Effect of hyperthermia on prognosis after acute ischemic stroke

<p><b>Background and Purpose:</b> Experimental studies have shown that hyperthermia is a determinant of poor outcome after ischemic stroke. Clinical studies evaluating the effect of temperature on poststroke outcome have, however, been limited by small sample sizes. We sought to evaluate the effect of temperature and timing of hyperthermia on outcome after ischemic stroke.</p> <p><b>Methods:</b> Data of 5305 patients in acute stroke trials from the Virtual International Stroke Trials Archive (VISTA) data set were analyzed. Data for temperatures at baseline, eighth, 24th, 48th, and 72nd hours, and seventh day were assessed in relation to outcome (poor versus good) based on the modified Rankin Scale at 3 months. Hyperthermia was defined as temperature >37.2°C and poor outcome as 90-day modified Rankin Scale >2. Hazard ratios with 95% CIs were reported for hyperthermia in relation to the outcome. Logistic regression models, in relation to hyperthermia, were fitted for a set of preselected covariates at different time points to identify predictors/determinants of hyperthermia.</p> <p><b>Results:</b> The average age of patients was 68.0±11.9 years, 2380 (44.9%) were females, and 42.3% (2233) received thrombolysis using recombinant tissue plasminogen activator. After adjustment, hyperthermia was a statistically significant predictor of poor outcome. The hazard ratios (95% CI) for poor outcome in relation to hyperthermia at different time points were: baseline 1.2 (1.0 to 1.4), eighth hour 1.7 (1.2 to 2.2), 24th hour 1.5 (1.2 to 1.9), 48th hour 2.0 (1.5 to 2.6), 72nd hour 2.2 (1.7 to 2.9), and seventh day 2.7 (2.0 to 3.8). Gender, stroke severity (National Institutes of Health Stroke Scale score >16), white blood cell count, and antibiotic use were significantly associated with hyperthermia (P≤0.01).</p> <p><b>Conclusions:</b> Hyperthermia, in acute ischemic stroke, is associated with a poor clinical outcome. The later the hyperthermia occurs within the first week, the worse the prognosis. Severity of stroke and inflammation are important determinants of hyperthermia after ischemic stroke. In patients with acute ischemic stroke, aggressive measures to prevent and treat hyperthermia could improve the clinical outcomes.</p&gt

The Effects of Dual Acceptor (Na, N) Doping on Zn and O Sites in ZnO

ZnO is an attracted semiconducting material because of the intriguing structural, electronic and optical properties as well as the properties can easily be tuned for applications. However, p-type doping is an essential research interest to overcome the hindering of the applications of n-type ZnO for next-generation advanced electronic and optoelectronic devices. In this article, we focus on the p-type acceptor mono (Na, N) and dual (Na-N) doping effects on the structural, electronic and optical properties of ZnO using first-principles calculations based on the density functional theory (DFT). Detailed DFT analysis reveals that the structure of ZnO distorted resulting in (Na, N) and (Na-N) doping, respectively. Band structure calculation highlights the confirmation of p-type ZnO for both types of doping introduced by acceptor impurity bands at the top of the valence band and pushing the Fermi level into the valence band. The band gap of ZnO is increased for Na and Na-N doping, while decreases for N doping.  The widening of the band gap with Na and Na-N doping could be explained by Burstein Moss effect. In this study the band gap can be tuned in between 0.58 and 0.93 eV. Importantly, enhancement of the absorption and photoconductivity in the near band edge region attributed to (Na, N) and (Na-N) could be extended its applications in high-performance p-type based electronic and optoelectronic devices

A New Data Mining Scheme Using Artificial Neural Networks

Classification is one of the data mining problems receiving enormous attention in the database community. Although artificial neural networks (ANNs) have been successfully applied in a wide range of machine learning applications, they are however often regarded as black boxes, i.e., their predictions cannot be explained. To enhance the explanation of ANNs, a novel algorithm to extract symbolic rules from ANNs has been proposed in this paper. ANN methods have not been effectively utilized for data mining tasks because how the classifications were made is not explicitly stated as symbolic rules that are suitable for verification or interpretation by human experts. With the proposed approach, concise symbolic rules with high accuracy, that are easily explainable, can be extracted from the trained ANNs. Extracted rules are comparable with other methods in terms of number of rules, average number of conditions for a rule, and the accuracy. The effectiveness of the proposed approach is clearly demonstrated by the experimental results on a set of benchmark data mining classification problems

Temporal variation in seed quality of Indian spinach preserved in different containers

The experiment was conducted to investigate the seed quality of Indian spinach for three months stored at different containers (Plastic pot, polythene bag and cloth bag) during 2013-2014 at Seed Technology Laboratory of Bangabandhu Sheikh Mujibur Rahman Agricultural University (BSMRAU), Gazipur. The initial moisture content of the seed was 9.89% with 75.00% germination. However, after two months, the seed had a different range of increased moisture content with reduced germination percentage depending upon the packing materials. Among the three containers, seeds stored at cloth bag absorbed more moisture (14.36%) from surrounding atmosphere followed by the moisture content, 12.67% and 11.50% of polythene bag and airtight plastic pot, respectively. Seeds stored at plastic pot, polythene bag and cloth bag exhibited 65.00, 64.00 and 50.00 per cent germination, respectively. Similarly, the fresh weight and dry weight of seedling and seedling vigor were also decreased. The seeds of plastic pot were good in term of germination capacity, fresh and dry weight and vigor index in comparison to those stored at cloth bag and polythene bag.Int. J. Agril. Res. Innov. & Tech. 5 (2): 51-57, December, 201

Green underwater wireless communications using hybrid optical-acoustic technologies

Underwater wireless communication is a rapidly growing field, especially with the recent emergence of technologies such as autonomous underwater vehicles (AUVs) and remotely operated vehicles (ROVs). To support the high-bandwidth applications using these technologies, underwater optics has attracted significant attention, alongside its complementary technology – underwater acoustics. In this paper, we propose a hybrid opto-acoustic underwater wireless communication model that reduces network power consumption and supports high-data rate underwater applications by selecting appropriate communication links in response to varying traffic loads and dynamic weather conditions. Underwater optics offers high data rates and consumes less power. However, due to the severe absorption of light in the medium, the communication range is short in underwater optics. Conversely, acoustics suffers from low data rate and high power consumption, but provides longer communication ranges. Since most underwater equipment relies on battery power, energy-efficient communication is critical for reliable underwater communications. In this work, we derive analytical models for both underwater acoustics and optics, and calculate the required transmit power for reliable communications in various underwater communication environments. We then formulate an optimization problem that minimizes the network power consumption for carrying data from underwater nodes to surface sinks under varying traffic loads and weather conditions. The proposed optimization model can be solved offline periodically, hence the additional computational complexity to find the optimum solution for larger networks is not a limiting factor for practical applications. Our results indicate that the proposed technique yields up to 35% power savings compared to existing opto-acoustic solutions

An Action Plan for Adaptation in Bangladesh Agriculture under Climate Change

This report is the outcome of a study titled 'An Action Plan for Adaptation in Bangladesh Agriculture under Climate Change', carried out with support from the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS). This report prepared by the Center for Environmental and Geographic Information Services (CEGIS) delineates an action plan to prioritize adaptation options after considering the inputs by experts and scrutiny by stakeholders. It aims to promote precision agriculture and commercialization of crop agriculture with strong institutional and financial footing to tackle climate change

An Intelligent Decision Support System for the Detection of Meat Spoilage using Multispectral Images

In food industry, quality and safety are considered important issues worldwide that are directly related to health and social progress. The use of vision technology for quality testing of food production has the obvious advantage of being able to continuously monitor a production using non-destructive methods, thus increasing the quality and minimizing cost. The performance of an intelligent decision support system has been evaluated in monitoring the spoilage of minced beef stored either aerobically or under modified atmosphere packaging, at different storage temperatures (0, 5, 10, and 15 °C) utilising multispectral imaging information. This paper utilises a neuro-fuzzy model which incorporates a clustering pre-processing stage for the definition of fuzzy rules, while its final fuzzy rule base is determined by competitive learning. Initially, meat samples are classified according to their storage conditions, while identification models are then utilised for the prediction of the Total Viable Counts of bacteria. The innovation of the proposed approach is further extended to the identification of the temperature used for storage, utilizing only imaging spectral information. Results indicated that spectral information in combination with the proposed modelling scheme could be considered as an alternative methodology for the accurate evaluation of meat spoilage