Records of predatory, con-specific and human induced mauls on fish from the Northeast Atlantic and Black Sea

Present study aims to describe records of mauled and con-specific injures in various fish inhabiting different environment and to discuss possible impacts on fish behavior and ontogenesis. The fish specimens were collected from the Black Sea and Azores Islands (NE Atlantic). Individuals of European flounder, Pleuronectes flesus, common stingray, Dasyatis pastinaca and turbot, Scophthalmus maximus with missing dorsal and caudal fins and flesh, were found in the Black Sea. Specimens with severe mauls of the ocean sunfish, Mola mola, almaco jack, Seriola rivoliana and sargo, Diplodus sargus were recorded from the Azores Islands. All of them were caught alive and survived severe mauls caused by predators or by accidents with propellers, fishing nets. The NE Atlantic records, although possibly caused by natural predation, are more probably than not the result of negative interactions with human activity. Numerous records of mauled fish species from both regions show that the problem with adverse effects of fisheries is quite important. Predatory and con-specific injuries obviously are compatible with basic fish vital functions of described cases. The problem with negative anthropogenic interactions seems to be insufficiently investigated and need more attention by responsible managers and decision makers

Probabilistic Modelling for Unsupervised Analysis of Human Behaviour in Smart Cities

The growth of urban areas in recent years has motivated a large amount of new sensor applications in smart cities. At the centre of many new applications stands the goal of gaining insights into human activity. Scalable monitoring of urban environments can facilitate better informed city planning, efficient security, regular transport and commerce. A large part of monitoring capabilities have already been deployed; however, most rely on expensive motion imagery and privacy invading video cameras. It is possible to use a low-cost sensor alternative, which enables deep understanding of population behaviour such as the Global Positioning System (GPS) data. However, the automated analysis of such low dimensional sensor data, requires new flexible and structured techniques that can describe the generative distribution and time dynamics of the observation data, while accounting for external contextual influences such as time of day or the difference between weekend/weekday trends. In this paper, we propose a novel time series analysis technique that allows for multiple different transition matrices depending on the data’s contextual realisations all following shared adaptive observational models that govern the global distribution of the data given a latent sequence. The proposed approach, which we name Adaptive Input Hidden Markov model (AI-HMM) is tested on two datasets from different sensor types: GPS trajectories of taxis and derived vehicle counts in populated areas. We demonstrate that our model can group different categories of behavioural trends and identify time specific anomalies

Simple approximate MAP Inference for Dirichlet processes

The Dirichlet process mixture (DPM) is a ubiquitous, flexible Bayesian nonparametric statistical model. However, full probabilistic inference in this model is analytically intractable, so that computationally intensive techniques such as Gibb's sampling are required. As a result, DPM-based methods, which have considerable potential, are restricted to applications in which computational resources and time for inference is plentiful. For example, they would not be practical for digital signal processing on embedded hardware, where computational resources are at a serious premium. Here, we develop simplified yet statistically rigorous approximate maximum a-posteriori (MAP) inference algorithms for DPMs. This algorithm is as simple as K-means clustering, performs in experiments as well as Gibb's sampling, while requiring only a fraction of the computational effort. Unlike related small variance asymptotics, our algorithm is non-degenerate and so inherits the "rich get richer" property of the Dirichlet process. It also retains a non-degenerate closed-form likelihood which enables standard tools such as cross-validation to be used. This is a well-posed approximation to the MAP solution of the probabilistic DPM model.Comment: 11 pages, 4 Figures, 5 Table

Simple approximate MAP inference for Dirichlet processes mixtures

The Dirichlet process mixture model (DPMM) is a ubiquitous, flexible Bayesian nonparametric statistical model. However, full probabilistic inference in this model is analytically intractable, so that computationally intensive techniques such as Gibbs sampling are required. As a result, DPMM-based methods, which have considerable potential, are restricted to applications in which computational resources and time for inference is plentiful. For example, they would not be practical for digital signal processing on embedded hardware, where computational resources are at a serious premium. Here, we develop a simplified yet statistically rigorous approximate maximum a-posteriori (MAP) inference algorithm for DPMMs. This algorithm is as simple as DP-means clustering, solves the MAP problem as well as Gibbs sampling, while requiring only a fraction of the computational effort. (For freely available code that implements the MAP-DP algorithm for Gaussian mixtures see http://www.maxlittle.net/.) Unlike related small variance asymptotics (SVA), our method is non-degenerate and so inherits the “rich get richer” property of the Dirichlet process. It also retains a non-degenerate closed-form likelihood which enables out-of-sample calculations and the use of standard tools such as cross-validation. We illustrate the benefits of our algorithm on a range of examples and contrast it to variational, SVA and sampling approaches from both a computational complexity perspective as well as in terms of clustering performance. We demonstrate the wide applicabiity of our approach by presenting an approximate MAP inference method for the infinite hidden Markov model whose performance contrasts favorably with a recently proposed hybrid SVA approach. Similarly, we show how our algorithm can applied to a semiparametric mixed-effects regression model where the random effects distribution is modelled using an infinite mixture model, as used in longitudinal progression modelling in population health science. Finally, we propose directions for future research on approximate MAP inference in Bayesian nonparametrics

Pancreatic Cancer Cell Lines Can Induce Prostaglandin E2 Production from Human Blood Mononuclear Cells

Accumulating evidence suggests an important role for cyclooxygenase-2 (COX-2) in the pathogenesis of a wide range of malignancies. The protumorigenic properties of COX-2 are generally thought to be mediated by its product, PGE2, which is shown to promote tumor spread and growth by multiple mechanisms but most importantly through modulation of the local immune response in the tumor. Pancreatic tumor cells produce various amounts of PGE2, some of them being even deficient in COX enzymes or other PGE2 synthases. Here we describe that, beside pancreatic tumor cells or stromal fibroblasts, human peripheral blood mononuclear cells can also produce PGE2 upon coculture with pancreatic cancer cells. Stimulating of cellular cPLA2 within PBMCs by secreted factors, presumably sPLA2, from tumor cells appeared crucial, while the direct contact between PBMCs and PDACs seemed to be dispensable for this effect. Our data is emphasizing the complex interactions participating in the formation of the tolerogenic immune milieu within pancreatic tumors

Predicting room occupancy with a single passive infrared (PIR) sensor through behavior extraction

Passive infrared sensors have widespread use in many applications, including motion detectors for alarms, lighting systems and hand dryers. Combinations of multiple PIR sensors have also been used to count the number of humans passing through doorways. In this paper, we demonstrate the potential of the PIR sensor as a tool for occupancy estimation inside of a monitored environment. Our approach shows how flexible nonparametric machine learning algorithms extract useful information about the occupancy from a single PIR sensor. The approach allows us to understand and make use of the motion patterns generated by people within the monitored environment. The proposed counting system uses information about those patterns to provide an accurate estimate of room occupancy which can be updated every 30 seconds. The system was successfully tested on data from more than 50 real office meetings consisting of at most 14 room occupants

Automated Quality Control for Sensor Based Symptom Measurement Performed Outside the Lab

The use of wearable sensing technology for objective, non-invasive and remote clinimetric testing of symptoms has considerable potential. However, the accuracy achievable with such technology is highly reliant on separating the useful from irrelevant sensor data. Monitoring patient symptoms using digital sensors outside of controlled, clinical lab settings creates a variety of practical challenges, such as recording unexpected user behaviors. These behaviors often violate the assumptions of clinimetric testing protocols, where these protocols are designed to probe for specific symptoms. Such violations are frequent outside the lab and affect the accuracy of the subsequent data analysis and scientific conclusions. To address these problems, we report on a unified algorithmic framework for automated sensor data quality control, which can identify those parts of the sensor data that are sufficiently reliable for further analysis. Combining both parametric and nonparametric signal processing and machine learning techniques, we demonstrate that across 100 subjects and 300 clinimetric tests from three different types of behavioral clinimetric protocols, the system shows an average segmentation accuracy of around 90%. By extracting reliable sensor data, it is possible to strip the data of confounding factors in the environment that may threaten reproducibility and replicability

TSDF: A simple yet comprehensive, unified data storage and exchange format standard for digital biosensor data in health applications

Digital sensors are increasingly being used to monitor the change over time of physiological processes in biological health and disease, often using wearable devices. This generates very large amounts of digital sensor data, for which, a consensus on a common storage, exchange and archival data format standard, has yet to be reached. To address this gap, we propose Time Series Data Format (TSDF): a unified, standardized format for storing all types of physiological sensor data, across diverse disease areas. We pose a series of format design criteria and review in detail current storage and exchange formats. When judged against these criteria, we find these current formats lacking, and propose a very simple, intuitive standard for both numerical sensor data and metadata, based on raw binary data and JSON-format text files, for sensor measurements/timestamps and metadata, respectively. By focusing on the common characteristics of diverse biosensor data, we define a set of necessary and sufficient metadata fields for storing, processing, exchanging, archiving and reliably interpreting, multi-channel biological time series data. Our aim is for this standardized format to increase the interpretability and exchangeability of data, thereby contributing to scientific reproducibility in studies where digital biosensor data forms a key evidence base