers in exploring different aspects of obfuscations that are affecting the IT world today.