Prevalence and clinical correlates of obsessive-compulsive disorder in schizophrenia

Obsessive compulsive symptoms frequently occur in a substantial proportion of patients with schizophrenia. The term schizoobsessive has been proposed to delineate this subgroup of schizophrenia patients who present with obsessive compulsive symptoms/disorder. However, whether this co-occurrence is more than just co-morbidity and represents a distinct subgroup remains controversial. A striking variation is noted across studies examining prevalence of obsessive compulsive symptoms/disorder in schizophrenia patients and their impact on clinical profile of schizophrenia. Hence, in this study, we examined the prevalence of obsessive compulsive symptoms/disorder in a large sample of consecutively hospitalized schizophrenia patients and compared the clinical and functional characteristics of schizophrenia patients with and without obsessive compulsive symptoms/disorder. We evaluated 200 consecutive subjects with the DSM-IV diagnosis of schizophrenia using the Structured Clinical Interview for DSM-IV Axis I disorders, Positive and Negative Syndrome Scale, Yale Brown Obsessive Compulsive Scale, Brown Assessment of Beliefs Scale, Clinical Global Impression-Severity scale, Global Assessment of Functioning Scale, Family Interview for Genetic Studies and World Health Organization Quality of Life scale. The prevalence of obsessive compulsive symptoms in patients with schizophrenia was 24% (n = 48); 37 of them had obsessive compulsive disorder (OCD) and II had obsessive compulsive symptoms not amounting to a clinical diagnosis of OCD (OCS). Schizophrenia patients with OCS/OCD had an earlier age at onset of schizophrenia symptoms, lower positive symptoms score, higher co-morbidity with Axis II disorders, higher occurrence of OCD in family and better quality of life. Findings of the study indicate a higher prevalence of OCS/OCD in schizophrenia. Schizophrenia patients with and without OCS/OCD have comparable clinical profile with few exceptions. High rates of OCD in first degree relatives suggest possible genetic contributions and differences in neurobiology. Finally, evidence to consider schizoobsessive as a distinct diagnostic entity is inconclusive and warrants further studies. (C) 2014 Elsevier Inc. All rights reserved.

No-reference image quality assessment using modified extreme learning machine classifier

In this paper, we present a machine learning approach to measure the visual quality of JPEG-coded images. The features for predicting the perceived image quality are extracted by considering key human visual sensitivity (HVS) factors such as edge amplitude, edge length, background activity and background luminance. Image quality assessment involves estimating the functional relationship between HVS features and subjective test scores. The quality of the compressed images are obtained without referring to their original images ('No Reference' metric). Here, the problem of quality estimation is transformed to a classification problem and solved using extreme learning machine (ELM) algorithm. In ELM, the input weights and the bias values are randomly chosen and the output weights are analytically calculated. The generalization performance of the ELM algorithm for classification problems with imbalance in the number of samples per quality class depends critically on the input weights and the bias values. Hence, we propose two schemes, namely the k-fold selection scheme (KS-ELM) and the real-coded genetic algorithm (RCGA-ELM) to select the input weights and the bias values such that the generalization performance of the classifier is a maximum. Results indicate that the proposed schemes significantly improve the performance of ELM classifier under imbalance condition for image quality assessment. The experimental results prove that the estimated visual quality of the proposed RCGA-ELM emulates the mean opinion score very well. The experimental results are compared with the existing JPEG no-reference image quality metric and full-reference structural similarity image quality metric.

A new intelligent algorithm for online voltage stability assessment and monitoring

This paper presents a new approach for assessing power system voltage stability based on artificial feed forward neural network (FFNN). The approach uses real and reactive power, as well as voltage vectors for generators and load buses to train the neural net (NN). The input properties of the NN are generated from offline training data with various simulated loading conditions using a conventional voltage stability algorithm based on the L-index. The performance of the trained NN is investigated on two systems under various voltage stability assessment conditions. Main advantage is that the proposed approach is fast, robust, accurate and can be used online for predicting the L-indices of all the power system buses simultaneously. The method can also be effectively used to determining local and global stability margin for further improvement measures.

The effect of horizontal resolution on simulation of very extreme US precipitation events in a global atmosphere model

We investigate the ability of a global atmospheric general circulation model (AGCM) to reproduce observed 20 year return values of the annual maximum daily precipitation totals over the continental United States as a function of horizontal resolution. We find that at the high resolutions enabled by contemporary supercomputers, the AGCM can produce values of comparable magnitude to high quality observations. However, at the resolutions typical of the coupled general circulation models used in the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, the precipitation return values are severely underestimated.

Stability of CU(2)Ln(2)O(5) compounds - Comparison, assessment and systematics

Phase diagram studies show that at ambient pressure only one ternary oxide, Cu(2)Ln(2)O(5), is stable in the ternary systems Cu-Ln-O (Ln = Tb, Dy, Ho, Er, Tm, Yb, Lu) at high temperatures. The crystal structure of Cu(2)Ln(2)O(5) can be described as a zig-zag arrangement of one-dimensional Cu2O5 chains parallel to-the a-axis with Ln atoms occupying distorted octahedral sites between these chains. Four sets of emf measurements on Gibbs energy of formation of Cu(2)Ln(2)O(5) (Ln = Tb, Dy, Ho, Er, Tm, Yb, Lu; Y) from component binary oxides and one set of high-temperature solution calorimetric data on enthalpy of formation have been reported in the literature. Except for Cu2Y2O5, the measured values for the Gibbs energies of formation of all other Cu(2)Ln(2)O(5) compounds fall in a narrow band (+/-1 kJ mol(-1)) and indicate a regular increase in stability with decreasing ionic radius of the lanthanide ion. The values for the second law enthalpy of formation, derived from the temperature dependence of emf obtained in different studies, show larger differences, as high as 25 kJ mol(-1) for Cu2Tm2O5. Though associated with an uncertainty of +/-4 kJ mol(-1), the calorimetric measurements help to identify the best set of emf data. The trends in thermodynamic data correlate well with the global instability index (GII) based on the overall deviation from the valence sum rule. Low values for the index calculated from crystallographic information indicate higher stability. Higher values are indicative of the larger stress in the structure.

An assessment of the reduction potential of hydrogen plasma

Hydrogen plasma can be used for deoxidation of functional materials containing reactive metals in both bulk and thin film forms. Since the different species in the plasma are not in thermodynamic equilibrium, application of classical thermodynamics to the analysis of such a system is associated with some difficulties. While global equilibrium approaches have been tried, with and without additional approximations or constraints, there is some ambiguity in the results obtained. Presented in this article is the application of a local equilibrium concept to assess the thermodynamic limit of the reaction of each species present in the gas with oxides or oxygen dissolved in metals. Each reaction results in a different pal tial pressure of H2O. Because of the higher reactivity of the dissociated and ionized species and the larger thermodynamic driving force for reactions involving these species, they act as powerful reducing agents. It is necessary to remove the products of reaction from the plasma to prevent back reaction and gradual approach to global equilibrium. A quantitative description using the framework of the Ellingham-Richardson-Jeffes diagrams is presented.

Albedo enhancement of marine clouds to counteract global warming: impacts on the hydrological cycle

Recent studies have shown that changes in solar radiation affect the hydrological cycle more strongly than equivalent CO(2) changes for the same change in global mean surface temperature. Thus, solar radiation management ``geoengineering'' proposals to completely offset global mean temperature increases by reducing the amount of absorbed sunlight might be expected to slow the global water cycle and reduce runoff over land. However, proposed countering of global warming by increasing the albedo of marine clouds would reduce surface solar radiation only over the oceans. Here, for an idealized scenario, we analyze the response of temperature and the hydrological cycle to increased reflection by clouds over the ocean using an atmospheric general circulation model coupled to a mixed layer ocean model. When cloud droplets are reduced in size over all oceans uniformly to offset the temperature increase from a doubling of atmospheric CO(2), the global-mean precipitation and evaporation decreases by about 1.3% but runoff over land increases by 7.5% primarily due to increases over tropical land. In the model, more reflective marine clouds cool the atmospheric column over ocean. The result is a sinking motion over oceans and upward motion over land. We attribute the increased runoff over land to this increased upward motion over land when marine clouds are made more reflective. Our results suggest that, in contrast to other proposals to increase planetary albedo, offsetting mean global warming by reducing marine cloud droplet size does not necessarily lead to a drying, on average, of the continents. However, we note that the changes in precipitation, evaporation and P-E are dominated by small but significant areas, and given the highly idealized nature of this study, a more thorough and broader assessment would be required for proposals of altering marine cloud properties on a large scale.

Comparative Assessment of Techniques for Bioresource Monitoring Using GIS and Remote Sensing

Growing concern over the status of global and regional bioenergy resources has necessitated the analysis and monitoring of land cover and land use parameters on spatial and temporal scales. The knowledge of land cover and land use is very important in understanding natural resources utilization, conversion and management. Land cover, land use intensity and land use diversity are land quality indicators for sustainable land management. Optimal management of resources aids in maintaining the ecosystem balance and thereby ensures the sustainable development of a region. Thus sustainable development of a region requires a synoptic ecosystem approach in the management of natural resources that relates to the dynamics of natural variability and the effects of human intervention on key indicators of biodiversity and productivity. Spatial and temporal tools such as remote sensing (RS), geographic information system (GIS) and global positioning system (GPS) provide spatial and attribute data at regular intervals with functionalities of a decision support system aid in visualisation, querying, analysis, etc., which would aid in sustainable management of natural resources. Remote sensing data and GIS technologies play an important role in spatially evaluating bioresource availability and demand. This paper explores various land cover and land use techniques that could be used for bioresources monitoring considering the spatial data of Kolar district, Karnataka state, India. Slope and distance based vegetation indices are computed for qualitative and quantitative assessment of land cover using remote spectral measurements. Differentscale mapping of land use pattern in Kolar district is done using supervised classification approaches. Slope based vegetation indices show area under vegetation range from 47.65 % to 49.05% while distance based vegetation indices shoes its range from 40.40% to 47.41%. Land use analyses using maximum likelihood classifier indicate that 46.69% is agricultural land, 42.33% is wasteland (barren land), 4.62% is built up, 3.07% of plantation, 2.77% natural forest and 0.53% water bodies. The comparative analysis of various classifiers, indicate that the Gaussian maximum likelihood classifier has least errors. The computation of talukwise bioresource status shows that Chikballapur Taluk has better availability of resources compared to other taluks in the district.

Solar energy potential assessment using GIS

Renewable energy resources are those having a cycling time less than 100 years and are renewed by the nature and their supply exceeds the rate of consumption. Renewable energy systems use resources that are constantly replaced in nature and are usually less polluting. In order to tap the potential of renewable energy sources, there is a need to assess the availability of resources spatially as well as temporally. Geographic Information Systems (GIS) along with Remote Sensing (RS) helps in mapping on spatial and temporal scales of the resources and demand. The spatial database of resource availability and the demand would help in the regional energy planning. This paper discusses the application of geographical information system (GIS) to map the solar potential in Karnataka state, India. Regions suitable for tapping solar energy are mapped on the basis of global solar radiation data, and this analysis provides a picture of the potential. The study identifies that Coastal parts of Karnataka with the higher global solar radiation is ideally suited for harvesting solar energy. The potential analysis reveals that, maximum global solar radiation is in districts such as Uttara Kannada and Dakshina Kannada. Global solar radiation in Uttara Kannada during summer, monsoon and winter are 6.31, 4.40 and 5.48 kWh/sq.m, respectively. Similarly, Dakshina Kannada has 6.16, 3.89 and 5.21 kWh/sq.m during summer, monsoon and winter.

Cerebroprotective activity of Wedelia calendulacea on global cerebral ischemia in rats

The present study was to investigate the effect of W. calendulacea on ischemia and reperfusion-induced cerebral injury. Cerebral ischemia was induced by occluding right and left common carotid arteries (global cerebral ischemia) for 30 min followed by reperfusion for 1 h and 4 h individually. Various biochemical alterations, produced subsequent to the application of bilateral carotid artery occlusion (BCAO) followed by reperfusion viz. increase in lipid peroxidation (LPO), hydrogen peroxide (H(2)O(2)), and decrease in reduced glutathione (GSH), catalase (CAT) and superoxide dismutase (SOD), level in the brain tissue, Western blot analysis (Cu-Zn-SOD and CAT) and assessment of cerebral infarct size were measured. All those enzymes are markedly reversed and restored to near normal level in the groups pre-treated with W. calendulacea (250 and 500 mg/kg given orally in single and double dose/day for 10 days) in dose-dependent way. The effect of W. calendulacea had increased significantly the protein expression of copper/zinc superoxide dismutase (Cu-Zn-SOD) and CAT in cerebral ischemia. W. claendulacea was markedly decrease cerebral infarct damages but results are not statistically significant. It can be concluded that W. calendulacea possesses a neuroprotective activity against cerebral ischemia in rat.

Ecological and socio-economic assessment of Varthur wetland, Bengaluru (India)

Wetlands are the most productive ecosystems, recognized globally for its vital role in sustaining a wide array of biodiversity and provide goods and services. However despite their important role in maintaining the ecology and economy, wetlands in India are endangered by inattention and lack of appreciation for their role. Increased anthropogenic activities such as intense agriculture practices, indiscriminate disposal of industrial effluents and sewage wastes have altered the physical, chemical as well as biological integrity of the ecosystem. This has resulted in the ecological degradation, which is evident from the current ecosystem valuation of Varthur wetland. Global valuation of coastal wetland ecosystem shows a total of 14,785/ha US$ annual economic value. An earlier study of relatively pristine wetland in Bangalore shows the value of Rs. 10,435/ha/day while the polluted wetland shows the value of Rs.20/ha/day. In contrast to this, Varthur, a sewage fed wetland has a value of Rs.118.9/ha/day. The pollutants and subsequent contamination of the wetland has telling effects such as disappearance of native species, dominance of invasive exotic species (such as African catfish), in addition to profuse breeding of disease vectors and pathogens. Water quality analysis revealed of high phosphates (4.22-5.76 ppm) level in addition to the enhanced BOD (119-140 ppm) and decreased DO (0-1.06 ppm). The amplified decline of ecosystem goods and services with degradation of water quality necessitates the implementation of sustainable management strategies to recover the lost wetland benefits.

Novel algorithm for online voltage stability assessment based on feed forward neural network

This paper presents an artificial feed forward neural network (FFNN) approach for the assessment of power system voltage stability. A novel approach based on the input-output relation between real and reactive power, as well as voltage vectors for generators and load buses is used to train the neural net (NN). The input properties of the feed forward network are generated from offline training data with various simulated loading conditions using a conventional voltage stability algorithm based on the L-index. The neural network is trained for the L-index output as the target vector for each of the system loads. Two separate trained NN, corresponding to normal loading and contingency, are investigated on the 367 node practical power system network. The performance of the trained artificial neural network (ANN) is also investigated on the system under various voltage stability assessment conditions. As compared to the computationally intensive benchmark conventional software, near accurate results in the value of L-index and thus the voltage profile were obtained. Proposed algorithm is fast, robust and accurate and can be used online for predicting the L-indices of all the power system buses. The proposed ANN approach is also shown to be effective and computationally feasible in voltage stability assessment as well as potential enhancements within an overall energy management system in order to determining local and global stability indices

Multiwalled-carbon-nanotube-induced miscibility in near-critical PS/PVME blends: assessment through concentration fluctuations and segmental relaxation

The effects of multiwalled carbon nanotubes (MWNTs) on the concentration fluctuations, interfacial driven elasticity, phase morphology, and local segmental dynamics of chains for near-critical compositions of polystyrene/poly(vinyl to methyl ether) (PS/PVME) blends were systematically investigated using dynamic shear rheology and dielectric spectroscopy. The contribution of the correlation length (xi) of the concentration fluctuations to the evolving stresses was monitored in situ to probe the different stages of demixing in the blends. The classical upturn in the dynamic moduli was taken as the rheological demixing temperature (T-rheo), which was also observed to be in close agreement with those obtained using concentration fluctuation variance, <(delta phi)(2)>, versus temperature curves. Further, Fredrickson and Larson's approach involving the mean-field approximation and the double-reptation self-concentration (DRSC) model was employed to evaluate the spinodal decomposition temperature (T-s). Interestingly, the values of both T-rheo and T-s shifted upward in the blends in the presence of MWNTs, manifesting in molecular-level miscibility. These phenomenal changes were further observed to be a function of the concentration of MWNTs. The evolution of morphology as a function of temperature was studied using polarized optical microscopy (POM). It was observed that PVME, which evolved as an interconnected network during the early stages of demixing, coarsened into a matrix-droplet morphology in the late stages. The preferential wetting of PVME onto MWNTs as a result of physicochemical interactions retained the interconnected network of PVME for longer time scales, as supported by POM and atomic force microscopy (AFM) images. Microscopic heterogeneity in macroscopically miscible systems was studied by dielectric relaxation spectroscopy. The slowing of segmental relaxations in PVME was observed in the presence of both ``frozen'' PS and MWNTs interestingly at temperatures much below the calorimetric glass transition temperature (T-g). This phenomenon was observed to be local rather than global and was addressed by monitoring the evolution of the relaxation spectra near and above the demixing temperature.

An assessment of terrestrial water storage, rainfall and river discharge over Northern India from satellite data

Terrestrial water storage (TWS) plays a key role in the global water cycle and is highly influenced by climate variability and human activities. In this study, monthly TWS, rainfall and Ganga-Brahmaputra river discharge (GBRD) are analysed over India for the period of 2003-12 using remote sensing satellite data. The spatial pattern of mean TWS shows a decrease over a large and populous region of Northern India comprising the foothills of the Himalayas, the Indo-Gangetic Plains and North East India. Over this region, the mean monthly TWS exhibits a pronounced seasonal cycle and a large interannual variability, highly correlated with rainfall and GBRD variations (r > 0.8) with a lag time of 2 months and 1 month respectively. The time series of monthly TWS shows a consistent and statistically significant decrease of about 1 cm year(-1) over Northern India, which is not associated with changes in rainfall and GBRD. This recent change in TWS suggests a possible impact of rapid industrialization, urbanization and increase in population on land water resources. Our analysis highlights the potential of the Earth-observation satellite data for hydrological applications.

Development of a global inundation map at high spatial resolution from topographic downscaling of coarse-scale remote sensing data

Large-scale estimates of the area of terrestrial surface waters have greatly improved over time, in particular through the development of multi-satellite methodologies, but the generally coarse spatial resolution (tens of kms) of global observations is still inadequate for many ecological applications. The goal of this study is to introduce a new, globally applicable downscaling method and to demonstrate its applicability to derive fine resolution results from coarse global inundation estimates. The downscaling procedure predicts the location of surface water cover with an inundation probability map that was generated by bagged derision trees using globally available topographic and hydrographic information from the SRTM-derived HydroSHEDS database and trained on the wetland extent of the GLC2000 global land cover map. We applied the downscaling technique to the Global Inundation Extent from Multi-Satellites (GIEMS) dataset to produce a new high-resolution inundation map at a pixel size of 15 arc-seconds, termed GIEMS-D15. GIEMS-D15 represents three states of land surface inundation extents: mean annual minimum (total area, 6.5 x 10(6) km(2)), mean annual maximum (12.1 x 10(6) km(2)), and long-term maximum (173 x 10(6) km(2)); the latter depicts the largest surface water area of any global map to date. While the accuracy of GIEMS-D15 reflects distribution errors introduced by the downscaling process as well as errors from the original satellite estimates, overall accuracy is good yet spatially variable. A comparison against regional wetland cover maps generated by independent observations shows that the results adequately represent large floodplains and wetlands. GIEMS-D15 offers a higher resolution delineation of inundated areas than previously available for the assessment of global freshwater resources and the study of large floodplain and wetland ecosystems. The technique of applying inundation probabilities also allows for coupling with coarse-scale hydro-climatological model simulations. (C) 2014 Elsevier Inc All rights reserved.