Elevated Circulating and Placental SPINT2 Is Associated with Placental Dysfunction

Biomarkers for placental dysfunction are currently lacking. We recently identified SPINT1 as a novel biomarker; SPINT2 is a functionally related placental protease inhibitor. This study aimed to characterise SPINT2 expression in placental insufficiency. Circulating SPINT2 was assessed in three prospective cohorts, collected at the following: (1) term delivery (n = 227), (2) 36 weeks (n = 364), and (3) 24–34 weeks’ (n = 294) gestation. SPINT2 was also measured in the plasma and placentas of women with established placental disease at preterm (<34 weeks) delivery. Using first-trimester human trophoblast stem cells, SPINT2 expression was assessed in hypoxia/normoxia (1% vs. 8% O2), and following inflammatory cytokine treatment (TNFα, IL-6). Placental SPINT2 mRNA was measured in a rat model of late-gestational foetal growth restriction. At 36 weeks, circulating SPINT2 was elevated in patients who later developed preeclampsia (p = 0.028; median = 2233 pg/mL vs. controls, median = 1644 pg/mL), or delivered a small-for-gestational-age infant (p = 0.002; median = 2109 pg/mL vs. controls, median = 1614 pg/mL). SPINT2 was elevated in the placentas of patients who required delivery for preterm preeclampsia (p = 0.025). Though inflammatory cytokines had no effect, hypoxia increased SPINT2 in cytotrophoblast stem cells, and its expression was elevated in the placental labyrinth of growth-restricted rats. These findings suggest elevated SPINT2 is associated with placental insufficiency.     

The Practical Application of Ozone Gas as an Anti-fungal (Anti-mold) Agent

We evaluated the ability of a portable ozone generating machine (Viroforce 1000) to inactivate 13 different species of environmental fungi. Samples, prepared as wet or dried films, were subjected to one or two cycles of treatment (35 ppm ozone for 20 minutes, with a short burst of?>90%?relative humidity), and measured for residual viability. Treatments could inactivate 3 log₁₀ cfu (colony forming units) of most of the fungi, both in the laboratory and in simulated field conditions, on various surfaces. We conclude that the ozone generator would be a valuable decontamination tool for mold removal in buildings.     

Design and Development of Density-Based Effective Document Clustering Method Using Ontology

Text document clustering is used to separate a collection of documents into several clusters by allowing the documents in a cluster to be substantially similar. The documents in one cluster are distinct from documents in other clusters. The high-dimensional sparse document term matrix reduces the clustering process efficiency. This study proposes a new way of clustering documents using domain ontology and WordNet ontology. The main objective of this work is to increase cluster output quality. This work aims to investigate and examine the method of selecting feature dimensions to minimize the features of the document name matrix. The sports documents are clustered using conventional K-Means with the dimension reduction features selection process and density-based clustering. A novel approach named ontology-based document clustering is proposed for grouping the text documents. Three critical steps were used in order to develop this technique. The initial step for an ontology-based clustering approach starts with data pre-processing, and the characteristics of the DR method are reduced with the Info-Gain collection. The documents are clustered using two clustering methods: K-Means and Density-Based clustering with DR Feature Selection Process. These methods validate the findings of ontology-based clustering, and this study compared them using the measurement metrics. The second step of this study examines the sports field ontology development and describes the principles and relationship of the terms using sports-related documents. The semantic web rational process is used to test the ontology for validation purposes. An algorithm for the synonym retrieval of the sports domain ontology terms has been proposed and implemented. The retrieved terms from the documents and sport ontology concepts are mapped to the retrieved synonym set words from the WorldNet ontology. The suggested technique is based on synonyms of mapped concepts. The proposed ontology approach employs the reduced feature set in order to clustering the text documents. The results are compared with two traditional approaches on two datasets. The proposed ontology-based clustering approach is found to be effective in clustering the documents with high precision, recall, and accuracy. In addition, this study also compared the different RDF serialization formats for sports ontology.

     

Retina disease prediction using modified convolutional neural network based on Inception-ResNet model with support vector machine classifier

Artificial intelligence and deep learning have aided ocular disease through experiments including automatic illness recognition from images of the iris, fundus, or retina. Automated diagnosis systems (ADSs) provide services for the benefit of humanity and are essential in the early detection of harmful diseases. In fact, early detection is essential to avoid total blindness. In real life, several diagnostic tests such as visual ocular tonometry, retinal exam, and acuity test are performed, but they are conclusively time demanding and stressful for the patient. To consume time and detect the retinal disease earlier, an efficient prediction method is designed. In this proposed model, the first process is data collection that consists of a retinal disease dataset for testing and training. The second process is pre-processing, which executes image resizing and noise filter for feature extraction. The third step is feature extraction, which extracts the image's form, size, color, and texture for classification with CNN based on Inception-ResNet V2. The classification process is done by using the SVM with the extracted features. The prediction of diseases is classified such as normal, cataract, glaucoma, and retinal disease. The suggested model's performance is assessed using performance indicators such as accuracy, error, sensitivity, precision, and so forth. The suggested model's accuracy, error, sensitivity, and precision are 0.96, 0.962, 0.964, and 0.04, respectively, higher than existing techniques such as VGG16, Mobilenet V1, ResNet, and AlexNet. Thus, the proposed model instantly predicts retinal disease.     

Performance comparison between Chaos and quantum-chaos based image encryption techniques

Multimedia Tools and Applications - Today’s digital era has undertaken most of the responsibilities of public and private sectors, not only the industries or big organizations dependent on...     

Models for minimum explosible concentration of organic dust clouds handled in industries

Dust explosion hazard exists in plants and facilities wherever combustible dusts are hardled. Minimum explosible concentration of dust clouds is an important factor requiring special attention for hazard evaluation if any technological equipment is to be protected by inertisation. The mathematical models available for prediction of this parameter have been analysed for their application to organic dust clouds. Solution of the most general mode for determination of minimum explosible concentration of dust clouds proposed by Mitsui and Tanaka is presented, together with the comparison with experimental data. It has been found that the model is not successful in predicting the minimum explosible concentration for organic dusts. Recommendations on requirement of development of a new model for prediction of minimum explosible concentration of an organic dust such as polyethylen have been given.     

Use of micelles in photogalvanic cell for solar energy conversion and storage: Azur A-glucose system

Sodium lauryl sulphate (NaLS), tetradecyl trimethyl ammonium bromide (TTAB) and Brij-35 were used in a photogalvanic cell containing azur A as a photosensitizer and glucose as a reductant for solar energy conversion and storage. The photopotential and photocurrent generated by the cell in the presence of NaLS were 811.0 mV and 1470 μA, respectively. The effect of variation of the concentrations of the surfactants on the electrical output, the fill factor, the conversion efficiency and the performance of the cell in the dark was studied in detail.     

Models for minimum ignition temperature of organic dust clouds

The minimum ignition temperature of dust clouds is one of the important factors required for the design of preventive measures against dust explosion. The mathematical models available to predict this parameter have been analyzed for thier application to organic dust clouds. A solution of the most general model proposed by Mitsui and Tanaka is presented, together with its comparision with experimental data. It has been found to be quite successful in predicting the minimum ignition temperature for metal dusts but not for organic dusts. Recommendations for the development of a new model to predict the minimum ignition temperature of an organic dust, such as polyethylene, have been given.     

Industrial fires and explosions due to electrostatic ignition

Static electricity is a frequent source of fires and explosions in industry. A variety of operations may generate static electricity leading to such fires and explosions. This requires adequate preventive and protective measures against this hazard. The present paper describes the theory and mechanism of electrostatic sparking, parameters needed to assess the respective hazard in a plant, safety measures to combat electrostatic problems, common operations where such problems exist and measures to eliminate or mitigate these problems.