Cheetahs, Acinonyx jubatus, balance turn capacity with pace when chasing prey

Predator–prey interactions are fundamental in the evolution and structure of ecological communities. Our understanding, however, of the strategies used in pursuit and evasion remains limited. Here, we report on the hunting dynamics of the world's fastest land animal, the cheetah, Acinonyx jubatus. Using miniaturized data loggers, we recorded fine-scale movement, speed and acceleration of free-ranging cheetahs to measure how hunting dynamics relate to chasing different sized prey. Cheetahs attained hunting speeds of up to 18.94 m s-1 and accelerated up to 7.5 m s-2 with greatest angular velocities achieved during the terminal phase of the hunt. The interplay between forward and lateral acceleration during chases showed that the total forces involved in speed changes and turning were approximately constant over time but varied with prey type. Thus, rather than a simple maximum speed chase, cheetahs first accelerate to decrease the distance to their prey, before reducing speed 5–8 s from the end of the hunt, so as to facilitate rapid turns to match prey escape tactics, varying the precise strategy according to prey species. Predator and prey thus pit a fine balance of speed against manoeuvring capability in a race for survival.

Remote, subsurface detection of the algal toxin domoic acid onboard the Environmental Sample Processor: Assay development and field trials

The ability to detect harmful algal bloom (HAB) species and their toxins in real- or near real-time is a critical need for researchers studying HAB/toxin dynamics, as well as for coastal resource managers charged with monitoring bloom populations in order to mitigate their wide ranging impacts. The Environmental Sample Processor (ESP), a robotic electromechanical/fluidic system, was developed for the autonomous, subsurface application of molecular diagnostic tests and has successfully detected several HAB species using DNA probe arrays during field deployments. Since toxin production and thus the potential for public health and ecosystem effects varies considerably in natural phytoplankton populations, the concurrent detection of HAB species and their toxins onboard the ESP is essential. We describe herein the development of methods for extracting the algal toxin domoic acid (DA) from Pseudonitzschia cells (extraction efficiency >90%) and testing of samples using a competitive ELISA onboard the ESP. The assay detection limit is in the low ng/mL range (in extract), which corresponds to low ng/L levels of DA in seawater for a 0.5 L sample volume acquired by the ESP. We also report the first in situ detection of both a HAB organism (i.e., Pseudo-nitzschia) and its toxin, domoic acid, via the sequential (within 2-3 h) conduct of species- and toxin-specific assays during ESP deployments in Monterey Bay, CA, USA. Efforts are now underway to further refine the assay and conduct additional calibration exercises with the aim of obtaining more reliable, accurate estimates of bloom toxicity and thus their potential impacts. Published by Elsevier B.V.

Distribution, occurrence and biotoxin composition of the main shellfish toxin producing microalgae within European waters: A comparison of methods of analysis

Harmful algal blooms (HABs) are a natural global phenomena emerging in severity and extent. Incidents have many economic, ecological and human health impacts. Monitoring and providing early warning of toxic HABs are critical for protecting public health. Current monitoring programmes include measuring the number of toxic phytoplankton cells in the water and biotoxin levels in shellfish tissue. As these efforts are demanding and labour intensive, methods which improve the efficiency are essential. This study compares the utilisation of a multitoxin surface plasmon resonance (multitoxin SPR) biosensor with enzyme-linked immunosorbent assay (ELISA) and analytical methods such as high performance liquid chromatography with fluorescence detection (HPLC-FLD) and liquid chromatography-tandem mass spectrometry (LC-MS/MS) for toxic HAB monitoring efforts in Europe. Seawater samples (n = 256) from European waters, collected 2009-2011, were analysed for biotoxins: saxitoxin and analogues, okadaic acid and dinophysistoxins 1/2 (DTX1/DTX2) and domoic acid responsible for paralytic shellfish poisoning (PSP), diarrheic shellfish poisoning (DSP) and amnesic shellfish poisoning (ASP), respectively. Biotoxins were detected mainly in samples from Spain and Ireland. France and Norway appeared to have the lowest number of toxic samples. Both the multitoxin SPR biosensor and the RNA microarray were more sensitive at detecting toxic HABs than standard light microscopy phytoplankton monitoring. Correlations between each of the detection methods were performed with the overall agreement, based on statistical 2 × 2 comparison tables, between each testing platform ranging between 32% and 74% for all three toxin families illustrating that one individual testing method may not be an ideal solution. An efficient early warning monitoring system for the detection of toxic HABs could therefore be achieved by combining both the multitoxin SPR biosensor and RNA microarray.

A Bayesian Network Hybrid Model for Representing Accident and Emergency Waiting Times

The paper introduces a new modeling approach that represents the waiting times in an Accident and Emergency (A&E) Department in a UK based National Health Service (NHS) hospital. The technique uses Bayesian networks to capture the heterogeneity of arriving patients by representing how patient covariates interact to influence their waiting times in the department. Such waiting times have been reviewed by the NHS as a means of investigating the efficiency of A&E departments (Emergency Rooms) and how they operate. As a result activity targets are now established based on the patient total waiting times with much emphasis on trolley waits.

Intertidal molluscan and algal species richness around the UK coast

Geographically referenced databases of species records are becoming increasingly available. Doubts over the heterogeneous quality of the underlying data may restrict analyses of such collated databases. We partitioned the spatial variation in species richness of littoral algae and molluscs from the UK National Biodiversity Network database into a smoothed mesoscale component and a local component. Trend surface analysis (TSA) was used to define the mesoscale patterns of species richness, leaving a local residual component that lacked spatial autocorrelation. The analysis was based on 10 km grid squares with 115035 records of littoral algae (729 species) and 66879 records of littoral molluscs (569 species). The TSA identified variation in algal and molluscan species richness with a characteristic length scale of approximately 120 km. Locations of the most species-rich grid squares were consistent with the southern and western bias of species richness in the UK marine flora and fauna. The TSA also identified areas which showed significant changes in the spatial pattern of species richness: breakpoints, which correspond to major headlands along the south coast of England. Patterns of algal and molluscan species richness were broadly congruent. Residual variability was strongly influenced by proxies of collection effort, but local environmental variables including length of the coastline and variability in wave exposure were also important. Relative to the underlying trend, local species richness hotspots occurred on all coasts. While there is some justification for scepticism in analyses of heterogeneous datasets, our results indicate that the analysis of collated datasets can be informative.

A Bayesian network hybrid model for representing accident and emergency waiting times

The paper introduces a new modeling approach that represents the waiting times in an accident and emergency (A&E) department in a UK based national health service (NHS) hospital. The technique uses Bayesian networks to capture the heterogeneity of arriving patients by representing how patient covariates interact to influence their waiting times in the department. Such waiting times have been reviewed by the NHS as a means of investigating the efficiency of A&E departments (emergency rooms) and how they operate. As a result activity targets are now established based on the patient total waiting times with much emphasis on trolley waits.

PROSTATIC INTRAEPITHELIAL NEOPLASIA (PIN) - PERFORMANCE OF BAYESIAN BELIEF NETWORK FOR DIAGNOSIS AND GRADING

Prostatic intraepithelial neoplasia (PIN) diagnosis and grading are affected by uncertainties which arise from the fact that almost all knowledge of PIN histopathology is expressed in concepts, descriptive linguistic terms, and words. A Bayesian belief network (BBN) was therefore used to reduce the problem of uncertainty in diagnostic clue assessment, while still considering the dependences between elements in the reasoning sequence. A shallow network was used with an open-tree topology, with eight first-level descendant nodes for the diagnostic clues (evidence nodes), each independently linked by a conditional probability matrix to a root node containing the diagnostic alternatives (decision node). One of the evidence nodes was based on the tissue architecture and the others were based on cell features. The system was designed to be interactive, in that the histopathologist entered evidence into the network in the form of likelihood ratios for outcomes at each evidence node. The efficiency of the network was tested on a series of 110 prostate specimens, subdivided as follows: 22 cases of non-neoplastic prostate or benign prostatic tissue (NP), 22 PINs of low grade (PINlow), 22 PINs of high grade (PINhigh), 22 prostatic adenocarcinomas with cribriform pattern (PACcri), and 22 prostatic adenocarcinomas with large acinar pattern (PAClgac). The results obtained in the benign and malignant categories showed that the belief for the diagnostic alternatives is very high, the values being in general more than 0.8 and often close to 1.0. When considering the PIN lesions, the network classified and graded most of the cases with high certainty. However, there were some cases which showed values less than 0.8 (13 cases out of 44), thus indicating that there are situations in which the feature changes are intermediate between contiguous categories or grades. Discrepancy between morphological grading and the BBN results was observed in four out of 44 PIN cases: one PINlow was classified as PINhigh and three PINhigh were classified as PINlow. In conclusion, the network can grade PlN lesions and differentiate them from other prostate lesions with certainty. In particular, it offers a descriptive classifier which is readily implemented and which allows the use of linguistic, fuzzy variables.

Bayesian network data imputation with application to survival tree analysis

Retrospective clinical datasets are often characterized by a relatively small sample size and many missing data. In this case, a common way for handling the missingness consists in discarding from the analysis patients with missing covariates, further reducing the sample size. Alternatively, if the mechanism that generated the missing allows, incomplete data can be imputed on the basis of the observed data, avoiding the reduction of the sample size and allowing methods to deal with complete data later on. Moreover, methodologies for data imputation might depend on the particular purpose and might achieve better results by considering specific characteristics of the domain. The problem of missing data treatment is studied in the context of survival tree analysis for the estimation of a prognostic patient stratification. Survival tree methods usually address this problem by using surrogate splits, that is, splitting rules that use other variables yielding similar results to the original ones. Instead, our methodology consists in modeling the dependencies among the clinical variables with a Bayesian network, which is then used to perform data imputation, thus allowing the survival tree to be applied on the completed dataset. The Bayesian network is directly learned from the incomplete data using a structural expectation–maximization (EM) procedure in which the maximization step is performed with an exact anytime method, so that the only source of approximation is due to the EM formulation itself. On both simulated and real data, our proposed methodology usually outperformed several existing methods for data imputation and the imputation so obtained improved the stratification estimated by the survival tree (especially with respect to using surrogate splits).

Proposing the deep dynamic Bayesian network as a future computer based medical system

The development of new learning models has been of great importance throughout recent years, with a focus on creating advances in the area of deep learning. Deep learning was first noted in 2006, and has since become a major area of research in a number of disciplines. This paper will delve into the area of deep learning to present its current limitations and provide a new idea for a fully integrated deep and dynamic probabilistic system. The new model will be applicable to a vast number of areas initially focusing on applications into medical image analysis with an overall goal of utilising this approach for prediction purposes in computer based medical systems.

rbcL sequences indicate a single evolutionary origin of multinucleate cells in the red algal tribe Callithamnieae.

In the Ceramiaceae, one of the largest families of the red algae, there are from 1 to 4000 nuclei in each vegetative cell, but each tribe is homogeneous with respect to the uninucleate/multinucleate character state, except for the Callithamnieae. The goals of this study were to analyze rbcL gene sequences to clarify the evolution of taxa within the tribe Callithamnieae and to evaluate the potential evolutionary significance of the development of multinucleate cells in certain taxa. The genus Aglaothamnion, segregated from Callithamnion because it is uninucleate, was paraphyletic in all analyses. Callithamnion (including Aristothamnion) was monophyletic although not robustly so, apparently due to variations between taxa in rate of sequence evolution. Morphological synapomorphies were identified at different depths in the tree, supporting the molecular phylogenetic analysis. The uninucleate character state is ancestral in this tribe. The evolution of multinucleate cells has occurred once in the Callithamnieae. Multiple nuclei in each cell may combine the benefits of small C values (rapid cell cycle) with large cells (permitting morphological elaboration) while maintaining a constant ratio of nuclear volume: cytoplasmic volume.