In vitro abrasion of resin-coated highly viscous glass ionomer cements: a confocal laser scanning microscopy study

The aim of this study was to evaluate the effect of resin coating on the wear depth of highly viscous glass ionomer cements (HVGICs) after 40,000 cycles, corresponding to over 8 years of tooth brushing

Advances in Polynesian prehistory: A review and assessment of the past decade (1993-2004)

The pace of archaeological research in Polynesia has intensified in recent years, resulting in more than 500 new literature citations over the past decade. Fieldwork has continued in such previously well-studied archipelagoes as Tonga and Samoa in Western Polynesia, and Hawai'i and New Zealand in Eastern Polynesia, and has expanded into previously neglected islands including Niue, the Equatorial Islands, the Austral Islands, and Mangareva. The emergence of Ancestral Polynesian culture out of its Eastern Lapita predecessor is increasingly well understood, and the chronology of Polynesian dispersal and expansion into Eastern Polynesia has engaged several researchers. Aside from these fundamental issues of origins and chronology, major research themes over the past decade include (1) defining the nature, extent, and timing of long-distance interaction spheres, particularly in Eastern Polynesia; (2) the impacts of human colonization and settlement on island ecosystems; (3) variation in Polynesian economic systems and their transformations over time; and (4) sociopolitical change, especially as viewed through the lens of household or microscale archaeology. Also noteworthy is the rapidly evolving nature of interactions between archaeologists and native communities, a critical aspect of archaeological practice in the region

Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity

The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management