In order to study transport in complex environments, it is extremely important to determine the physical mechanism underlying diffusion and precisely characterize its nature and parameters. Often, this task is strongly impacted by data consisting of trajectories with short length (either due to brief recordings or previous trajectory segmentation) and limited localization precision. In this paper, we propose a machine learning method based on a random forest architecture, which is able to associate single trajectories to the underlying diffusion mechanism with high accuracy. In addition, the algorithm is able to determine the anomalous exponent with a small error, thus inherently providing a classification of the motion as normal or anomalous (subor super-diffusion). The method provides highly accurate outputs even when working with very short trajectories and in the presence of experimental noise. We further demonstrate the application of transfer learning to experimental and simulated data not included in the training/test dataset. This allows for a full, high-accuracy characterization of experimental trajectories without the need of any prior information.In the last decades, the research in biophysics has conveyed large efforts toward the development of experimental techniques allowing the visualization of biological processes one molecule at a time [1-4]. These efforts have been mainly driven by the concept that ensemble-averaging hides important features that are relevant for cellular function. Somehow expectedly, experiments performed by means of these techniques have shown a large heterogeneity in the behavior of biological molecules, thus fully justifying the use of these raffinate tools.Besides, experiments performed using single particle tracking [3] have revealed that even chemically-identical molecules in biological media can display very different behaviors, as a consequence of the complex environment where diffusion takes place. By way of example, this heterogeneity is reflected in the broad distribution of dynamic parameters of distinct individual trajectories corresponding to the same molecular species, such as the diffusion coefficient, well above stochastic indetermination. Typically, the trajectories are analyzed by quantifying the (time-averaged) mean square displacement (tMSD) as a function of the time lag τ [5]:The calculation of this quantity-expected to scale linearly for a Brownian walker in a homogeneous environment-has provided a ubiquitous evidence of anomalous behaviors in biological systems, characterized by an asymptotic nonlinear scaling of the tMSD curve d t a 2. More experiments have shown that the anomalous exponent can vary from particle to particle ( figure 1(a)) as a consequence of molecular interactions and that these changes can be experienced by the same particle in space/time [6]. Several methods have been OPEN ACCESS RECEIVED

This multicenter cohort study investigated the differences between coronavirus disease 2019 (COVID-19) related symptoms and post-COVID symptoms between male and female COVID-19 survivors. Clinical and hospitalization data were collected from hospital medical records in a sample of individuals recovered from COVID-19 at five public hospitals in Spain. A predefined list of post-COVID symptoms was systematically assessed, but patients were free to report any symptom. Anxiety/depressive levels and sleep quality were also assessed. Adjusted multivariate logistic regressions were used to identify the association of sex with post-COVID related-symptoms. A total of 1969 individuals (age: 61, SD: 16 years, 46.4% women) were assessed 8.4 months after discharge. No overall significant sex differences in COVID-19 onset symptoms at hospital admission were found. Post-COVID symptoms were present in up to 60% of hospitalized COVID-19 survivors eight months after the infection. The number of post-COVID symptoms was 2.25 for females and 1.5 for males. After adjusting by all variables, female sex was associated with ≥3 post-COVID symptoms (adj OR 2.54, 95%CI 1.671–3.865, p < 0.001), the presence of post-COVID fatigue (adj OR 1.514, 95%CI 1.040–2.205), dyspnea (rest: adj OR 1.428, 95%CI 1.081–1.886, exertion: adj OR 1.409, 95%CI 1.109–1.791), pain (adj OR 1.349, 95%CI 1.059–1.720), hair loss (adj OR 4.529, 95%CI 2.784–7.368), ocular problems (adj OR 1.981, 95%CI 1.185–3.312), depressive levels (adj OR 1.606, 95%CI 1.002–2.572) and worse sleep quality (adj OR 1.634, 95%CI 1.097–2.434). Female sex was a risk factor for the development of some long-term post-COVID symptoms including mood disorders. Healthcare systems should consider sex differences in the management of long haulers.

No abstract

This study investigated the prevalence of long-term musculoskeletal post-COVID pain and their risk factors in a large cohort of COVID-19 survivors. A multicenter cohort study including patients hospitalised because of COVID-19 in 5 hospitals of Madrid (Spain) during the first wave of the pandemic was conducted. Hospitalisation and clinical data were collected from medical records. Patients were scheduled for a telephone interview after hospital discharge for collecting data about the musculoskeletal post-COVID pain. Anxiety/depressive levels and sleep quality were likewise assessed. From 2000 patients recruited, a total of 1969 individuals (46.4% women, age: 61 years, SD: 16 years) were assessed on average at 8.4 (SD: 1.5) months after discharge. At the time of the study, 887 (45% women) reported musculoskeletal post-COVID pain. According to the presence of previous pain symptoms, the prevalence of "de novo" (new-onset) musculoskeletal post-COVID pain was 74.9%, whereas 25.1% experienced an increase in previous symptoms (exacerbated COVID-related pain). Female sex (odds ratio [OR]: 1.349, 95% confidence interval [CI]: 1.059-1.720), history of musculoskeletal pain (OR 1.553, 95% CI 1.271-1.898), presence of myalgia (OR 1.546, 95% CI 1.155-2.070) and headache (1.866, 95% CI 1.349-2.580) as COVID-19-associated onset symptoms, and days at hospital (OR 1.013, 95% CI 1.004-1.022) were risk factors associated with musculoskeletal post-COVID pain. In conclusion, musculoskeletal post-COVID pain is present in 45.1% of COVID-19 survivors at 8 months after hospital discharge with most patients developing de novo post-COVID pain. Female sex, history of musculoskeletal pain, presence of myalgia and headache as COVID-19 symptoms at the acute phase, and days at hospital were risk factors associated with musculoskeletal post-COVID pain.

No abstract

The quantum autoencoder is a recent paradigm in the field of quantum machine learning, which may enable an enhanced use of resources in quantum technologies. To this end, quantum neural networks with less nodes in the inner than in the outer layers were considered. Here, we propose a useful connection between quantum autoencoders and quantum adders, which approximately add two unknown quantum states supported in different quantum systems. Specifically, this link allows us to employ optimized approximate quantum adders, obtained with genetic algorithms, for the implementation of quantum autoencoders for a variety of initial states. Furthermore, we can also directly optimize the quantum autoencoders via genetic algorithms. Our approach opens a different path for the design of quantum autoencoders in controllable quantum platforms.

Objective: Anemia is a frequent comorbidity in hemodialysis patients that can be successfully treated by administering erythropoiesis-stimulating agents (ESAs). ESAs dosing is currently based on clinical protocols that often do not account for the high inter-and intra-individual variability in the patient's response. As a result, the hemoglobin level of some patients oscillates around the target range, which is associated with multiple risks and side-effects. This work proposes a methodology based on reinforcement learning (RL) to optimize ESA therapy. Methods: RL is a data-driven approach for solving sequential decision-making problems that are formulated as Markov decision processes (MDPs). Computing optimal drug administration strategies for chronic diseases is a sequential decision-making problem in which the goal is to find the best sequence of drug doses. MDPs are particularly suitable for modeling these problems due to their ability to capture the uncertainty associated with the outcome of the treatment and the stochastic nature of the underlying process. The RL algorithm employed in the proposed methodology is fitted Q iteration, which stands out for its ability to make an efficient use of data. Results:The experiments reported here are based on a computational model that describes the effect of ESAs on the hemoglobin level. The performance of the proposed method is evaluated and compared with the well-known Q-learning algorithm and with a standard protocol. Simulation results show that the performance of Q-learning is substantially lower than FQI and the protocol. When comparing FQI and the protocol, FQI achieves an increment of 27.6% in the proportion of patients that are within the targeted range of hemoglobin during the period of treatment. In addition, the quantity of drug needed is reduced by 5.13%, which indicates a more efficient use of ESAs. Conclusion: Although prospective validation is required, promising results demonstrate the potential of RL to become an alternative to current protocols.

We propose a quantum machine learning algorithm for efficiently solving a class of problems encoded in quantum controlled unitary operations. The central physical mechanism of the protocol is the iteration of a quantum time-delayed equation that introduces feedback in the dynamics and eliminates the necessity of intermediate measurements. The performance of the quantum algorithm is analyzed by comparing the results obtained in numerical simulations with the outcome of classical machine learning methods for the same problem. The use of time-delayed equations enhances the toolbox of the field of quantum machine learning, which may enable unprecedented applications in quantum technologies.