Reconstruction of eye movements during blinks

In eye movement research in reading, the amount of data plays a crucial role for the validation of results. A methodological problem for the analysis of the eye movement in reading are blinks, when readers close their eyes. Blinking rate increases with increasing reading time, resulting in high data losses, especially for older adults or reading impaired subjects. We present a method, based on the symbolic sequence dynamics of the eye movements, that reconstructs the horizontal position of the eyes while the reader blinks. The method makes use of an observed fact that the movements of the eyes before closing or after opening contain information about the eyes movements during blinks. Test results indicate that our reconstruction method is superior to methods that use simpler interpolation approaches. In addition, analyses of the reconstructed data show no significant deviation from the usual behavior observed in readers

Self-Supervised Learning for Cardiac MR Image Segmentation by Anatomical Position Prediction

In the recent years, convolutional neural networks have transformed the field of medical image analysis due to their capacity to learn discriminative image features for a variety of classification and regression tasks. However, successfully learning these features requires a large amount of manually annotated data, which is expensive to acquire and limited by the available resources of expert image analysts. Therefore, unsupervised, weakly-supervised and self-supervised feature learning techniques receive a lot of attention, which aim to utilise the vast amount of available data, while at the same time avoid or substantially reduce the effort of manual annotation. In this paper, we propose a novel way for training a cardiac MR image segmentation network, in which features are learnt in a self-supervised manner by predicting anatomical positions. The anatomical positions serve as a supervisory signal and do not require extra manual annotation. We demonstrate that this seemingly simple task provides a strong signal for feature learning and with self-supervised learning, we achieve a high segmentation accuracy that is better than or comparable to a U-net trained from scratch, especially at a small data setting. When only five annotated subjects are available, the proposed method improves the mean Dice metric from 0.811 to 0.852 for short-axis image segmentation, compared to the baseline U-net

High dimensional biological data retrieval optimization with NoSQL technology.

Background High-throughput transcriptomic data generated by microarray experiments is the most abundant and frequently stored kind of data currently used in translational medicine studies. Although microarray data is supported in data warehouses such as tranSMART, when querying relational databases for hundreds of different patient gene expression records queries are slow due to poor performance. Non-relational data models, such as the key-value model implemented in NoSQL databases, hold promise to be more performant solutions. Our motivation is to improve the performance of the tranSMART data warehouse with a view to supporting Next Generation Sequencing data. Results In this paper we introduce a new data model better suited for high-dimensional data storage and querying, optimized for database scalability and performance. We have designed a key-value pair data model to support faster queries over large-scale microarray data and implemented the model using HBase, an implementation of Google's BigTable storage system. An experimental performance comparison was carried out against the traditional relational data model implemented in both MySQL Cluster and MongoDB, using a large publicly available transcriptomic data set taken from NCBI GEO concerning Multiple Myeloma. Our new key-value data model implemented on HBase exhibits an average 5.24-fold increase in high-dimensional biological data query performance compared to the relational model implemented on MySQL Cluster, and an average 6.47-fold increase on query performance on MongoDB. Conclusions The performance evaluation found that the new key-value data model, in particular its implementation in HBase, outperforms the relational model currently implemented in tranSMART. We propose that NoSQL technology holds great promise for large-scale data management, in particular for high-dimensional biological data such as that demonstrated in the performance evaluation described in this paper. We aim to use this new data model as a basis for migrating tranSMART's implementation to a more scalable solution for Big Data

Localization of a 64-kDa phosphoprotein in the lumen between the outer and inner envelopes of pea chloroplasts

The identification and localization of a marker protein for the intermembrane space between the outer and inner chloroplast envelopes is described. This 64-kDa protein is very rapidly labeled by [γ-32P]ATP at very low (30 nM) ATP concentrations and the phosphoryl group exhibits a high turnover rate. It was possible to establish the presence of the 64-kDa protein in this plastid compartment by using different chloroplast envelope separation and isolation techniques. In addition comparison of labeling kinetics by intact and hypotonically lysed pea chloroplasts support the localization of the 64-kDa protein in the intermembrane space. The 64-kDa protein was present and could be labeled in mixed envelope membranes isolated from hypotonically lysed plastids. Mixed envelope membranes incorporated high amounts of 32P from [γ-32P]ATP into the 64-kDa protein, whereas separated outer and inner envelope membranes did not show significant phosphorylation of this protein. Water/Triton X-114 phase partitioning demonstrated that the 64-kDa protein is a hydrophilic polypeptide. These findings suggest that the 64-kDa protein is a soluble protein trapped in the space between the inner and outer envelope membranes. After sonication of mixed envelope membranes, the 64-kDa protein was no longer present in the membrane fraction, but could be found in the supernatant after a 110000 × g centrifugation

Adenylate effects on protein phosphorylation in the interenvelope lumen of pea chloroplasts

A 64-kilodalton (kDa) protein, situated in the lumen between the inner and outer envelopes of pea (Pisum sativum L.) chloroplasts (Soll and Bennett 1988, Eur. J. Biochem., 175, 301–307) is shown to undergo reversible phosphorylation in isolated mixed envelope vesicles. It is the most conspicuously labelled protein after incubation of envelopes with 33 nmol·1-1 [-32P]ATP whereas incubation with 50 mol·1-1 [-32P]ATP labels most prominently two outer envelope proteins (86 and 23 kDa). Half-maximum velocity for phosphorylation of the 64-kDa protein occurs with 200 nmol·1-1 ATP, and around 40 mol·1-1 ATP for phosphorylation of the 86- and 23-kDa proteins, indicating the operation of two distinct kinases. GGuanosine-, uridine-, cytidine 5-triphosphate and AMP are poor inhibitors of the labelling of the 64-kDa protein with [-32P]ATP. On the other hand, ADP has a potent influence on the extent of labelling (half-maximal inhibition at 1–5 mol·1-1). The ADP-dependent appearance of 32P in ATP indicates that ADP acts by reversal of kinase activity and not as a competitive inhibitor. However, the most rapid loss of 32P from pre-labelled 64-kDa protein occurs when envelope vesicles are incubated with ATP t1/2=15 s at 20 molsd1-1 ATP). This induced turnover of phosphate appears to be responsible for the rapid phosphoryl turnover seen in situ

An overview of the mid-infrared spectro-interferometer MATISSE: science, concept, and current status

MATISSE is the second-generation mid-infrared spectrograph and imager for the Very Large Telescope Interferometer (VLTI) at Paranal. This new interferometric instrument will allow significant advances by opening new avenues in various fundamental research fields: studying the planet-forming region of disks around young stellar objects, understanding the surface structures and mass loss phenomena affecting evolved stars, and probing the environments of black holes in active galactic nuclei. As a first breakthrough, MATISSE will enlarge the spectral domain of current optical interferometers by offering the L and M bands in addition to the N band. This will open a wide wavelength domain, ranging from 2.8 to 13 um, exploring angular scales as small as 3 mas (L band) / 10 mas (N band). As a second breakthrough, MATISSE will allow mid-infrared imaging - closure-phase aperture-synthesis imaging - with up to four Unit Telescopes (UT) or Auxiliary Telescopes (AT) of the VLTI. Moreover, MATISSE will offer a spectral resolution range from R ~ 30 to R ~ 5000. Here, we present one of the main science objectives, the study of protoplanetary disks, that has driven the instrument design and motivated several VLTI upgrades (GRA4MAT and NAOMI). We introduce the physical concept of MATISSE including a description of the signal on the detectors and an evaluation of the expected performances. We also discuss the current status of the MATISSE instrument, which is entering its testing phase, and the foreseen schedule for the next two years that will lead to the first light at Paranal.Comment: SPIE Astronomical Telescopes and Instrumentation conference, June 2016, 11 pages, 6 Figure

Interobserver reliability of classification and characterization of proximal humeral fractures: a comparison of two and three-dimensional CT

Interobserver reliability for the classification of proximal humeral fractures is limited. The aim of this study was to test the null hypothesis that interobserver reliability of the AO classification of proximal humeral fractures, the preferred treatment, and fracture characteristics is the same for two-dimensional (2-D) and three-dimensional (3-D) computed tomography (CT). Members of the Science of Variation Group--fully trained practicing orthopaedic and trauma surgeons from around the world--were randomized to evaluate radiographs and either 2-D CT or 3-D CT images of fifteen proximal humeral fractures via a web-based survey and respond to the following four questions: (1) Is the greater tuberosity displaced? (2) Is the humeral head split? (3) Is the arterial supply compromised? (4) Is the glenohumeral joint dislocated? They also classified the fracture according to the AO system and indicated their preferred treatment of the fracture (operative or nonoperative). Agreement among observers was assessed with use of the multirater kappa (κ) measure. Interobserver reliability of the AO classification, fracture characteristics, and preferred treatment generally ranged from "slight" to "fair." A few small but statistically significant differences were found. Observers randomized to the 2-D CT group had slightly but significantly better agreement on displacement of the greater tuberosity (κ = 0.35 compared with 0.30, p < 0.001) and on the AO classification (κ = 0.18 compared with 0.17, p = 0.018). A subgroup analysis of the AO classification results revealed that shoulder and elbow surgeons, orthopaedic trauma surgeons, and surgeons in the United States had slightly greater reliability on 2-D CT, whereas surgeons in practice for ten years or less and surgeons from other subspecialties had slightly greater reliability on 3-D CT. Proximal humeral fracture classifications may be helpful conceptually, but they have poor interobserver reliability even when 3-D rather than 2-D CT is utilized. This may contribute to the similarly poor interobserver reliability that was observed for selection of the treatment for proximal humeral fractures. The lack of a reliable classification confounds efforts to compare the outcomes of treatment methods among different clinical trials and reports

An fMRI Investigation of Preparatory Set in the Human Cerebral Cortex and Superior Colliculus for Pro- and Anti-Saccades

Previous studies have identified several cortical regions that show larger BOLD responses during preparation and execution of anti-saccades than pro-saccades. We confirmed this finding with a greater BOLD response for anti-saccades than pro-saccades during the preparation phase in the FEF, IPS and DLPFC and in the FEF and IPS in the execution phase. We then applied multi-voxel pattern analysis (MVPA) to establish whether different neural populations are involved in the two types of saccade. Pro-saccades and anti-saccades were reliably decoded during saccade execution in all three cortical regions (FEF, DLPFC and IPS) and in IPS during saccade preparation. This indicates neural specialization, for programming the desired response depending on the task rule, in these regions. In a further study tailored for imaging the superior colliculus in the midbrain a similar magnitude BOLD response was observed for pro-saccades and anti-saccades and the two saccade types could not be decoded with MVPA. This was the case both for activity related to the preparation phase and also for that elicited during the execution phase. We conclude that separate cortical neural populations are involved in the task-specific programming of a saccade while in contrast, the SC has a role in response preparation but may be less involved in high-level, task-specific aspects of the control of saccades

The future of metabolomics in ELIXIR.

Metabolomics, the youngest of the major omics technologies, is supported by an active community of researchers and infrastructure developers across Europe. To coordinate and focus efforts around infrastructure building for metabolomics within Europe, a workshop on the "Future of metabolomics in ELIXIR" was organised at Frankfurt Airport in Germany. This one-day strategic workshop involved representatives of ELIXIR Nodes, members of the PhenoMeNal consortium developing an e-infrastructure that supports workflow-based metabolomics analysis pipelines, and experts from the international metabolomics community. The workshop established metabolite identification as the critical area, where a maximal impact of computational metabolomics and data management on other fields could be achieved. In particular, the existing four ELIXIR Use Cases, where the metabolomics community - both industry and academia - would benefit most, and which could be exhaustively mapped onto the current five ELIXIR Platforms were discussed. This opinion article is a call for support for a new ELIXIR metabolomics Use Case, which aligns with and complements the existing and planned ELIXIR Platforms and Use Cases