11 research outputs found

    A polynomial quantum computing algorithm for solving the dualization problem

    Full text link
    Given two prime monotone boolean functions f:{0,1}n{0,1}f:\{0,1\}^n \to \{0,1\} and g:{0,1}n{0,1}g:\{0,1\}^n \to \{0,1\} the dualization problem consists in determining if gg is the dual of ff, that is if f(x1,,xn)=g(x1,xn)f(x_1, \dots, x_n)= \overline{g}(\overline{x_1}, \dots \overline{x_n}) for all (x1,xn){0,1}n(x_1, \dots x_n) \in \{0,1\}^n. Associated to the dualization problem there is the corresponding decision problem: given two monotone prime boolean functions ff and gg is gg the dual of ff? In this paper we present a quantum computing algorithm that solves the decision version of the dualization problem in polynomial time

    Four-month incidence of suicidal thoughts and behaviors among healthcare workers after the first wave of the Spain COVID-19 pandemic

    Get PDF
    [EN] Healthcare workers (HCW) are at high risk for suicide, yet little is known about the onset of suicidal thoughts and behaviors (STB) in this important segment of the population in conjunction with the COVID-19 pandemic. We conducted a multicenter, prospective cohort study of Spanish HCW active during the COVID-9 pandemic. A total of n = 4809 HCW participated at baseline (May–September 2020; i.e., just after the first wave of the pandemic) and at a four-month follow-up assessment (October–December 2020) using web-based surveys. Logistic regression assessed the individual- and population-level associations of separate proximal (pandemic) risk factors with four-month STB incidence (i.e., 30-day STB among HCW negative for 30-day STB at baseline), each time adjusting for distal (pre-pandemic) factors. STB incidence was estimated at 4.2% (SE = 0.5; n = 1 suicide attempt). Adjusted for distal factors, proximal risk factors most strongly associated with STB incidence were various sources of interpersonal stress (scaled 0–4; odds ratio [OR] range = 1.23–1.57) followed by personal health-related stress and stress related to the health of loved ones (scaled 0–4; OR range 1.30–1.32), and the perceived lack of healthcare center preparedness (scaled 0–4; OR = 1.34). Population-attributable risk proportions for these proximal risk factors were in the range 45.3–57.6%. Other significant risk factors were financial stressors (OR range 1.26–1.81), isolation/quarantine due to COVID-19 (OR = 1.53) and having changed to a specific COVID-19 related work location (OR = 1.72). Among other interventions, our findings call for healthcare systems to implement adequate conflict communication and resolution strategies and to improve family-work balance embedded in organizational justice strategies.S

    IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2012

    No full text
    The use of social networking sites, such as Twitter, for various purposes, continues to grown since its first appearance. This social net is a microblogging site to share short messages on a variety of topics. In particular, political elections are a very interesting field to exchange views using this platform. In this paper we have used Spanish elections to investigate the use of Twitter for this purpose, and to find out if the conversations maintained there can anticipate, in some way, the results of the elections. In order to do this, we have developed a tool, called Tara tweet, to define experiments and to capture the defined conversations, and have applied it to the cases of three Spanish elections during 2011 and 2012. Our results show that Twitter is used for political discussion, and that the references to the different political parties correlate, significatively, with the votes of the electors. This is an indicator that Twitter may be used by social researchers as a tool, among others, to predict future results of the elections. Of course, with due caution because the measured data correspond to distinct actions, so obviously, much more research and studies should be done in this field

    The cognitive and psychiatric subacute impairment in severe Covid-19.

    No full text
    Neurologic impairment persisting months after acute severe SARS-CoV-2 infection has been described because of several pathogenic mechanisms, including persistent systemic inflammation. The objective of this study is to analyze the selective involvement of the different cognitive domains and the existence of related biomarkers. Cross-sectional multicentric study of patients who survived severe infection with SARS-CoV-2 consecutively recruited between 90 and 120 days after hospital discharge. All patients underwent an exhaustive study of cognitive functions as well as plasma determination of pro-inflammatory, neurotrophic factors and light-chain neurofilaments. A principal component analysis extracted the main independent characteristics of the syndrome. 152 patients were recruited. The results of our study preferential involvement of episodic and working memory, executive functions, and attention and relatively less affectation of other cortical functions. In addition, anxiety and depression pictures are constant in our cohort. Several plasma chemokines concentrations were elevated compared with both, a non-SARS-Cov2 infected cohort of neurological outpatients or a control healthy general population. Severe Covid-19 patients can develop an amnesic and dysexecutive syndrome with neuropsychiatric manifestations. We do not know if the deficits detected can persist in the long term and if this can trigger or accelerate the onset of neurodegenerative diseases

    Endogenous neural precursor cells in health and disease

    No full text

    Evaluation of a quality improvement intervention to reduce anastomotic leak following right colectomy (EAGLE): pragmatic, batched stepped-wedge, cluster-randomized trial in 64 countries

    No full text
    corecore