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The devastating consequences of successful security breaches that have been observed recently have forced more and more software development enterprises to shift their focus towards building software products that are highly secure (i.e., vulnerability-free) from the ground up. In order to produce secure software applications, appropriate mechanisms are required for enabling project managers and developers to monitor the security level of their products during their development and identify and eliminate vulnerabilities prior to their release. A large number of such mechanisms have been proposed in the literature over the years, but limited attempts with respect to their industrial applicability, relevance, and practicality can be found. To this end, in the present paper, we demonstrate an integrated security platform, the VM4SEC platform, which exhibits cutting-edge solutions for software security monitoring and optimization, based on static and textual source code analysis. The platform was built in a way to satisfy the actual security needs of a real software development company. For this purpose, an industrial case study was conducted in order to identify the current security state of the company and its security needs in order for the employed security mechanisms to be adapted to the specific needs of the company. Based on this analysis, the overall architecture of the platform and the parameters of the selected models and mechanisms were properly defined and demonstrated in the present paper. The purpose of this paper is to showcase how cutting-edge security monitoring and optimization mechanisms can be adapted to the needs of a dedicated company and to be used as a blueprint for constructing similar security monitoring platforms and pipelines.

Due to rapid industrialization, novel water-quality monitoring techniques for the detection of highly toxic and hazardous heavy metal ions are essential. Herein, a hybrid noble nanoparticle/DNAzyme electrochemical biosensor is proposed for the simultaneous and label-free detection of Pb2+ and Cr3+ in aqueous solutions. The sensor is based on the combination of a two-dimensional naked-platinum nanoparticle film and DNAzymes, whose double-helix configuration disassembles into smaller fragments in the presence of target-specific heavy metal ions. The electrochemical behavior of the fabricated sensor was investigated with non-faradaic electrochemical impedance spectroscopy (EIS), resulting in the successful detection of Pb2+ and Cr3+ well below their maximum permitted levels in tap water. So far, there has been no report on the successful detection of heavy metal ions utilizing the non-faradaic electrochemical impedance spectroscopy technique based on advanced nanomaterials paired with DNAzymes. This is also one of the few reports on the successful detection of chromium (III) via a sensor incorporating DNAzymes.

Aging is a complex biological process. Main factors that interplay in the aging process include free radicals and oxidation, insulin and insulin growth factors, sirtuins, mTOR, microbiome, lack of micronutrients, and declining proteasome activity which lead to cellular damage. Damaged cells are being replaced by somatic stem cells which proliferate to generate new cells. For each cell replication the telomeres of the related stem cells become shorter and this is the basic factor that modulates aging. Telomere length shortens with age and leads to senescence. Shorter telomeres are associated with increased incidence of aging related diseases and shorter lifespan. The percentage of short telomeres and rate of telomere shortening predicts longevity in mammals. Measurement of single telomeres through Q-FISH is the only reliable method to evaluate single telomere length and percentage of short telomeres. Repeated measurements at a distance of 6 months or a year can reveal the rate of change of the short telomeres, and response ofpatients to treatments, lifestyle, diet, supplementation and exercise modifications. A natural product telomerase activator TA-65, an astragalus extract, has been found to lengthen telomere in humans. By experimenting with different combinations of cycloastragenol (astragalus extract active molecule) we've able to increase telomerase activation in relation to the control cells.

Developing embedded software applications is a challenging task, chiefly due to the limitations that are imposed by the hardware devices or platforms on which they operate, as well as due to the heterogeneous non-functional requirements that they need to exhibit. Modern embedded systems need to be energy efficient and dependable, whereas their maintenance costs should be minimized, in order to ensure the success and longevity of their application. Being able to build embedded software that satisfies the imposed hardware limitations, while maintaining high quality with respect to critical non-functional requirements is a difficult task that requires proper assistance. To this end, in the present paper, we present the SDK4ED Platform, which facilitates the development of embedded software that exhibits high quality with respect to important quality attributes, with a main focus on energy consumption, dependability, and maintainability. This is achieved through the provision of state-of-the-art and novel quality attribute-specific monitoring and optimization mechanisms, as well as through a novel fuzzy multi-criteria decision-making mechanism for facilitating the selection of code refactorings, which is based on trade-off analysis among the three main attributes of choice. Novel forecasting techniques are also proposed to further support decision making during the development of embedded software. The usefulness, practicality, and industrial relevance of the SDK4ED platform were evaluated in a real-world setting, through three use cases on actual commercial embedded software applications stemming from the airborne, automotive, and healthcare domains, as well as through an industrial study. To the best of our knowledge, this is the first quality analysis platform that focuses on multiple quality criteria, which also takes into account their trade-offs to facilitate code refactoring selection.

Technical debt (TD) describes quality compromises that can yield short-term benefits but may negatively affect the quality of software products in the long run. A wide range of tools and techniques have been introduced over the years in order for the developers to be able to determine and manage TD. However, being able to also predict its future evolution is of equal importance to avoid its accumulation, and, in turn, the unlikely event of making the project unmaintainable. Although recent research endeavors have showcased the feasibility of building accurate project-specific TD forecasting models, there is a gap in the field regarding cross-project TD forecasting. Cross-project TD forecasting is of practical importance, since it would enable the application of pre-existing forecasting models on previously unknown software projects, especially new projects that do not exhibit sufficient commit history to enable the construction of project-specific models. To this end, in the present paper, we focus on cross-project TD forecasting, and we examine whether the consideration of similarities between software projects could be the key for more accurate forecasting. More specifically, we propose an approach based on data clustering. In fact, a relatively large repository of software projects is divided into clusters of similar projects with respect to their TD aspects, and specific TD forecasting models are built for each cluster, using regression algorithms. According to our approach, previously unknown software projects are assigned to one of the defined clusters and the cluster-specific TD forecasting model is applied to predict future TD values. The approach was evaluated through several experiments based on real-world applications. The results of the analysis suggest that the proposed approach comprises a promising solution for accurate cross-project TD forecasting.

Software security is a critical aspect of modern software products. The vulnerabilities that reside in their source code could become a major weakness for enterprises that build or utilize these products, as their exploitation could lead to devastating financial consequences. Therefore, the development of mechanisms capable of identifying and discovering software vulnerabilities has recently attracted the interest of the research community. Besides the studies that examine software attributes in order to predict the existence of vulnerabilities in software components, there are also studies that attempt to predict the future number of vulnerabilities based on the already reported vulnerabilities of a project. In this paper, the evolution of vulnerabilities in a horizon of up to 24 months ahead is predicted using a univariate time series forecasting approach. Both statistical and deep learning models are developed and compared based on security data coming from five popular software projects. In contrast to related literature, the results indicate that the capacity of Deep Learning and statistical models in forecasting the evolution of software vulnerabilities, as well as the selection of the best-performing model, depends on the respective software project. In some cases, statistical models provided better accuracy, whereas in other cases, Deep Learning models demonstrated better predictive power. However, the difference in their performance was not found to be statistically significant. In general, the two model categories produced similar forecasts for the number of vulnerabilities expected in the future, without significant diversities.

Fatty acids (FAs) play critical roles in health and disease. The detection of FA imbalances through metabolomics can provide an overview of an individual's health status, particularly as regards chronic inflammatory disorders. In this study, we aimed to establish sensitive reference value ranges for targeted plasma FAs in a well-defined population of healthy adults. Plasma samples were collected from 159 participants admitted as outpatients. A total of 24 FAs were analyzed using gas chromatography-mass spectrometry, and physiological values and 95% reference intervals were calculated using an approximate method of analysis. The differences among the age groups for the relative levels of stearic acid (P=0.005), the omega-6/omega-3 ratio (P=0.027), the arachidonic acid/eicosapentaenoic acid ratio (P<0.001) and the linoleic acid-produced dihomo-gamma-linolenic acid (P=0.046) were statistically significant. The majority of relative FA levels were higher in males than in females. The levels of myristic acid (P=0.0170) and docosahexaenoic acid (P=0.033) were signifi-cantly different between the sexes. The reference values for the FAs examined in this study represent a baseline for further studies examining the reproducibility of this methodology and sensitivities for nutrient deficiency detection and investigating the biochemical background of pathological conditions. The application of these values to clinical practice will allow for the discrimination between health and disease and contribute to early prevention and treatment.

Telomeres are specific DNA regions positioned at the ends of chromosomes and composed of functional non-coding repeats. Upon cell division, the telomeres decrease in length by a preordained amount. When the telomeres become critically short, cells lose the ability to divide and enter a specific functioning mode designated as 'cellular senescence'. However, human tissues express an enzyme that deters the shrinking of the telomeres, the telomerase. Due to its ability to maintain telomere length, the telomerase slows down and possibly suspends the aging of the cells. In regard to this, solid evidence demonstrates that female human fertility decreases with increased maternal age and that various adverse factors, including alterations in telomerase activity, can contribute to age-associated infertility in women. The fact that telomerase activity is regulated in a time- and location-dependent manner in both embryo and placental tissues, highlights it potential importance to the successful completion of pregnancy. Since maternal age is a crucial determining factor for the success of in vitro and in vivo fertilization, numerous studies have focused on telomerase activity and its correlation with mammalian fertilization, as well as the following cleavage and pre-implantation developmental processes. Associations between telomerase activity and pregnancy complications have been previously observed. Our aim in this review was to summarize and critically discuss evidence correlating telomerase activity with pregnancy complications.

The incidence of human papillomavirus (HPV) in the human cancer domain is still a subject of intensive study. In this study, we examined cervical swab samples from 713 females with genital warts, and tested the samples for high- and low-risk genital HPV. HPV genotyping was assessed using a Genotyping test that detects HPV by the amplification of target DNA using polymerase chain reaction and nucleic acid hybridization. In total, we detected 37 anogenital HPV DNA genotypes [6, 11, 16, 18, 26, 31, 33, 35, 39, 40, 42, 45, 51, 52, 53, 54, 55, 56, 58, 59, 61, 62, 64, 66, 67, 68, 69, 70, 71, 72, 73 (MM9), 81, 82 (MM4), 83 (MM7), 84 (MM8), IS39 and CP6108] and investigated the incidence of these genotypes in the patients with genital warts. We found differences in the distribution of high-/low-risk strains and the incidence of high-risk strains was found to occur mainly in females under 35 years of age. The data from our study suggest that a detailed oral, rectal and genital identification of high-risk strains should be performed to visualize the entire pattern of possible triggers of carcinogenesis.

This study analyzes macroeconomic shocks in Cyprus and the EMU-area from the beginning of 1990 to the end of 2004. We examine the relative importance of aggregate demand and supply shocks along with money, in explaining short-run real output fluctuations. The empirical results for the analysis are obtained by using the framework of structural vector autoregression model (SVAR). The structural impulses in the VAR model are defined as shocks in aggregate demand, aggregate supply and money growth. Results indicate that shocks in AD, AS, the money growth are all sources of macro shocks in Cyprus and the EMU-area.