Lung nodules detection using semantic segmentation and classification with optimal features

Neural Computing and Applications - Lung cancer is a deadly disease if not diagnosed in its early stages. However, early detection of lung cancer is a challenging task due to the shape and size of...     

New fatty acid derivatives from <Emphasis Type="Italic">Homaxinella</Emphasis> sp., a marine sponge

Fractionation of the MeOH extract of Homaxinella sp., a marine sponge, led to the isolation of a sodium salt of a new brominated FA (1), two new MG (2 and 4), and a new lysoPC (6). The geometry of the double bonds in 1 and 2 was defined by comparison of the NMR chemical shifts of the allylic carbons, nuclear Overhauser effect spectroscopy correlations of the allylic protons, and coupling constants of the vinylic protons with those reported. Evidence mainly from NMR and MS analyses established the planar structures of the compounds. Compounds 1, 2, 4, and 6 were evaluated for cytotoxicity against a panel of five human solid tumor cell lines. Only compound 1 showed moderate activity.     

Intelligent Machine Learning with Metaheuristics Based Sentiment Analysis and Classification

Sentiment Analysis (SA) is one of the subfields in Natural Language Processing (NLP) which focuses on identification and extraction of opinions that exist in the text provided across reviews, social media, blogs, news, and so on. SA has the ability to handle the drastically-increasing unstructured text by transforming them into structured data with the help of NLP and open source tools. The current research work designs a novel Modified Red Deer Algorithm (MRDA) Extreme Learning Machine Sparse Autoencoder (ELMSAE) model for SA and classification. The proposed MRDA-ELMSAE technique initially performs preprocessing to transform the data into a compatible format. Moreover, TF-IDF vectorizer is employed in the extraction of features while ELMSAE model is applied in the classification of sentiments. Furthermore, optimal parameter tuning is done for ELMSAE model using MRDA technique. A wide range of simulation analyses was carried out and results from comparative analysis establish the enhanced efficiency of MRDA-ELMSAE technique against other recent techniques.     

Optimized Identification with Severity Factors of Gastric Cancer for Internet of Medical Things

The Internet of Medical Things (IoMT) emerges with the vision of the Wireless Body Sensor Network (WBSN) to improve the health monitoring systems and has an enormous impact on the healthcare system for recognizing the levels of risk/severity factors (premature diagnosis, treatment, and supervision of chronic disease i.e., cancer) via wearable/electronic health sensor i.e., wireless endoscopic capsule. However, AI-assisted endoscopy plays a very significant role in the detection of gastric cancer. Convolutional Neural Network (CNN) has been widely used to diagnose gastric cancer based on various feature extraction models, consequently, limiting the identification and categorization performance in terms of cancerous stages and grades associated with each type of gastric cancer. This paper proposed an optimized AI-based approach to diagnose and assess the risk factor of gastric cancer based on its type, stage, and grade in the endoscopic images for smart healthcare applications. The proposed method is categorized into five phases such as image pre-processing, Four-Dimensional (4D) image conversion, image segmentation, K-Nearest Neighbour (K-NN) classification, and multi-grading and staging of image intensities. Moreover, the performance of the proposed method has experimented on two different datasets consisting of color and black and white endoscopic images. The simulation results verified that the proposed approach is capable of perceiving gastric cancer with 88.09% sensitivity, 95.77% specificity, and 96.55% overall accuracy respectively.     

Isolation and identification of lipase producing thermophilic Geobacillus sp. SBS-4S: cloning and characterization of the lipase

A thermophilic microorganism, SBS-4S, was isolated from a hot spring located in Gilgit, Northern Areas of Pakistan. It was found to be an aerobic, gram-positive, rod-shaped, thermophilic bacterium that grew on various sugars, carboxylic acids and hydrocarbons at temperatures between 45°C and 75°C. Complete 16S rRNA gene sequence of the microorganism exhibited homology to various species of genus Geobacillus. A highest homology of 99.8% was found with Geobacillus kaustophilus. A partial (0.7 kbp) chaperonin gene sequence also showed a highest homology of 99.4% to that of G. kaustophilus whereas biochemical characteristics of the microorganism were similar to Geobacillus uzenensis. Based on biochemical characterization, 16S rRNA and chaperonin gene sequences, we identified SBS-4S as a strain of genus Geobacillus. Strain SBS-4S produced several extracellular enzymes including amylase, protease and lipase. The lipase encoding gene was cloned, expressed in Escherichia coli and the gene product was characterized. The recombinant lipase was optimally active at 60°C with stability at wide pH range (6-12). The enzyme activity was enhanced remarkably in the presence of Ca(+2). The K(m) and the V(max) for the hydrolysis of p-nitrophenyl acetate were 3.8mM and 2273 μmol min(-1)mg(-1), respectively. The ability of the recombinant enzyme to be stable at a wide pH range makes it a potential candidate for use in industry.     

New Type Anode for Calcium Ion Batteries Based on Silicon Carbide Monolayer

Silicon - A theoretical research study was conducted into the possibility of using a two-dimensional graphene-like material silicon carbide nano-sheet (2D-SiCNS) as an anode in rechargeable Ca-ion...     

Interfacial performance and durability of textile reinforced concrete

The aim of this work is to understand the bonding characteristics between various yarn structures and cement matrix which was studied by yarn pull out test. In this work, the effect of accelerated ageing under alkaline environment for different types of fibers i.e. Basalt, Polypropylene, Polyester, and Jute is studied in order to comparatively evaluate their weight loss and reduction of tensile strength. SEM images are also used for studying the morphology of alkali degraded fibers. Material characterization can also be done by FTIR, XRD, ICP, Zeta potential, and UV spectrometer.     

Subcritical Crack Growth in a Phosphate Laser Glass

The rate of subcritical crack growth in a metaphosphate Nd-doped laser glass was measured using the double-cleavage-drilled compression (DCDC) method. The crack velocity is reported as a function of stress intensity at temperatures ranging from 296 to 573 K and in nitrogen with water vapor pressures ranging from 40 Pa (0.3 mmHg) to 4.7 × 10⁴ Pa (355 mmHg). The measured crack velocities follow region I, II, and III behavior similar to that reported for silicate glasses. A chemical and mass-transport-limited reaction rate model explains the behavior of the data except at high temperatures and high water vapor pressures where crack tip blunting is observed. Blunting is characterized by an arrest in the crack growth followed by the inability to reinitiate slow crack growth at higher stresses. A dynamic crack tip blunting mechanism is proposed to explain the deviation from the reaction rate model.     

Enzymolysis reaction kinetics and thermodynamics of rapeseed protein with sequential dual‐frequency ultrasound pretreatment

We evaluated the effect of sequential dual‐frequency ultrasound (SDFU) pretreatments on rapeseed protein enzymolysis, using alcalase as a model enzyme. Hydrolysed protein concentrations, enzymolysis kinetics and thermodynamic parameters were investigated. The results showed that the hydrolysed rapeseed protein concentration following SDFU pretreatments was higher compared to that of the control for up to 75 min of enzymolysis at various substrate concentrations of 5–25 g L^?1; both control and SDFU pretreatment groups showed first‐order reaction kinetics. Compared to the control, the Michaelis–Menten constant (K_M) value decreased remarkably by 17.61%, while an increase in the binding frequency between enzyme and substrate (K_A) by 10.47% was observed. The thermodynamic parameters, enthalpy, entropy and activation energy were reduced in the SDFU pretreatment group compared to the control by 31.78%, 18.0% and 29.56%, respectively. SDFU pretreatment showed little effect on Gibbs free energy at the various temperatures studied.