Miracle-dz/UniFAR-FTUs huggingface dataset:
Study of Brain Region Segmentation Using Convolutional Neural Network

Magnetic Resonance Imaging (MRI) is used in medical imaging for detection of tumours and visualize brain tissues.This is done manually by expert radiologist and this takes good amount of time. The traditional method of MRI evaluation of tumour depends greatly on qualitative features,like density of tumour,growth pattern etc. Brain Region Segmentation is important in neuroimaging application, for example, alignment of images, surface reconstruction etc.The previous methods depends upon the qualitative features and is very sensitive to errors. Noise and errors need to be reduced and efficiently delineated, very less work is done in automatic tumour detection using deep learning methods and there is lot of areas which can be explored. The deep learning method is very much different from the machine learning method. The machine learning method uses algorithms to input data, learn from given data, and make decision based on the experience or learning whereas the deep learning can learn and make decisions on its own. Deep learning has a capability of learning from data that is unstructured or unlabeled. In deep learning, the algorithms try to learn using method of feature extraction which is very different and makes the model fully automatic, here we don't require any handcrafted feature. In traditional method we need to develop feature extractor for different problem, so we use deep learning which reduces effort of developing different feature extractor for different problem. In one of method of 2-D patch extraction could achieve accuracy of 88% where the network architecture is inspired by VGG Network, high grade and low grade network differs in number of convolutional layer preceding a max-pooling layer. In other,they have used encoder-decoder type neural network and achieved accuracy of 87.2%. In a single forward pass, previously discussed patch based technique are slow as network predicts only centre pixel of patch. In the present study, we have used supervised learning to learn the features from the input images and found that Convolutional Neural Network can achieve good accuracy.In CNN, the network in starting phase learns low level feature like lines or edges and then slowly learns the high level features. The present method achieved accuracy of 90-94% which is a good achievement in this field. MICCAI-BRATS challenge 2015 dataset is utilized in the present study. In present method, there are total of 245 MRI images which are further divided into 110 image for training the network and 145 images for testing the data.
Rheumatoid Arthritis Patients On Persistent Moderate Disease Activity On Biologics Have Adverse 5-years Outcome Compared To Persistent Low-remission Status Yet They Represent A Heterogeneous Group: Lower-moderate Has Better Functionality And Fewer Serious Adverse Events Than Higher-moderate Subgroup

Background The long-term outcome of rheumatoid arthritis (RA) patients who in clinical practice, exhibit persistent moderate disease activity (pMDA) despite treatment with biologics has not been adequately studied. Herein, we analyzed the 5-year outcome of the pMDA group and assessed for within-group heterogeneity. Methods We included longitudinally-monitored RA patients from the Hellenic Registry of Biologic Therapies with persistent (cumulative time ≥50% of a 5-year period) moderate (pMDA, 3.2<DAS28≤5.1) or remission/low (pRLDA, DAS28≤3.2) disease activity. The former was further classified into persistent lower-moderate (plMDA, DAS28<4.2) and higher-moderate (phMDA, DAS28≥4.2) subgroups. Five-year trajectories of functionality (HAQ) was the primary outcome in comparing pRLDA versus pMDA and assessing heterogeneity within the pMDA subgroups through multivariable mixed-effect regression. Results We identified 295 patients with pMDA and 90 patients with pRLDA, the former group comprising of plMDA (n=133, 45%) and phMDA (n=162, 55%). pMDA was associated with worse 5-year functionality trajectory than pRLDA (+0.27 HAQ units, CI 95% +0.22 to +0.33; p<0.0001), while the phMDA subgroup had worse 5-year functionality than plMDA (+0.26 HAQ units, CI 95% 0.18 to 0.36; p<0.0001). Importantly, higher persistent disease activity was associated with more serious adverse events (SAEs) [pRLDA: 0.2±0.48 vs pMDA: 0.5±0.96, p=0.006; plMDA: 0.32 ±0.6 vs phMDA: 0.64 ±1.16, p=0.038). Male gender (p=0.017), lower baseline DAS28 (p<0.001), HAQ improvement >0.22 (p=0.029) and lower average DAS28 during the first trimester since treatment initiation (p=0.001), independently predicted grouping into pRLDA. Conclusions In clinical practice, RA patients with pMDA while on bDMARDs have adverse long-term outcome compared to lower disease activity status, while heterogeneity exists within the pMDA group in terms of 5-year functionality and SAEs. Targeted studies to better characterize pMDA subgroups are needed, in order to assist clinicians in tailoring treatments.
Client’s satisfaction towards emergency medical care at Ayder comprehensive specialized hospital, Mekelle, Ethiopia

Background: As assessing client’s experience are important guide towards services improvement, providers will need to fully understand patient satisfaction level and their determinants. This study was aimed to assess the level of satisfaction towards the emergency medical care and associated factors at emergency department of Ayder specialized comprehensive hospital, Mekelle, Ethiopia.Methods: A cross-sectional study was conducted from March 1–30, 2019. Systematic random sampling method was used to enroll study participants. Data was collected using a standard Brief Emergency Department Patient Satisfaction Scale questionnaire by trained data collectors. Data was entered into EpiData 3.1 then exported and analyzed by SPSS version 22. Binary and multiple logistic regression was used to assess the predictor’s patient satisfaction.Results: A total of 299 participants were enrolled in the study with a response rate of 99.3%. The overall client’s satisfaction was 81.9%. The lowest satisfaction rate was reported towards emergency room environment, and patient family satisfaction 37.5%, and 49.8% respectively. Educational status those who were able to read and write (AOR = 3.9, 95% CI: 1.4, 10, P = 0.008), those who arrived during the morning shift of the day (AOR = 3.6, 95% CI: 1.6, 8.7, p = 0.002), those who waited < 15 minutes to be seen by a doctor (AOR = 1.3, 95% CI: 1.003, 1.4), having felt discriminated (AOR = 1.5, 95% CI: 1.003, 1.4) were significantly associated determinants of client satisfaction.Conclusions: The overall client’s satisfaction in this study was found to be good. There is a low satisfaction towards emergency room environment and patient’s family courtesy. It is important to readjust emergency room services to improve client’s courtesy, avoid feeling of discrimination by clients and overall to improve the quality of care in the emergency department.
Price discovery in emerging market ETFs

ABSTRACT This study investigates the price discovery role of exchange-traded funds (ETFs) by examining the predictive relation between the returns of emerging market ETFs traded in the US and the returns to the aggregate equity indices that they track. In a sample that covers 18 countries, we find that ETF returns can predict one-day-ahead returns of their underlying indices. This relation is robust after controlling for the non-synchronicity between markets, serial correlation in index returns, and various determinants of aggregate returns. Moreover, the predictive relation is more pronounced during periods of higher volatility and evidence for bidirectional spillover effects is weak. We also find that an out-of-sample rolling window strategy outperforms investing in the market index several-fold in the majority of the markets, especially in the high-volatility subsample.
Dynamics of leptin, insulin resistance, parathyroid hormone, 25(OH)D in the implementation of the eras-protocol in patients of surgical profile

The aim : to increase the effectiveness of treatment of patients of surgical profile with overweight by developing algorithms for perioperative intensive care for the successful implementation of the ERAS protocol. Material and methods . The basis of this study is the analysis of the results of a comprehensive clinical and instrumental dynamic examination of 122 patients with surgical herniological profile for a period of 1 day to 1 month from the date of surgery. The study included patients with ventral hernias of the anterior abdominal wall, which were determined by the SWR classification. The conditions for admission to the study under the conditions of inclusion were a fence 10 days before surgery to determine the analysis of vitamin D concentration. 3 groups of patients were identified (control, with addition to the protocol of treatment of cholecalciferol, with addition to the protocol of treatment of cholecalciferol and a solution of D-fructose-1,6-diphosphate sodium salt of hydrate). Determined the type of fat distribution, index of visceral obesity, triglycerides, high-density lipoprotein, leptin, fasting glucose, endogenous insulin, calculated the index of HOMA. Parametric statistics methods were used to process the obtained data. Results . In the vast majority of overweight patients (90 %) the abdominal type of fat distribution with the presence of visceral index obesity was determined. At the time of screening, the concentration of leptin in the blood of all studied patients exceeded the upper limit of normal by almost 4 times. The absence of a probable connection between the level of 25 (OH) D and leptin was determined, which confirms the presence of obesity due to reduced muscle mass and impaired energy metabolism, the presence of a relationship between the level of 25 (OH) D, HOMA, concentration of parathyroid hormone in the blood. Conclusions . Implementation of a planned surgical profile in overweight patients at the screening stage 10 days before surgery to determine the level of 25 (OH) D in the blood is a key point in deciding the possibility of conducting the perioperative period according to the ERAS program. Additional purpose to its classical protocol of cholecalciferol and solution of D-fructose-1,6-diphosphate sodium salt of hydrate increases the quality of motor activity of patients after surgery, increases their adaptive potential by restoring lost muscle function. The optimized classical algorithm of the ERAS-program significantly (p <0.05) improved the quality of life in the long term (30 days after surgery), such as physical functioning, general health, viability scale, mental health (SF-36 scale) and decreased body mass index
Sexual propagation of common Bahia cocoa seedlings (Theobroma cacao L.) influenced by irrigation depths and the planting seasons

Cocoa (Theobroma cacao L.) is one of the most economically important species due to its use in the production of chocolate, honey, pulp, animal feed, fertilizers, jellies, butter and cocoa powder. The production of seedlings such as through sexual propagation is the essential stage for the establishment of new cocoa crops, in which the seedlings are produced by seeds. This is the most commonly used method in cocoa. Several factors interfere with the final quality of the seedlings, such as the planting season and the irrigation management. Thus, the objective of this study was to evaluate the effects of different irrigation depths and planting seasons on the quality and growth of Bahia genotype cocoa seedlings produced through sexual propagation. The study took place at the Federal Institute of Espírito Santo, Campus Itapina, in Colatina, located in the Northwest region of the State of Espírito Santo, Brazil. The experimental design was completely randomized in a split-plot scheme. The plots consisted of six different irrigation depths: 4, 6, 8, 10, 12 and 14 mm d-1 and the subplots, of three different planting seasons: autumn, winter and spring. At 50 days after sowing, the seedlings were evaluated for the following morphological characteristics: plant height, stem diameter, leaf area, length of the root system, dry mass of the aerial part, dry mass of the root system, total dry mass and Dickson quality index. The production of Common Bahia genotype cacao seedlings is more effective in the irrigation depth of 9.44 mm d-1, demonstrating better development and higher plant quality.
Radiosynthesis and preclinical evaluation of [18F] 4-(2-fluoroethoxy)-2H-chromen-2-one as a novel myocardial perfusion imaging agent

Abstract Recently we developed [18F] 4-(2-fluoroethoxy)-2H-chromen-2-one as a novel 18F myocardial perfusion imaging radiotracer. It was synthesized in good radiochemical yield (>90%). The total time from radiosynthesis to its purification was less than 40 min, with excellent radiochemical purity (≥99%). It showed good stability over a period of 5 h at room temperature. The partition coefficient (log P) of radiotracer was found to be 2.70, suggesting the lipophilic nature of radiotracer. Ex vivo biodistribution study of radiotracer in normal Wistar rats for 30 min post-injection, demonstrated a good heart uptake (>1.3% ID/g) and favorable pharmacokinetics. Additionally, the radiotracer showed significant excretion (>11% ID) by liver, which is indicative of its rapid clearance. Further, in vivo biodistribution study of radiotracer in New Zealand White rabbit provided the clear PET/CT images of cardiomyocytes and myocardial perfusion. All these experimental findings suggest that [18F] 4-(2-fluoroethoxy)-2H-chromen-2-one could be used as a potential hit for myocardial perfusion imaging.
TEACHING METHODS PREFERRED BY SCHOOL SCIENCE TEACHERS AND STUDENTS IN THEIR CLASSROOM

This study was designed to stipulate an image of occurring science practices in secondary schools. The main objective of this study is to investigate the types of science teaching practices that have been preferred to use by science teachers to teach science subjects. This study delimits the secondary schools of Lahore, which is the second most populated city in Pakistan. The research was conducted on a sample of 60 schools [60 science teachers and 600 students]. The researchers captured the complete list of secondary schools of Lahore from official websites and schools were selected through random technique. The most senior science teacher opted for data collection in case of more than one science teacher availability at a single school. The study adopted a survey research design and data was collected through a newly developed reliable questionnaire with Cronbach alpha = 0.95. The data was entered and analysed by using SPSS. The study result reveals that secondary school science teachers prefer lecture and discussion methods for science teaching. Significantly, science teachers are less interested in using the inquiry method and science project-based learning in their classrooms. Science teachers should adopt recommended teaching methods to develop a better understanding of science among students.
The Mediating Role of Golden Square of Marketing between Effective Product Packaging Elements and Customer Patronage: An Empirical Study on Cosmetics and Beauty Care Products in Egypt

Purpose – Product packaging is recognized nowadays as one of the pivotal component in attracting consumers and thus persuading consumers in an indirect way to purchase these products and build customer patronage, but still lack of empirical investigation. Hence, to narrow this gap, this paper aims to investigate the impact of Golden Square of Marketing (IPSL) as a mediator of the relationship between product packaging elements and customer patronage. In this study product packaging elements comprises five factors which are packaging color, packaging material, packaging design, printed information and creative packaging. And also Golden Square of Marketing "IPSL" includes Image, Positioning, Satisfaction and Loyalty, this paper reviewing the Egyptian customers’ preferences about the packaging elements of cosmetics and beauty care products such as (L’Oréal, Maybelline, Mac, Flormar, Sephora, Makeup forever, Deborah and other brands) The Mediating Role of Golden Square of Marketing between . Dr. Sherif Taher Mohammed & Dr. Sara Abd-El Fattah Hammad رشع يداحلا دلجملا عبارلا ددعلا 0202 616 Design/methodology/approach – The design of this research is descriptive and quantitative in nature. And two kinds of data were used; Secondary Data For a theoretical overview, secondary data were collected from various websites and research papers that dealt with the topics related to the research problem. Primary Data The needed primary data to achieve the research objectives were collected via the E-Questionnaire method introduced by Google models to apply the questionnaire through the Internet and was filled with different people. A total of 276 valid responses were obtained. Hypotheses were tested using the statistical package for social sciences (SPSS V26) for basic descriptive statistics, and (Smart PLS 3.2.7) for SEM-PLS modelling. Findings – The results indicated that effective product packaging elements have a significant positive influence on both Golden Square of Marketing (IPSL) and Customer Patronage. In addition, Golden Square of Marketing (IPSL) mediates the relationship between product packaging elements and customer patronage with a positive and statistically significant relationship. Originality/value – This research further extended the current knowledge by demonstrating that the influence of product packaging elements and customer patronage is mediated by Golden Square of Marketing (IPSL). Also, this research utilised the strength of PLS–SEM in dealing with higher-order The Mediating Role of Golden Square of Marketing between . Dr. Sherif Taher Mohammed & Dr. Sara Abd-El Fattah Hammad رشع يداحلا دلجملا عبارلا ددعلا 0202 617 constructs, allowing us to develop and test a parsimonious model that is useful for practitioners.
New Chemometric Approaches to Non-targeted GCMS Fingerprinting Analysis of Wine Volatiles

In contrast to targeted gas chromatography mass spectrometry (GC-MS) analysis of wine volatiles, non-targeted GC-MS approaches take information of known and unknown compounds into account, are faster, inherently more comprehensive and give a more holistic representation of the sample composition. Although several non-targeted approaches have been developed, there is still a great demand for automated data processing tools, especially for complex multi-way data such as chromatographic data obtained from multichannel detectors (e.g. GC-MS chromatograms of multiple samples). This work therefore aimed at the development of data processing procedures for non-targeted GC-MS analysis of volatile wine compounds. The two developed approaches use basic matrix manipulation of segmented GC-MS chromatograms and PCA or PARAFAC multi-way modelling. The approaches take retention time shifts between samples into account and avoid peak integration. A demonstration of the new fingerprinting approaches is presented using an artificial GC-MS data set and an experimental full-scan GC-MS data set obtained for a set of experimental wines. Results of the new approaches were also compared to a references method. Furthermore, the combination of one of the developed GC-MS fingerprinting approaches with the fast sensory screening technique projective mapping was exploited as a powerful approach to simultaneously study the volatile composition and the sensory characteristics of experimental wines. This methodology was used to study the impact of different malolactic fermentation scenarios on two different Pinotage wine styles and for a full factorial investigation of the impact of grape vine age, must turbidity and yeast strain on the aroma of Riesling experimental wines.
Water permeability prediction of sponge city pavement materials based on different machine learning algorithms

Permeable pavement material is one of the most important supporting materials in the construction of sponge city, and its water permeability is the most important performance index. The water permeability test of permeable pavement materials is a tedious and complicated experimental work. It is of great research significance to predict the water permeability of permeable pavement materials through structural parameters modeling. In this paper, the database is first established by experimental means, and then the prediction models of LASSO (Least absolute shrinkage and selection operator), SVR (Support vector regression) and GBR (Gradient Boosting Regression) machine learning algorithms are established. Through the four factors of particle size, particle size distribution, shape parameters and binder content predict the water permeability of sponge city pavement materials. The results show that different machine learning algorithms have different sensitivity to the distribution of data samples. The fitting effect of GBR model water permeability prediction is better than that of SVR and LASSO models. The test value-predicted value MSE is 0.0051 and R2 is 0.92, which can effectively predict the water permeability of sponge city pavement materials.
Synthesis and Properties of Monolayer-Protected Cox(SC2H4Ph)m Nanoclusters

Nanometer-sized and stable thiolate-protected cobalt clusters were synthesized by a wet chemical method, leading to a pink solution with well-defined optical activity (ultraviolet–visible) and photoluminescence. The cobalt cluster core of ∼1.3 nm size was metallic (as indicated by scanning transmission electron microscopy, scanning tunneling microscopy, X-ray photoelectron spectroscopy, electron-energy loss spectroscopy) and was surrounded by a specific configuration of thiolate staples (according to Raman spectra, Fourier transfrom infrared spectroscopy, X-ray absorption fine structure, matrix-assisted laser desorption ionization) that is similar to that of corresponding gold clusters.
Automatic Detection of Liver Disease Using Voting Ensemble Method

Liver disease is one of the prominent causes of death which can be tackled by providing detection at an early stage along with possible countermeasures. Liver diseases caused by factors like genetic predisposition, infections and the environment. It requires diverse and targeted treatment options. The increasing of hepatic conditions worldwide is due to lifestyle actions such as intake of alcohol and drug with the consultation of physicians. The cause of numerous infections and disorders are not yet well understood. A voting ensemble method is proposed in this paper that considers influential factors responsible for liver disease. This predictive model aims to enhance forecasting reports with respect to other peer intelligent model. The enhanced efficiency reaches an accuracy of 77.2% which is quite promising towards early liver disease prediction.
Internet of Things (IoT) to Study the Wild Life: A Review

: Internet of Things (IOT) has emerged as an efficient technical support for the biological researchers. Present study has been focused on four applications of IOT used for wildlife monitoring; location tracking, habitat, in situ observation, and behavioral studies. IOT based monitoring unit for wildlife studies mainly includes sensing, communication, software and relative hardware components.