Feed supplements of oil and selenium have been studied for their effect on absolute weight growth and a descriptive picture of the nutritional content of protein, fat, cholesterol in tilapia baby fish. Feed experiments using Complete Randomized Design (6x3), R1 (basal/protein ration 28%); R2 addition of a mixture of coconut oil and hazelnut oil without Se and R3 (oil mixture 4%+Se 0.15 mg/kg); R4 (4% coconut oil + Se) and R5 (4% hazelnut oil + Se) and Rs (standard ration of protein 32%). Coconut is dominated by saturated fatty acids (lauric acid 42.67%), while hazelnut is dominated by linoleic unsaturated fatty acids (34.4%) and oleic acid (48.99%). Basal ration with the addition of a mixture of vegetable oils + Se resulted in an absolute growth of 27.33 g and a daily growth rate (DGR) of 0.43 g/day, and matched the Ration with high protein (32%). The addition of vegetable fats and selenium provides fish meat protein content 54.62%-58.54% and meat protein conversion (protein productive value) 27.68-32.03%. The fat content of meat and cholesterol ranges from 7.15%-10.20% and 75.43-103.97 mg/dL, respectively, and Se in tilapia meat ranges from 0.502-0.753 mg/kg).

Respiratory diseases become burden to affect health of the people and five lung related diseases namely COPD, Asthma, Tuberculosis, Lower respiratory tract infection and Lung cancer are leading causes of death worldwide. X-ray or CT scan images of lungs of patients are analysed for prediction of any lung related respiratory diseases clinically. Respiratory sounds also can be analysed to diagnose the respiratory illness prevailing among humans. Sound based respiratory disease classification against healthy subjects is done by extracting spectrogram from the respiratory sound signal and Convolutional neural network (CNN) templates are created by applying the extracted features on the layered CNN architecture. Test sound is classified to be associated with respiratory disease or healthy subjects by applying the testing procedure on the test feature frames of spectrogram. Evaluation of the respiratory disease binary classification is performed by considering 80% and 20% of the extracted spectrogram features for training and testing. An automated system is developed to classify the respiratory diseases namely upper respiratory tract infection (URTI), pneumonia, bronchitis, bronchiectasis, and coronary obstructive pulmonary disease (COPD) against healthy subjects from breathing & wheezing sounds. Decision level fusion of spectrogram, Melspectrogram and Gammatone gram features with CNN for modelling & classification is done and the system has deliberated the accuracy of 98%. Combination of Gammatone gram and CNN has provided very good results for binary classification of pulmonary diseases against healthy subjects. This system is realized in real time by using Raspberry Pi hardware and this system provides the validation error of 14%. This automated system would be useful for COVID testing using breathing sounds if respiratory sound database with breathing sound recordings from COVID patients would be available.

In the current machine vision technology, accurate detection and classification of the crop dis-eases can protect against spoilage. Different diseases of tomato leaf have similar features or traits, making image disease detection confusing and challenging. Farmers cannot recognize whether a crop is infected or not just by looking at its leaves, because the healthy and infected crops resemble the same at first. Deep learning models can be used to overcome this prob-lem within less computational time. As a result, a new framework is implemented in this work through fine tuning the Deep Convolutional Neural Networks (DCNN) model using hyper parameters like learning rate, batch size, and epochs by applying transfer learning techniques for detecting tomato leaf disease. The data in this work is collected from the Plant Vil-lage database, which includes 20,639 images. The pro-posed model is implemented on three pre trained DCNN models-Alex Net, ResNet50 and VGG16. The proposed framework attains highest classification ac-curacy of 99.26% for fine tuning DCNN. The simula-tion results demonstrates that the fine-tuning Res-Net50 performs better classification of crop diseases when compared to the other DCNN models.

In response to the global call for strategic information to comprehend prostate cancer, this study evaluated the health communication on behavioral practice of prostate cancer in Kwara state, Nigeria. Existing studies in Nigeria on prostate cancer have mostly focused on health practitioners and their patients, ignoring specific empirical data on semi-urban and urban context. This study looks at health communication channels as predictors of knowledge, attitude, and behavioral practices, with a focus on Ilorin, Nigeria’s Kwara state, which has the highest prostate cancer prevalence rate. A total of 336 respondents from Kwara State, Nigeria, were randomly selected using the multistage sample procedure for the survey. The findings show Knowledge of prostate cancer was highest amongst study participants who used the radio (4.00 ± 1.06) and television (3.64 ± 0.51) while it was low amongst those who relied on the internet (3.48 ± 0.50) and health professionals (3.16 ± 0.66) as their primary source of information. Contrastingly, practice was highest amongst persons who used the internet (3.60 ± 0.20) as their primary information source and lowest amongst those who used the television (2.50 ± 1.52) and Health Professionals (2.44 ± 0.65). Demographically, respondents in the 46-55 age group scored the highest (3.93 ± 0.71) as compared to those in the 26-35 (3.43 ± 0.68) who scored the lowest on the knowledge scale.The study concludes that health communication outlets such as television, the Internet, radio, newspapers, and health workers have a good impact on the people of Ilorin, Kwara State, Nigeria. The study suggests creating a nationwide prostate cancer communication system to improve the knowledge, attitude and practice of people, towards the attainment of Sustainable Development Goal 3.

The purpose of this study was to determine the levels and components of essential oils between the rhizome and tuber parts of the white turmeric (Kaempferi rotunda) plant. Sampling of white turmeric was done purposively. The plant parts analyzed were the rhizome and tuber of white turmeric. The study was conducted in August 2021. Sampling of white turmeric was carried out in Hampatung Village, Kapuas Hilir District, Kapuas Regency. Laboratory studies were carried out in 3 places, namely the Laboratory of Chemical Technology for Forest Products, Department of Forestry, University of Palangka Raya, BPOM Laboratory of Palangka Raya City and the Test Laboratory of the Academy of Analytical Chemistry, Bogor Polytechnic. From the results of the analysis of white turmeric essential oil content in the rhizome (0.2969%). The results of GC-MS analysis of essential oils obtained from the rhizome showed 33 components and there were 4 main component compounds, namely Bornyl acetate (64.81%), Champhene (35.07%), Pentadecane (47.53%) and ethyl cinnamate (48.57%).

Brain Tumor (BT) categorization is an indispensable task for evaluating Tumors and making an appropriate treatment. Magnetic Resonance Imaging (MRI) modality is commonly used for such an errand due to its unparalleled nature of the imaging and the actuality that it doesn’t rely upon ionizing radiations. The pertinence of Deep Learning (DL) in the space of imaging has cleared the way for exceptional advancements in identifying and classifying complex medical conditions, similar to a BT. Here in the presented paper, the classification of BT through DL techniques is put forward for the characterizing BTs using open dataset which categorize them into benign and malignant. The proposed framework achieves a striking precision of 96.65.

The mantle plays important role in the mechanism of oyster protection caused by environmental pollutants. This study aims to analyze the effect of water accommodated fraction of crude oil on the mantle of Slipper-Shaped Oyster (Crassostrea iredalei) at different doses and time exposure. The ventral and posterior segments of the mantle were fixed, and tissue sections were stained with hematoxylin-eosin, PAS-Periodic acid–Schiff, and TEM-transmission electron microscopy techniques. HE-hematoxylin and eosin, PAS-alcian, and TEM-transmission electron microscopy were used to characterize the different mucosubstances and to describe the ultrastructure-related response on a certain part of the mantle after exposure. The tissues of epithelium, connective tissue, mucus cells, pigmented cells, numerous hemolymph sinuses, shell formation, and blood sinus were recognized under a light microscope. The mucous cell was excreted in all the concentrations (control, 12.5, 25, 50, and 100% Water Acomodate Fraction) and also in the time exposure (24, 48, 72, and 96 hours). A large number of mucous cells was produced in the inner mantle cavity (IMC) and outer mantle cavity (OMC). Mucous cells increased in number with increasing WAF concentration as well as the length of exposure time. The highest number of mucus cells was observed at 100% Water Accommodate Fraction (WAF) concentration and 96 hours of exposure. The structure and function of the mantle, the shell formation, the edge of the mantle, mucous cell, muscle bundles, nerve fibers, and epithelium layer of the Slipper-Shaped Oyster (Crassostrea iredalei) were documented in this study.

The number of deaths worldwide caused by COVID-19 continues to increase and the variants of the virus whose process we do not yet master are aggravating this situation. To deal with this global pandemic, early diagnosis has become important. New investigation methods are needed to improve diagnostic performance. A very large number of patients with COVID-19 have with cardiac arrhythmias often with ST segment elevation or depression on an electrocardiogram. Can ST-segment changes contribute to automatic diagnosis of COVID-19? In this article, we have tried to answer this question. We propose in this work a method for the automatic identification of COVID patients which exploits in particular the modifications of the ST segment observed on recordings of the ECG signal. Two sources of data allowed the development of the database for this study: 300 ECGs from the "physioNet" database with prior measurement of the ST segments, and 100 paper ECGs of patients from the cardiology department of the hospital X in Tunis registered on (non-covid) topics and covid topics. Four learning algorithms (ANN, CNN-LSTM, Xgboost, Random forest) were then applied on this database. The evaluation results show that CNN-LSTM and Xgboost present better accuracy in terms of classifying covid and non-covid patients with an accuracy rate of 87% and 88.7% respectively.

Bivariate calibration algorithm is compared with the results obtained by the usage of high-dimensional calibration methods such as partial least squares (PLS) and multi-way partial least-squares (N-PLS) by using UV-Vis spectrophotometric data of first and second-order. The algorithms were applied to the determination of a mixture of an analgesic and a stimulant compound and their actual concentrations of them were calculated by using spectroscopic data. The direct reading of absorbance values at 227 nm and 271 nm were employed for quantification of the compounds in the case of the bivariate method. The approaches of first-order and multi-way methods were applied with a previous optimization of the calibration matrix by constructing sets of calibration and validation with 20 and 10 samples (mixtures) respectively according to a central composite design and their UV absorption spectra were recorded at 200-350 nm. All algorithms were satisfactorily applied to the simultaneous determination of these compounds in pharmaceutical formulations with mean percentage recovery of 100.5 ± 3.67, 98.7 ± 3.42, and 100.5 ± 3.74 for bivariate, PLS-1, and N-PLS, respectively. The statistical evaluation of the bivariate method showed that this procedure is comparable with those algorithms that employ high-dimensional structured information. The aim of the work is to compare the methods under study and it can be seen that there are no significant differences, so a simple spectrophotometer can be used up to a very specialized one. However, the advantage of bivariate calibration is its simplicity, due to the minimal experimental manipulation.