Comparison of Machine Learning Algorithms for Modeling Species Distributions: Application to Stream Invertebrates from Western USA Reference Sites

Machine learning algorithms are increasingly being used by ecologists to model and predict the distributions of individual species and entire assemblages of sites. Accurate prediction of distribution of species is an important factor in any modeling. We compared prediction accuracy of four machine learning algorithms-random forests, classification trees, support vector machines, and gradient boosting machines to a traditional method, linear discriminant models (LDM), on a large set of stream invertebrate data collected at 728 reference sites in the western United States. Classifications were constructed for individual species and for assemblages of sites clustered a priori by similarity on biological characteristics. Predictive accuracy of the classifications was evaluated by computing the percent of sites correctly classified, sensitivity, specificity, kappa, and the area under the receiver operating characteristic curve on 10-fold crossvalidated predictions from each classification method on each individual species and assemblage of sites. The predictions from each type of classification were used to estimate the Observed over Expected (O/E) index of taxa richness. Random Forests generally produced the most accurate individual species models . However, none of the machine learning algorithms showed significant improvement over LDMs for classifications of assemblages of sites and precision of the O/E index. The performance of Support Vector Machines was particularly poor for classifying individual species and assemblages of sites, and resulted in greater bias in the O/E index. We believe that the performance of models developed for species at such large spatial scales may depend more on the predictor variables available than the classification technique

Studies on some novel bioactive synthetic compounds

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Design of Adaptive Headlights for Automobiles

The highest fatal traffic accident rate occurs on curved roads at nighttime. Night time driving with conventional headlamps is particularly unsafe. Only 25% of the driving is done at night but 55% of the driving accidents occur during this period. The existing conventional light systems do not provide illumination in the right direction on curve roads. Due to this constrain, a need to understand an alternative technology solution. The aim is to improve visibility for driver and so achieve a significant increase in safety and driving comfort. This calls for a flexible front light for automobiles to illuminate road ahead in the night at corner. Adaptive front lighting system (AFS) helps improve driver’s visibility at night time hence achieving enhance safety. AFS (adaptive front-lighting system) used to detect information about corner in advance with help of sensor which detect the information send it to motor to adjust headlamps to get the lighting beam which was suitable for the corner. Through this way, it could avoid "blind spot" caused by the fixed lighting area when coming into the corner, and improve driving safety. DOI: 10.17762/ijritcc2321-8169.150315

Estrogen and neuroprotection: from clinical observations to molecular mechanisms

We now appreciate that estrogen is a pleiotropic gonadal steroid that exerts profound effects on the plasticity and cell survival of the adult brain. Over the past century, the life span of women has increased, but the age of the menopause remains constant. This means that women may now live over one third of their lives in a hypoestrogenic, postmenopausal state. The impact of prolonged hypoestrogenicity on the brain is now a critical health concern as we realize that these women may suffer an increased risk of cognitive dysfunction and neurodegeneration due to a variety of diseases. Accumulating evidence from both clinical and basic science studies indicates that estrogen exerts critical protective actions against neurodegenerative conditions such as Alzheimer's disease and stroke. Here, we review the discoveries that comprise our current understanding of estrogen action against neurodegeneration. These findings carry far-reaching possibilities for improving the quality of life in our aging population

INVESTIGATION OF CHEMICAL COMPOSITION FROM DRYOPTERIS CHOCHLEATA (D.DON) C. CHR. (DRYOPTERIDACEAE)

ABSTRACTObjective: The present study was for the determination of bioactive volatile compounds from chloroform and ethanolic extract.Methods: For present investigation of the samples were carried out by using Shimadzu Make QP-2010 with nonpolar 60 M RTX 5MS Column.Interpretation on Mass-Spectrum GC-MS was conducted using the database of National Institute Slandered and Technology (NIST) having more62,000 patterns.Results: In Ethanolic extract seven compounds and chloroform extract fourteen compounds were identified. The common compounds in these twoextracts are Germacrene D; 1, 3-cyclohexanedione, 2-methyl-2 (3-Oxobutyl); Neoisolongifolene, 8, 9-dehydro.Coclusion: Present investigation emphasizes the efficacy of traditional remedies and that it inspires the people to realize the importance of naturalresources for its potent pharmaceutical use.Keywords: Medicinal pteridophyte, Gas chromatography-mass spectroscopy analysis, Ethanol extract, Chloroform extract, Bioactive compounds