Gene Therapy and Modification as a Therapeutic Strategy for Cancer

Gene therapy is an exciting new field of personalized medicine, allowing for medical procedures that can target diseases such as cancer in novel ways. Technologies that involve gene transfer treatments allow for the insertion of foreign DNA into tumour cells, resulting in restored protein expression or altered function. Gene therapy can also be used as a form of immunotherapy, either by modifying cancer cells to make them better targeted by the immune system, or by modifying the body’s immune cells to make them more ag­gressive towards tumours. Additionally, oncolytic virotherapy uses classes of genetically modified viruses that can specifically target and interfere with tumour cells. The ongoing development of the CRISPR/Cas9 gene editing tool may also have promise in future therapeutic applications, with the tool being capable of removing cancer-causing, latent viral infections, such as HPV, from afflicted cells. Nonetheless, there are still many questions of safety, efficacy, and commercial viability which remain to be resolved with many gene therapy procedures. There is also emerging controversy over the ethical, legal, and moral implications that modifying the genetic content of human beings will have on society. These concerns must be confronted and addressed if the benefits promised by gene therapy are to be properly realized.   La thérapie génétique est un nouveau domaine d’étude médicale personnalisée qui permet de cibler des maladies spécifiques comme le cancer de façon innovatrice. Cette thérapie utilise le transfert de gènes avec une insertion d’ADN étrangère dans les cellules can­céreuses dans le but de restaurer l’expression des protéines et de retrouver la fonction cellulaire. La thérapie génétique peut aussi être utilisée comme une forme d’immunothérapie, soit en modifiant les cellules cancéreuses pour qu’elles soient mieux ciblées par le système immunitaire ou en modifiant les cellules immunitaires du corps pour les rendre plus agressives envers les tumeurs. De plus, une virothérapie oncolytique utilise des virus génétiquement modifiés qui peuvent cibler spécifiquement et interférer avec des cellules cancéreuses. Le développement du système d’édition génétique CRISPR/Cas9 s’avère prometteur pour les applications thérapeutiques futures. Cet outil est capable d’enlever les infections virales latentes dans les cellules affectées qui peuvent causer le cancer, tel que l’HPV. Malgré ces découvertes, plusieurs questions importantes demeurent quant à la sécurité et à l’efficacité de leur application. Il s’agit d’un domaine controversé avec des implications éthiques, légales, et morales, car le tout implique une modification du contenu génétique humain. Ces inquiétudes doivent être adressées afin de pouvoir continuer à explorer les bienfaits de cette thérapie géné­tique. En poursuivant la recherche dans ce domaine, il serait possible de valider cette thérapie et optimiser ses bienfaits

Hardware/software co-design of fractal features based fall detection system

Falls are a leading cause of death in older adults and result in high levels of mortality, morbidity and immobility. Fall Detection Systems (FDS) are imperative for timely medical aid and have been known to reduce death rate by 80%. We propose a novel wearable sensor FDS which exploits fractal dynamics of fall accelerometer signals. Fractal dynamics can be used as an irregularity measure of signals and our work shows that it is a key discriminant for classification of falls from other activities of life. We design, implement and evaluate a hardware feature accelerator for computation of fractal features through multi-level wavelet transform on a reconfigurable embedded System on Chip, Zynq device for evaluating wearable accelerometer sensors. The proposed FDS utilises a hardware/software co-design approach with hardware accelerator for fractal features and software implementation of Linear Discriminant Analysis on an embedded ARM core for high accuracy and energy efficiency. The proposed system achieves 99.38% fall detection accuracy, 7.3× speed-up and 6.53× improvements in power consumption, compared to the software only execution with an overall performance per Watt advantage of 47.6×, while consuming low reconfigurable resources at 28.67%

Experimental and Numerical Seismic Evaluation of RC Walls Under Axial Compression

Recent studies show that code-based equations usually do not provide an accurate estimate for the shear strength of short reinforced concrete (RC) walls due to the negligence of many important factors including the beneficial effect of axial compression. In the current study, quasi-static reversed cyclic testing is conducted for two RC wall specimens, one under axial load and one without axial load to assess the effect of the axial compression on the shear strength of RC walls in high-rise buildings. The results of the experimental study show that the axial compression load significantly improves the shear strength of RC walls. Results are also compared with the performance-based seismic evaluation code practices. Based on the experimental findings, recommendations are made for improvements in the existing codes. The experimental results are further compared with different numerical models to explore the suitable computer modeling options for non-linear response prediction of RC walls

Data portability for activities of daily living and fall detection in different environments using radar micro-doppler

The health status of an older or vulnerable person can be determined by looking into the additive effects of aging as well as any associated diseases. This status can lead the person to a situation of ‘unstable incapacity’ for normal aging and is determined by the decrease in response to the environment and to specific pathologies with apparent decrease of independence in activities of daily living (ADL). In this paper, we use micro-Doppler images obtained using a frequency-modulated continuous wave radar (FMCW) operating at 5.8 GHz with 400 MHz bandwidth as the sensor to perform assessment of this health status. The core idea is to develop a generalized system where the data obtained for ADL can be portable across different environments and groups of subjects, and critical events such as falls in mature individuals can be detected. In this context, we have conducted comprehensive experimental campaigns at nine different locations including four laboratory environments and five elderly care homes. A total of 99 subjects participated in the experiments where 1453 micro-Doppler signatures were recorded for six activities. Different machine learning, deep learning algorithms and transfer learning technique were used to classify the ADL. The support vector machine (SVM), K-nearest neighbor (KNN) and convolutional neural network (CNN) provided adequate classification accuracies for particular scenarios; however, the autoencoder neural network outperformed the mentioned classifiers by providing classification accuracy of ~ 88%. The proposed system for fall detection in elderly people can be deployed in care centers and is application for any indoor settings with various age group of people. For future work, we would focus on monitoring multiple older adults, concurrently in indoor settings using continuous radar sensor data stream which is limitation of the present system

Forecasting Flashover Parameters of Polymeric Insulators under Contaminated Conditions Using the Machine Learning Technique

There is a vital need to understand the flashover process of polymeric insulators for safe and reliable power system operation. This paper provides a rigorous investigation of forecasting the flashover parameters of High Temperature Vulcanized (HTV) silicone rubber based on environmental and polluted conditions using machine learning. The modified solid layer method based on the IEC 60507 standard was utilised to prepare samples in the laboratory. The effect of various factors including Equivalent Salt Deposit Density (ESDD), Non-soluble Salt Deposit Density (NSDD), relative humidity and ambient temperature, were investigated on arc inception voltage, flashover voltage and surface resistance. The experimental results were utilised to engineer a machine learning based intelligent system for predicting the aforementioned flashover parameters. A number of machine learning algorithms such as Artificial Neural Network (ANN), Polynomial Support Vector Machine (PSVM), Gaussian SVM (GSVM), Decision Tree (DT) and Least-Squares Boosting Ensemble (LSBE) were explored in forecasting of the flashover parameters. The prediction accuracy of the model was validated with a number of error cost functions, such as Root Mean Squared Error (RMSE), Normalized RMSE (NRMSE), Mean Absolute Percentage Error (MAPE) and R. For improved prediction accuracy, bootstrapping was used to increase the sample space. The proposed PSVM technique demonstrated the best performance accuracy compared to other machine learning models. The presented machine learning model provides promising results and demonstrates highly accurate prediction of the arc inception voltage, flashover voltage and surface resistance of silicone rubber insulators in various contaminated and humid conditions

Contactless privacy-preserving head movement recognition using deep learning for driver fatigue detection

Head movement holds significant importance in con-veying body language, expressing specific gestures, and reflecting emotional and character aspects. The detection of head movement in smart or assistive driving applications can play an important role in preventing major accidents and potentially saving lives. Additionally, it aids in identifying driver fatigue, a significant contributor to deadly road accidents worldwide. However, most existing head movement detection systems rely on cameras, which raise privacy concerns, face challenges with lighting conditions, and require complex training with long video sequences. This novel privacy-preserving system utilizes UWB-radar technology and leverages Deep Learning (DL) techniques to address the mentioned issues. The system focuses on classifying the five most common head gestures: Head 45L (HL45), Head 45R (HR45), Head 90L (HL90), Head 90R (HR90), and Head Down (HD). By processing the recorded data as spectrograms and leveraging the advanced DL model VGG16, the proposed system accurately detects these head gestures, achieving a maximum classification accuracy of 84.00% across all classes. This study presents a proof of concept for an effective and privacy-conscious approach to head position classification.</p

Social sensing for sentiment analysis of policing authority performance in smart cities

High-tech services in smart cities, ubiquity of smart phones, and proliferation of social media platforms have enabled social sensing, either through direct human observers or through humans as sensor carriers and operators, such as through the use of smart phones, cameras, etc. We performed a sentiment analysis (SA) and mined public opinion on the civil services and policing authority in a smart city. The establishment of high-tech policing in Lahore, Pakistan, known as the Punjab Safe Cities Authority (PSCA), Lahore, along with integrated command and control centers and various equipments, such as 8,000 cameras, monitoring sensors, etc., has resulted in a requirement for its performance evaluation and social media–enabled opinion mining to determine the broader impact on communities. Social sensing of civil services has been enabled through the presence of the PSCA on Facebook, Twitter, YouTube, and Web TV. The SA of the local civil services is not possible without taking into account the local language. In this article, we utilize machine learning techniques to perform multi-class SA of public opinion on policing authority and the provided civil services in both the local languages Urdu and English. The support vector machine provides the highest performance multi-classification accuracy of 86.87% for positive, negative, and neutral sentiments. The temporal sentiments are determined over time from January 2020 to July 2021, with an overall positive sentiment of 62.40% and a negative sentiment of 13.51%, which shows high satisfaction of policing authority and the provided civil services

HRNN4F: Hybrid deep random neural network for multi-channel fall activity detection

Falls are a major health concern in older adults. Falls lead to mortality, immobility and high costs to social and health care services. Early detection and classification of falls is imperative for timely and appropriate medical aid response. Traditional machine learning models have been explored for fall classification. While newly developed deep learning techniques have the ability to potentially extract high-level features from raw sensor data providing high accuracy and robustness to variations in sensor position, orientation and diversity of work environments that may skew traditional classification models. However, frequently used deep learning models like Convolutional Neural Networks (CNN) are computationally intensive. To the best of our knowledge, we present the first instance of a Hybrid Multichannel Random Neural Network (HMCRNN) architecture for fall detection and classification. The proposed architecture provides the highest accuracy of 92.23% with dropout regularization, compared to other deep learning implementations. The performance of the proposed technique is approximately comparable to a CNN yet requires only half the computation cost of the CNN-based implementation. Furthermore, the proposed HMCRNN architecture provides 34.12% improvement in accuracy on average than a Multilayer Perceptron