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

State of the art in bile analysis in forensic toxicology

AbstractIn forensic toxicology, alternative matrices to blood are useful in case of limited, unavailable or unusable blood sample, suspected postmortem redistribution or long drug intake-to-sampling interval. The present article provides an update on the state of knowledge for the use of bile in forensic toxicology, through a review of the Medline literature from 1970 to May 2015. Bile physiology and technical aspects of analysis (sampling, storage, sample preparation and analytical methods) are reported, to highlight specificities and consequences from an analytical and interpretative point of view. A table summarizes cause of death and quantification in bile and blood of 133 compounds from more than 200 case reports, providing a useful tool for forensic physicians and toxicologists involved in interpreting bile analysis. Qualitative and quantitative interpretation is discussed. As bile/blood concentration ratios are high for numerous molecules or metabolites, bile is a matrix of choice for screening when blood concentrations are low or non-detectable: e.g., cases of weak exposure or long intake-to-death interval. Quantitative applications have been little investigated, but small molecules with low bile/blood concentration ratios seem to be good candidates for quantitative bile-based interpretation. Further experimental data on the mechanism and properties of biliary extraction of xenobiotics of forensic interest are required to improve quantitative interpretation

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

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

Spatial representation of corticofugal input in the inferior colliculus: a multicontact silicon probe approach

The inferior colliculus (IC) is a well-established target of descending projections from the auditory cortex (AC). However, our understanding of these pathways has been limited by an incomplete picture of their functional influence within the three-dimensional space of the IC. Our goal was to study the properties and spatial representation of corticofugal input in the IC of guinea pigs with a high degree of spatial resolution. We systematically mapped neural activity in the IC using two types of silicon substrate probes that allow for simultaneous recording at multiple neural sites. One probe provided a high resolution in the dorsal-ventral plane and the other provided spatial resolution in the medial-lateral plane. Electrical stimulation of the ipsilateral AC produced excitatory responses in the IC with thresholds usually below 5–10 µA. First spike latencies were predominantly in the 6–20 ms range, although latencies from 3–5 ms were also observed. Broadly distributed unimodal spike patterns with modal latencies greater than 30 ms were occasionally seen. The excitatory responses to cortical stimulation were mostly unimodal and occasionally bimodal with a wide range of spike distribution patterns and response durations. Excitation was often followed by suppression of spontaneous activity. Suppression of acoustic responses was observed even when there was little or no response to electrical stimulation, suggesting spatial-temporal integration. A few of the responding neurons showed purely inhibitory responses to electrical stimulation, suggesting that there are disynaptic routes of corticocollicular inhibition. Detailed spatial mapping revealed that the response patterns and their durations had a characteristic spatial distribution in the IC.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/46534/1/221_2003_Article_1671.pd

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