The aim of the study was to compare the bone mineral apposition rate (BMAR) of immediately loaded implants with an unloaded control during the early healing phase in the partially edentulous mandible. In seven mini pigs, three premolars and the first molar were removed in the left mandible. Three months later, five implants were installed. Four implants received a fixed provisional restoration and were loaded immediately. The most anterior implant was used as unloaded control. Polychromatic fluorescence labelling was performed to assess the BMAR. After 4 months, the implants were retrieved together with the adjacent bone. Histological specimens were prepared and subjected to a fluorescence microscopic and histomorphometric analysis. Two provisional restorations were found partially lost at the end of the observation period. One implant that had lost the splinting fixation showed soft connective tissue healing. The BMAR did not differ statistically significantly between loaded and unloaded implants and within the single groups during the observation period (BMARloaded days 14-42=1.8+/-0.2 microm/d, BMARloaded days 42-70=1.8+/-0.1 microm/d, BMARloaded days 70-98=1.6+/-0.1 microm/d, pBMARloaded days 14-42/42-70/70-98 =0.156, BMARunloaded days 14-42=1.7+/-0.1 microm/d, BMARunloaded days 42-70=1.8+/-0.2 microm/d, BMARunloaded days 70-98=1.6+/-0.4 microm/d, pBMARunloaded days 14-42/42-70/70-98=0.368, pBMARloaded/unloaded days 14-42=0.073, pBMARloaded/unloaded days 42-70=0.098, pBMARloaded/unloaded days 70-98=0.262). Four months after implant placement, the bone-to-implant contact was 77.8+/-17.3% for the loaded and 78.0+/-5.8% for the unloaded implants (P=0.753). Immediate loading does not affect the bone mineral apposition rate when compared with unloaded implants. Rigid splinting seems to be the crucial factor for implant success. Uncontrolled masticatory forces can cause failure after partial loss of the provisional restoration.

Motivated by the observation that one of the biggest problems in automatic singing voice detection is the confusion of vocals with other pitch-continuous and pitch-varying instruments, we propose a set of three new audio features designed to reduce the amount of false vocal detections. This is borne out in comparative experiments with three different musical corpora. The resulting singing voice detector appears to be at least on par with more complex state-of-the-art methods. New features and classifier are very light-weight and in principle suitable for on-line use.

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The small method errors, high intraclass correlation coefficients, and comparable repeatability values for cleft and noncleft sides reveal that the new technique is appropriate for clinical use.

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Objective To evaluate and compare the effects of early primary closure of the hard palate on the anterior and posterior width of the maxillary arch in children with bilateral (BCLP) and unilateral (UCLP) cleft lip and palate during the first 4 years of life. Design A retrospective, mixed-longitudinal study. Setting Cleft Palate Center of the University of Erlangen-Nuremberg. Subjects and Methods The present investigation analyzes longitudinally 42 children with UCLP and 8 children with BCLP between 1996 and 2000 with early simultaneous primary closure of lip and hard palate (4 to 5 months). Palatal arch width was measured on dental casts with a computer-controlled three-dimensional digitizing system, and their growth velocities were calculated from consecutive periods (mean follow-up 39 months). Differences in growth velocities were compared with those of 25 children with UCLP and 15 children with BCLP with delayed closure of hard palate (12 to 14 months). Results and Conclusions There was no significant difference in terms of anterior and posterior maxillary width between early and delayed closure of hard palate within the first 4 years of life.

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Abstract-In Acoustic Scene Classification (ASC) two major approaches have been followed . While one utilizes engineered features such as mel-frequency-cepstral-coefficients (MFCCs), the other uses learned features that are the outcome of an optimization algorithm. I-vectors are the result of a modeling technique that usually takes engineered features as input. It has been shown that standard MFCCs extracted from monaural audio signals lead to i-vectors that exhibit poor performance, especially on indoor acoustic scenes. At the same time, Convolutional Neural Networks (CNNs) are well known for their ability to learn features by optimizing their filters. They have been applied on ASC and have shown promising results. In this paper, we first propose a novel multi-channel i-vector extraction scheme for ASC, improving their performance on indoor and outdoor scenes. Second, we propose a CNN architecture that achieves promising ASC results. Further, we show that i-vectors and CNNs capture complementary information from acoustic scenes. Finally, we propose a hybrid system for ASC using multi-channel i-vectors and CNNs by utilizing a score fusion technique. Using our method, we participated in the ASC task of the DCASE-2016 challenge. Our hybrid approach achieved 1 st rank among 49 submissions, substantially improving the previous state of the art.