Study of Cerebral Venous Thrombosis in Males.

Cerebral venous thrombosis (CVT), is the thrombosis of the intracranial veins or dural sinuses.1 It is a relatively rare disorder, affecting about 5 persons per million per year with huge regional variations. It accounts less than 1% of all strokes. It has differential geographic distribution with a higher incidence in the Asian countries. In contrast to arterial stroke, thrombosis of the cerebral venous sinuses and the cerebral cortical veins most often affects children and young adults. Its presentation is highly variable, etiological factors are diverse and more heterogeneous making cerebral cortical venous thrombosis (CVT) a distinctively unique entity. The Virchow’s triad, which compromises the features of endothelial damage, stasis and hypercoagulability of blood, plays a very important role in the pathogenesis of cerebral venous sinus thrombosis. These haemodynamic factors vary with each patient. They may operate together incidentally or accidentally to produce the clinical manifestation of cerebral cortical venous sinus thrombosis. AIM OF THE STUDY : To study the risk and etiological factors in pathogenesis of Cerebral Venous Thrombosis and the varied clinical presentation in males. CONCLUSION : Cerebral venous thrombosis (CVT) which was previously thought to be an uncommon condition is now being diagnosed frequently due to increasing awareness and improvement in imaging modalities. Cerebral venous sinus thrombosis presents with a wide variety of clinical manifestation, the most common being acute onset headache, seizures, features of raised intracranial tension and focal neurological deficits not pertaining to arterial territorial region. Thrombophilic conditions and hyper oestrogenic states in a setting of dehydration are known factors postulated in the causation of CVT. This study, exclusively done in male patients is the first of its kind to our knowledge from the literature. From the results obtained in this study, alcoholism, which is very much rampant in this part of Chennai, seems to be cause for the higher incidence of CVT in males. The correlations of the various studies quoted from the literature, makes it plausible to consider folate deficiency in alcoholics with the resultant hyper homocystinemia as the cause for the hyper coagulability. Further, in our study group which includes people who are manual labourers, belonging to low socioeconomic class, pre-existent nutritional deficiency might also contribute to the clinical scenario. Hence these nutritional deficiencies, which are amenable for correction, if sought earlier and corrected in the high risk group, might decrease the incidence of this catastrophic disease. Further large scale studies are needed to establish a clear relationship between these factors in our population. Studies are also needed in the context of treatment with folic acid, methyl cobalamine, and pyridoxine in the acute setting of CVT. Further studies are also needed in pregnant and the puerperal women who will also have decreased folate levels due to increased demand. The appropriate dose of folic acid needed to overcome these deficient states in various population subgroups needs to be quantified by further studies

Green and Sustainability in Software Development Lifecycle Process

This chapter gives an insight of GREENSOFT Model for sustainable software engineering. In today’s world, computing devices are extensively by all for many purposes. They consume lots of energy even though they reduce energy consumption. Computers are used extensively while developing software. Existing software engineering models do not pay much attention to green computing that focuses on the effective use of natural resources. Sustainability of resources is the key. The GREENSOFT model of software engineering proposes a methodology in which Green IT practices are used, which will reduce the energy consumption of computers while developing software

HEPATOPROTECTIVE EVALUATION OF EPALTES DIVARICATA (L.) CASS. WHOLE PLANT EXTRACTS AGAINST PARACETAMOL-INDUCED HEPATOTOXICITY IN RATS

Objective: The plant of Epaltes divaricata (L.) Cass. Traditionally used for jaundice. The present work aimed to investigate the hepatoprotective activity of alcohol and aqueous extract of the whole plant against paracetamol-induced hepatotoxicity in rats to substantiate its traditional use.Methods: The alcohol and aqueous (200 and 400 mg/kg) extract of Epaltes divaricata prepared by cold maceration were administered orally to the animals with hepatotoxicity induced by paracetamol (1000 mg/kg). Silymarine (40 mg/k) was given as reference standard. Hepatoprotective activity was assessed by estimating marker enzymes and by histopathological studies.Results: Both alcohol and aqueous (200 and 400 mg/kg) extract treatment significantly restored the paracetamol-induced elevations in levels of serum enzymes aspartate transaminase (AST), alanine transaminase (ALT), alkaline phosphate (ALP) and total bilirubin in a dose-dependent manner. Histopathological examination revealed that the treatment attenuated the paracetamol-induced damage to the liver. The hepatoprotective effect of both extracts was comparable to that of the standard hepatoprotective agent, silymarin.Conclusion: The alcohol and aqueous extract of E. divaricata exhibited hepatoprotective effect against paracetamol-induced liver damage in rats. This study also validated their traditional medicinal use in jaundice

Synthesis, characterization and photocatalytic application of ZnWO4/ZrO2 nanocomposite towards degradation of methyl orange dye

Visible light active ZnWO4/ZrO2 nanocomposite was prepared via hydrothermal method. The nanocomposite was characterized by UV-visible diffuse reflectance spectroscopy (UV-vis-DRS), Fourier transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), Scanning Electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDX) and transmission electron microscopy (TEM) techniques. The XRD results showed that average particle size of ZrO2, ZnWO4 and ZnWO4/ZrO2 were found to be 29.20 nm, 23.78 nm and 20.14 nm respectively and the phase structure for ZrO2 and ZnWO4 in the composite was Rhombohedral and Monoclinic respectively. The UV–vis absorption spectra of the ZnWO4/ZrO2 nanocomposite noticeably shifted to the visible light region compared to that of the ZrO2. The prepared photocatalyst were composed of plate and spongy sphere with little agglomeration was seen from SEM result. The photocatalytic activities of the prepared nanocomposite was evaluated for the degradation of methyl orange (MO) under visible light irradiations. The effect of operational parameters such as initial dye concentration, pH, catalyst concentration and irradiation time have been investigated in detail. The photocatalytic degradation efficiency of ZnWO4/ZrO2, ZnWO4 and ZrO2 for 95%, 72% and 60 % respevtively. The high photocatalytic activity can be attributed to stronger absorption in the visible light region, a greater specific surface area, smaller crystal sizes, more surface OH groups, and to the effect of ZnWO4 doping, which resulted in a lower band gap energy

A Hybrid DE-RGSO-ELM for Brain Tumor Tissue Categorization in 3D Magnetic Resonance Images

Medical diagnostics, a technique used for visualizing the internal structures and functions of human body, serves as a scientific tool to assist physicians and involves direct use of digital imaging system analysis. In this scenario, identification of brain tumors is complex in the diagnostic process. Magnetic resonance imaging (MRI) technique is noted to best assist tissue contrast for anatomical details and also carries out mechanisms for investigating the brain by functional imaging in tumor predictions. Considering 3D MRI model, analyzing the anatomy features and tissue characteristics of brain tumor is complex in nature. Henceforth, in this work, feature extraction is carried out by computing 3D gray-level cooccurence matrix (3D GLCM) and run-length matrix (RLM) and feature subselection for dimensionality reduction is performed with basic differential evolution (DE) algorithm. Classification is performed using proposed extreme learning machine (ELM), with refined group search optimizer (RGSO) technique, to select the best parameters for better simplification and training of the classifier for brain tissue and tumor characterization as white matter (WM), gray matter (GM), cerebrospinal fluid (CSF), and tumor. Extreme learning machine outperforms the standard binary linear SVM and BPN for medical image classifier and proves better in classifying healthy and tumor tissues. The comparison between the algorithms proves that the mean and standard deviation produced by volumetric feature extraction analysis are higher than the other approaches. The proposed work is designed for pathological brain tumor classification and for 3D MRI tumor image segmentation. The proposed approaches are applied for real time datasets and benchmark datasets taken from dataset repositories

Efficient Discovery of Expressive Multi-label Rules using Relaxed Pruning

Being able to model correlations between labels is considered crucial in multi-label classification. Rule-based models enable to expose such dependencies, e.g., implications, subsumptions, or exclusions, in an interpretable and human-comprehensible manner. Albeit the number of possible label combinations increases exponentially with the number of available labels, it has been shown that rules with multiple labels in their heads, which are a natural form to model local label dependencies, can be induced efficiently by exploiting certain properties of rule evaluation measures and pruning the label search space accordingly. However, experiments have revealed that multi-label heads are unlikely to be learned by existing methods due to their restrictiveness. To overcome this limitation, we propose a plug-in approach that relaxes the search space pruning used by existing methods in order to introduce a bias towards larger multi-label heads resulting in more expressive rules. We further demonstrate the effectiveness of our approach empirically and show that it does not come with drawbacks in terms of training time or predictive performance.Comment: Preprint version. To appear in Proceedings of the 22nd International Conference on Discovery Science, 201

Learning Interpretable Rules for Multi-label Classification

Multi-label classification (MLC) is a supervised learning problem in which, contrary to standard multiclass classification, an instance can be associated with several class labels simultaneously. In this chapter, we advocate a rule-based approach to multi-label classification. Rule learning algorithms are often employed when one is not only interested in accurate predictions, but also requires an interpretable theory that can be understood, analyzed, and qualitatively evaluated by domain experts. Ideally, by revealing patterns and regularities contained in the data, a rule-based theory yields new insights in the application domain. Recently, several authors have started to investigate how rule-based models can be used for modeling multi-label data. Discussing this task in detail, we highlight some of the problems that make rule learning considerably more challenging for MLC than for conventional classification. While mainly focusing on our own previous work, we also provide a short overview of related work in this area.Comment: Preprint version. To appear in: Explainable and Interpretable Models in Computer Vision and Machine Learning. The Springer Series on Challenges in Machine Learning. Springer (2018). See http://www.ke.tu-darmstadt.de/bibtex/publications/show/3077 for further informatio

Real Time Soil Moisture (RTSM) Based Irrigation Scheduling to Improve Yield and Water-Use Efficiency of Green Pea (Pisum sativum L.) Grown in North India

A field experiment on green pea (Pisum Sativum L.) was conducted under drip irrigation to determine the irrigation schedule based on real-time soil moisture measurements with irrigation treatments (main plots) and fertilizer treatments (sub-plots) in a split-plot design with three replications. Main plots consisted of fourirrigation levels at different matric potential ranges (I1: −20 kPa; I2: −30 kPa; I3: −35 kPa; and I4: −40 kPa), while the sub-plots consisted of three fertigation levels (F1: 120%, F2: 100% and F3: 80%) of recommended dose of fertilizers (40:60:50 kg/ha of NPK). The tensiometer with digital pressure transducer transferred the soil matric potential data to the irrigation controller, which activated the solenoid valves for irrigation. Observations were collected on plant growth parameters, pod yield, and quality parameters. Descriptive statistics of different plant growth parameters were made. The higher SMP threshold (−20 kPa) and lower SMP threshold (−40 kPa) greatly reduced the yield and water-use efficiency. Considering the results, real-time soil moisture-based irrigation at the soil matric potential threshold level of −30 kPa with 120% of recommended dose of fertilizers through fertigation was recommended for attaining maximum green pea pod yield and water-use efficiency under semi-arid Inceptisols

Design of Filtration System for Aerated Sewage Water

The study investigated the potential of sand and activated charcoal filtration systems to enhance water quality for irrigation by treating aerated sewage effluent from. Setup involved a 60 cm deep sand filter connected as the inlet to another 30 cm deep sand filter and this filter linked as the inlet to a 30 cm deep charcoal filter. These filters were operated in series at hydraulic loading rates (HLR) of 60 m/h and 10 m/h. Notably, operating the filters in series at an HLR of 10 m/h yielded superior effluent water quality compared to an HLR of 60 m/h. System achieved significant removal efficiencies for turbidity, BOD5, COD, Total Nitrogen (Total-N), Total Phosphorous (Total-P) with 71.9%, 54.4%, 71.9%, 44.4%, 39.1%, and 42.9% with a 90 cm deep sand filter at an HLR of 10 m/h, and also with a combination of sand and charcoal filters at an HLR of 25 m/h system achieved 81.6%, 80.3%, 63.5%, 47.5%, and 64.3% respectively. We also examined the chemical characteristics of both untreated and treated sewage water samples, revealing a hierarchy of cation and anion prevalence as follows: Na+ > Ca2+ > Mg2+ > K+ for cations, and Cl- > HCO3- > SO42- > CO32- for anions. Our study demonstrates that the combination of aeration and sand filtration effectively ensures safety by preventing water body pollution and unpleasant odours with high-quality treated wastewater suitable for sustainable agricultural use

Assessing Land Use Dynamics of Lower Bhavani Basin Using Multiple GIS Platforms

Land use describes the actual form of land, such as a forest or open water and classification based on human utilization. Land use map provides the information about the current landscape of an area. In this study, the Lower Bhavani basin's land use and land cover were classified using GIS platforms and data from the Landsat 8 satellite. The platform utilized in this study were Semi-Automated Plugin (SAP) in QGIS and Random forest method in Google Earth Engine (GEE). The findings suggested that both platforms performed efficiently and displayed comparable percentages of land covered by various land use features. The accuracy of the resulting land use map was evaluated using a Google Earth image, and it was discovered that SAP and GEE hold 91.8% and 92.6% of the total accuracy. This study aids in evaluating and classifying the various Geographic Information System platforms land use trends