Classification of COVID-19 from CT chest images using Convolutional Wavelet Neural Network

Analyzing X-rays and computed tomography-scan (CT scan) images using a convolutional neural network (CNN) method is a very interesting subject, especially after coronavirus disease 2019 (COVID-19) pandemic. In this paper, a study is made on 423 patients’ CT scan images from Al-Kadhimiya (Madenat Al Emammain Al Kadhmain) hospital in Baghdad, Iraq, to diagnose if they have COVID or not using CNN. The total data being tested has 15000 CT-scan images chosen in a specific way to give a correct diagnosis. The activation function used in this research is the wavelet function, which differs from CNN activation functions. The convolutional wavelet neural network (CWNN) model proposed in this paper is compared with regular convolutional neural network that uses other activation functions (exponential linear unit (ELU), rectified linear unit (ReLU), Swish, Leaky ReLU, Sigmoid), and the result is that utilizing CWNN gave better results for all performance metrics (accuracy, sensitivity, specificity, precision, and F1-score). The results obtained show that the prediction accuracies of CWNN were 99.97%, 99.9%, 99.97%, and 99.04% when using wavelet filters (rational function with quadratic poles (RASP1), (RASP2), and polynomials windowed (POLYWOG1), superposed logistic function (SLOG1)) as activation function, respectively. Using this algorithm can reduce the time required for the radiologist to detect whether a patient has COVID or not with very high accuracy

The Frequency of Neuropsychiatric Sequelae After Traumatic Brain In-jury in the Global South: A systematic review and meta-analysis

Countries in the 'global south' are characterized by factors that contribute to the increased incidence of traumatic brain injury (TBI). This systematic review and meta-analysis aimed to assess the prevalence of neuropsychiatric sequelae following a TBI, specifically among the Western Asian, South Asian, and African regions of the global south. A literature review was conducted until August 20, 2021, for publications that measured psychiatric or cognitive impairment after TBI from the 83 countries that constitute the aforementioned regions. The main databases, such as PsycINFO, Scopus, PubMed/MEDLINE, ProQuest (English), Al-Manhal (Arabic) and Google Scholar, were selected for grey literature. Following the evaluation of the articles using the Joanna Briggs Institute guidelines, the random effects model was used to estimate the prevalence of depression, anxiety, posttraumatic stress disorders (PTSD), sleep disturbance related to TBI (TBI-SD), obsessive–compulsive disorder (OCD), and cognitive impairment. Of 56 non-duplicated studies identified by the initial search, 27 studies were eligible for systematic review and 23 for meta-analysis. The pooled prevalence of depression in a total sample of 1882 was 35·35% (95% CI=24·64–46·87%), of anxiety in a total sample of 1211 was 28·64% (95% CI=17·99–40·65%), of PTSD in a total sample of 426 was 19·94% (95% CI=2·35–46·37%), of OCD in a total sample of 313 was 19·48% (95% CI=0·23–58·06%), of TBI–SD in a total sample of 562 was 26·67% (95% CI=15·63–39·44%), and cognitive impairment in a total sample of 941 was 49·10% (95% CI=31·26–67·07%). To date, this is the first critical review that has examined the spectrum of post–TBI neuropsychiatric sequelae in the specified regions. While existing studies lack homogeneous data due to variability in the diagnostic tools and outcome measures utilised, the reported prevalence rates are significant and comparable to statistics from the global north. Keywords: traumatic brain injury; neuropsychiatric sequelae; global south; systematic review; meta-analysis; cognitive impairment; anxiety; depressio

Multistrata exploration and production study

The project objective is to verify a development strategy for high grading areas of multistrate (shallow gas sands and coalbeds) potential in southern West Virginia and test it in up to five wells. Activities for the Multistrata Project began in Fiscal Year 1993 with the placement of the three Test Wells into production full force. By turning all three wells in line October 1, 1992, gas began flowing as designed by the agreements negotiated in Fiscal Year 1992. All the parties involved began to see tangible results as the gas moved from the College`s gathering lines into the systems of Ramco, Columbia, and Mountaineer Gas Companies. The gas production overall from the three Project Test Wells has been exceptional. Work is currently underway which will allow for enhanced production from the College wells. This involves installation of a two-phase, 20 hp gas operated compressor into the main sales line. The College of West Virginia gathering system will be tied directly into a system owned by another local producer and will necessitate the laying of 700 feet of additional pipeline. The compressor will be equipped to suction 1 psi and discharge at 75 psi for delivery of the College`s gas into the host system

Classification of COVID-19 from CT chest images using convolutional wavelet neural network

Analyzing X-rays and computed tomography-scan (CT scan) images using a convolutional neural network (CNN) method is a very interesting subject, especially after coronavirus disease 2019 (COVID-19) pandemic. In this paper, a study is made on 423 patients’ CT scan images from Al-Kadhimiya (Madenat Al Emammain Al Kadhmain) hospital in Baghdad, Iraq, to diagnose if they have COVID or not using CNN. The total data being tested has 15000 CT-scan images chosen in a specific way to give a correct diagnosis. The activation function used in this research is the wavelet function, which differs from CNN activation functions. The convolutional wavelet neural network (CWNN) model proposed in this paper is compared with regular convolutional neural network that uses other activation functions (exponential linear unit (ELU), rectified linear unit (ReLU), Swish, Leaky ReLU, Sigmoid), and the result is that utilizing CWNN gave better results for all performance metrics (accuracy, sensitivity, specificity, precision, and F1-score). The results obtained show that the prediction accuracies of CWNN were 99.97%, 99.9%, 99.97%, and 99.04% when using wavelet filters (rational function with quadratic poles (RASP1), (RASP2), and polynomials windowed (POLYWOG1), superposed logistic function (SLOG1)) as activation function, respectively. Using this algorithm can reduce the time required for the radiologist to detect whether a patient has COVID or not with very high accuracy

Removal of amoxicillin from contaminated water using modified bentonite as a reactive material

This study concerns the removal of a trihydrate antibiotic (Amoxicillin) from synthetically contaminated water by adsorption on modified bentonite. The bentonite was modified using hexadecyl trimethyl ammonium bromide (HTAB), which turned it from a hydrophilic to a hydrophobic material. The effects of different parameters were studied in batch experiments. These parameters were contact time, solution pH, agitation speed, initial concentration (C0) of the contaminant, and adsorbent dosage. Maximum removal of amoxicillin (93 %) was achieved at contact time = 240 min, pH = 10, agitation speed = 200 rpm, initial concentration = 30 ppm, and adsorbent dosage = 3 g bentonite per 1L of pollutant solution. The characterization of the adsorbent, modified bentonite, was accomplished using Fourier transform infrared spectroscopy, scanning electron microscopy, X-ray diffraction, and Brunauer-Emmett-Teller. The isotherm models were also investigated, and it was found that the Freundlich isotherm model fitted well with the experimental data (R2 = 94.77), which suggests heterogeneity in the multilayer adsorption of amoxicillin onto modified bentonite. The kinetics of the adsorption process were studied. The experimental data were found to obey the pseudo-first-order kinetic model (R2 = 95.1). Thermodynamic studies indicated that the adsorption process was physisorption and endothermic. Finally, the modified bentonite proved to be a good adsorbent for the removal of amoxicillin from contaminated solutions

Prenatal and Peripartum Exposure to Antibiotics and Cesarean Section Delivery Are Associated with Differences in Diversity and Composition of the Infant Meconium Microbiome

The meconium microbiome may provide insight into intrauterine and peripartum exposures and the very earliest intestinal pioneering microbes. Prenatal antibiotics have been associated with later obesity in children, which is thought to be driven by microbiome dependent mechanisms. However, there is little data regarding associations of prenatal or peripartum antibiotic exposure, with or without cesarean section (CS), with the features of the meconium microbiome. In this study, 16S ribosomal RNA gene sequencing was performed on bacterial DNA of meconium samples from 105 infants in a birth cohort study. After multivariable adjustment, delivery mode (p = 0.044), prenatal antibiotic use (p = 0.005) and peripartum antibiotic use (p < 0.001) were associated with beta diversity of the infant meconium microbiome. CS (vs. vaginal delivery) and peripartum antibiotics were also associated with greater alpha diversity of the meconium microbiome (Shannon and Simpson, p < 0.05). Meconium from infants born by CS (vs. vaginal delivery) had lower relative abundance of the genus Escherichia (p < 0.001). Prenatal antibiotic use and peripartum antibiotic use (both in the overall analytic sample and when restricting to vaginally delivered infants) were associated with differential abundance of several bacterial taxa in the meconium. Bacterial taxa in the meconium microbiome were also differentially associated with infant excess weight at 12 months of age, however, sample size was limited for this comparison. In conclusion, prenatal and peripartum antibiotic use along with CS delivery were associated with differences in the diversity and composition of the meconium microbiome. Whether or not these differences in the meconium microbiome portend risk for long-term health outcomes warrants further exploration