Evolving always‐critical networks

Living beings share several common features at the molecular level, but there are very few large‐scale “operating principles” which hold for all (or almost all) organisms. However, biology is subject to a deluge of data, and as such, general concepts such as this would be extremely valuable. One interesting candidate is the “criticality” principle, which claims that biological evolution favors those dynamical regimes that are intermediaries between ordered and disordered states (i.e., “at the edge of chaos”). The reasons why this should be the case and experimental evidence are briefly discussed, observing that gene regulatory networks are indeed often found on, or close to, the critical boundaries. Therefore, assuming that criticality provides an edge, it is important to ascertain whether systems that are critical can further evolve while remaining critical. In order to explore the possibility of achieving such “always‐critical” evolution, we resort to simulated evolution, by suitably modifying a genetic algorithm in such a way that the newly‐generated individuals are constrained to be critical. It is then shown that these modified genetic algorithms can actually develop critical gene regulatory networks with two interesting (and quite different) features of biological significance, involving, in one case, the average gene activation values and, in the other case, the response to perturbations. These two cases suggest that it is often possible to evolve networks with interesting properties without losing the advantages of criticality. The evolved networks also show some interesting features which are discussed

A Hybrid Training-Time and Run-Time Defense Against Adversarial Attacks in Modulation Classification

Motivated by the superior performance of deep learning in many applications including computer vision and natural language processing, several recent studies have focused on applying deep neural network for devising future generations of wireless networks. However, several recent works have pointed out that imperceptible and carefully designed adversarial examples (attacks) can significantly deteriorate the classification accuracy. In this letter, we investigate a defense mechanism based on both training-time and run-time defense techniques for protecting machine learning-based radio signal (modulation) classification against adversarial attacks. The training-time defense consists of adversarial training and label smoothing, while the run-time defense employs a support vector machine-based neural rejection (NR). Considering a white-box scenario and real datasets, we demonstrate that our proposed techniques outperform existing state-of-the-art technologies

Analysis of Score-Level Fusion Rules for Deepfake Detection

Deepfake detection is of fundamental importance to preserve the reliability of multimedia communications. Modern deepfake detection systems are often specialized on one or more types of manipulation but are not able to generalize. On the other hand, when properly designed, ensemble learning and fusion techniques can reduce this issue. In this paper, we exploit the complementarity of different individual classifiers and evaluate which fusion rules are best suited to increase the generalization capacity of modern deepfake detection systems. We also give some insights to designers for selecting the most appropriate approach

The influence of environment on the sperm quality: a comprehensive, retrospective, cohort study.

influence of environment on the sperm quality: a comprehensive, retrospective, cohort stud

Predictive biometrics: A review and analysis of predicting personal characteristics from biometric data

Interest in the exploitation of soft biometrics information has continued to develop over the last decade or so. In comparison with traditional biometrics, which focuses principally on person identification, the idea of soft biometrics processing is to study the utilisation of more general information regarding a system user, which is not necessarily unique. There are increasing indications that this type of data will have great value in providing complementary information for user authentication. However, the authors have also seen a growing interest in broadening the predictive capabilities of biometric data, encompassing both easily definable characteristics such as subject age and, most recently, `higher level' characteristics such as emotional or mental states. This study will present a selective review of the predictive capabilities, in the widest sense, of biometric data processing, providing an analysis of the key issues still adequately to be addressed if this concept of predictive biometrics is to be fully exploited in the future

Sarcopenic obesity and reduced BMD in young men living with HIV: body composition and sex steroids interplay

Purpose Sex steroids play a key role on male bone homeostasis and body composition (BC), their role in men living with HIV (MLWH) is less recognized. This study aimed at investigating the prevalence of low BMD, sarcopenia, and sarcopenic obesity (SO) and their relationship with sex steroids in MLWH aged < 50. Methods Prospective, cross-sectional, observational study on MLWH younger than 50 (median age 47.0 years). BC and BMD were evaluated with DXA. Two different definitions of sarcopenia were applied: appendicular lean mass/height(2) (ALMI) < 7.26 kg/m(2) or appendicular lean mass/body weight (ALM/W) < 28.27%. Low BMD was defined for Z-score < -2.0. Sarcopenia coupled with obesity identified SO. Serum total testosterone (T) and estradiol (E2) were measured by LC-MS/MS; free testosterone (cFT) was calculated by Vermeulen equation. Results Sarcopenia was detected in 107 (34.9%) and 44 (14.3%) out of 307 MLWH according to ALMI and ALM/W, respectively. The prevalence of SO was similar by using both ALMI (11.4%) and ALM/W (12.4%). Sarcopenic and SO MLWH had lower total T and cFT in both the definition for sarcopenia. BMD was reduced in 43/307 (14.0%). Serum E2 < 18 pg/mL was an independent contributing factor for sarcopenia, SO, and low BMD. Conclusions T and E2 are important determinants of BC even in MLWH. This is among the first studies investigating the distribution of obesity phenotypes and the prevalence of SO among MLWH showing that SO is present in 11-12% of enrolled MLWH regardless of the definition used. However, deep differences emerged using two different diagnostic definitions

PATIENT VOICES, a project for the integration of the systematic assessment of patient reported outcomes and experiences within a comprehensive cancer center: a protocol for a mixed method feasibility study

BACKGROUND: Listening to "patient voices" in terms of symptoms, emotional status and experiences with care, is crucial for patient empowerment in clinical practice. Despite convincing evidence that routine patient reported outcomes and experience measurements (PRMs) with rapid feed-back to oncologists can improve symptom control, patient well-being and cost effectiveness, PRMs are not commonly used in cancer care, due to barriers at various level. Part of these barriers may be overcome through electronic PRMs collection (ePRMs) integrated with the electronic medical record (EMR). The PATIENT VOICES initiative is aimed at achieving a stepwise integration of ePRMs assessment into routine cancer care. The feasibility project presented here is aimed at assessing the knowledge, use and attitudes toward PRMs in a comprehensive cancer centre; developing and assessing feasibility of a flexible system for ePRM assessment; identifying barriers to and developing strategies for implementation and integration of ePRMs clinical practice. METHODS: The project has been organized into four phases: a) pre-development; b) software development and piloting; c) feasibility assessment; d) post-development. A convergent mixed method design, based on concurrent quantitative and qualitative data collection will be applied. A web-survey on health care providers (HCPs), qualitative studies on patients and HCPs (semi-structured interviews and focus groups) as well as longitudinal and cross-sectional quantitative studies will be carried out. The quantitative studies will enroll 600 patients: 200 attending out-patient clinics (physical symptom assessement), 200 attending inpatient wards (psychological distress assessment) and 200 patients followed by multidisciplinary teams (patient experience with care assessment). The Edmonton symptom assessment scale, the Distress Thermometer, and a tool adapted from existing patient reported experience with cancer care questionnaires, will be used in quantitative studies. A multi-disciplinary stakeholder team including researchers, clinicians, health informatics professionals, health system administrators and patients will be involved in the development of potentially effective implementation strategies in the post development phase. DISCUSSION: The documentation of potential advantages and implementation barriers achieved within this feasibility project, will serve as a starting point for future and more focused interventions aimed at achieving effective ePRMs routine assessment in cancer care. TRIAL REGISTRATION: ClinicalTrials.gov ( NCT03968718 ) May 30th, 2019