Deep learning for supervised classification of temporal data in ecology

Temporal data is ubiquitous in ecology and ecologists often face the challenge of accurately differentiating these data into predefined classes, such as biological entities or ecological states. The usual approach consists of transforming the time series into user-defined features and then using these features as predictors in conventional statistical or machine learning models. Here we suggest the use of deep learning models as an alternative to this approach. Recent deep learning techniques can perform the classification directly from the time series, eliminating subjective and resource-consuming data transformation steps, and potentially improving classification results. We describe some of the deep learning architectures relevant for time series classification and show how these architectures and their hyper-parameters can be tested and used for the classification problems at hand. We illustrate the approach using three case studies from distinct ecological subdisciplines: i) insect species identification from wingbeat spectrograms; ii) species distribution modelling from climate time series and iii) the classification of phenological phases from continuous meteorological data. The deep learning approach delivered ecologically sensible and accurate classifications demonstrating its potential for wide applicability across subfields of ecology.info:eu-repo/semantics/publishedVersio

Implant-Supported Three-Unit Fixed Dental Prosthesis Using Coded Healing Abutments and Fabricated Using a Digital Workflow:A 1-Year Prospective Case Series Study

PURPOSE: To test the applicability of coded healing abutments, intraoral scanners, and monolithic zirconia for the fabrication of three-unit fixed dental prostheses (FDPs) on two dental implants. MATERIALS AND METHODS: Patients with three missing teeth in the posterior region of either the maxilla or mandible received two dental implants. After healing, coded healing abutments were placed. Full-arch intraoral scans were made to produce individual titanium abutments and a three-unit FDP. Peri-implant tissues were assessed 2 weeks after placement of the FDP and again after 1 year. Patient-reported outcome measures were registered prior to treatment and after 1 year. The quality of the FDPs was assessed using modified United States Public Health Service criteria after 1 year of service. RESULTS: A total of 54 patients were treated with 60 restorations, and 51 patients with 56 restorations were available at the 1-year follow-up. Implant survival was 99.1%, and prosthesis survival was 100%. The peri-implant tissues remained healthy, and patient satisfaction was high. However, the USPHS evaluation showed that some prostheses exhibited fit or color issues that needed to be addressed, although most were rated as successful (80.4%). CONCLUSION: The use of coded healing abutments and intraoral scanners to produce full-zirconia three-unit FDPs on two dental implants proved to be a feasible technique, with promising objective and subjective results. However, technical challenges still impacted the treatment results, resulting in a number of restorations having clinical or radiographic marginal gaps or reduced color match

Three-unit fixed dental prostheses supported by either two abutment implants or two abutment teeth:A comparative retrospective cohort study

Objectives: In general, similar restorative constructions are made on natural teeth and on dental implants. The assumption is made that implants and their restoration perform the same as natural roots and their prosthetic restoration. Evaluating cohorts of three-unit bridges on teeth and on implants, this retrospective clinical study aimed to compare implants and teeth as supporting units, including the reconstructions, in terms of survival, success, clinical, radiographic, and patient-reported outcomes. Material and Methods: From an 8-year period, all patients treated with a posterior three-unit fixed reconstruction on either implants or teeth, with a follow-up of at least 2 years, were identified. For each implant-supported reconstruction, a comparable tooth-supported reconstruction was selected, based on the length of follow-up, the material of the reconstruction, and the location in either the maxilla or mandible. Results: For the Implant-group, 24 patients could be matched with 24 best matching patients with tooth-supported fixed dental prostheses (FPDs). Supporting implants and implant-supported reconstructions were all in function with a mean follow-up of 52 +/- 23 months. Two tooth-supported reconstructions had been replaced (91.7% survival) (mean follow-up: 52 +/- 19 months). Radiographic bone levels and soft tissue conditions were favorable in both groups with minor differences. There was no significant difference in overall patient satisfaction. The modified USPHS-score revealed an 87.5% overall success in the Implant-group and 91.7% in the Tooth-group. Conclusions: Implant-supported three-unit FDPs area reliable treatment option with survival and success rates not significantly different from the results of tooth-supported three-unit FDPs

Deep Learning Assisted Data Inspection for Radio Astronomy

Modern radio telescopes combine thousands of receivers, long-distance networks, large-scale compute hardware, and intricate software. Due to this complexity, failures occur relatively frequently. In this work we propose novel use of unsupervised deep learning to diagnose system health for modern radio telescopes. The model is a convolutional Variational Autoencoder (VAE) that enables the projection of the high dimensional time-frequency data to a low-dimensional prescriptive space. Using this projection, telescope operators are able to visually inspect failures thereby maintaining system health. We have trained and evaluated the performance of the VAE quantitatively in controlled experiments on simulated data from HERA. Moreover, we present a qualitative assessment of the the model trained and tested on real LOFAR data. Through the use of a naive SVM classifier on the projected synthesised data, we show that there is a trade-off between the dimensionality of the projection and the number of compounded features in a given spectrogram. The VAE and SVM combination scores between 65% and 90% accuracy depending on the number of features in a given input. Finally, we show the prototype system-health-diagnostic web framework that integrates the evaluated model. The system is currently undergoing testing at the ASTRON observatory

Full-zirconia single-tooth molar implant-supported restorations with angulated screw channel abutments:A 1-year prospective case series study

Background Implant-supported restorations in the posterior region are subjected to various complications that could be prevented by changing either the design or the material. Purpose The aim of this prospective case series study was to evaluate full-zirconia implant-supported restorations with angulated screw channel abutments in the molar region of the maxilla and mandible and their effect on hard and soft peri-implant tissues, during a 1-year follow-up period. Materials and Methods Thirty consecutive patients with a single missing molar, sufficient bone height, and implant site free of infection were included. Each patient was to receive a parallel-walled implant with conical connection according to a two-staged surgical protocol. After 3 months, a full-contour screw-retained zirconia restoration with angulated screw channel abutment was provided. Clinical and radiographic examinations were performed 1 and 12 months after placement of the restoration. Patients' satisfaction was scored prior to treatment and after 12 months with the restoration in function. Primary outcome measure was success of the restoration. Results All patients could be evaluated after 12 months. Success of the restorations was 100%. From loading to the 12-month follow-up, the mean marginal bone loss was 0.16 mm (SD: 0.26). Mean scores for plaque, calculus, peri-implant mucosa, bleeding, and pocket probing depth were low, depicting healthy peri-implant conditions. Patients' satisfaction was high and had improved after treatment. Conclusion Full-contour zirconia implant-supported restorations with angulated screw channel abutments in the molar region have an excellent clinical performance after 1 year of function

matchms - processing and similarity evaluation of mass spectrometry data

Mass spectrometry data is at the heart of numerous applications in the biomedical and lifesciences. With growing use of high-throughput techniques, researchers need to analyze largerand more complex datasets. In particular through joint effort in the research community,fragmentation mass spectrometry datasets are growing in size and number. Platforms such asMassBank (Horai et al., 2010), GNPS (Wang et al., 2016) or MetaboLights (Haug et al., 2020)serve as an open-access hub for sharing of raw, processed, or annotated fragmentation massspectrometry data. Without suitable tools, however, exploitation of such datasets remainsoverly challenging. In particular, large collected datasets contain data acquired using differentinstruments and measurement conditions, and can further contain a significant fraction ofinconsistent, wrongly labeled, or incorrect metadata (annotations)