COVID research across the social sciences in 2020: a bibliometric approach.

Research on the COVID-19 pandemic has produced an incredible volume of social science research. To explore the initial areas of COVID-19 scholarship, the following study uses bibliometric co-citation network analysis on data from Clarivate's Web of Science database to analyze 3327 peer-reviewed studies published during the first year of the pandemic and their 107,396 shared references. Findings indicate nine distinct disciplinary research clusters centered around a single medical core of COVID-19 pandemic research. Topics ranging from tourism collapse, fear scales, financial contagion, health surveillance, shifts in crime rates, quarantine psychology, and collective trauma among others are found to have emerged in this initial phase of research as covid spread across the world. A corresponding infodemic highlights early communication challenges and a broader need to thwart misinformation. As this body of work continues to grow across the social sciences, key intersections, shared themes, and long-term implications of this historic event are brought into view.

Toxic Colonialism in the Territorial Isles: A Geospatial Analysis of Environmental Crime Across U.S. Territorial Islands 2013-2017.

The following study explores the geographic distribution of EPA environmental violations across the unincorporated territories within a criminological framework. Using data obtained from the EPA ECHO database, we conduct a geospatial legal analysis to identify island areas bearing a disproportionate number of green criminal activity between 2013 and 2017. Puerto Rico, the U.S. Virgin Islands, Guam, Mariana Islands, and American Samoa reflect a combination of green criminal patterns tied to tourism, commercial production, militarism, and nuclear testing. These remote and relatively biodiverse isles are consistently found to be bearing the burden of toxic waste which originated on the other side of the sea.

Embedding MRI information into MRSI data source extraction improves brain tumour delineation in animal models.

Glioblastoma is the most frequent malignant intra-cranial tumour. Magnetic resonance imaging is the modality of choice in diagnosis, aggressiveness assessment, and follow-up. However, there are examples where it lacks diagnostic accuracy. Magnetic resonance spectroscopy enables the identification of molecules present in the tissue, providing a precise metabolomic signature. Previous research shows that combining imaging and spectroscopy information results in more accurate outcomes and superior diagnostic value. This study proposes a method to combine them, which builds upon a previous methodology whose main objective is to guide the extraction of sources. To this aim, prior knowledge about class-specific information is integrated into the methodology by setting the metric of a latent variable space where Non-negative Matrix Factorisation is performed. The former methodology, which only used spectroscopy and involved combining spectra from different subjects, was adapted to use selected areas of interest that arise from segmenting the T2-weighted image. Results showed that embedding imaging information into the source extraction (the proposed semi-supervised analysis) improved the quality of the tumour delineation, as compared to those obtained without this information (unsupervised analysis). Both approaches were applied to pre-clinical data, involving thirteen brain tumour-bearing mice, and tested against histopathological data. On results of twenty-eight images, the proposed Semi-Supervised Source Extraction (SSSE) method greatly outperformed the unsupervised one, as well as an alternative semi-supervised approach from the literature, with differences being statistically significant. SSSE has proven successful in the delineation of the tumour, while bringing benefits such as 1) not constricting the metabolomic-based prediction to the image-segmented area, 2) ability to deal with signal-to-noise issues, 3) opportunity to answer specific questions by allowing researchers/radiologists define areas of interest that guide the source extraction, 4) creation of an intra-subject model and avoiding contamination from inter-subject overlaps, and 5) extraction of meaningful, good-quality sources that adds interpretability, conferring validation and better understanding of each case.

Community-led comparative genomic and phenotypic analysis of the aquaculture pathogen Pseudomonas baetica a390T sequenced by Ion semiconductor and Nanopore technologies.

Pseudomonas baetica strain a390T is the type strain of this recently described species and here we present its high-contiguity draft genome. To celebrate the 16th International Conference on Pseudomonas, the genome of P. baetica strain a390T was sequenced using a unique combination of Ion Torrent semiconductor and Oxford Nanopore methods as part of a collaborative community-led project. The use of high-quality Ion Torrent sequences with long Nanopore reads gave rapid, high-contiguity and -quality, 16-contig genome sequence. Whole genome phylogenetic analysis places P. baetica within the P. koreensis clade of the P. fluorescens group. Comparison of the main genomic features of P. baetica with a variety of other Pseudomonas spp. suggests that it is a highly adaptable organism, typical of the genus. This strain was originally isolated from the liver of a diseased wedge sole fish, and genotypic and phenotypic analyses show that it is tolerant to osmotic stress and to oxytetracycline.