FoGGAN: Generating Realistic Parkinson's Disease Freezing of Gait Data Using GANs

Data scarcity in the healthcare domain is a major drawback for most state-of-the-art technologies engaging artificial intelligence. The unavailability of quality data due to both the difficulty to gather and label them as well as due to their sensitive nature create a breeding ground for data augmentation solutions. Parkinson's Disease (PD) which can have a wide range of symptoms including motor impairments consists of a very challenging case for quality data acquisition. Generative Adversarial Networks (GANs) can help alleviate such data availability issues. In this light, this study focuses on a data augmentation solution engaging Generative Adversarial Networks (GANs) using a freezing of gait (FoG) symptom dataset as input. The data generated by the so-called FoGGAN architecture presented in this study are almost identical to the original as concluded by a variety of similarity metrics. This highlights the significance of such solutions as they can provide credible synthetically generated data which can be utilized as training dataset inputs to AI applications. Additionally, a DNN classifier's performance is evaluated using three different evaluation datasets and the accuracy results were quite encouraging, highlighting that the FOGGAN solution could lead to the alleviation of the data shortage matter.

The Effectiveness of Zero-Day Attacks Data Samples Generated via GANs on Deep Learning Classifiers

Digitization of most of the services that people use in their everyday life has, among others, led to increased needs for cybersecurity. As digital tools increase day by day and new software and hardware launch out-of-the box, detection of known existing vulnerabilities, or zero-day as they are commonly known, becomes one of the most challenging situations for cybersecurity experts. Zero-day vulnerabilities, which can be found in almost every new launched software and/or hardware, can be exploited instantly by malicious actors with different motives, posing threats for end-users. In this context, this study proposes and describes a holistic methodology starting from the generation of zero-day-type, yet realistic, data in tabular format and concluding to the evaluation of a Neural Network zero-day attacks' detector which is trained with and without synthetic data. This methodology involves the design and employment of Generative Adversarial Networks (GANs) for synthetically generating a new and larger dataset of zero-day attacks data. The newly generated, by the Zero-Day GAN (ZDGAN), dataset is then used to train and evaluate a Neural Network classifier for zero-day attacks. The results show that the generation of zero-day attacks data in tabular format reaches an equilibrium after about 5000 iterations and produces data that are almost identical to the original data samples. Last but not least, it should be mentioned that the Neural Network model that was trained with the dataset containing the ZDGAN generated samples outperformed the same model when the later was trained with only the original dataset and achieved results of high validation accuracy and minimal validation loss.

A Distributed Big Data Analytics Architecture for Vehicle Sensor Data

The unceasingly increasing needs for data acquisition, storage and analysis in transportation systems have led to the adoption of new technologies and methods in order to provide efficient and reliable solutions. Both highways and vehicles, nowadays, host a vast variety of sensors collecting different types of highly fluctuating data such as speed, acceleration, direction, and so on. From the vast volume and variety of these data emerges the need for the employment of big data techniques and analytics in the context of state-of-the-art intelligent transportation systems (ITS). Moreover, the scalability needs of fleet and traffic management systems point to the direction of designing and deploying distributed architecture solutions that can be expanded in order to avoid technological and/or technical entrapments. Based on the needs and gaps detected in the literature as well as the available technologies for data gathering, storage and analysis for ITS, the aim of this study is to provide a distributed architecture platform to address these deficiencies. The architectural design of the system proposed, engages big data frameworks and tools (e.g., NoSQL Mongo DB, Apache Hadoop, etc.) as well as analytics tools (e.g., Apache Spark). The main contribution of this study is the introduction of a holistic platform that can be used for the needs of the ITS domain offering continuous collection, storage and data analysis capabilities. To achieve that, different modules of state-of-the-art methods and tools were utilized and combined in a unified platform that supports the entire cycle of data acquisition, storage and analysis in a single point. This leads to a complete solution for ITS applications which lifts the limitations imposed in legacy and current systems by the vast amounts of rapidly changing data, while offering a reliable system for acquisition, storage as well as timely analysis and reporting capabilities of these data.

Driving Behaviour Analysis Using Machine and Deep Learning Methods for Continuous Streams of Vehicular Data

In the last few decades, vehicles are equipped with a plethora of sensors which can provide useful measurements and diagnostics for both the vehicle’s condition as well as the driver’s behaviour. Furthermore, the rapid increase for transportation needs of people and goods together with the evolution of Information and Communication Technologies (ICT) push the transportation domain towards a new more intelligent and efficient era. The reduction of CO₂ emissions and the minimization of the environmental footprint is, undeniably, of utmost importance for the protection of the environment. In this light, it is widely acceptable that the driving behaviour is directly associated with the vehicle’s fuel consumption and gas emissions. Thus, given the fact that, nowadays, vehicles are equipped with sensors that can collect a variety of data, such as speed, acceleration, fuel consumption, direction, etc. is more feasible than ever to put forward solutions which aim not only to monitor but also improve the drivers’ behaviour from an environmental point of view. The approach presented in this paper describes a holistic integrated platform which combines well-known machine and deep learning algorithms together with open-source-based tools in order to gather, store, process, analyze and correlate different data flows originating from vehicles. Particularly, data streamed from different vehicles are processed and analyzed with the utilization of clustering techniques in order to classify the driver’s behaviour as eco-friendly or not, followed by a comparative analysis of supervised machine and deep learning algorithms in the given labelled dataset.

Survey on Security Threats in Agricultural IoT and Smart Farming

The agriculture sector has held a major role in human societies across the planet throughout history. The rapid evolution in Information and Communication Technologies (ICT) strongly affects the structure and the procedures of modern agriculture. Despite the advantages gained from this evolution, there are several existing as well as emerging security threats that can severely impact the agricultural domain. The present paper provides an overview of the main existing and potential threats for agriculture. Initially, the paper presents an overview of the evolution of ICT solutions and how these may be utilized and affect the agriculture sector. It then conducts an extensive literature review on the use of ICT in agriculture, as well as on the associated emerging threats and vulnerabilities. The authors highlight the main ICT innovations, techniques, benefits, threats and mitigation measures by studying the literature on them and by providing a concise discussion on the possible impacts these could have on the agri-sector.

Humoral immune activation in amyotrophic lateral sclerosis patients

There is evidence that immunological factors may involved in the pathogenetic mechanisms of amyotrophic lateral sclerosis (ALS). Few studies to date have explored the status of the humoral immune response in patients with ALS. We examined the presence of humoral immune activation in ALS patients, serum immunoglobulins (IgG, IgA and IgM) levels were measured in 36 patients with ALS and 35 normal controls. Serum IgG, IgM and IgA levels were not significantly different in our ALS patients compared with the control group (P=ns). No correlations of serum IgG, IgM and IgA concentrations with duration, severity of the disease or the clinical form of onset (bulbar or spinal) were found in our ALS patients. Our results do not suggest a humoral immune activation in ALS patients. This does not exclude that immunological mechanisms may be involved in ALS pathogenesis.

Alterations of T cell subsets in ALS: a systemic immune activation?

INTRODUCTION

There is evidence that immunological factors may involved in pathogenetic mechanisms of amyotrophic lateral sclerosis (ALS). Few studies to date have explored the status of the systemic immune response in patients with ALS.

PATIENTS AND METHODS

In order to examine whether systemic immune activation is observed in patients with ALS, we measured the number of T cell subsets by flow cytometry in 36 patients with ALS and 35 normal controls.

RESULTS

CD8 cytotoxic T cells and natural killer (NK) T cells were significantly increased in our patients with ALS compared with the control group (P = 0.02 and P = 0.04, respectively). Treg cells were significantly reduced compared with normal controls (P = 0.01). Treg cells were also negatively correlated with progression of the disease (P = 0.017).

CONCLUSIONS

Our results suggest a systemic immune activation in patients with ALS. The high production of CD8(+) T and NKT cells may suggest an immunological reaction to some unknown or undetected endogenous proteins or viruses. A probably dual (neurodestructive or neuroprotective) inflammatory function of Treg cells cannot be excluded.

Interleukin-17 and interleukin-23 are elevated in serum and cerebrospinal fluid of patients with ALS: a reflection of Th17 cells activation?

BACKGROUND

There is evidence that immunological factors may involved in pathogenetic mechanisms of amyotrophic lateral sclerosis (ALS). Th17 cells are characterized by predominant production of IL-17 and are suggested to be crucial in destructive autoimmunity. Interleukin-23 (IL-23) appears to play a supporting role in the continued stimulation and survival of Th17.

PATIENTS AND METHODS

We measured by enzyme-like immunosorbent assay (ELISA) serum and cerebrospinal fluid (CSF) levels of IL-17 and IL-23 in 22 patients with ALS and 19 patients with other non-inflammatory neurological disorders (NIND) studied as a control group. IL-17 and IL-23 serum and CSF levels were also correlated with duration of the disease, the disability level and the clinical subtype of the disease onset in patients with ALS.

RESULTS

IL-17 and IL-23 serum levels were higher in patients with ALS as compared with patients with NIND (P = 0.015 and P = 0.002 respectively). IL-17 and IL-23 CSF levels were also increased in patients with ALS (P = 0.0006 and P = 0.000001 respectively). IL-17 and IL-23 levels were not correlated with disease duration, disability scale or clinical subtype of the disease onset in ALS patients.

CONCLUSIONS

Our findings suggest that these molecules may be involved in the pathogenetic mechanisms acting as potential markers of Th17 cells activation in ALS.

Interleukin-15 and interleukin-12 are elevated in serum and cerebrospinal fluid of patients with amyotrophic lateral sclerosis

BACKGROUND

There is evidence that immunological factors may be involved in pathogenic mechanisms of amyotrophic lateral sclerosis (ALS). Interleukin (IL)-15 and IL-12 are proinflammatory cytokines produced by activated blood and glial cells. They promote T cell differentiation and proliferation.

PATIENTS AND METHODS

We measured by ELISA serum and cerebrospinal fluid (CSF) levels of IL-15 and IL-12 in 21 patients with ALS and 19 patients with other noninflammatory neurological disorders (NIND) studied as a control group. IL-15 and IL-12 serum and CSF levels were also correlated with duration of the disease, the disability level determined using the revised ALS Functional Rating Scale and the clinical subtype of the disease onset in patients with ALS.

RESULTS

IL-15 and IL-12 serum levels were higher in patients with ALS as compared with patients with NIND (p = 0.014 and p = 0.011, respectively). IL-15 and IL-12 CSF levels were also increased in patients with ALS (p = 0.011 and p = 0.005, respectively). IL-15 and IL-12 levels were not correlated with disease duration, disability scale or clinical subtype of the disease onset in ALS patients.

CONCLUSIONS

Our findings suggest that these molecules may be involved in the pathogenic mechanisms acting as potential markers of immune activation in ALS.

Microencapsulation of DNA within alginate microspheres and crosslinked chitosan membranes for in vivo application

Calf thymus DNA was microencapsulated within crosslinked chitosan membranes, or immobilized within chitosan-coated alginate microspheres. Microcapsules were prepared by interfacial polymerization of chitosan, and alginate microspheres formed by emulsification/internal gelation. Diameters ranged from 20 to 500 microns, depending on the formulation conditions. Encapsulated DNA was quantified in situ by direct spectrophotometry (260 nm) and ethidium bromide fluorimetry, and compared to DNA measurements on the fractions following disruption and dissolution of the microspheres. Approximately 84% of the DNA was released upon core dissolution and membrane disruption, with 12% membrane bound. The yield of encapsulation was 96%. Leakage of DNA from intact microspheres/capsules was not observed. DNA microcapsules and microspheres were recovered intact from rat feces following gavage and gastrointestinal transit. Higher recoveries (60%) and reduced shrinkage during transit were obtained with the alginate microspheres. DNA was recovered and purified from the microcapsules and microspheres by chromatography and differential precipitation with ethanol. This is the first report of microcapsules or microspheres containing biologically active material (DNA) being passed through the gastrointestinal tract, with the potential for substantial recovery.

Microencapsulation within crosslinked polyethyleneimine membranes

A microencapsulation technique is proposed involving the formation of a polyethyleneimine (PEI) membrane crosslinked by an acid dichloride. The membranes were formed at pH 8 in a non-polar solvent, conditions which are better suited for the encapsulation of biocatalysts or fragile biochemicals than those using polyamide membranes. The mean diameter and size distribution of the PEI microcapsules were similar to that observed with nylon membranes. The resultant microcapsules were spherical, free-flowing with a strong membrane. The mass of membrane was seen to be independent of the reaction time (1-4 min), insensitive to the PEI concentration and proportional to the concentration of crosslinking agent.