Simulation is a key component of population genetics. It helps to train our intuition and is important for the development, testing and comparison of inference methods. Because population genetic models such as the ancestral recombination and selection graphs (Griffiths & Marjoram, 1997;Neuhauser & Krone, 1997) are computationally intractable for inference but relatively easy to simulate, simulations are also heavily used for parameter inference.Approximate Bayesian Computation (ABC; Beaumont et al., 2002) is a widely used example. Regardless of the application, the goal is to simulate data that is 'realistic' in the sense that it resembles real data from the population(s) of interest. Typically this is done by fixing some parameters that are fairly well-known, then choosing other parameters to match some property of the real data, usually based on summary statistics. However, this involves a potential loss of information in the reduction in summary statistics and then an implicit weighting on the relative importance of different summary statistics. Often, parameters that create simulations that match one type of summary statistic (e.g. the site frequency spectrum) do not match others (e.g. linkage disequilibrium patterns; Beichman et al., 2017). Here, we present a novel parameter learning approach using Generative Adversarial Networks (GANs). Our approach creates both realistic simulated data and a quantitative way of determining

Geriatric depression is more common in nursing homes and social support is a mechanism that mitigates the stressors of life factors and simultaneously promotes wellness and health. The purpose of the study was to assess the levels of depression and social support among elderly in nursing homes. During the period February 2016-March 2016 170 elderly residents in nursing homes completed the Geriatric Depression Scale-15 (GDS-15) and the Multidimensional Scale of Perceived Social Support (MSPSS). Statistical analysis was conducted with IBM SPSS Statistics 23. 37, 1% of the sample had depressive symptoms. Depression is statistically correlated with age and it is affected by the years of education (p = 0.003), the number of the children (p = 0.006), whether the elderly person is bedridden or not (p < 0.001), the frequency of visits by family members (p < 0.001) and whether the elderly performs activities outside the nursing home (0.001). Higher GDS score had those who were illiterate (6.41), those with one or no children (6.82 and 6.59 respectively), the bedridden (6.70), people without visits from relatives (7.69) and without activities outside (5.64). Also, social support is affected by the family status (p < 0.001), the number of children (p < 0.001), the frequency of visits by relatives (p < 0.001) and whether the elderly performs activities outside the foundation (p < 0.008). Higher MSPSS score had those who were married (61.60), those who had four children (63.50), people who accept visits from relatives every day (64.58) and people who do activities outside the institution (58.07). The appearance of this increased rate of depression symptoms in this elderly population leads to the need for more aid social support.

The purpose of this research was to explore the association between state and trait anxiety experienced by patients who had undergone traumatic amputation and their family caregivers. The sample studied consisted of 50 hospitalized patients who had undergone traumatic amputation and 50 family caregivers. The collected data included patients’ and caregivers’ characteristics and the State Trait Anxiety Inventory scores. Fifty percent of patients and caregivers scored below 50 and 47, respectively (median), in trait anxiety. In terms of state anxiety, at least 50% of patients and caregivers scored below 56 and 50.5, respectively. These values indicate moderate to high levels of the impact of amputation on the trait and state anxiety of amputees and their caregivers. A positive linear correlation was found between the trait and state anxiety of the patients as well as between the trait and state anxiety of caregivers, as expected (ρ = 0.915, P < .001, and ρ = 0.920, P < .001, respectively). A statistically significant positive correlation was also observed between state patient anxiety and state anxiety of caregivers (ρ = 0.239 and P = .039) and between trait patient anxiety and trait anxiety of caregivers (ρ = 0.322 and P = .030). More specifically, as the patient’s anxiety score (either trait temporary) increases, the score of the caregivers’ anxiety increases and vice versa. Nurses should be aware of the association between anxiety of amputees and caregivers and, therefore, work in multidisciplinary teams to maximize clinical outcomes for patients after amputation and their families.

Population genetics relies heavily on simulated data for validation, inference, and intuition. In particular, since real data is always limited, simulated data is crucial for training machine learning methods. Simulation software can accurately model evolutionary processes, but requires many hand-selected input parameters. As a result, simulated data often fails to mirror the properties of real genetic data, which limits the scope of methods that rely on it. In this work, we develop a novel approach to estimating parameters in population genetic models that automatically adapts to data from any population. Our method is based on a generative adversarial network that gradually learns to generate realistic synthetic data. We demonstrate that our method is able to recover input parameters in a simulated isolation-with-migration model. We then apply our method to human data from the 1000 Genomes Project, and show that we can accurately recapitulate the features of real data.

IntroductIon This study aimed to investigate the knowledge, practices and belief of psychiatric nurses and nurses' assistants towards patients' smoking habits and clinical practice.Methods A questionnaire-based study was conducted among psychiatric nurses and nurses' assistants working in two major psychiatric hospitals in Athens from January to March 2015. The final sample consisted 297 psychiatric nurses and nurses' assistants who were current smokers and agreed to complete the anonymous questionnaire.results The majority of nurses noted that the psychiatric patient (always 45.5%, sometimes 34.3%) should be excluded from the ban on smoking in hospitals. Various practices were noted among nurses concerning the assessment of patients' smoking history smoking habits and cessation plans. Nurses had some knowledge about the health effects of smoking (96%) and feel responsible to help their patient quit smoking (37.4%). However they proved to be unaware of the relation between smoking, psychiatric symptoms and psychotropic medication. The findings indicated that almost half of psychiatric nurses smoke in their work environment and are against the application of the anti-smoking law in psychiatric hospitals (42.4%), as they believe that psychiatric patients should be handled different from other patients even though they are aware of the dangers of smoking (56.6%).conclusIons Our survey showed the existence of a number of misperceptions concerning psychiatric patients and a serious deficiency in knowledge on tobacco dependency and quitting. Mental health nurses should have a key-role in patient smoking cessation and should also act as role models for their patients. Future research should focus on the implementation of smoking cessation training programs focused on changing nurses' attitudes and beliefs towards their patients' addiction to tobacco. IntroductIonFollowing European and International standards, Greece has developed its legislation banning smoking inside hospitals and other health services since 2002, targeted on limiting smoking and protecting public health (law 3730/2008 and 3370/2005). According to the tobacco control legislation, smoking tobacco products are prohibited in all public or private healthcare services, enclosed or covered 1 . According to mental health care reform in Greece, psychiatric hospitals will be closed for their greater part and mental health care units will be integrated into general hospitals, during the following year (2014)(2015), with psychiatric nurses integrated into the larger team of nursing staff 2 . The psychiatric care reform is not yet completed. Due to the economic depression, an indefinite extension for the implementation of the psychiatric care reform program is in place. The situation of psychiatric hospitals remains the same and psychiatric nurses undertake the majority of the daily workload. Despite the legislation, smoking has not been eliminated in Greek hospitals, especially in mental health care settings, and prevalence of smoking among healthcare wo...