Abstract
Breast Ultrasound has long been used to support diagnostic and exploratory procedures concerning breast cancer, with an interesting success rate, specially when complemented with other radiology information. This usability can further enhance visualization tasks during pre-treatment clinical analysis by coupling the B-Mode images to 3D space, as found in Magnetic Resonance Imaging (MRI) per instance. In fact, Lesions in B-mode are visible and present high detail when comparing with other 3D sequences. This coupling, however, would be largely benefited from the ability to match the various structures present in the B-Mode, apart from the broadly studied lesion. In this work we focus on structures such as skin, subcutaneous fat, mammary gland and thoracic region. We provide a preliminary insight to several structure segmentation approaches in the hopes of obtaining a functional and dependable pipeline for delineating these potential reference regions that will assist in multi-modal radiological data alignment. For this, we experiment with pre-processing stages that include Anisotropic Diffusion guided by Log-Gabor filters (ADLG) and main segmentation steps using K-Means, Meanshift and Watershed. Among the pipeline configurations tested, the best results were found using the ADLG filter that ran for 50 iterations and H-Maxima suppression of 20% and the K-Means method with $$K=6$$. The results present several cases that closely approach the ground truth despite overall having larger average errors. This encourages the experimentation of other approaches that could withstand the innate data variability that makes this task very challenging. © Springer Nature Switzerland AG 2020.

Abstract
Large amounts of mobility data are being generated from many different sources, and several data mining methods have been proposed for this data. One of the most critical steps for trajectory data mining is segmentation. This task can be seen as a pre-processing step in which a trajectory is divided into several meaningful consecutive sub-sequences. This process is necessary because trajectory patterns may not hold in the entire trajectory but on trajectory parts. In this work we propose a supervised trajectory segmentation algorithm, called Wise Sliding Window Segmentation (WS-II). It processes the trajectory coordinates to find behavioral changes in space and time, generating an error signal that is further used to train a binary classifier for segmenting trajectory data. This algorithm is flexible and can be used in different domains. We evaluate our method over three real datasets from different domains (meteorology, fishing, and individuals movements), and compare it with four other trajectory segmentation algorithms: OWS, GRASP-UTS, CB-SMoT, and SPD. We observed that the proposed algorithm achieves the highest performance for all datasets with statistically significant differences in terms of the harmonic mean of purity and coverage. © Springer Nature Switzerland AG 2020.

Abstract
Background Palliative care is an essential part of medical practice but it remains limited, inaccessible, or even absent in low and middle income countries. Objectives To evaluate the general knowledge, attitudes, and practices of Mozambican physicians on palliative care. Methods A cross-sectional observational study was conducted between August 2018 and January 2019 in the 3 main hospitals of Mozambique, in addition to the only hospital with a standalone palliative care service. Data was collected from a self-administered survey directed to physicians in services with oncology patients. Results Two hundred and seven out of 306 physicians surveyed answered the questionnaire. The median physician age was 38 years. Fifty-five percent were males, and 49.8% residents. The most common medical specialty was surgery with 26.1%. Eighty percent of physicians answered that palliative care should be provided to patients when no curative treatments are available; 87% believed that early integration of palliative care can improve patients' quality of life; 73% regularly inform patients of a cancer diagnosis; 60% prefer to inform the diagnosis and prognosis to the family/caregivers. Fifty percent knew what a "do-not-resuscitate" order is, and 51% knew what palliative sedation is. Only 25% of the participants answered correctly all questions on palliative care general knowledge, and only 24% of the participants knew all answers about euthanasia. Conclusions Mozambican physicians in the main hospitals of Mozambique have cursory knowledge about palliative care. Paternalism and the family-centered model are the most prevalent. More interventions and training of professionals are needed to improve palliative care knowledge and practice in the country.

Abstract

Abstract
INTRODUCTION: The human genome contains tens of thousands of rare (minor allele frequency <1%) variants, some of which contribute to disease risk. Using 838 samples with whole-genome and multitissue transcriptome sequencing data in the Genotype-Tissue Expression (GTEx) project version 8, we assessed how rare genetic variants contribute to extreme patterns in gene expression (eOutliers), allelic expression (aseOutliers), and alternative splicing (sOutliers). We integrated these three signals across 49 tissues with genomic annotations to prioritize high-impact rare variants (RVs) that associate with human traits. RATIONALE: Outlier gene expression aids in identifying functional RVs. Transcriptome sequencing provides diverse measurements beyond gene expression, including allele-specific expression and alternative splicing, which can provide additional insight into RV functional effects. RESULTS: After identifying multitissue eOutliers, aseOutliers, and sOutliers, we found that outlier individuals of each type were significantly more likely to carry an RV near the corresponding gene. Among eOutliers, we observed strong enrichment of rare structural variants. sOutliers were particularly enriched for RVs that disrupted or created a splicing consensus sequence. aseOutliers provided the strongest enrichment signal when evaluated from just a single tissue. We developed Watershed, a probabilistic model for personal genome interpretation that improves over standard genomic annotation–based methods for scoring RVs by integrating these three transcriptomic signals from the same individual and replicates in an independent cohort. To assess whether outlier RVs identified in GTEx associate with traits, we evaluated these variants for association with diverse traits in the UK Biobank, the Million Veterans Program, and the Jackson Heart Study. We found that transcriptome-assisted prioritization identified RVs with larger trait effect sizes and were better predictors of effect size than genomic annotation alone. CONCLUSION: With >800 genomes matched with transcriptomes across 49 tissues, we were able to study RVs that underlie extreme changes in the transcriptome. To capture the diversity of these extreme changes, we developed and integrated approaches to identify expression, allele-specific expression, and alternative splicing outliers, and characterized the RV landscape underlying each outlier signal. We demonstrate that personal genome interpretation and RV discovery is enhanced by using these signals. This approach provides a new means to integrate a richer set of functional RVs into models of genetic burden, improve disease gene identification, and enable the delivery of precision genomics.

Abstract
Current tools for self-management of chronic obstructive respiratory diseases (CORD) are difficult to use, not individualized and requiring laborious analysis by health professionals, discouraging their use in healthcare. There is an opportunity for cost-effective and easy-to-disseminate advanced technological solutions directed to patients and attractive to different stakeholders. The strategy of AIRDOC is to develop and integrate self-monitoring and self-managing tools, making use of the smartphone's presence in everyday life. AIRDOC intends to innovate on: i) technologies for remote monitoring of respiratory function and computerized lung auscultation; ii) coaching solutions, integrating psychoeducation, gamification and disease management support systems; and iii) management of personal health data, focusing on security, privacy and interoperability. It is expected that AIRDOC results will contribute for the innovation in CORD healthcare, with increased patient involvement and empowerment while providing quality prospective information for better clinical decisions, allowing more efficient and sustainable healthcare delivery.