Accuracy of photogrammetry, intraoral scanning, and conventional impression techniques for complete-arch implant rehabilitation: an in vitro comparative study

Background To compare the accuracy of photogrammetry, intraoral scanning and conventional impression techniques for complete-arch implant rehabilitation. Methods A master cast containing 6 implant abutment replicas was fabricated. Group PG: digital impressions were taken 10 times using a photogrammetry system; Group IOS: intraoral scanning was performed to fabricate 10 digital impressions; Group CNV: splinted open-tray impression technique was used to fabricate 10 definitive casts. The master cast and conventional definitive casts were digitized with a laboratory reference scanner. For all STL files obtained, scan bodies were converted to implant abutment replicas using a digital library. The accuracy of a digitizer was defined by 2 main parameters, trueness and precision. "Trueness" was used to describe the deviation between test files and reference file, and "precision" was used to describe the closeness between test files. Then, the trueness and precision of three impression techniques were evaluated and statistically compared (α = 0.05). Results The median trueness was 24.45, 43.45 and 28.70 μm for group PG, IOS and CNV; Group PG gave more accurate trueness than group IOS (P < 0.001) and group CNV (P = 0.033), group CNV showed more accurate trueness than group IOS (P = 0.033). The median precision was 2.00, 36.00 and 29.40 μm for group PG, IOS and CNV; Group PG gave more accurate precision than group IOS (P < 0.001) and group CNV (P < 0.001), group CNV showed more accurate precision than IOS (P = 0.002). Conclusions For complete-arch implant rehabilitation, the photogrammetry system showed the best accuracy of all the impression techniques evaluated, followed by the conventional impression technique, and the intraoral scanner provided the least accuracy.

The importance of residue-level filtering, and the Top2018 best-parts dataset of high-quality protein residues

We have curated a high-quality, “best parts” reference dataset of about 3 million protein residues in about 15,000 PDB-format coordinate files, each containing only residues with good electron density support for a physically acceptable model conformation. The resulting pre-filtered data typically contains the entire core of each chain, in quite long continuous fragments. Each reference file is a single protein chain, and the total set of files were selected for low redundancy, high resolution, good MolProbity score, and other chain-level criteria. Then each residue was critically tested for adequate local map quality to firmly support its conformation, which must also be free of serious clashes or covalent-geometry outliers. The resulting Top2018 pre-filtered datasets have been released on the Zenodo online web service and is freely available for all uses under a Creative Commons license. Currently, one dataset is residue-filtered on mainchain plus Cβ atoms, and a second dataset is full-residue filtered; each is available at 4 different sequence-identity levels. Here, we illustrate both statistics and examples that show the beneficial consequences of residue-level filtering. That process is necessary because even the best of structures contain a few highly disordered local regions with poor density and low-confidence conformations that should not be included in reference data. Therefore the open distribution of these very large, pre-filtered reference datasets constitutes a notable advance for structural bioinformatics and the fields that depend upon it.The Top2018 dataset provides the first representative sample of 3D protein structure for which excellence of experimental data constrains the detailed local conformation to be correct for essentially all 3 million residues included. Earlier generations of residue-filtered datasets were central in developing MolProbity validation used worldwide, and now Zenodo has enabled anyone to use out latest version as a sound basis for structural bioinformatics, protein design, prediction, improving biomedically important structures, or other applications.

Clustering by Similarity of Brazilian Legal Documents Using Natural Language Processing Approaches

The Brazilian legal system postulates the expeditious resolution of judicial proceedings. However, legal courts are working under budgetary constraints and with reduced staff. As a way to face these restrictions, artificial intelligence (AI) has been tackling many complex problems in natural language processing (NLP). This work aims to detect the degree of similarity between judicial documents that can be achieved in the inference group using unsupervised learning, by applying three NLP techniques, namely term frequency-inverse document frequency (TF-IDF), Word2Vec CBoW, and Word2Vec Skip-gram, the last two being specialized with a Brazilian language corpus. We developed a template for grouping lawsuits, which is calculated based on the cosine distance between the elements of the group to its centroid. The Ordinary Appeal was chosen as a reference file since it triggers legal proceedings to follow to the higher court and because of the existence of a relevant contingent of lawsuits awaiting judgment. After the data-processing steps, documents had their content transformed into a vector representation, using the three NLP techniques. We notice that specialized word-embedding models—like Word2Vec—present better performance, making it possible to advance in the current state of the art in the area of NLP applied to the legal sector.

Bedshift: perturbation of genomic interval sets

Functional genomics experiments, like ChIP-Seq or ATAC-Seq, produce results that are summarized as a region set. There is no way to objectively evaluate the effectiveness of region set similarity metrics. We present Bedshift, a tool for perturbing BED files by randomly shifting, adding, and dropping regions from a reference file. The perturbed files can be used to benchmark similarity metrics, as well as for other applications. We highlight differences in behavior between metrics, such as that the Jaccard score is most sensitive to added or dropped regions, while coverage score is most sensitive to shifted regions.

Monolith Dental Bridge by Soft Machining of Dried Ceramic Dough

All-ceramic fixed restorations are gaining popularity because of increased esthetic consciousness in the younger population. Monolith ceramic crowns and bridges are preferred to withstand masticatory loads where the availability of occlusal height is limited. Machining green blanks of ceramic with organic binders have been studied to reduce loss of final strength, cost, and wastage associated with milling partial sintered and sintered blanks. Notches on the surface/edge associated with green milling will disappear after sintering in contrast to sintered state machining where diamond polishing is essential. In this study, we explored a novel ceramic dough process to form a green body of alumina or 3 Mol% yttria-stabilized zirconium oxide (YSZ), and computer numerical control (CNC) machining was performed on the dried dough. Micro Computer Tomography analysis of the bridges after sintering revealed a negligible void volume, 0.06–0.08% of the total volume, with randomly dispersed voids. Precision analysis of the sintered bridges with respect to the reference file resulted in a deviation range of +0.56 to −0.79 mm, with negligible deviation on the cementation surface. The green machined surface had a roughness profile of 1.2–1.7 μm after machining and 2.2–2.4 μm after sintering, as revealed by 3D profilometry.

Dimensional Accuracy of Dental Models for Three-Unit Prostheses Fabricated by Various 3D Printing Technologies

Previous studies on accuracy of three-dimensional (3D) printed model focused on full arch measurements at few points. The aim of this study was to examine the dimensional accuracy of 3D-printed models which were teeth-prepped for three-unit fixed prostheses, especially at margin and proximal contact areas. The prepped dental model was scanned with a desktop scanner. Using this reference file, test models were fabricated by digital light processing (DLP), Multi-Jet printing (MJP), and stereo-lithography apparatus (SLA) techniques. We calculated the accuracy (trueness and precision) of 3D-printed models on 3D planes, and deviations of each measured points at buccolingual and mesiodistal planes. We also analyzed the surface roughness of resin printed models. For overall 3D analysis, MJP showed significantly higher accuracy (trueness) than DLP and SLA techniques; however, there was not any statistically significant difference on precision. For deviations on margins of molar tooth and distance to proximal contact, MJP showed significantly accurate results; however, for a premolar tooth, there was no significant difference between the groups. 3D color maps of printed models showed contraction buccolingually, and surface roughness of the models fabricated by MJP technique was observed as the lowest. The accuracy of the 3D-printed resin models by DLP, MJP, and SLA techniques showed a clinically acceptable range to use as a working model for manufacturing dental prostheses