Produktivitas Getah Pinus (Pinus Merkusii Jungh Et De Vriese) Berdasarkan Ketinggian Tempat Dan Konsentrasi Stimulansia Asam Cuka (C2H4O2)

The Production of oleoresin is affected by the application of stimulant and effect of elevation. The purpose of this research were to know the effect of C2H4O2 stimulant and effect of elevation to P. merkusii and to know the C2H4O2 concentration and the best of elevation. This research were carried at working area of PT. Inhutani IV, Siborong-borong in March – April 2014 using factorial randomized block design with two factors, i.e. the concentration of stimulant (0, 10, 20 and 30%) and elevation (900 m, 1050-1150 m, 1150-1250 m, 1250-1350 m and 1350-1400 m). Parameter measured were quality and quantity of oleoresin (gram). Result of this research showed that the application of C2H4O2 stimulant on the tapping of pine trees increase considerably to oleoresin. The concentration of C2H4O2 (30%) and the elevation 900 meters can gave the best product of oleoresin, i.e. 154,15 grams/tree/month. Quality of oleoresin were got at this research was B quality

Application of synthetic polymers as adsorbents for the removal of cadmium from aqueous solutions: batch experimental studies

In the present study seven synthetic polymers were used as adsorbents for the removal of Cd(II) from aqueous solution. The equilibrium studies were systematically carried out in a batch process, covering various process parameters that include agitation time, adsorbent dosage, and pH of the aqueous solution.The analyzing system was an atomic absorption spectrometer (Perkin-Analyst 100). It was observed in adsorption and desorption tests that synthetic polymers showed significant pH dependence, which affected the removal efficiency, robustly. Adsorption behavior was found to follow Freundlich and Longmuir isotherms. A regeneration study was also carried out

Using Deep Learning for Predicting Stock Trends

Deep learning has shown great promise in solving complicated problems in recent years. One applicable area is finance. In this study, deep learning will be used to test the predictability of stock trends. Stock markets are known to be volatile, prices fluctuate, and there are many complicated financial indicators involved. While the opinion of researchers differ about the predictability of stocks, it has been shown by previous empirical studies that some aspects of stock markets can be predictable to some extent. Various data including news or financial indicators can be used to predict stock prices. In this study, the focus will be on using past stock prices and using technical indicators to increase the performance of the results. The goal of this study is to measure the accuracy of predictions and evaluate the results. Historical data is gathered for Apple, Microsoft, Google and Intel stocks. A prediction model is created by using past data and technical indicators were used as features in the model. The experiments were performed by using long short-term memory networks. Different approaches and techniques were tested to boost the performance of the results. To prove the usability of the final model in the real world and measure the profitability of results backtesting was performed. The final results show that while it is not possible to predict the exact price of a stock in the future to gain profitable results, deep learning can be used to predict the trend of stock markets to generate buy and sell signals

Deep Learning for the Prediction of Stock Market Trends

In this study, deep learning will be used to test the predictability of stock trends. Stock markets are known to be volatile, prices fluctuate, and there are many complicated financial indicators involved. Various data including news or financial indicators can be used to predict stock prices. In this study, the focus will be on using past stock prices and using technical indicators to increase the performance of the results. The goal of this study is to measure the accuracy of predictions and evaluate the results. Historical data is gathered for Apple, Microsoft, Google and Intel stocks. A prediction model is created by using past data and technical indicators were used as features in the model. The experiments were performed by using long short-term memory networks. Different approaches and techniques were tested to boost the performance of the results. To prove the usability of the final model in the real world and measure the profitability of results backtesting was performed. The final results show that while it is not possible to predict the exact price of a stock in the future to gain profitable results, deep learning can be used to predict the trend of stock markets to generate buy and sell signals

Susceptibility of hamsters to clostridium difficile isolates of differing toxinotype

Clostridium difficile is the most commonly associated cause of antibiotic associated disease (AAD), which caused ~21,000 cases of AAD in 2011 in the U.K. alone. The golden Syrian hamster model of CDI is an acute model displaying many of the clinical features of C. difficile disease. Using this model we characterised three clinical strains of C. difficile, all differing in toxinotype; CD1342 (PaLoc negative), M68 (toxinotype VIII) and BI-7 (toxinotype III). The naturally occurring non-toxic strain colonised all hamsters within 1-day post challenge (d.p.c.) with high-levels of spores being shed in the faeces of animals that appeared well throughout the entire experiment. However, some changes including increased neutrophil influx and unclotted red blood cells were observed at early time points despite the fact that the known C. difficile toxins (TcdA, TcdB and CDT) are absent from the genome. In contrast, hamsters challenged with strain M68 resulted in a 45% mortality rate, with those that survived challenge remaining highly colonised. It is currently unclear why some hamsters survive infection, as bacterial and toxin levels and histology scores were similar to those culled at a similar time-point. Hamsters challenged with strain BI-7 resulted in a rapid fatal infection in 100% of the hamsters approximately 26 hr post challenge. Severe caecal pathology, including transmural neutrophil infiltrates and extensive submucosal damage correlated with high levels of toxin measured in gut filtrates ex vivo. These data describes the infection kinetics and disease outcomes of 3 clinical C. difficile isolates differing in toxin carriage and provides additional insights to the role of each toxin in disease progression

Prolonged Bartonella henselae Bacteremia Caused by Reinfection in Cats

We analyzed the genetic relatedness of blood culture isolates of Bartonella henselae from 2 cats of patients with cat-scratch disease at admission and after 12 months. Isolates from each cat at different times were clonally unrelated, which suggested reinfection by a second strain

Polycation-π Interactions Are a Driving Force for Molecular Recognition by an Intrinsically Disordered Oncoprotein Family

Molecular recognition by intrinsically disordered proteins (IDPs) commonly involves specific localized contacts and target-induced disorder to order transitions. However, some IDPs remain disordered in the bound state, a phenomenon coined "fuzziness", often characterized by IDP polyvalency, sequence-insensitivity and a dynamic ensemble of disordered bound-state conformations. Besides the above general features, specific biophysical models for fuzzy interactions are mostly lacking. The transcriptional activation domain of the Ewing's Sarcoma oncoprotein family (EAD) is an IDP that exhibits many features of fuzziness, with multiple EAD aromatic side chains driving molecular recognition. Considering the prevalent role of cation-π interactions at various protein-protein interfaces, we hypothesized that EAD-target binding involves polycation- π contacts between a disordered EAD and basic residues on the target. Herein we evaluated the polycation-π hypothesis via functional and theoretical interrogation of EAD variants. The experimental effects of a range of EAD sequence variations, including aromatic number, aromatic density and charge perturbations, all support the cation-π model. Moreover, the activity trends observed are well captured by a coarse-grained EAD chain model and a corresponding analytical model based on interaction between EAD aromatics and surface cations of a generic globular target. EAD-target binding, in the context of pathological Ewing's Sarcoma oncoproteins, is thus seen to be driven by a balance between EAD conformational entropy and favorable EAD-target cation-π contacts. Such a highly versatile mode of molecular recognition offers a general conceptual framework for promiscuous target recognition by polyvalent IDPs. © 2013 Song et al