With resting-state functional MRI (rs-fMRI) there are a variety of post-processing methods that quantify the human brain connectome. However, there is a choice of which preprocessing steps will be used prior to calculating the functional connectivity of the brain,. In this paper, we have tested seven different preprocessing schemes and assessed the reliability between and reproducibility within the various strategies by means of graph theoretical measures. Different schemes were tested on a publicly available dataset with rs-fMRI of healthy controls. The brain was parcellated into 190 nodes and four graph theoretical (GT) measures were calculated; global efficiency (GEFF), characteristic path length (CPL), average clustering coefficient (ACC), and average local efficiency (ALE). Our findings indicate that results can significantly differ based on which preprocessing steps are selected. We also found dependence between motion and GT measurements in most preprocessing strategies. We conclude that with the use of censoring based on outliers within the functional time-series, results indicate an increase in reliability of GT measurements with a reduction in dependency with head motion.

Objective: To investigate the influence of aging on conventional MRI and magnetic resonance spectroscopy (MRS) findings of mucopolysaccharidosis (MPS) patients and to test the correlation of enzyme levels, urinary glycosaminoglycans (GAG), and neuroimaging findings. Methods: Sixty patients with MPS types I (n = 8), II (n = 31), IV-A (n = 4), and VI (n = 17) underwent T2, fluid-attenuated inversion recovery (FLAIR), and MRS of the brain. For analysis of MRI variables, we measured the normalized cerebral volume (NCV), CSF volume (NCSFV), ventricular volume (NVV), and lesion load (NLL) on FLAIR using semiautomated and automated segmentation techniques. For MRS, a point-resolved spectroscopy technique was used. Voxels were positioned at the white and gray matter. Statistical analysis involved Pearson or Spearman tests for correlation between neuroimaging, age, enzyme levels, and urinary GAG. Results: The median age at onset of the disease was 20 months. Patients with longer disease duration had more NLL in the white matter (r = 0.28, p = 0.03), and this difference was more pronounced in MPS II patients (r = 0.44, p = 0.02). Metabolites ratios in MRS, NCV, NCSFV, and NVV did not correlate with disease duration or age of the patients (p > 0.05). MRI and MRS variables in either the white or the gray matter did not correlate with enzymatic activity or GAG levels. Patients with MPS II had a lower mean NCV (p < 0.001). Conclusions: Our data showed that white matter lesion is more extensive as disease duration increases, especially in mucopolysaccharidosis type II patients. MRI and magnetic resonance spectroscopy findings did not correlate with either enzymatic or glycosaminoglycan levels.

(1) Background: This paper aims to present and discuss the most significant challenges encountered by STEM professionals associated with remote working during the COVID-19 lockdowns. (2) Methods: We performed a qualitative analysis of 921 responses from professionals from 76 countries to the open-ended question: “What has been most challenging during the lockdown for you, and/or your family?” (3) Findings: Participants reported challenges within the immediate family to include responsibilities for school, childcare, and children’s wellbeing; and the loss of social interactions with family and friends. Participants reported increased domestic duties, blurred lines between home and work, and long workdays. Finding adequate workspace was a problem, and adaptations were necessary, especially when adults shared the same setting for working and childcare. Connectivity issues and concentration difficulties emerged. While some participants reported employers’ expectations did not change, others revealed concerns about efficiency. Mental health issues were expressed as anxiety and depression symptoms, exhaustion and burnout, and no outlets for stress. Fear of becoming infected with COVID-19 and uncertainties about the future also emerged. Pressure points related to gender, relationship status, and ethnicities were also evaluated. Public policies differed substantially across countries, raising concerns about the adherence to unnecessary restrictions, and similarly, restrictions being not tight enough. Beyond challenges, some benefits emerged, such as increased productivity and less time spent getting ready for work and commuting. Confinement resulted in more quality time and stronger relationships with family. (4) Interpretation: Viewpoints on positive and negative aspects of remote working differed by gender. Females were more affected professionally, socially, and personally than males. Mental stress and the feeling of inadequate work efficiency in women were caused by employers’ expectations and lack of flexibility. Working from home turned out to be challenging, primarily due to a lack of preparedness, limited access to a dedicated home-office, and lack of previous experience in multi-layer/multi-scale environments.

Both reverse transcription-PCR (RT-PCR) and chest X-rays are used for the diagnosis of the coronavirus disease-2019 (COVID-19). However, COVID-19 pneumonia does not have a defined set of radiological findings. Our work aims to investigate radiomic features and classification models to differentiate chest X-ray images of COVID-19-based pneumonia and other types of lung patterns. The goal is to provide grounds for understanding the distinctive COVID-19 radiographic texture features using supervised ensemble machine learning methods based on trees through the interpretable Shapley Additive Explanations (SHAP) approach. We use 2,611 COVID-19 chest X-ray images and 2,611 non-COVID-19 chest X-rays. After segmenting the lung in three zones and laterally, a histogram normalization is applied, and radiomic features are extracted. SHAP recursive feature elimination with cross-validation is used to select features. Hyperparameter optimization of XGBoost and Random Forest ensemble tree models is applied using random search. The best classification model was XGBoost, with an accuracy of 0.82 and a sensitivity of 0.82. The explainable model showed the importance of the middle left and superior right lung zones in classifying COVID-19 pneumonia from other lung patterns.