Mental, physical and socio-economic status of adults living in spain during the late stages of the state of emergency caused by COVID-19

Research has shown that the confinement measures implemented to curb the spread of COVID-19 can have negative effects on people’s lives at multiple levels. The objective of this cross-sectional study was to better understand the mental, physical, and socio-economic status of adults living in Spain during the late stages of the state of emergency caused by COVID-19. Five hundred and forty-four individuals responded to an online survey between 3 June and 30 July 2020. They were asked to report data about their mental and physical health, financial situation, and satisfaction with the information received about the pandemic. Means, percentages, t-test, ANOVAs, and logistic regressions were computed. A third of the participants reported symptoms of anxiety, depression, and stress, and worries about their health and the future. Participants also described mild levels of fatigue and pain during lockdown (66%), and a reduction in household income (39%). Respondents that were female, younger, single, and with lower levels of education reported experiencing a greater impact of the COVID-19 pandemic. The data showed that the negative effects of lockdown were present in the late stages of the state of emergency. The findings can be used to contribute to the development of programs to prevent or mitigate the negative impact of confinement measures.Fundação para a Ciência e Tecnologia - FCTinfo:eu-repo/semantics/publishedVersio

A novel thermostable protein-tag: optimization of the Sulfolobus solfataricus DNA- alkyl-transferase by protein engineering

In the last decade, a powerful biotechnological tool for the in vivo and in vitro specific labeling of proteins (SNAP-tag™ technology) was proposed as a valid alternative to classical protein-tags (green fluorescent proteins, GFPs). This was made possible by the discovery of the irreversible reaction of the human alkylguanine-DNA-alkyl-transferase (hAGT) in the presence of benzyl-guanine derivatives. However, the mild reaction conditions and the general instability of the mesophilic SNAP-tag™ make this new approach not fully applicable to (hyper-)thermophilic and, in general, extremophilic organisms. Here, we introduce an engineered variant of the thermostable alkylguanine-DNA-alkyl-transferase from the Archaea Sulfolobus solfataricus (SsOGT-H5), which displays a catalytic efficiency comparable to the SNAP-tag™ protein, but showing high intrinsic stability typical of proteins from this organism. The successful heterologous expression obtained in a thermophilic model organism makes SsOGT-H5 a valid candidate as protein-tag for organisms living in extreme environments

Genome stability: recent insights in the topoisomerase reverse gyrase and thermophilic DNA alkyltransferase

Repair and defence of genome integrity from endogenous and environmental hazard is a primary need for all organisms. Natural selection has driven the evolution of multiple cell pathways to deal with different DNA damaging agents. Failure of such processes can hamper cell functions and induce inheritable mutations, which in humans may cause cancerogenicity or certain genetic syndromes, and ultimately cell death. A special case is that of hyperthermophilic bacteria and archaea, flourishing at temperatures higher than 80 °C, conditions that favor genome instability and thus call for specific, highly efficient or peculiar mechanisms to keep their genome intact and functional. Over the last few years, numerous studies have been performed on the activity, function, regulation, physical and functional interaction of enzymes and proteins from hyperthermophilic microorganisms that are able to bind, repair, bypass damaged DNA, or modify its structure or conformation. The present review is focused on two enzymes that act on DNA catalyzing unique reactions: reverse gyrase and DNA alkyltransferase. Although both enzymes belong to evolutionary highly conserved protein families present in organisms of the three domains (Eucarya, Bacteria and Archaea), recently characterized members from hyperthermophilic archaea show both common and peculiar features

Hypnotizability-related interoceptive awareness and inhibitory/activating emotional traits

Emotions are influenced by several individual factors. Hypnotizability - a psychophysiological trait associated with morpho-functional cerebral and cerebellar variations able to sustain differences in interoception and emotion - could be one of them. The aims of the study were to find out possible differences in Interoceptive Awareness (IA) and in the emotional traits sustained by the Behavioral Inhibition/Activation System (BIS/BAS) in participants with high (highs), medium (mediums) and low (lows) hypnotizability and to investigate the association of interoceptive awareness and BIS/BAS related emotional traits as a function of hypnotizability. Thus, IA and BIS/BAS were studied in 284 subjects of both genders by the Multidimensional Assessment of Interoceptive Awareness (MAIA) and by BIS/BAS scales, respectively. Significantly lower BIS scores (lower inhibitory control/conflict monitoring) in highs and lows with respect to mediums and significantly higher IA (proneness to notice and interpret interoceptive information) in highs with respect to mediums and lows were found. In addition, different correlations between MAIA and BIS/BAS scales were observed in the three groups, indicating different hypnotizability-related associations. The hypnotizability-related relation between interoceptive awareness and emotional traits could be accounted for by different models and their knowledge may be relevant to the science of emotion and to clinical applicatio

Non-linear analysis of the heart rate variability during passive Tilt test.

In recent years, new dynamic methods of HRV quantification have been used to uncover nonlinear fluctuations in heart rate that are otherwise not apparent. These nonlinear variations would enable the cardiovascular system to respond more quickly to changing conditions. Several methods have been proposed to quantify these fluctuations on the basis of the scaling properties of the heart rate variations Detrended Fluctuation Analysis, Correlation Dimension (CD), Entropy (ApEn and SampEn) or other nonlinear properties (Poincaré plots). Moreover, fractal analysis of HRV was more sensitive to the classical spectral and time-domain analysis of HRV. The investigation of new parameters, descriptive of the complex regulation mechanisms of heart rate, has to be encouraged because not all information in the HRV signal is captured by traditional methods. The new technologies thus could provide additional physiological, and clinical meaning

Towards Explainable Quantum Machine Learning for Mobile Malware Detection and Classification

Through the years, the market for mobile devices has been rapidly increasing, and as a result of this trend, mobile malware has become sophisticated. Researchers are focused on the design and development of malware detection systems to strengthen the security and integrity of sensitive and private information. In this context, deep learning is exploited, also in cybersecurity, showing the ability to build models aimed at detecting whether an application is Trusted or malicious. Recently, with the introduction of quantum computing, we have been witnessing the introduction of quantum algorithms in Machine Learning. In this paper, we provide a comparison between five state-of-the-art Convolutional Neural Network models (i.e., AlexNet, MobileNet, EfficientNet, VGG16, and VGG19), one network developed by the authors (called Standard-CNN), and two quantum models (i.e., a hybrid quantum model and a fully quantum neural network) to classify malware. In addition to the classification, we provide explainability behind the model predictions, by adopting the Gradient-weighted Class Activation Mapping to highlight the areas of the image obtained from the application symptomatic of a certain prediction, to the convolutional and to the quantum models obtaining the best performances in Android malware detection. Real-world experiments were performed on a dataset composed of 8446 Android malicious and legitimate applications, obtaining interesting results