Dimensionality reduction, and function approximation of poly(lactic-co-glycolic acid) micro- and nanoparticle dissolution rate

Prediction of poly(lactic-co-glycolic acid) (PLGA) micro- and nanoparticles’ dissolution rates plays a significant role in pharmaceutical and medical industries. The prediction of PLGA dissolution rate is crucial for drug manufacturing. Therefore, a model that predicts the PLGA dissolution rate could be beneficial. PLGA dissolution is influenced by numerous factors (features), and counting the known features leads to a dataset with 300 features. This large number of features and high redundancy within the dataset makes the prediction task very difficult and inaccurate. In this study, dimensionality reduction techniques were applied in order to simplify the task and eliminate irrelevant and redundant features. A heterogeneous pool of several regression algorithms were independently tested and evaluated. In addition, several ensemble methods were tested in order to improve the accuracy of prediction. The empirical results revealed that the proposed evolutionary weighted ensemble method offered the lowest margin of error and significantly outperformed the individual algorithms and the other ensemble techniques.Web of Science101129111

Implants in patients with oral manifestations of autoimmune or muco-cutaneous diseases : a systematic review

To give an overview on implant survival rates in patients with oral manifestations of systemic autoimmune (oral Lichen planus (oLp), Pemphigus (Pe)), muco-cutaneous (Epidermolysis bullosa (EB)), autoimmune multisystemic rheumatic diseases (Sjögren´s syndrome (SjS), systemic Lupus erythematosus (sLE), or systemic Sclerosis (sSc)). Systematic literature review (PubMed/Medline, Embase) using MESH and search term combinations, published between 1980 and August 2018 in English language reporting on dental implant-prosthetic rehabilitation of patients with oLp, Pe, EB, SjS, sLE, sSc, study design, age, gender, follow-up period (? 12 months), implant survival rate. Implant-related weighed mean values of implant survival rate (wmSR) were calculated. After a mean follow-up period (mfp) of 44.6 months, a wmSR of 98.3 % was calculated from data published for patients with oLp (100 patients with 302 implants). Data of 27 patients (152 implants) with EB revealed wmSR of 98.7 % following mfp of 32.6 months. For 71 patients (272 implants) with SjS, wmSR was 94.2 % following a mfp of 45.2 months, and for 6 patients (44 implants) with sSc, wmSR was 97.7 % after mfp of 37.5 months. One case report on one patient each with Pe (two implants) as well as sLE (6 implants) showed 100 % SR following at least 24 months. Guidelines regarding implant treatment of patients with oLp, Pe, EB, SjS, sLE or sSc do not exist nor are contraindicating conditions defined. Implant survival rates of patients affected are comparable to those of healthy patients. For implant-prosthetic rehabilitation of patients with Pe and sLE no conclusions can be drawn due to lack of sufficient clinical data. Implant-prosthetic treatment guidelines regarding healthy patients should be strictly followed, but frequent recall is recommended in patients affected with oLp, SjS, EB, SSc, Pe or sLE

Feature selection and ensemble of regression models for predicting the protein macromolecule dissolution profile

Predicting the dissolution rate of proteins plays a significant role in pharmaceutical/medical applications. The rate of dissolution of Poly Lactic-co-Glycolic Acid (PLGA) micro- and nanoparticles is influenced by several factors. Considering all factors leads to a dataset with three hundred features, making the prediction difficult and inaccurate. Our present study consists of three phases. Firstly, dimensionality reduction techniques are applied in order to simplify the task and eliminate irrelevant and redundant attributes. Subsequently, a heterogeneous pool of several classical regression algorithms is created and evaluated. Regression algorithms in the pool are independently trained to identify the problem at hand. Finally, we test several ensemble methods in order to elevate the accuracy of the prediction. The Evolutionary Weighted Ensemble method proposed in this paper offered the lowest RMSE and significantly outperformed competing classical algorithms and other ensemble techniques

Modelling Dental Milling Process with Machine Learning-Based Regression Algorithms

Control of dental milling processes is a task which can significantly reduce production costs due to possible savings in time. Appropriate setup of production parameters can be done in a course of optimisation aiming at minimising selected objective function, e.g. time. Nonetheless, the main obstacle here is lack of explicitly defined objective functions, while model of relationship between the parameters and outputs (such as costs or time) is not known. Therefore, the model must be discovered in advance to use it for optimisation. Machine learning algorithms serve this purpose perfectly. There are plethoras of competing methods and the question is which shall be selected. In this paper, we present results of extensive investigation on this question. We evaluated several well-known classical regression algorithms, ensemble approaches and feature selection techniques in order to find the best model for dental milling model

LTER HAUSGARTEN 2018 - Long-Term Ecological Research in the Fram Strait, Cruise No. MSM77, September 15 - October 13, 2018, Longyearbyen (Svalbard) - Edinburgh (Scotland)

The 77th cruise of the RV MARIA S. MERIAN contributed to various large national and international research and infrastructure projects (FRAM, ARCHES, INTAROS, ICOS, SIOS) as well as to the research programme PACES-II (Polar Regions and Coasts in the changing Earth System) of the Alfred-Wegener-Institute Helmholtz-Center for Polar and Marine Research (AWI). Investigations within Work Package 4 (Arctic sea ice and its interaction with ocean and ecosystems) of the PACES-II programme, aim at assessing and quantifying ecosystem changes from surface waters to the deep ocean in response to the retreating sea ice, and at exploring the most important (feedback) processes determining temporal and spatial variability. Contributions to the PACES-II Work Package 6 (Large scale variability and change in polar benthic biota and ecosystem functions) include the identification of spatial patterns and temporal trends in relevant benthic community functions, and the development of a comprehensive science community reference collection of observational data. Work carried out within WPs 4 and 6 will support the time-series studies at the LTER (Long-Term Ecological Research) observatory HAUSGARTEN (Fig. 1.1), where we document Global Change induced environmental variations on a polar deep-water ecosystem. This work is carried out in close co-operation between the HGF-MPG Joint Research Group on Deep-Sea Ecology and Technology and the PEBCAO Group (Phytoplankton Ecology and Biogeochemistry in the Changing Arctic Ocean) at AWI as well as the working group Microbial Geochemistry at the GEOMAR and the HGF Young Investigators Group SEAPUMP (Seasonal and regional food web interactions with the biological pump)

Improved adaptive splitting and selection: the hybrid training method of a classifier based on a feature space partitioning

Currently, methods of combined classification are the focus of intense research. A properly designed group of combined classifiers exploiting knowledge gathered in a pool of elementary classifiers can successfully outperform a single classifier. There are two essential issues to consider when creating combined classifiers: how to establish the most comprehensive pool and how to design a fusion model that allows for taking full advantage of the collected knowledge. In this work, we address the issues and propose an AdaSS+, training algorithm dedicated for the compound classifier system that effectively exploits local specialization of the elementary classifiers. An effective training procedure consists of two phases. The first phase detects the classifier competencies and adjusts the respective fusion parameters. The second phase boosts classification accuracy by elevating the degree of local specialization. The quality of the proposed algorithms are evaluated on the basis of a wide range of computer experiments that show that AdaSS+ can outperform the original method and several reference classifiers.Web of Science243art. no. 143000

Learning Decision Trees from Data Streams with Concept Drift

AbstractThis paper addresses a data mining task of classifying data stream with concept drift. The proposed algorithm, named Concept-adapting Evolutionary Algorithm For Decision Tree does not require any knowledge of the environment such as numbers and rates of drifts. The novelty of the approach is combining tree learner and evolutionary algorithm, where the decision tree is learned incrementally and all information is stored in an internal structure of the trees’ population. The proposed algorithm is experimentally compared with state-of-the-art stream methods on several real live and synthetic datasets. Results indicate its high performance in term of accuracy and processing time