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.

We propose the use of support vector machines (SVMs) for automatic hyperspectral data classification and knowledge discovery. In the first stage of the study, we use SVMs for crop classification and analyze their performance in terms of efficiency and robustness, as compared to extensively used neural and fuzzy methods. Efficiency is assessed by evaluating accuracy and statistical differences in several scenes. Robustness is analyzed in terms of: (1) suitability to working conditions when a feature selection stage is not possible and (2) performance when different levels of Gaussian noise are introduced at their inputs. In the second stage of this work, we analyze the distribution of the support vectors (SVs) and perform sensitivity analysis on the best classifier in order to analyze the significance of the input spectral bands. For classification purposes, six hyperspectral images acquired with the 128-band HyMAP spectrometer during the DAISEX-1999 campaign are used. Six crop classes were labeled for each image. A reduced set of labeled samples is used to train the models, and the entire images are used to assess their performance. Several conclusions are drawn: (1) SVMs yield better outcomes than neural networks regarding accuracy, simplicity, and robustness; (2) training neural and neurofuzzy models is unfeasible when working with high-dimensional input spaces and great amounts of training data; (3) SVMs perform similarly for different training subsets with varying input dimension, which indicates that noisy bands are successfully detected; and (4) a valuable ranking of bands through sensitivity analysis is achieved.

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 (sub- or 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.

The theory of extreme learning machine (ELM) has become very popular on the last few years. ELM is a new approach for learning the parameters of the hidden layers of a multilayer neural network (as the multilayer perceptron or the radial basis function neural network). Its main advantage is the lower computational cost, which is especially relevant when dealing with many patterns defined in a high-dimensional space. This brief proposes a Bayesian approach to ELM, which presents some advantages over other approaches: it allows the introduction of a priori knowledge; obtains the confidence intervals (CIs) without the need of applying methods that are computationally intensive, e.g., bootstrap; and presents high generalization capabilities. Bayesian ELM is benchmarked against classical ELM in several artificial and real datasets that are widely used for the evaluation of machine learning algorithms. Achieved results show that the proposed approach produces a competitive accuracy with some additional advantages, namely, automatic production of CIs, reduction of probability of model overfitting, and use of a priori knowledge.