Fracture Resistance of Zirconia Oral Implants In Vitro: A Systematic Review and Meta-Analysis

Various protocols are available to preclinically assess the fracture resistance of zirconia oral implants. The objective of the present review was to determine the impact of different treatments (dynamic loading, hydrothermal aging) and implant features (e.g., material, design or manufacturing) on the fracture resistance of zirconia implants. An electronic screening of two databases (MEDLINE/Pubmed, Embase) was performed. Investigations including > 5 screw-shaped implants providing information to calculate the bending moment at the time point of static loading to fracture were considered. Data was extracted and meta-analyses were conducted using multilevel mixed-effects generalized linear models (GLMs). The Šidák method was used to correct for multiple testing. The initial search resulted in 1864 articles, and finally 19 investigations loading 731 zirconia implants to fracture were analyzed. In general, fracture resistance was affected by the implant design (1-piece > 2-piece, p = 0.004), material (alumina-toughened zirconia/ATZ > yttria-stabilized tetragonal zirconia polycrystal/Y-TZP, p = 0.002) and abutment preparation (untouched > modified/grinded, p < 0.001). In case of 2-piece implants, the amount of dynamic loading cycles prior to static loading (p < 0.001) or anatomical crown supply (p < 0.001) negatively affected the outcome. No impact was found for hydrothermal aging. Heterogeneous findings of the present review highlight the importance of thoroughly and individually evaluating the fracture resistance of every zirconia implant system prior to market release

Duration of Immunity After Rubella Vaccination: A Long-Term Study in Switzerland

In Switzerland 319 of 594 young women seronegative for rubella antibody vaccinated at 15-25 years of age against rubella with the Cendehill vaccine strain were retested 15 years later with three tests (hemagglutination inhibition, enzyme-linked immunosorbent assay, and a neutralization technique) for the presence of rubella antibodies. For 307 women rubella antibodies were still detectable by all three techniques. For nine women rubella antibodies were demonstrable by only one or two tests. Only three vaccinees were seronegative by all three tests. These three women also showed no booster response after challenge with the vaccine strain. The high percentage of women with persistent rubella antibodies 15 years after vaccination might be explained in part by the presence of subclinical reinfections due to a wild rubella viru

MicroRNAs as salivary markers for periodontal diseases

The aim of this review is to discuss current findings regarding the roles of miRNAs in periodontal diseases and the potential use of saliva as a diagnostic medium for corresponding miRNA investigations. For periodontal disease, investigations have been restricted to tissue samples and five miRNAs, that is, miR-142-3p, miR-146a, miR-155, miR-203, and miR-223, were repeatedly validated in vivo and in vitro by different validation methods. Particularly noticeable are the small sample sizes, different internal controls, and different case definitions of periodontitis in in vivo studies. Beside of that, the validated miRNAs are associated with inflammation and therefore with various diseases. Furthermore, several studies successfully explored the use of salivary miRNA species for the diagnosis of oral cancer. Different cancer types were investigated and heterogeneous methodology was used; moreover, no overlap of resultswas found. In conclusion, fivemiRNAs have consistently been reported for periodontitis; however, their disease specificity, detectability, and expression in saliva and their importance as noninvasive markers are questionable. In principle, a salivary miRNA diagnostic method seems feasible.However, standardized criteria and protocols for preanalytics, measurements, and analysis should be established to obtain comparable results across different studies

A survey on perceived speaker traits: personality, likability, pathology, and the first challenge

The INTERSPEECH 2012 Speaker Trait Challenge aimed at a unified test-bed for perceived speaker traits – the first challenge of this kind: personality in the five OCEAN personality dimensions, likability of speakers, and intelligibility of pathologic speakers. In the present article, we give a brief overview of the state-of-the-art in these three fields of research and describe the three sub-challenges in terms of the challenge conditions, the baseline results provided by the organisers, and a new openSMILE feature set, which has been used for computing the baselines and which has been provided to the participants. Furthermore, we summarise the approaches and the results presented by the participants to show the various techniques that are currently applied to solve these classification tasks

Probing speech emotion recognition transformers for linguistic knowledge

Large, pre-trained neural networks consisting of self-attention layers (transformers) have recently achieved state-of-the-art results on several speech emotion recognition (SER) datasets. These models are typically pre-trained in self-supervised manner with the goal to improve automatic speech recognition performance -- and thus, to understand linguistic information. In this work, we investigate the extent in which this information is exploited during SER fine-tuning. Using a reproducible methodology based on open-source tools, we synthesise prosodically neutral speech utterances while varying the sentiment of the text. Valence predictions of the transformer model are very reactive to positive and negative sentiment content, as well as negations, but not to intensifiers or reducers, while none of those linguistic features impact arousal or dominance. These findings show that transformers can successfully leverage linguistic information to improve their valence predictions, and that linguistic analysis should be included in their testing

Age and gender recognition for telephone applications based on GMM supervectors and support vector machines

This paper compares two approaches of automatic age and gen-der classification with 7 classes. The first approach are Gaus-sian Mixture Models (GMMs) with Universal Background Models (UBMs), which is well known for the task of speaker identifica-tion/verification. The training is performed by the EM algorithm or MAP adaptation respectively. For the second approach for each speaker of the test and training set a GMM model is trained. The means of each model are extracted and concatenated, which results in a GMM supervector for each speaker. These supervectors are then used in a support vector machine (SVM). Three different ker-nels were employed for the SVM approach: a polynomial kernel (with different polynomials), an RBF kernel and a linear GMM dis-tance kernel, based on the KL divergence. With the SVM approach we improved the recognition rate to 74 % (p &lt; 0.001) and are in the same range as humans. Index Terms — Acoustic signal analysis, speaker classification, age, gender, Gaussian mixture models (GMM), support vector ma-chine (SVM) 1

Probing Speech Emotion Recognition Transformers for Linguistic Knowledge

Large, pre-trained neural networks consisting of self-attention layers (transformers) have recently achieved state-of-the-art results on several speech emotion recognition (SER) datasets. These models are typically pre-trained in self-supervised manner with the goal to improve automatic speech recognition performance -- and thus, to understand linguistic information. In this work, we investigate the extent in which this information is exploited during SER fine-tuning. Using a reproducible methodology based on open-source tools, we synthesise prosodically neutral speech utterances while varying the sentiment of the text. Valence predictions of the transformer model are very reactive to positive and negative sentiment content, as well as negations, but not to intensifiers or reducers, while none of those linguistic features impact arousal or dominance. These findings show that transformers can successfully leverage linguistic information to improve their valence predictions, and that linguistic analysis should be included in their testing.Comment: This work has been submitted for publication to Interspeech 202

Methoden zur Charakterisierung optischer Gitter über einen großen Ortsfrequenzbereich

In unserem Beitrag berichten wir über Methoden zur Streulicht- und Wellenfrontmessung optischer Gitter. Ein selbst entwickelter Streulichtmessplatz in der Czerny-Turner-Geometrie erlaubt die Messung von BRDF-Werten über bis zu 14 Größenordnungen mit hoher Winkel-Auflösung. Zur quantitativen Erfassung von Wellenfrontfehlern holografisch-abbildender Gitter wird ein Moiré-Verfahren verwendet