Changing influenza activity in the Southern hemisphere countries during the COVID-19 pandemic

Introduction: While the reduction in influenza cases in the Northern hemisphere in 2020 has been widely reported, the influenza transmission dynamics in the Southern hemisphere remain uncharacterized. Methods: This study analysed the change in influenza-positive proportion (IPP) between 2010–2019 and 2020 in countries in the Southern hemisphere with ≤40% missing IPP data in FluNet to assess how coronavirus disease 2019 (COVID-19) relates to influenza activity. The analysis considered the incidence of COVID-19 reported by the World Health Organization and the implementation date of non-pharmaceutical interventions (NPIs) reported by the Oxford COVID-19 Government Response Tracker. Results: In each of the seven included countries, the average IPP was lower in 2020 than in 2010–2019 (P < 0.01), with the largest difference being 31.1% (95% confidence interval 28.4–33.7%). In Argentina, Bolivia, Chile and South Africa, higher IPPs were observed during epidemiological weeks 4–16 in 2020 compared with the same weeks in 2010–2019. The IPP increased after NPIs were implemented in Argentina and South Africa, but started to decline in Bolivia, Chile, Madagascar and Paraguay before NPI implementation. Conclusions: Influenza burden and activity decreased in 2020 in the Southern hemisphere. The temporal decline in influenza activity varied between countries

Machine learning application for classification of Alzheimer's disease stages using 18F-flortaucipir positron emission tomography

Abstract Background The progression of Alzheimer’s dementia (AD) can be classified into three stages: cognitive unimpairment (CU), mild cognitive impairment (MCI), and AD. The purpose of this study was to implement a machine learning (ML) framework for AD stage classification using the standard uptake value ratio (SUVR) extracted from 18F-flortaucipir positron emission tomography (PET) images. We demonstrate the utility of tau SUVR for AD stage classification. We used clinical variables (age, sex, education, mini-mental state examination scores) and SUVR extracted from PET images scanned at baseline. Four types of ML frameworks, such as logistic regression, support vector machine (SVM), extreme gradient boosting, and multilayer perceptron (MLP), were used and explained by Shapley Additive Explanations (SHAP) to classify the AD stage. Results Of a total of 199 participants, 74, 69, and 56 patients were in the CU, MCI, and AD groups, respectively; their mean age was 71.5 years, and 106 (53.3%) were men. In the classification between CU and AD, the effect of clinical and tau SUVR was high in all classification tasks and all models had a mean area under the receiver operating characteristic curve (AUC) > 0.96. In the classification between MCI and AD, the independent effect of tau SUVR in SVM had an AUC of 0.88 (p < 0.05), which was the highest compared to other models. In the classification between MCI and CU, the AUC of each classification model was higher with tau SUVR variables than with clinical variables independently, which yielded an AUC of 0.75(p < 0.05) in MLP, which was the highest. As an explanation by SHAP for the classification between MCI and CU, and AD and CU, the amygdala and entorhinal cortex greatly affected the classification results. In the classification between MCI and AD, the para-hippocampal and temporal cortex affected model performance. Especially entorhinal cortex and amygdala showed a higher effect on model performance than all clinical variables in the classification between MCI and CU. Conclusions The independent effect of tau deposition indicates that it is an effective biomarker in classifying CU and MCI into clinical stages using MLP. It is also very effective in classifying AD stages using SVM with clinical information that can be easily obtained at clinical screening

Arctic Primary Aerosol Production Strongly Influenced by Riverine Organic Matter

The sources of primary and secondary aerosols in the Arctic are still poorly known. A number of surface seawater samples-with varying degrees of Arctic riverine and sea ice influences-were used in a sea spray generation chamber to test them for their potential to produce sea spray aerosols (SSA) and cloud condensation nuclei (CCN). Our interdisciplinary data showed that both sea salt and organic matter (OM) significantly influenced the SSA production. The number concentration of SSA in the coastal samples was negatively correlated with salinity and positively correlated with a number of OM tracers, including dissolved and chromophoric organic carbon (DOC, CDOM), marine microgels and chlorophyll a (Chl-a) but not for viral and bacterial abundances; indicating that OM of riverine origin enhances primary aerosol production. When all samples were considered, transparent exopolymer particles (TEP) were found to be the best indicator correlating positively with the ratio number concentration of SSA/salinity. CCN efficiency was not observed to differ between the SSA from the various samples, despite differences in organic characteristics. It is suggested that the large amount of freshwater from river runoff have a substantial impact on primary aerosols production mechanisms, possibly affecting the cloud radiative forcing

Predicting progression to dementia with &quot;comprehensive visual rating scale&quot; and machine learning algorithms

Background and ObjectiveIdentifying biomarkers for predicting progression to dementia in patients with mild cognitive impairment (MCI) is crucial. To this end, the comprehensive visual rating scale (CVRS), which is based on magnetic resonance imaging (MRI), was developed for the assessment of structural changes in the brains of patients with MCI. This study aimed to investigate the use of the CVRS score for predicting dementia in patients with MCI over a 2-year follow-up period using various machine learning (ML) algorithms. MethodsWe included 197 patients with MCI who were followed up more than once. The data used for this study were obtained from the Japanese-Alzheimer&apos;s Disease Neuroimaging Initiative study. We assessed all the patients using their CVRS scores, cortical thickness data, and clinical data to determine their progression to dementia during a follow-up period of over 2 years. ML algorithms, such as logistic regression, random forest (RF), XGBoost, and LightGBM, were applied to the combination of the dataset. Further, feature importance that contributed to the progression from MCI to dementia was analyzed to confirm the risk predictors among the various variables evaluated. ResultsOf the 197 patients, 108 (54.8%) showed progression from MCI to dementia. Tree-based classifiers, such as XGBoost, LightGBM, and RF, achieved relatively high performance. In addition, the prediction models showed better performance when clinical data and CVRS score (accuracy 0.701-0.711) were used than when clinical data and cortical thickness (accuracy 0.650-0.685) were used. The features related to CVRS helped predict progression to dementia using the tree-based models compared to logistic regression. ConclusionsTree-based ML algorithms can predict progression from MCI to dementia using baseline CVRS scores combined with clinical data.N