Long-term stability of contour augmentation with early implant placement following single tooth extraction in the esthetic zone: a prospective, cross-sectional study in 41 patients with a 5- to 9-year follow-up

BACKGROUND Early implant placement with simultaneous contour augmentation is documented with short- and medium-term studies. The long-term stability of contour augmentation is uncertain. METHODS In this prospective, cross-sectional study, 41 patients with an implant-borne single crown were examined twice, in 2006 and 2010. Clinical, radiologic, and esthetic parameters were assessed at both examinations. In addition, a cone beam computed tomographic (CBCT) image was obtained during the second examination to assess the dimensions of the facial bone wall. RESULTS All 41 implants demonstrated ankylotic stability without signs of peri-implant infection at both examinations. The clinical parameters remained stable over time. Satisfactory esthetic outcomes were noted, as assessed by the pink and white esthetic score (PES/WES) indices. Overall, the PES scores were slightly higher than the WES scores. None of the implants developed mucosal recession over time, as confirmed by values of the distance between implant shoulder and mucosal margin and cast measurements. The periapical radiographs yielded stable peri-implant bone levels, with a mean distance between implant shoulder and first visible bone-implant contact value of 2.18 mm. The CBCT analysis demonstrated a mean thickness of the facial bone wall ≈2.2 mm. In two implants (4.9%) no facial bone wall was detectable radiographically. CONCLUSIONS This prospective cross-sectional study demonstrates stable peri-implant hard and soft tissues for all 41 implants examined and satisfactory esthetic outcomes overall. The follow-up of 5 to 9 years confirmed again that the risk for mucosal recession is low with early implant placement. In addition, contour augmentation with guided bone regeneration was able to establish and maintain a facial bone wall in 95% of patients.

Lake ice phenology from AVHRR data for European lakes: An automated two-step extraction method

Several lake ice phenology studies from satellite data have been undertaken. However, the availability of long-term lake freeze-thaw-cycles, required to understand this proxy for climate variability and change, is scarce for European lakes. Long time series from space observations are limited to few satellite sensors. Data of the Advanced Very High Resolution Radiometer (AVHRR) are used in account of their unique potential as they offer each day global coverage from the early 1980s expectedly until 2022. An automatic two-step extraction was developed, which makes use of near-infrared reflectance values and thermal infrared derived lake surface water temperatures to extract lake ice phenology dates. In contrast to other studies utilizing thermal infrared, the thresholds are derived from the data itself, making it unnecessary to define arbitrary or lake specific thresholds. Two lakes in the Baltic region and a steppe lake on the Austrian–Hungarian border were selected. The later one was used to test the applicability of the approach to another climatic region for the time period 1990 to 2012. A comparison of the extracted event dates with in situ data provided good agreements of about 10 d mean absolute error. The two-step extraction was found to be applicable for European lakes in different climate regions and could fill existing data gaps in future applications. The extension of the time series to the full AVHRR record length (early 1980 until today) with adequate length for trend estimations would be of interest to assess climate variability and change. Furthermore, the two-step extraction itself is not sensor-specific and could be applied to other sensors with equivalent near- and thermal infrared spectral bands.

Long-term evaluation of Class II subdivision treatment with unilateral maxillary first molar extraction

OBJECTIVE To evaluate the long-term effects of asymmetrical maxillary first molar (M1) extraction in Class II subdivision treatment. MATERIALS AND METHODS Records of 20 Class II subdivision whites (7 boys, 13 girls; mean age, 13.0 years; SD, 1.7 years) consecutively treated with the Begg technique and M1 extraction, and 15 untreated asymmetrical Class II adolescents (4 boys, 11 girls; mean age, 12.2 years; SD, 1.3 years) were examined in this study. Cephalometric analysis and PAR assessment were carried out before treatment (T1), after treatment (T2), and on average 2.5 years posttreatment (T3) for the treatment group, and at similar time points and average follow-up of 1.8 years for the controls. RESULTS The adjusted analysis indicated that the maxillary incisors were 2.3 mm more retracted in relation to A-Pog between T1 and T3 (β  =  2.31; 95% CI; 0.76, 3.87), whereas the mandibular incisors were 1.3 mm more protracted (β  =  1.34; 95% CI; 0.09, 2.59), and 5.9° more proclined to the mandibular plane (β  =  5.92; 95% CI; 1.43, 10.41) compared with controls. The lower lip appeared 1.4 mm more protrusive relative to the subnasale-soft tissue-Pog line throughout the observation period in the treated adolescents (β  =  1.43; 95% CI; 0.18, 2.67). There was a significant PAR score reduction over the entire follow-up period in the molar extraction group (β  =  -6.73; 95% CI; -10.7, -2.7). At T2, 65% of the subjects had maxillary midlines perfectly aligned with the face. CONCLUSIONS Unilateral M1 extraction in asymmetrical Class II cases may lead to favorable occlusal outcomes in the long term without harming the midline esthetics and soft tissue profile.

Automatic extraction of syntactic semantic patterns for multilingual resources

In this paper we present an automatic system for the extraction of syntactic semantic patterns applied to the development of multilingual processing tools. In order to achieve optimum methods for the automatic treatment of more than one language, we propose the use of syntactic semantic patterns. These patterns are formed by a verbal head and the main arguments, and they are aligned among languages. In this paper we present an automatic system for the extraction and alignment of syntactic semantic patterns from two manually annotated corpora, and evaluate the main linguistic problems that we must deal with in the alignment process.

Web mining for translators: automatic construction of comparable, genre-driven corpora

The aim of this paper is to evaluate the efficacy of the application WebBootCaT to create specialised corpora automatically, investigating the translation of articles of association from Italian into English. The first section reflects on the relevant literature and proposes the utility of corpora for translators. The second section discusses the methodology employed, and the third section analyses the results obtained and comments on how language professionals could possibly exploit the application to its full. The fourth section provides a few concrete usage examples of the thus built corpora, to then conclude that WebBootCaT is a genuinely powerful tool that could be implemented by professional translators in order to save time and improve their translations in the long term.

Entropy feature extraction of EEG signals for automatic person identification

The use of human brain electroencephalography (EEG) signals for automatic person identi cation has been investigated for a decade. It has been found that the performance of an EEG-based person identication system highly depends on what feature to be extracted from multi-channel EEG signals. Linear methods such as Power Spectral Density and Autoregressive Model have been used to extract EEG features. However these methods assumed that EEG signals are stationary. In fact, EEG signals are complex, non-linear, non-stationary, and random in nature. In addition, other factors such as brain condition or human characteristics may have impacts on the performance, however these factors have not been investigated and evaluated in previous studies. It has been found in the literature that entropy is used to measure the randomness of non-linear time series data. Entropy is also used to measure the level of chaos of braincomputer interface systems. Therefore, this thesis proposes to study the role of entropy in non-linear analysis of EEG signals to discover new features for EEG-based person identi- cation. Five dierent entropy methods including Shannon Entropy, Approximate Entropy, Sample Entropy, Spectral Entropy, and Conditional Entropy have been proposed to extract entropy features that are used to evaluate the performance of EEG-based person identication systems and the impacts of epilepsy, alcohol, age and gender characteristics on these systems. Experiments were performed on the Australian EEG and Alcoholism datasets. Experimental results have shown that, in most cases, the proposed entropy features yield very fast person identication, yet with compatible accuracy because the feature dimension is low. In real life security operation, timely response is critical. The experimental results have also shown that epilepsy, alcohol, age and gender characteristics have impacts on the EEG-based person identication systems.

Automatic Glaucoma Detection Based on Optic Disc Segmentation and Texture Feature Extraction

The use of digital image processing techniques is prominent in medical settings for the automatic diagnosis of diseases. Glaucoma is the second leading cause of blindness in the world and it has no cure. Currently, there are treatments to prevent vision loss, but the disease must be detected in the early stages. Thus, the objective of this work is to develop an automatic detection method of Glaucoma in retinal images. The methodology used in the study were: acquisition of image database, Optic Disc segmentation, texture feature extraction in different color models and classification of images in glaucomatous or not. We obtained results of 93% accuracy