Effect of monoalgal diet on the growth, survival and egg production in Nannocalanus minor (Copepoda: Calanoida)

1579-1584The effect of monoalgal diet on the growth, survival, egg production and egg hatching succession in calanoid copepod Nannocalanus minor was studied under laboratory condition. There are seven different microalgae such as Chlorella marina, Dunaliella salina, Isochrysis galbana, Nannochloropsis sp., Coscinodiscus centralis, Chaetoceros affinis and Skeletonema costatum were tested for their efficacy on survival of N. minor at two different algal cell concentrations viz. 10,000 and 20,000 cells/ml. Among the six diets tested, Chlorella marina shows the extensive survival in both lowest and highest algal concentrations where the 100% survival extends for 7th and 9th days of experiment while the least survival was obtained in diatom Skeletonema costatum. Likewise, copepod N. minor grew faster at C. marina than other algal feed tested presently. The egg production (32±1.52 eggs/female/day) and hatching succession (93.75%) both are proportionally increased with increasing algal concentration (20,000 cells/ml) while at low algal concentration (1000 cells/ml) it was recorded as 3±1 eggs/female/day and 44.33% respectively. The study provides a realistic basis for formulating suitable algal food and algal concentration required for copepod N. minor to achieve utmost growth, survival and fecundity in captive condition. This information can help in developing the culture technology on copepod Nannocalanus minor for it use in larval fish culture

Automated Epileptic Seizure Detection in Pediatric Subjects of CHB-MIT EEG Database—A Survey

Epilepsy is a neurological disorder of the brain that causes frequent occurrence of seizures. Electroencephalography (EEG) is a tool that assists neurologists in detecting epileptic seizures caused by an unexpected flow of electrical activities in the brain. Automated detection of an epileptic seizure is a crucial task in diagnosing epilepsy which overcomes the drawback of a visual diagnosis. The dataset analyzed in this article, collected from Children’s Hospital Boston (CHB) and the Massachusetts Institute of Technology (MIT), contains long-term EEG records from 24 pediatric patients. This review paper focuses on various patient-dependent and patient-independent personalized medicine approaches involved in the computer-aided diagnosis of epileptic seizures in pediatric subjects by analyzing EEG signals, thus summarizing the existing body of knowledge and opening up an enormous research area for biomedical engineers. This review paper focuses on the features of four domains, such as time, frequency, time-frequency, and nonlinear features, extracted from the EEG records, which were fed into several classifiers to classify between seizure and non-seizure EEG signals. Performance metrics such as classification accuracy, sensitivity, and specificity were examined, and challenges in automatic seizure detection using the CHB-MIT database were addressed.</jats:p

Automated Epileptic Seizure Detection in Pediatric Subjects of CHB-MIT EEG Database—A Survey

Epilepsy is a neurological disorder of the brain that causes frequent occurrence of seizures. Electroencephalography (EEG) is a tool that assists neurologists in detecting epileptic seizures caused by an unexpected flow of electrical activities in the brain. Automated detection of an epileptic seizure is a crucial task in diagnosing epilepsy which overcomes the drawback of a visual diagnosis. The dataset analyzed in this article, collected from Children’s Hospital Boston (CHB) and the Massachusetts Institute of Technology (MIT), contains long-term EEG records from 24 pediatric patients. This review paper focuses on various patient-dependent and patient-independent personalized medicine approaches involved in the computer-aided diagnosis of epileptic seizures in pediatric subjects by analyzing EEG signals, thus summarizing the existing body of knowledge and opening up an enormous research area for biomedical engineers. This review paper focuses on the features of four domains, such as time, frequency, time-frequency, and nonlinear features, extracted from the EEG records, which were fed into several classifiers to classify between seizure and non-seizure EEG signals. Performance metrics such as classification accuracy, sensitivity, and specificity were examined, and challenges in automatic seizure detection using the CHB-MIT database were addressed

Modelling and Blood Flow Analysis of Internal Pudendal Artery

A common male sexual disorder is erectile dysfunction which has multidimensions. In this fast-moving world, it is prominently seen in lots of males. There are many causes for Erectile Dysfunction, one of the major causes is the improper supply of blood to the penile organ. That may be due to vasoconstriction or blockage in the internal pudendal artery which supplies oxygen to the penile organ. A simulated model of the internal iliac artery to the internal prudential artery is designed and a flow simulation is done using Solid works software. The Computed Tomography of a male subject is obtained and a three-dimensional model of the abdominal artery is extracted using MIMICS (Materialize Interactive Medical Image Control System) software. By making use of the measured dimensions from the three-dimensional image. The 3D models (Normal condition, Abnormal condition with blockage, and Abnormal condition with constrictions) are designed and the Flow analysis is done in Solid works software. By the end of the study, we came to a conclusion that at normal temperature and pressure, the simulated normal volumetric blood flow at the internal pudendal artery is 6.88701e-09 m3/s and for abnormal cases the simulated volumetric blood flow is 2.6107e-09m3/s.</jats:p