Tracking sentiments toward fat acceptance over a decade on Twitter

The fat acceptance (FA) movement aims to counteract weight stigma and discrimination against individuals who are overweight/obese. We developed a supervised neural network model to classify sentiment toward the FA movement in tweets and identify links between FA sentiment and various Twitter user characteristics. We collected any tweet containing either “fat acceptance” or “#fatacceptance” from 2010–2019 and obtained 48,974 unique tweets. We independently labeled 2000 of them and implemented/trained an Average stochastic gradient descent Weight-Dropped Long Short-Term Memory (AWD-LSTM) neural network that incorporates transfer learning from language modeling to automatically identify each tweet’s stance toward the FA movement. Our model achieved nearly 80% average precision and recall in classifying “supporting” and “opposing” tweets. Applying this model to the complete dataset, we observed that the majority of tweets at the beginning of the last decade supported FA, but sentiment trended downward until 2016, when support was at its lowest. Overall, public sentiment is negative across Twitter. Users who tweet more about FA or use FA-related hashtags are more supportive than general users. Our findings reveal both challenges to and strengths of the modern FA movement, with implications for those who wish to reduce societal weight stigma.

Unsupervised Clustering of Continuous Ambient Noise Data to Get Higher Signal Quality in Seismic Surveys

Seismic interferometry has been shown to extract body wave arrivals from ambient noise seismic data. However, surface waves dominate ambient noise data, so cross-correlating and stacking all available data may not succeed in extracting body wave arrivals. A better strategy is to find portions of the data in which body wave energy dominates and to process only those portions. One challenge is that passive seismic recordings comprise huge volumes of data, so identifying portions with strong body-wave energy could be difficult or time-consuming. We use spatio-temporal features, calculated with data recorded by all receivers together, to perform unsupervised clustering. Using data recorded by a dense seismic array in Sweetwater, TX we were able to identify five clusters, representing a subsets of the complete dataset that contain similar features, and extract a 7 km/s body wave arrival from one cluster. This arrival did not emerge when we performed the same cross-correlation and stacking regimen on the entire dataset.

A Deep Learning Ensemble Approach for Automated COVID-19 Detection from Chest CT Images

Background: The aim of this study was to evaluate the performance of an automated COVID-19 detection method based on a transfer learning technique that makes use of chest computed tomography (CT) images. Method: In this study, we used a publicly available multiclass CT scan dataset containing 4171 CT scans of 210 different patients. In particular, we extracted features from the CT images using a set of convolutional neural networks (CNNs) that had been pretrained on the ImageNet dataset as feature extractors, and we then selected a subset of these features using the Information Gain filter. The resulting feature vectors were then used to train a set of k Nearest Neighbors classifiers with 10-fold cross validation to assess the classification performance of the features that had been extracted by each CNN. Finally, a majority voting approach was used to classify each image into two different classes: COVID-19 and NO COVID-19. Results: A total of 414 images of the test set (10% of the complete dataset) were correctly classified, and only 4 were misclassified, yielding a final classification accuracy of 99.04%. Conclusions: The high performance that was achieved by the method could make it feasible option that could be used to assist radiologists in COVID-19 diagnosis through the use of CT images.

Streamflow and weather conditions of seven small coastal watersheds, British Columbia, Canada, 2013–2019

Hydrometeorological observations of small watersheds of the northeast Pacific coastal temperate rainforest (NPCTR) of North America are important to understand land to ocean ecological connections and to provide the scientific basis for regional environmental management decisions. The Hakai Institute operates a densely networked and long-term hydrometeorological monitoring observatory, that fills a spatial data gap in the remote and sparsely gauged outer coast of the NPCTR. Here we present the first five water years (October 2013–October 2019) of hourly streamflow and weather data from seven small (< 13 km2), coastal watersheds. Average yearly rainfall was 3267 mm, resulting in 2317 mm of runoff and 0.1087 km3 of freshwater exports from all seven watersheds per year. However, rainfall and runoff were highly variable depending on location and elevation. The seven watersheds have rainfall-dominated (pluvial) streamflow regimes, streamflow responses are rapid and most water exports are driven by high-intensity fall and winter storm events. Measuring rainfall and streamflow in remote and topographically complex rainforest environments is challenging, hence advanced and novel automated measurement methods were used. These methods, specifically for stream flow measurement allowed us to quantify uncertainty and identify key sources of error, which varied by gauging location. Links to the complete dataset, watershed delineations with metrics, and calculation scripts can be found in Sect. 6 and 7.

Streptococcus pneumoniae: a Plethora of Temperate Bacteriophages With a Role in Host Genome Rearrangement

Bacteriophages (phages) are viruses that infect bacteria. They are the most abundant biological entity on Earth (current estimates suggest there to be perhaps 1031 particles) and are found nearly everywhere. Temperate phages can integrate into the chromosome of their host, and prophages have been found in abundance in sequenced bacterial genomes. Prophages may modulate the virulence of their host in different ways, e.g., by the secretion of phage-encoded toxins or by mediating bacterial infectivity. Some 70% of Streptococcus pneumoniae (the pneumococcus)—a frequent cause of otitis media, pneumonia, bacteremia and meningitis—isolates harbor one or more prophages. In the present study, over 4000 S. pneumoniae genomes were examined for the presence of prophages, and nearly 90% were found to contain at least one prophage, either defective (47%) or present in full (43%). More than 7000 complete putative integrases, either of the tyrosine (6243) or serine (957) families, and 1210 full-sized endolysins (among them 1180 enzymes corresponding to 318 amino acid-long N-acetylmuramoyl-L-alanine amidases [LytAPPH]) were found. Based on their integration site, 26 different pneumococcal prophage groups were documented. Prophages coding for tRNAs, putative virulence factors and different methyltransferases were also detected. The members of one group of diverse prophages (PPH090) were found to integrate into the 3’ end of the host lytASpn gene encoding the major S. pneumoniae autolysin without disrupting it. The great similarity of the lytASpnand lytAPPH genes (85–92% identity) allowed them to recombine, via an apparent integrase-independent mechanism, to produce different DNA rearrangements within the pneumococcal chromosome. This study provides a complete dataset that can be used to further analyze pneumococcal prophages, their evolutionary relationships, and their role in the pathogenesis of pneumococcal disease.

Operating Room Time Prediction: An Application of Latent Class Analysis and Machine Learning

Objective: The objective of this work is to build a prediction model for Operating Room Time (ORT) to be used in an intelligent scheduling system. This prediction is a complex exercise due to its high variability and multiple influential variables. Materials and methods: We assessed a new strategy using Latent Class Analysis (LCA) and clustering methods to identify subgroups of procedures and surgeries that are combined with prediction models to improve ORT estimates. Three tree-based models are assessed, Classification and Regression Trees (CART), Conditional Random Forest (CFOREST) and Gradient Boosting Machine (GBM), under two scenarios: (i) basic dataset of predictors and (ii) complete dataset with binary procedures. To evaluate the model, we use a test dataset and a training dataset to tune parameters. Results and discussion: The best results are obtained with GBM model using the complete dataset and the grouping variables, with an operational accuracy of 57.3% in the test set. Conclusion: The results indicate the GBM model outperforms other models and it improves with the inclusion of the procedures as binary variables and the addition of the grouping variables obtained with LCA and hierarchical clustering that perform the identification of homogeneous groups of procedures and surgeries.

Missing data methods for intensive care unit SOFA scores in electronic health records studies: results from a Monte Carlo simulation

Aim: Missing data cause problems through decreasing sample size and the potential for introducing bias. We tested four missing data methods on the Sequential Organ Failure Assessment (SOFA) score, an intensive care research severity adjuster. Methods: Simulation study using 2015–2017 electronic health record data, where the complete dataset was sampled, missing SOFA score elements imposed and performance examined of four missing data methods – complete case analysis, median imputation, zero imputation (recommended by SOFA score creators) and multiple imputation (MI) – on the outcome of in-hospital mortality. Results: MI performed well, whereas other methods introduced varying amounts of bias or decreased sample size. Conclusion: We recommend using MI in analyses where SOFA score component values are missing in administrative data research.

Palm-based Oleochemicals in Non-Food Applications: Meeting the Environmental Regulatory Challenges

The oleochemical manufacturing is one of the industrial sectors that contributed significantly to the economic growth of the country. Palm oil (PO) and palm kernel oil (PKO) have been utilised as feedstocks for production of the five basic oleochemicals, namely fatty acid, methyl ester, fatty alcohol, fatty amine and glycerol. These basic oleochemicals could be used without further treatment or they could be processed further for the purpose of purification and improving functionality, and then formulated with other ingredients into finished products. The industries, however, are challenged with the growing concern on safety, toxicity and eco-toxicity level, biodegradability profile and hence marketability of the products and new technologies developed. While Malaysian Palm Oil Board (MPOB)’s R & D focused on developing various oleochemicals derivatives, the method to assess the environmental impact of the production of these products are also being looked into together with process feasibility and technology viability study. With a complete entity comprises of laboratories, facilities and expertise, MPOB can now assist Malaysian manufacturers/exporters to establish a complete dataset on biodegradation, ecotoxicity and life cycle assessment (LCA) to facilitate the market access of their products and ensure conformation to regulation set by importing countries.

Birds specimens of the zoological collection of the Universidad de Nariño, Colombia

We provide a complete dataset of bird specimens of the zoological collection at the Universidad de Nariño, Colombia. For every specimen, we reviewed taxonomic identifications to species level by applying curatorial procedures, including the comparison of skins, the use of taxonomic keys and primary literature, and by confirming georeferenced locality data. We present 1249 specimens from 419 species. Most records come from ecosystems in southwestern Colombia, department of Nariño. All records are in the Darwin Core standard and have been made available through the Colombian biodiversity portal (SiB-Colombia) and the GBIF. In addition, we projected these bird occurrences in a geographic context to analyze the density, representation of ecosystems, biogeographic regions, and administrative units (municipalities). We also examine the representation of relevant species regarding their endemism, migratory, or conservation status. With this information, we want to support research and training initiatives to support ecological planning with biogeographic approaches to understand the temporal changes in bird faunas.

Late reinfection with a different severe acute respiratory syndrome coronavirus-2 clade in a patient with refractory arterial hypertension: a case report

Background Differentiating between persistent infection with intermittent viral shedding and reinfection with severe acute respiratory syndrome coronavirus 2 remains challenging. Although a small number of cases with genomic evidence of second infection have been reported, limited information exists on frequency and determinants of reinfection, time between infections, and duration of immunity after the primary infection. Case presentation We report a reinfection with severe acute respiratory syndrome coronavirus 2 in a 52-year-old caucasian male whose primary infection was diagnosed in May 2020, during the first wave of the pandemic in Spain, and the second occurred 8 months later, in January 2021. We present a complete dataset including results from real-time polymerase chain reaction, serology, and genome sequencing confirming reinfection with a different clade. Noteworthy was that the patient was immunocompetent but had multiple cardiometabolic comorbidities, including refractory arterial hypertension, that might increase the individual risk in coronavirus disease 2019. Conclusions This case of reinfection with severe acute respiratory syndrome coronavirus 2 occurring several months after the primary infection reports the longest time interval between reinfection and initial infection described to date. It raises concerns on the duration of protective immunity, suggesting that it may begin to wane in patients who acquired the initial infection during the first wave of the pandemic. The potential contributing role of arterial hypertension and cardiometabolic comorbidities as risk factors for reinfection deserves investigation.