Advanced deep learning approaches for biosingnals applications

University of Technology Sydney. Faculty of Engineering and Information Technology.A wide gap exists between clinical application results and those from laboratory observations concerning hand rehabilitation devices. In most instances, laboratory observations show superior outcomes the real-time applications demonstrate poor consequences. The robust nature of the electromyography signal and limited laboratory applications are the principal reasons for the gap. This thesis aims to introduce and develop a deep learning model that is capable of learning features from biosignals. The deep learning model is expected to tame the variable nature of the electromyography signal which will lead to the best available outcomes. Furthermore, the suggested deep learning scheme will be trained to be skilled in learning the best features that match the biosignal application regardless of the number of classes. Moreover, traditional feature extraction is time consuming and extremely reliant on the user’s experience and the application. The objective of this research is accomplished via the following four implemented models. 1. Developing a deep learning model via implementing a two-stage autoencoder along with applying different signal representations like spectrogram, wavelet and wavelet packet to tame variations of the electromyography signal. Support vector machine, extreme learning machine with two activation functions (sigmoid and radial basis function) and softmax layer were used for classifications. Moreover, the classifier fusion layer achieved testing accuracy of more than 92% and training attained more than 98%. The same dataset was implemented for superimposed signal representations for two stages autoencoder and softmax layer, support vector machine, k-nearest neighbor and discriminant analysis for classification besides the classifier fusion which led to testing accuracy of more than 90%. 2. Presenting principal component analysis and independent component analysis for feature learning purposes after applying different signal representations algorithms such as spectrogram, wavelet and wavelet packet. Discriminant analysis, extreme learning machine and support vector machine were used for classification. Furthermore, the two proposed models showed acceptable accuracy along with shorter simulation time. The testing accuracy achieved more than 90% by implementing a classifier fusion layer. Manhattan index was estimated for all features and only the top 50 Manhattan index features were included to decrease the simulation time while attaining acceptable accuracy values. 3. Introducing a self-organising map for deep learning whereby the biosignal was represented by spectrograms, wavelet and wavelet packet. The presented biosignal was introduced to a layer of self- organising map then the suggested system performance was evaluated by extreme learning machine, self-adaptive evolutionally extreme learning machine, discriminant analysis and support vector machine for classification. Adding a classifier fusion layer increased the testing accuracy to 96.60% for ten-finger movements and 99.73% for training. The proposed system showed superior behavior regarding accuracy and simulation time. 4. Presenting a deep learning model where 1) the data was augmented after representing the biosignal by a spectrogram, 2) the augmented signal was represented by a tensor, and finally 3) The signal was introduced to the two-stage autoencoder. The same dataset was used with traditional pattern recognition for comparison purposes. Classifier fusion layer was executed in deep learning scheme whereby the ten-finger movements achieved 90.25% and 87.11% attained by pattern recognition. Besides, the six finger movement dataset was acquired from amputee participants and accomplished 91.85% for deep learning and reached 89.64% for traditional pattern recognition. Furthermore, different datasets for different applications were tested using the recommended deep learning model. Eventually, feeding the deep learning model with various datasets for different applications afforded the model with higher fidelity, combined with real outcomes and generalization

Secondary bacterial and fungal infections in critically ill COVID‐19 patients: Impact on antimicrobial resistance

Background: The primary burden among severely ill COVID-19 cases allocated to ICUs is secondary bacterial and fungal infections. Antimicrobial resistance is aggravated more likely by empiric overusing of antimicrobials. This study aimed to assess the microbiological profile of fungal and bacterial superinfections in laboratory confirmed COVID-19 cases and their antimicrobial susceptibility pattern. Methods: Various clinical samples were obtained from 117 critically ill COVID-19 patients in the clinical suspicion of secondary infections for assessing the pathogens accountable for the superinfections and their antimicrobial susceptibility pattern according to standard microbiological procedures. Results: Among 117 COVID-19 patients allocated to ICU, 68 (58%) had secondary infections. The most prevalent infection was of the lower respiratory tract. Most infections were bacterial 85.8%. Gram-negative isolates were the most predominant strains, accounting for 71.7%. among them, Klebsiella pneumoniae 43.4 % and Acinetobacter baumannii 20.7% were the most predominant. Majority of the bacterial strains were multidrug-resistant, all gram-negative strains showed one hundred percent resistance rate to cephalosporins, amoxicillin, and amoxicillin-clavulanic. The lowest resistance was observed for tigecycline. All gram-positive strains were susceptible to linezolid and vancomycin. Additionally, all candida isolates were susceptible to the tested antifungals. Conclusions: In hospitalized severely ill COVID-19 patients, secondary infections are most frequently caused by Gram-negative pathogens exhibiting high rate of antibiotic resistance and are associated with poor outcomes. Strict adherence to infection control measures as well as regular microbiological surveillance are required

McKenzie, Ellen

This interview features Ellen McKenzie, an African-American lesbian woman living in Portland, Maine. Having lived in Portland for almost her entire life, Ellen can provide insight on growing up in one of the only black families in her community, the intersections between race and sexuality, co-parenting children from a spouse’s previous marriage and generally navigating the world and her career as a queer woman of color. Throughout this interview, we hear a lot about her childhood and her family’s history as civil rights activists in Maine, her relationship with her spouse and and co-parenting their children with both her spouse, and the children’s father and stepmother. This insight into LGBTQ blended families is insightful and interesting. We also hear about the loss of one of their sons to suicide. Lastly, she provides insight into her career as a social worker, and how to navigate the workplace as a woman of color and how she handles racism and discrimination. Citation Please cite as: Querying the Past: LGBTQ Maine Oral History Project Collection, Lesbian, Gay, Bisexual, Transgender, and Queer+ Collection, Jean Byers Sampson Center for Diversity in Maine, University of Southern Maine Libraries. For more information about the Querying the Past: Maine LGBTQ Oral History Project, please contact Dr. Wendy Chapkis.https://digitalcommons.usm.maine.edu/querying_ohproject/1033/thumbnail.jp

Optimization of Renewable Energy-Based Smart Micro-Grid System

Optimization of renewable energy-based micro-grids is presently attracting significant consideration. Hence the main objective of this chapter is to evaluate the technical and economic performance of a micro-grid (MG) comparing between two operation modes; stand-alone (off-grid), and grid connected (on-grid). The micro-grid system (MGS) suggested components are; PV panels, wind turbine(s) inverter, and control unit in case of grid connected. In the stand alone mode diesel generator and short term storage are added to the renewable generators. To investigate the performance of the MGS; technically, detailed models for each component will be presented then the complete MGS model is developed. Another objective of this study is the economical evaluation of MGS by comparing the system net present cost (NPC) and cost of generated electricity for the two modes of operation; off-grid and on-grid

Language Policy in Sudanese-Arabic Speaking Families

In countries like the United States, where English is the dominant language, minority languages spoken in families and communities are at risk of being lost by the second and subsequent generations. This study thus examines the language maintenance and family language policies that exist in families of first-generation immigrant Sudanese residing in a Sudanese community in a Midwest university town of 74,000. The study aims to answer how family language policy, spoken or unspoken, affects the maintenance of the children\u27s first language. The participants in this study were first generation immigrants, identified themselves as bilingual, resided in the United States for more than ten years and identified as parents of at least one child over the age of seven years old. The data were based on a written survey and a follow-up interview with the parents, eliciting information about their children\u27s linguistic behavior. From the ten participants surveyed, half were interviewed. The participants selected to be interviewed consisted of parents whose children represented a range of fluency in Arabic, some being quite fluent, and other barely fluent, often in the same family. The findings indicate that the closer the parents felt their children were to their heritage cultural identity, the more likely these offspring were to maintain their Sudanese Arabic linguistic skills. The parents interviewed believed that involvement in and closeness to the heritage community on the part of the children played the most significant role in the child\u27s heritage language maintenance and degree of cultural loyalty in their families

Designing an optical frequency comb generator for visible light communication applications

The optical frequency comb generator (OFCG) is an efficient optoelectronic device that is included in many important applications over a various field such as microwave and optical communication. A novel scheme of OFCG presented in this work for visible light communication application based on amplitude modulation, radio frequency (RF) signal, phase shift and two Mach-Zehnder modulators (MZMs), our design features are simple with more efficient power and premium flatness of comb lines, the number of generating frequencies lines was 64 with a power stronger than -2 dBm over a 340 GHz bandwidth from a single continuous laser diode. Different chirping factor (α) of MZMs are implemented (3, 5, 7), as the results the best results related to α=5 with extra flatness, the system was designed and simulated by VPI design suite 9.8

Sugar Alcohols Have a Key Role in Pathogenesis of Chronic Liver Disease and Hepatocellular Carcinoma in Whole Blood and Liver Tissues.

The major risk factors for hepatocellular carcinoma (HCC) are hepatitis C and B viral infections that proceed to Chronic Liver Disease (CLD). Yet, the early diagnosis and treatment of HCC are challenging because the pathogenesis of HCC is not fully defined. To better understand the onset and development of HCC, untargeted GC-TOF MS metabolomics data were acquired from resected human HCC tissues and their paired non-tumor hepatic tissues (n = 46). Blood samples of the same HCC subjects (n = 23) were compared to CLD (n = 15) and healthy control (n = 15) blood samples. The participants were recruited from the National Liver Institute in Egypt. The GC-TOF MS data yielded 194 structurally annotated compounds. The most strikingly significant alteration was found for the class of sugar alcohols that were up-regulated in blood of HCC patients compared to CLD subjects (p < 2.4 × 10-12) and CLD compared to healthy controls (p = 4.1 × 10-7). In HCC tissues, sugar alcohols were the most significant (p < 1 × 10-6) class differentiating resected HCC tissues from non-malignant hepatic tissues for all HCC patients. Alteration of sugar alcohol levels in liver tissues also defined early-stage HCC from their paired non-malignant hepatic tissues (p = 2.7 × 10-6). In blood, sugar alcohols differentiated HCC from CLD subjects with an ROC-curve of 0.875 compared to 0.685 for the classic HCC biomarker alpha-fetoprotein. Blood sugar alcohol levels steadily increased from healthy controls to CLD to early stages of HCC and finally, to late-stage HCC patients. The increase in sugar alcohol levels indicates a role of aldo-keto reductases in the pathogenesis of HCC, possibly opening novel diagnostic and therapeutic options after in-depth validation

The role of Melatonin in reducing Obesity and its safety of use: A Review

Melatonin is the chiefly hormone formed via the pineal gland, its endogenous synthesis occur during the dark phase and controlled by the Suprachiasmatic Nucleus SCN, melatonin is a long-established and widely distributed chemical in nature that exhibits a variety of modes of action and functions in almost every living thing regulating the circadian rhythms, sleep and wakefulness cycle, energy metabolism in addition to its ability to regulate the raleasing of many cytokines participate in weight plus appetite control . It has been established that the hormone is participated in the controlling of body weight, food intake, glucose metabolism and energy balance, the important role of melatonin in modifiable adipose tissue, lipid profile, inflammation and oxidative stress opens up great hopes for the treatment of obesity. Since obesity is a serious public health issue which results from the imbalance between the amount of calories eaten and the amount of energy expended and predisposes to various metabolic diseases, so this review has been focused on some physiological function of melatonin , its role in the controlling of energy equilibrium and reducing obesity in addition to the benefits of its supplementation