A marked point process model with strong prior shape information for extraction of multiple, arbitrarily-shaped objects

We define a method for incorporating strong prior shape information into a recently extended Markov point process model for the extraction of arbitrarily-shaped objects from images. To estimate the optimal configuration of objects, the process is sampled using a Markov chain based on a stochastic birth-and-death process defined in a space of multiple objects. The single objects considered are defined by both the image data and the prior information in a way that controls the computational complexity of the estimation problem. The method is tested via experiments on a very high resolution aerial image of a scene composed of tree crowns

A multi-layer `gas of circles' Markov random field model for the extraction of overlapping near-circular objects

We propose a multi-layer binary Markov random field (MRF) model that assigns high probability to object configurations in the image domain consisting of an unknown number of possibly touching or overlapping near-circular objects of approximately a given size. Each layer has an associated binary field that specifies a region corresponding to objects. Overlapping objects are represented by regions in different layers. Within each layer, long-range interactions favor connected components of approximately circular shape, while regions in different layers that overlap are penalized. Used as a prior coupled with a suitable data likelihood, the model can be used for object extraction from images, e.g. cells in biological images or densely-packed tree crowns in remote sensing images. We present a theoretical and experimental analysis of the model, and demonstrate its performance on various synthetic and biomedical images

Comparison of sequencing methods and data processing pipelines for whole genome sequencing and minority single nucleotide variant (mSNV) analysis during an influenza A/H5N8 outbreak

As high-throughput sequencing technologies are becoming more widely adopted for analysing pathogens in disease outbreaks there needs to be assurance that the different sequencing technologies and approaches to data analysis will yield reliable and comparable results. Conversely, understanding where agreement cannot be achieved provides insight into the limitations of these approaches and also allows efforts to be focused on areas of the process that need improvement. This manuscript describes the next-generation sequencing of three closely related viruses, each analysed using different sequencing strategies, sequencing instruments and data processing pipelines. In order to determine the comparability of consensus sequences and minority (sub-consensus) single nucleotide variant (mSNV) identification, the biological samples, the sequence data from 3 sequencing platforms and the *.bam quality-trimmed alignment files of raw data of 3 influenza A/H5N8 viruses were shared. This analysis demonstrated that variation in the final result could be attributed to all stages in the process, but the most critical were the well-known homopolymer errors introduced by 454 sequencing, and the alignment processes in the different data processing pipelines which affected the consistency of mSNV detection. However, homopolymer errors aside, there was generally a good agreement between consensus sequences that were obtained for all combinations of sequencing platforms and data processing pipelines. Nevertheless, minority variant analysis will need a different level of careful standardization and awareness about the possible limitations, as shown in this study

Factors affecting the prey preferences of jackals (Canidae)

Prey selection by carnivores can be affected by top-down and bottom-up factors. For example, large carnivores may facilitate food resources for mesocarnivores by providing carcasses to scavenge, however mesocarnivores may hunt large prey themselves, and their diets might be affected by prey size and behaviour. We reviewed jackal diet studies and determined how the presence of large carnivores and various bottom-up factors affected jackal prey selection. We found 20 studies of black-backed jackals (Canis mesomelas) from 43 different times or places, and 13 studies of Eurasian golden jackals (Canis aureus) from 23 different times or places reporting on 3900 and 2440 dietary records (i.e. scats or stomach contents), respectively. Black-backed jackals significantly preferred small (< 30 kg) ungulate 3 species that hide their young (duiker Sylvicapra grimmia, bushbuck Tragelaphus scriptus and springbok Antidorcas marsupialis), and avoided large (> 120 kg) hider species and follower species of any body size. They had a preferred and accessible prey weight range of 14-26 kg, and a predator to ideal prey mass ratio of 1:3.1. Eurasian golden jackal significantly prefer to prey on brown hare (Lepus europaeus; 4 kg), yielding a predator to preferred prey mass ratio of 1:0.6, and a preferred and accessible prey weight range of 0 – 4 kg and 0 – 15 kg, respectively. Prey preferences of jackals differed significantly in the presence of apex predators, but it was not entirely due to carrion availability of larger prey species. Our results show that jackal diets are affected by both top-down and bottom-up factors, because apex predators as well as prey size and birthing behaviour affected prey preferences of jackals. A better understanding of the factors affecting jackal prey preferences, as presented here, could lead to greater acceptance of mesocarnivores and reduced human-wildlife conflict

Comparative study of classification algorithms using molecular descriptors in toxicological databases

The rational development of new drugs is a complex and expensive process, comprising several steps. Typically, it starts by screening databases of small organic molecules for chemical structures with potential of binding to a target receptor and prioritizing the most promising ones. Only a few of these will be selected for biological evaluation and further refinement through chemical synthesis. Despite the accumulated knowledge by pharmaceutical companies that continually improve the process of finding new drugs, a myriad of factors affect the activity of putative candidate molecules in vivo and the propensity for causing adverse and toxic effects is recognized as the major hurdle behind the current "target-rich, lead-poor" scenario. In this study we evaluate the use of several Machine Learning algorithms to find useful rules to the elucidation and prediction of toxicity using ID and 2D molecular descriptors. The results indicate that: i) Machine Learning algorithms can effectively use ID molecular descriptors to construct accurate and simple models; ii) extending the set of descriptors to include 2D descriptors improve the accuracy of the models

Community structure of a southern Chihuahuan Desert grassland under different grazing pressures

"The effect of different grazing intensities on the semiarid grasslands of the southern Chihuahuan Desert was studied using a fence-line contrast between a moderately grazed cattle ranch, from which goats and sheep had been excluded for forty-five years, and an ejido, heavily overgrazed for at least the last century. Each plant species density and cover was quantified in three distinctive microhabitats on the ranch and on the adjacent common-use rangelands. The results indicated that three grass species were important in the dynamics of this rangeland. Hilaria belangeri, a stoloniferous, mat-forming grass species, was dominant on the heavily grazed ejido, but is in the process of being replaced by two taller grasses, Bouteloua gracilis and Boutleloua curtipendula, on the more moderately grazed ranch. These data suggest that the dynamics of this system in the semiarid grasslands of the southern Chihuahuan Desert are based primarily on two functional groups of species. Members of the first functional group are stoloniferous and clonal. Their growth form slows the rate of surface water movement, thereby controlling erosion while building up the soil by entrapping debris. In contrast, the second functional group is comprised of non-stoloniferous grasses that are described variously as bunch, tufted or tussock grasses in the literature. These species have the capacity to invade, overtop and replace individuals of the first functional group. They are limited in their lateral growth by their rhizomatous growth habit, which is much less efficient in co-opting space. Finally, there is, at least, a third functional group of much taller grasses present that are limited in these rangelands to refuges by the heavy grazing. These species are usually obligate seed producers. Such species have the potential to replace members of the earlier functional groups by overtopping them. They are limited by their inability to reproduce vegetatively, once established by their seed bank. This research suggests that restoration of the heavily eroded, semiarid grasslands of the southern Chihuahuan Desert must begin with the re-establishment of members of the first functional group. These species have the ability to facilitate the entry of the later functional groups and, in turn, be replaced competitively by them.

Adaptive probabilistic models of wavelet packets for the analysis and segmentation of textured remote sensing images

Remote sensing imagery plays an important role in many fields. It has become an invaluable tool for diverse applications ranging from cartography to ecosystem management. In many of the images processed in these types of applications, semantic entities in the scene are correlated with textures in the image. In this paper, we propose a new method of analysing such textures based on adaptive probabilistic models of wavelet packets. Our approach adapts to the principal periodicities present in the textures, and can capture long-range correlations while preserving the independence of the wavelet packet coefficients. This technique has been applied to several remote sensing images, the results of which are presented.

Adaptive Probabilistic Models of Wavelet Packets for the Analysis and Segmentation of Textured Remote Sensing Images

Remote sensing imagery plays an important role in many fields. It has become an invaluable tool for diverse applications ranging from cartography to ecosystem management. In many of the images processed in these types of applications, semantic entities in the scene are correlated with textures in the image. In this paper, we propose a new method of analysing such textures based on adaptive probabilistic models of wavelet packets. Our approach adapts to the principal periodicities present in the textures, and can capture long-range correlations while preserving the independence of the wavelet packet coefficients. This technique has been applied to several remote sensing images, the results of which are presented