Multi-stage Biomarker Models for Progression Estimation in Alzheimer’s Disease

The estimation of disease progression in Alzheimer’s disease (AD) based on a vector of quantitative biomarkers is of high interest to clinicians, patients, and biomedical researchers alike. In this work, quantile regression is employed to learn statistical models describing the evolution of such biomarkers. Two separate models are constructed using (1) subjects that progress from a cognitively normal (CN) stage to mild cognitive impairment (MCI) and (2) subjects that progress from MCI to AD during the observation window of a longitudinal study. These models are then automatically combined to develop a multi-stage disease progression model for the whole disease course. A probabilistic approach is derived to estimate the current disease progress (DP) and the disease progression rate (DPR) of a given individual by fitting any acquired biomarkers to these models. A particular strength of this method is that it is applicable even if individual biomarker measurements are missing for the subject. Employing cognitive scores and image-based biomarkers, the presented method is used to estimate DP and DPR for subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Further, the potential use of these values as features for different classification tasks is demonstrated. For example, accuracy of 64% is reached for CN vs. MCI vs. AD classification

Complete Genome Sequence of the Type Strain Corynebacterium Epidermidicanis DSM 45586, Isolated from the Skin of a Dog Suffering from Pruritus

The complete genome sequence of Corynebacterium epidermidicanis DSM 45586 comprises 2,692,072 bp with 58.06% G+C content. The annotation revealed 2,466 protein-coding regions, including genes for surface-anchored proteins with Cna B-type or bacterial Ig-like domains and for an adhesive SpaABC-type pilus with similarity to fimbrial subunits of Corynebacterium resistens DSM 45100

Complete Genome Sequence of the Type Strain Corynebacterium Mustelae DSM 45274, Isolated from Various Tissues of a Male Ferret with Lethal Sepsis

The complete genome of Corynebacterium mustelae DSM 45274 comprises 3,474,226 bp and 3,188 genes. Prominent niche and virulence factors are SpaBCA- and SpaDEF-type pili with similarity to pilus proteins of Corynebacterium resistens and Corynebacterium urealyticum and an immunomodulatory EndoS-like endoglycosidase probably catalyzing the removal of distinct glycans from IgG antibodies

Complete Genome Sequence of the Type Strain Corynebacterium testudinoris DSM 44614, Recovered from Necrotic Lesions in the Mouth of a Tortoise

The complete genome sequence of the type strain Corynebacterium testudinoris DSM 44614 from the mouth of a tortoise comprises 2,721,226 bp with a mean G+C content of 63.14%. The automatic annotation of the genome sequence revealed 4 rRNA operons, 51 tRNA genes, 7 other RNA genes, and 2,561 protein-coding regions.Medical Microbiology and Genomics fund (eKVV 200937)Germany. Federal Ministry of Education and Research (German Network for Bioinformatics Intrastructure Initiative FKZ 031A533A

SonoNet: Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand Ultrasound

Identifying and interpreting fetal standard scan planes during 2D ultrasound mid-pregnancy examinations are highly complex tasks which require years of training. Apart from guiding the probe to the correct location, it can be equally difficult for a non-expert to identify relevant structures within the image. Automatic image processing can provide tools to help experienced as well as inexperienced operators with these tasks. In this paper, we propose a novel method based on convolutional neural networks which can automatically detect 13 fetal standard views in freehand 2D ultrasound data as well as provide a localisation of the fetal structures via a bounding box. An important contribution is that the network learns to localise the target anatomy using weak supervision based on image-level labels only. The network architecture is designed to operate in real-time while providing optimal output for the localisation task. We present results for real-time annotation, retrospective frame retrieval from saved videos, and localisation on a very large and challenging dataset consisting of images and video recordings of full clinical anomaly screenings. We found that the proposed method achieved an average F1-score of 0.798 in a realistic classification experiment modelling real-time detection, and obtained a 90.09% accuracy for retrospective frame retrieval. Moreover, an accuracy of 77.8% was achieved on the localisation task.Comment: 12 pages, 8 figures, published in IEEE Transactions in Medical Imagin

Virulence Factor Genes Detected in the Complete Genome Sequence of Corynebacterium uterequi DSM 45634, Isolated from the Uterus of a Maiden Mare

The complete genome sequence of the type strain Corynebacterium uterequi DSM 45634 from an equine urogenital tract specimen comprises 2,419,437 bp and 2,163 protein-coding genes. Candidate virulence factors are homologs of DIP0733, DIP1281, and DIP1621 from Corynebacterium diphtheriae and of sialidase precursors from Trueperella pyogenes and Chlamydia trachomatis.Medical Microbiology and Genomics fund (eKVV 200937)Germany. Federal Ministry of Education and Research (German Network for Bioinformatics Intrastructure Initiative FKZ 031A533A

Multi-Atlas Segmentation using Partially Annotated Data: Methods and Annotation Strategies

Multi-atlas segmentation is a widely used tool in medical image analysis, providing robust and accurate results by learning from annotated atlas datasets. However, the availability of fully annotated atlas images for training is limited due to the time required for the labelling task. Segmentation methods requiring only a proportion of each atlas image to be labelled could therefore reduce the workload on expert raters tasked with annotating atlas images. To address this issue, we first re-examine the labelling problem common in many existing approaches and formulate its solution in terms of a Markov Random Field energy minimisation problem on a graph connecting atlases and the target image. This provides a unifying framework for multi-atlas segmentation. We then show how modifications in the graph configuration of the proposed framework enable the use of partially annotated atlas images and investigate different partial annotation strategies. The proposed method was evaluated on two Magnetic Resonance Imaging (MRI) datasets for hippocampal and cardiac segmentation. Experiments were performed aimed at (1) recreating existing segmentation techniques with the proposed framework and (2) demonstrating the potential of employing sparsely annotated atlas data for multi-atlas segmentation

Autoadaptive motion modelling for MR-based respiratory motion estimation

This repository contains four T1-weighted 2D MR slice datasets from multiple slice positions covering the entire thorax during free breathing and breath holds. The data was used to evaluate our novel autoadaptive respiratory motion model which we proposed in [1]. In particular, the datasets contain the following: Acquisition of all sagittal slice positions covering the thorax and one coronal slice position acquired during a breath hold. Results of registration between adjacent sagittal slice positions [control point displacements (cpp) and displacement fields (dfs)] 40 dynamic acquisitions of each slice position also present in the breath-hold acquired during free breathing. Results of registration of the dynamic acquisitions to the respective breath-holds slices (cpp's and dfs's). The data is divided into 4 zip files, each containing the data of one volunteer. The folder structure for each is as follows: |-- bhs (breath hold data) | |-- images (images) | | |-- cor | | `-- sag | `-- mfs_slpos2slpos (registration results) | `-- sag `-- dyn (dynamic free-breathing data) |-- images (images) | |-- cor | `-- sag `-- mfs_tpos2tpos (registration results) |-- cor `-- sag Please, see our publication [1] for details on the acquisition sequence and registration used. -- [1]: CF Baumgartner, C Kolbitsch, JR McClelland, D Rueckert, AP King, Autoadaptive motion modelling for MR-based respiratory motion estimation, Medical Image Analysis (2016), http://dx.doi.org/10.1016/j.media.2016.06.00

Whole genome sequence comparisons in taxonomoy

This chapter is devoted to application of whole genome sequence comparisons in taxonomy. Driven by the rapid progress in sequencing technologies, “low budget” bacterial genomes become increasingly available in a nearly unlimited number. During finalizing this chapter, completed genomes representing 1,604 bacterial and 85 archaeal species were present in the public data bank (http://www.ncbi.nlm.nih.gov/sutils/genom_table.cgi) reflecting the enormous progress made within sequencing microbial genomes in the last years. With the advent of next generation sequencing, whole genome sequence comparisons will be more and more important for taxonomy, especially valuable in elucidating relationship of groups of closely related bacterial strains which might form a single taxon, a subspecies or just an ecovar within a given species. The aim of this chapter is to hand out a tool set for applying genomics to the interested taxonomist. Using these tools might prove as being useful especially in refining groups of closely related strains, which are not resolved by their 16S rRNA sequence. Here, we will exemplify this approach by selecting a specific group of plant – associated Bacillus amyloliquefaciens strains with plant growth promoting properties. In recent years, those strains were increasingly applied as biological substitutes of agrochemicals, mainly used as biofertilizer and for biocontrol of phytopathogenic microorganisms, and nematodes (Chen et al., 2007).http://www.sciencedirect.com/science/bookseries/05809517hb2016Biochemistr