Continuous plankton records stand the test of time: evaluation of flow rates, clogging and the continuity of the CPR time-series

The Continuous Plankton Recorder (CPR) survey is one of the most extensive biological time-series in existence and has been in operation over major regions of the North Atlantic since 1932. However, there is little information about the volume of water filtered through each sample, but rather a general assumption has persisted that each sample represents 3 m3. Data from electromagnetic flowmeters, deployed on CPRs between 1995 and 1998, was examined. The mean volume filtered through samples was 3.11 m3 and the effect of clogging on filtration efficiencies was not great. Consequently, even when the likely variations in flow due to clogging are taken into account, previously identified links between zooplankton abundance and climatic signals remain strong.

First records of dive durations for a hibernating sea turtle

The first published record, from the early 1970s, of hibernation in sea turtles is based on the reports of the indigenous Indians and fishermen from Mexico, who hunted dormant green turtles (Chelonia mydas) in the Gulf of California. However, there were no successful attempts to investigate the biology of this particular behaviour further. Hence, data such as the exact duration and energetic requirements of dormant winter submergences are lacking. We used new satellite relay data loggers to obtain the first records of up to 7 h long dives of a loggerhead turtle (Caretta caretta) overwintering in Greek waters. These represent the longest dives ever reported for a diving marine vertebrate. There is strong evidence that the dives were aerobic, because the turtle surfaced only for short intervals and before the calculated oxygen stores were depleted. This evidence suggests that the common belief that sea turtles hibernate underwater, as some freshwater turtles do, is incorrect.

Stomach temperature records reveal nursing behaviour and transition to solid food consumption in an unweaned mammal, the harbour seal pup (Phoca vitulina)

Knowledge of milk transfer from mother to offspring and early solid food ingestions in mammals allows for a greater understanding of the factors affecting transition to nutritional independence and pre-weaning growth and survival. Yet studies monitoring suckling behaviour have often relied on visual observations, which might not accurately represent milk intake. We assessed the use of stomach temperature telemetry to monitor suckling and foraging behaviour in free-ranging harbour seal (Phoca vitulina) pups during lactation. Stomach temperature declines were analysed using principal component and cluster analyses, as well as trials using simulated stomachs resulting in a precise classification of stomach temperature drops into milk, seawater and solid food ingestions. Seawater and solid food ingestions represented on average 15.361.6% [0-40.0%] and 0.760.2% [0-13.0%], respectively, of individual ingestions. Overall, 63.7% of milk ingestions occurred while the pups were in the water, of which 13.9% were preceded by seawater ingestion. The average time between subsequent ingestions was significantly less for seawater than for milk ingestions. These results suggest that seawater ingestion might represent collateral ingestion during aquatic suckling attempts. Alternatively, as solid food ingestions (n = 19) were observed among 7 pups, seawater ingestion could result from missed prey capture attempts. This study shows that some harbour seals start ingesting prey while still being nursed, indicating that weaning occurs more gradually than previously thought in this species. Stomach temperature telemetry represents a promising method to study suckling behaviour in wild mammals and transition to nutritional independence in various endotherm species.

A possible anatomical and biomechanical explanation of the 10% rule used in the clinical assessment of prehensile hand movements and handed dominance

Abstract
A current doctrine in the dynamometric approach to determine lateralization of hand function states that in 10% of cases, the non-dominant hand will be stronger than the dominant hand. In this study, a novel MRI based modelling approach was applied to the first dorsal introsseus muscle (FDI), to determine whether the 10% rule may be applied to the FDI and may be partially explained by the arrangement of the anatomical components of the FDI.

Methods
Initially the force generated by the thumb segment during an isometric pushing task in the horizontal plane was measured from 25 strongly right-handed young males. Nine of these participants then had structural magnetic resonance imaging (sMRI) of the thumb and index osseous compartment. A modelling technique was developed to extract the muscle data and quantify the muscle line of action onto to the first metacarpal bone segment in order to quantify the muscle force at the point of momentary rotation – equilibrium.

Results
Eight of 25 subjects exhibited stronger force from the left hand. Six out of nine subjects from the MRI possessed significantly greater angles of attachment of the index osseous compartment on the left (non-dominant) hand. These six subjects also generated greater maximal isometric forces from the FDI of the left side. There was a significantly greater muscle volume for the right FDI muscle as compared to the left as measured from the reconstructed MRI slice data.

Conclusions
The calculated force produced by the muscle is related to the angle of attachment of the muscle to bone in the index osseous compartment. The MRI findings indicate that the 10% rule may be anatomically and biomechanically explained.

Machine-learning prediction of cancer survival: a retrospective study using electronic administrative records and a cancer registry

Using the prediction of cancer outcome as a model, we have tested the hypothesis that through analysing routinely collected digital data contained in an electronic administrative record (EAR), using machine-learning techniques, we could enhance conventional methods in predicting clinical outcomes.

Improved risk predictions via sparse imputation of patient conditions in electronic medical records

Electronic Medical Records (EMR) are increasingly used for risk prediction. EMR analysis is complicated by missing entries. There are two reasons - the “primary reason for admission” is included in EMR, but the co-morbidities (other chronic diseases) are left uncoded, and, many zero values in the data are accurate, reflecting that a patient has not accessed medical facilities. A key challenge is to deal with the peculiarities of this data - unlike many other datasets, EMR is sparse, reflecting the fact that patients have some, but not all diseases. We propose a novel model to fill-in these missing values, and use the new representation for prediction of key hospital events. To “fill-in” missing values, we represent the feature-patient matrix as a product of two low rank factors, preserving the sparsity property in the product. Intuitively, the product regularization allows sparse imputation of patient conditions reflecting common comorbidities across patients. We develop a scalable optimization algorithm based on Block coordinate descent method to find an optimal solution. We evaluate the proposed framework on two real world EMR cohorts: Cancer (7000 admissions) and Acute Myocardial Infarction (2652 admissions). Our result shows that the AUC for 3 months admission prediction is improved significantly from (0.741 to 0.786) for Cancer data and (0.678 to 0.724) for AMI data. We also extend the proposed method to a supervised model for predicting of multiple related risk outcomes (e.g. emergency presentations and admissions in hospital over 3, 6 and 12 months period) in an integrated framework. For this model, the AUC averaged over outcomes is improved significantly from (0.768 to 0.806) for Cancer data and (0.685 to 0.748) for AMI data.