Search

The classification of a pre-processed fMRI dataset using functional connectivity (FC)-based features is considered a challenging task because of the set of high-dimensional FC features and the small dataset size. To tackle this specific set of FC high-dimensional features and a small-sized dataset, we propose here a conditional Generative Adversarial Network (cGAN)-based dataset augmenter to first train the cGAN on computed connectivity features of NYU dataset and use the trained cGAN to generate synthetic connectivity features per category. After obtaining a sufficient number of connectivity features per category, a Multi-Head attention mechanism is used as a head for the classification. We name our proposed approach "ASD-GANNet", which is end-to-end and does not require hand-crafted features, as the Multi-Head attention mechanism focuses on the features that are more relevant. Moreover, we compare our results with the six available state-of-the-art techniques from the literature. Our proposed approach results using the "NYU" site as a training set for generating a cGAN-based synthetic dataset are promising. We achieve an overall 10-fold cross-validation-based accuracy of 82%, sensitivity of 82%, and specificity of 81%, outperforming available state-of-the art approaches. A sitewise comparison of our proposed approach also outperforms the available state-of-the-art, as out of the 17 sites, our proposed approach has better results in the 10 sites. [ABSTRACT FROM AUTHOR]

Introduction: Congenital anomalies vary substantially in severity. Anomalies which affect an infant's life expectancy, health status, physcial or social functioning may be described as "major anomalies". In contrast, "minor anomalies" are those with little or no impact on health or short term or long term function. Prenatal screening for fetal malformations is an important component of prenatal care. Recent progress in the fields of maternal fetal medicine, radiology, and genetics has resulted in great advances in prenatal diagnosis. The use of fetal MRI can confirm the presence of lesions noted by ultrasound and may demonstrate additional anomalies. Fetal MRI has been performed since 1983, primarily to study central nervous system(CNS).Magnetic resonance imaging (MRI) is now being used in conjunction with ultrasound to provide additional information for prenatal diagnosis. The advantages of MRI include the lack of ionizing radiation, superior spatial and soft tissue resolution, relatively operator independent, better fetal anatomy delineation independent of fetal position and maternal obesity, use of multiple planes for reconstruction & a large field of view, making the visualization of complicated anomalies easier. Aim & objective: The present study was conducted with the aim to study the additional value of fetal MRI in assessment of fetuses with abnormal findings on ultrasound and focus on, Confirmation of inconclusive sonographic findings on MRI & detection of additional anomalies on MRI leading to a change in diagnosis, prognosis and management during pregnancy. Material and methods: Present study was conducted in the Postgraduate Department of Radiodiagnosis and Imaging, Government Medical College, Srinagar over a period of 18 months, after obtaining ethical clearance from institutional ethics committee. This cross- sectional study where pregnant patients with detected congenital fetal anomalies on ultrasonography were subjected to fetal MRI. The study included 68 patients with ultrasound documented fetal anomaly. The age of pregnancy was confirmed. It was made sure that the mother takes nothing by mouth for at least 4 h prior the scan to prevent postprandial motion and that she empties her bladder before the study. Results: The mean age of study group was 29 years with age ranging from 18-40 years. Maximum number of pregnant females with congenital fetal anomalies were in the age group of 28-32 years followed by 23-27 years. Most of the studies were done in 3rd trimester at 34-35 weeks followed by 28-29 weeks. The highest number (16%) of subjects were within the Gestational age 34-35 weeks. Ultrasonography detected 74 anomalies in 65 patients of the study group. The maximum anomalies detected included fetal brain and spine malformations followed by urogenital malformations. Among craniospinal anomalies the most commonly detected abnormality was Ventriculomegaly (17% ) and Chiari 2 malformation (10 %). MRI detected 88 anomalies in 65 patients of the study group. The maximum anomalies detected included fetal brain and spine malformations followed by urogenital malformations. Corpus callosum agenesis/dysgenesis and Chiari 2 malformations were the most commonly detected craniospinal malformations by MRI with a prevalence of 9.1 % each. MRI confirmed USG diagnosis with additional findings in 29.5% of cases, changed USG diagnosis in 20.5% of cases and changed USG diagnosis to normal in 9% of the cases resulting in an overall Change in diagnosis in 59% patients of the study group. [ABSTRACT FROM AUTHOR]

Simulations were performed under continuous processing conditions using the 2D continuum model to describe the internal state of a multi-solid system, comprising solid particles of different sizes and densities at the same time. The feed consisted of 35 types of solid particle species with five different sizes, 1.70, 1.20, 0.85, 0.60 and 0.35 mm, and seven different densities ranging from 1400 to 2000 kg/m3. The simulations results have been used to plot the concentration profiles of solid particles along the bed height. The concentration profiles of the solid particles depicted that the fine dense particles, 0.60 mm, having density equal to 1900 kg/m³ and terminal velocity 0.058 m/s moved downwards and discharged into the underflow. However, the low-density coarse particles, 1.20 mm, having density equal to 1400 kg/m3 and terminal velocity 0.068 m/s moved upwards and conveyed to the overflow, hence, show a separation process based on the density difference. Furthermore, simulation results showed that the particle species having densities close to the value of the separation relative density exhibited higher concentrations along the system height, and the suspension within the system was mainly composed of these species. [ABSTRACT FROM AUTHOR]

Cracking of edible oils occurs at high temperature and forms valued low molecular weight chemical species. The aim of the current study was to find a catalyst which can break these heavy molecules at the lower ranges of temperatures. From the analysis prospective, the non-condensable hydrocarbons (gaseous product species) were not determined and reactions study was carried out in a batch reactor. There was no evident conversion up to a temperature of 450 °C in the absence of catalyst whereas the reaction mixture was left inside a batch reactor for a long duration of an hour. Reaction parameters, such as catalyst types (ZnO and Al2O3), amount of catalyst, reaction temperature, residence or holding time, and heating rate to reach a reaction temperature were systematically examined. Powdered form of catalyst samples (ZnO and Al2O3) were characterized by using XRD, EDX, and Nitrogen adsorption isotherms. Temperatures studied over ZnO catalyst were 400 °C, 425 °C, 450 °C, 475 °C, and 500 °C. The maximum oil conversion was 81 % at a temperature of 450 °C. We observed that the conversion increases from 400 °C to 450 °C, whereas above 450 °C it starts to decrease. However, in comparison to ZnO catalyst the reaction rate was much higher over the Al2O3, i.e. a considerable conversion occurred at lower ranges of temperatures. Thus here a different set of temperatures (330 °C, 370 °C, 390 °C, 410 °C, and 430°C) were used. When reacting for an hour at a temperature of 390 °C, and in the presence of 8 wt.% of Al2O3 (same catalyst mass was used in ZnO reacting system) the conversion reached to 71 %. Above 390 °C the conversion decreased. Over both tested metal oxide catalysts the caloric value, density, flash point, and kinematic viscosity of the liquid product species were similar to petro fuels. The XRD and EDX signature of the catalyst samples corresponds to the standard ZnO and Al2O3 patterns. Finally, when compared to ZnO the better activity over the Al2O3 (higher conversion at lower temperature) catalyst can be linked with a high external surface area. [ABSTRACT FROM AUTHOR]

Abstract: Developing countries like Pakistan need continuous supply of cheap energy. It is common fear in today’s world that fossil fuels will be depleted soon. The cost of energy is increasing continuously and is expected to be at its peak by 2050. Many technologically advanced countries are successfully using renewable energy sources for their energy needs, however, they still believe in the importance of fossil fuel. In renewable energy field, Pakistan is using hydropower for energy needs successfully, whereas project regarding solar and wind energy is in progress. Biomass, a renewable energy source, is gaining interest in many researchers because it produces similar type of fuel extracted from crude oil. Energy from biomasses only depends upon the availability of cheap raw material. Biodiesel, which is produced by the reaction of vegetable oil and alcohol, can be used with same or with better performance in diesel engine. It is a clean fuel that causes less environment pollution as compared to petro diesel. High cost and non-continuous supply of vegetable oil is the main hurdle for its general acceptance. Many advanced countries have developed strategy for continuous supply of cheap price energy crops (source of biomass). Biodiesel is the only possible reciprocal to petro diesel or otherwise diesel engine will be useless after the depletion of crude oil. In this study, biodiesel as an energy source has been discussed; this is indigenous diesel engine fuel and is beneficial for our environment, economy, and more importantly will increase the income of our farmers. [Copyright &y& Elsevier]

Attention Deficit Hyperactivity Disorder (ADHD) is a brain disorder with characteristics such as lack of concentration, excessive fidgeting, outbursts of emotions, lack of patience, difficulty in organizing tasks, increased forgetfulness, and interrupting conversation, and it is affecting millions of people worldwide. There is, until now, not a gold standard test using which an ADHD expert can differentiate between an individual with ADHD and a healthy subject, making accurate diagnosis of ADHD a challenging task. We are proposing a Knowledge Distillation-based approach to search for discriminating features between the ADHD and healthy subjects. Learned embeddings from a large neural network, trained on the functional connectivity features, were fed to one hidden layer Autoencoder for reproduction of the embeddings using the same connectivity features. Finally, a forward feature selection algorithm was used to select a combination of most discriminating features between the ADHD and the Healthy Controls. We achieved promising classification results for each of the five individual sites. A combined accuracy of 81% in KKI, 60% Peking, 56% in NYU, 64% NI, and 56% OHSU and individual site wise accuracy of 72% in KKI, 60% Peking, 73% in NYU, 70% NI, and 71% OHSU were obtained using our extracted features. Our results also outperformed state-of-the-art methods in literature which validates the efficacy of our proposed approach. [ABSTRACT FROM AUTHOR]

In this study, a 2D segregation-dispersion model has been used to study the effect of shear induced lift on elutriation within a Reflux Classifier under continuous process conditions. The Reflux Classifier is a beneficiating technology comprising a fluidized bed with a set of parallel inclined channels above it. A 2D segregation-dispersion model of the device was utilized to study the mechanism of shear induced lift introduced as a contribution to the dispersion in the x-direction within the inclined channel. A multi-solid feed, 42 types of particle species having different densities and sizes, was simulated. It was observed that due to the shear induced lift, a phenomenon of particle resuspension occurred causing relatively higher solid volume fractions in the middle zone of the inclined channel. This happened because the coarse low-density particles experienced a higher shear induced lift and their concentration increased toward the middle zone. These coarse low-density particles and fine low-density particles moved out from the system via the overflow. Whereas, the fine dense particles experienced a low shear induced lift, hence, settled, slide backward, and discharged in the underflow. The lower Ep values ranging from 0.03 to 0.05 were obtained showing an improved separation performance. [ABSTRACT FROM AUTHOR]

Autism disorder, generally known as Autism Spectrum Disorder (ASD) is a brain disorder characterized by lack of communication skills, social aloofness and repetitions in the actions in the patients, which is affecting millions of the people across the globe. Accurate identification of autistic patients is considered a challenging task in the domain of brain disorder science. To address this problem, we have proposed a three-stage feature selection approach for the classification of ASD on the preprocessed Autism Brain Imaging Data Exchange (ABIDE) rs-fMRI Dataset. In the first stage, a large neural network which we call a "Teacher " was trained on the correlation-based connectivity matrix to learn the latent representation of the input. In the second stage an autoencoder which we call a "Student" autoencoder was given the task to learn those trained "Teacher" embeddings using the connectivity matrix input. Lastly, an SFFS-based algorithm was employed to select the subset of most discriminating features between the autistic and healthy controls. On the combined site data across 17 sites, we achieved the maximum 10-fold accuracy of 82% and for the individual site-wise data, based on 5-fold accuracy, our results outperformed other state of the art methods in 13 out of the total 17 site-wise comparisons. [ABSTRACT FROM AUTHOR]

Arabic is one of the most semantically and syntactically complex languages in the world. A key challenging issue in text mining is text summarization, so we propose an unsupervised score-based method which combines the vector space model, continuous bag of words (CBOW), clustering, and a statistically-based method. The problems with multidocument text summarization are the noisy data, redundancy, diminished readability, and sentence incoherency. In this study, we adopt a preprocessing strategy to solve the noise problem and use the word2vec model for two purposes, first, to map the words to fixed-length vectors and, second, to obtain the semantic relationship between each vector based on the dimensions. Similarly, we use a k-means algorithm for two purposes: (1) Selecting the distinctive documents and tokenizing these documents to sentences, and (2) using another iteration of the k-means algorithm to select the key sentences based on the similarity metric to overcome the redundancy problem and generate the initial summary. Lastly, we use weighted principal component analysis (W-PCA) to map the sentences' encoded weights based on a list of features. This selects the highest set of weights, which relates to important sentences for solving incoherency and readability problems. We adopted Recall-Oriented Understudy for Gisting Evaluation (ROUGE) as an evaluation measure to examine our proposed technique and compare it with state-of-the-art methods. Finally, an experiment on the Essex Arabic Summaries Corpus (EASC) using the ROUGE-1 and ROUGE-2 metrics showed promising results in comparison with existing methods. [ABSTRACT FROM AUTHOR]

In this study, a careful survey was conducted at twenty-nine different car-wash stations in Peshawar city to collect wastewater samples and evaluate the usage of fresh water in those stations. Experiments were carried out on a prototype treatment unit comprising sedimentation and filtration. After treatment, the TSS reduced from 1000 to 200 mg/L, hardness 321.60 to 120 mg/L and turbidity 253 to 3.70 NTU, TDS 591 to 400 mg/L and oil contents 27 to 14 mg/L. The quality of treated water was found suitable for car washing again. Furthermore, a design and cost estimation of a commercial scale low-cost wastewater treatment unit has been provided. It was found that the installation of low-cost wastewater treatment units can save 12 m3/day offresh water in a carwash station. [ABSTRACT FROM AUTHOR]

Objective: To compare the efficacy of metformin in the treatment of gestational diabetes mellitus (GDM) with insulin and to compare the frequency of hypoglycaemia in neonates of the mothers treated with metformin and insulin. Study Design: Randomized control trial to compare the efficacy of metformin with insulin in the treatment of GDM. Place and Duration of Study: Outpatient department and labour ward of Obstetric and Gynaecology department of Benazir Bhutto Hospital Rawalpindi from August 2012 to January 2013. Patients and Method: A total of 110 pregnant ladies with GDM diagnosed after 20 weeks of gestation were included and divided into group A and group B with 55 patients in each group. Group A patients were treated with insulin and group B with metformin. Plasma fasting glucose and two hours postprandial glucose levels were determined on weekly basis for four weeks after starting the treatment to determine the efficacy of insulin and metformin. At birth plasma glucose levels of all the neonates were carried out two hourly, and more frequently depending upon the requirement, during first 24 hours in both the groups to determine neonatal hypoglycaemia. Results: Fasting plasma glucose in group A and B were calculated as 5.96 ± 0.58 and 5.76 ± 0.46 mmol/L respectively (p=0.280), while two hours post-prandial plasma glucose levels were 7.34 ± 0.48 and 7.28 ± 0.58 mmol/L respectively (p=0.650). Efficacy in group A was 78.18% and in group B was 70.91% (p=0.381) while frequency of neonatal hypoglycaemia was calculated as 61.54% in group A and 41% in group B (p=0.113). Conclusion: The efficacy of metformin in treatment of gestational diabetes mellitus is similar as with insulin and the frequency of hypoglycemia in neonates of the mother treated with metformin and insulin is also similar. [ABSTRACT FROM AUTHOR]

Objective: To determine the frequency of asymptomatic spontaneous bacterial peritonitis in patients of liver cirrhosis with ascites. Study Design: Descriptive study. Place and Duration of Study: The study was conducted at indoor and outpatient departments of Medicine, Military Hospital, Rawalpindi, Pakistan from 29th February 2008 to 28th August 2008. Subject and Methods: One hundred and ninety five patients of liver cirrhosis with ascites were selected. To standardize the study; patients of both genders over 18 years of age, diagnosed with liver cirrhosis and ascites were included in the study after obtaining their informed consent. Patients with abdominal tenderness and fever, hepatic encephalopathy, intra-abdominal surgically treatable cause, with any co-morbid disease (hypertension and diabetes mellitus) or refusing to give consent were excluded from the study. Strict inclusion/exclusion criteria were observed to control the confounding variables. Diagnosis of spontaneous bacterial peritonitis was based on increased ascitic fluid absolute polymorphonuclear leukocyte (PMN) count (≥ 250 cells / mm³) and/or positive bacterial culture. Results: Out of 195 patients, 10 patients (5%) had neutrocytic ascites (absolute neutrophil count ≥ 250 cell/mm³). Out of these 195 patients, three patients (1.5%) were found to have positive ascitic fluid culture. Conclusion: In our study the frequency of asymptomatic spontaneous bacterial peritonitis in patients of liver cirrhosis with ascites turned out to be 5% which is low. Therefore, analysis of ascitic fluid through diagnostic paracentesis should be reserved for inpatient or outpatient with clinically apparent new onset ascities or in cirrhotic patients with ascities whose general condition deteriorates. [ABSTRACT FROM AUTHOR]