Probability Of Presence Research Articles

Identification of the Properties and Function of the Unknown Protein with Accession Number AT2G15110.1 on the TAIR Website

Background: Nowadays, with the help of genomics and proteomics, numerous genes and proteins have been discovered, however, the function and role of most of them are still unknown. Using bioinformatics tools can be a major step in the identification of the function of these genes and proteins. Methods: In this study, we applied various bioinformatics software to identify the unknown protein properties with the AT2G15110.1 accession number on the Arabidopsis Information Resource website. Operations, such as identification of general protein properties, blasting amino acid sequences, detection of motifs and domains in the sequence, examination of the second and third protein structures, exploration of ligands, assessment of proteins involved with the target protein as well as recognition of the target protein location in the cell, were carried out. Results: The results showed that the query protein had no significant homology in terms of sequence, three-dimensional structure, and any interaction with known proteins. Additionally, it was observed that the presence probability of this protein in the nucleus organelle was more than in other organelles, and it only has one domain of unknown function. Conclusions: The results of this study can be basic information for other researchers who seek to identify this unknown protein and determine the right pathway for the identification of mentioned protein function through bioinformatics tools or laboratory methods. Based on the results of this study, laboratory methods are recommended for subsequent studies.

Influence of Rising Water Temperature on the Temperate Seagrass Species Eelgrass (Zostera marina L.) in the Northeast USA

Sea surface temperature (SST) has increased worldwide since the beginning of the 20th century, a trend which is expected to continue. Changes in SST can have significant impacts on marine biota, including population-level shifts and alterations in community structure and diversity, and changes in the timing of ecosystem events. Seagrasses are a group of foundation species that grow in shallow coastal and estuarine systems, where they provide many ecosystem services. Eelgrass, Zostera marina L., is the dominant seagrass species in the Northeast United States of America (USA). Multiple factors have been cited for losses in this region, including light reduction, eutrophication, and physical disturbance. Warming has the potential to exacerbate seagrass loss. Here, we investigate regional changes in eelgrass presence and abundance in relation to local water temperature using monitoring data from eight sites in the Northeastern USA (New Hampshire to Maryland) where a consistent monitoring protocol, SeagrassNet, has been applied. We use a hurdle model consisting of a generalized additive mixed model (GAMM) with binomial and beta response distributions for modeling eelgrass presence and abundance, respectively, in relation to the local summer average water temperature. We show that summer water temperature one year prior to monitoring is a significant predictor of eelgrass presence, but not abundance, on a regional scale. Above average summer temperatures correspond to a decrease in probability of eelgrass presence (and increased probability of eelgrass absence) the following year. Cooler than average temperatures in the preceding year, down to approximately 0.5°C below the site average, are associated with the highest predicted probability of eelgrass presence. Our findings suggest vulnerability in eelgrass meadows of the Northeast USA and emphasize the value of unified approaches to seagrass monitoring, conservation and management at the seascape scale.

Associations Between Posterior Communicating Artery Aneurysms and Morphological Characteristics of Surrounding Arteries.

ObjectivesTo explore the associations between posterior communicating artery (PComA) aneurysms and morphological characteristics of arteries upstream of and around the PComA bifurcation site.MethodsIn this study, fifty-seven patients with PComA aneurysms and sixty-two control subjects without aneurysms were enrolled. The centerlines of the internal carotid artery (ICA) and important branches were generated for the measurement and analysis of morphological parameters, such as carotid siphon types, diameters of two fitting circles, and the angle formed by them (D1, D2, and ϕ), length (L) and tortuosity (TL) of ICA segment between an ophthalmic artery and PComA bifurcations, bifurcation angle (θ), tortuosity (TICA and TPComA), and flow direction changes (θICA and θPComA) around the PComA bifurcation site.ResultsNo significant difference (p > 0.05) was found in the siphon types (p = 0.467) or L (p = 0.114). Significant differences (p < 0.05) were detected in D1 (p = 0.036), TL (p < 0.001), D2 (p = 0.004), ϕ (p = 0.008), θ (p = 0.001), TICA (p < 0.001), TPComA (p = 0.012), θICA (p < 0.001), and θPComA (p < 0.001) between the two groups. TICA had the largest area under the curve (AUC) (0.843) in the receiver operating characteristic (ROC) analysis in diagnosing the probability of PComA aneurysms presence and was identified as the only potent morphological parameter (OR = 11.909) associated with PComA aneurysms presence.ConclusionsThe high tortuosity of the ICA segment around the PComA bifurcation is associated with PComA aneurysm presence.

Anatomy of Subpancreatic Transverse Colon Vessel and Its Clinical Significance: An Observational Study.

PurposeTo observe and count the probability of presence and the anatomy of the vessel arising via the inferior margin of the pancreas and traveling within the transverse mesocolon, and analyze its clinical significance.MethodsPatients who underwent radical operation for transverse colon cancer or descending colon cancer from January 2020 to November 2021 and a nonspecific cadaver were included in this study. We observed and recorded intraoperatively for the probability of presence and the anatomy of the vessel arising via the inferior margin of the pancreas and traveling within the transverse mesocolon. And its property was determined by tissue slice.ResultsA total of 84 patients were included, of which, the vessel was observed in 72 (85.7%) patients, and its property was confirmed by tissue slice of one patient after surgery. The vessel was also observed in a nonspecific cadaver. Originating from transverse pancreatic artery, often one, occasionally two, rarely three vessels arose via the inferior margin of pancreas and supplied the left transverse colon. Artery and vein parallel ran, and it was difficult to separate them due to their small diameter, but the vessels may thicken under certain conditions for increasing blood supply.ConclusionThe vessel, which is not yet reported and named in the literature, can be called the subpancreatic transverse colon vessel, which has a high probability of presence in humans and may be of great significance to human physiological anatomy, surgery, and oncology, and deserves recognition and attention from surgeons.

Non‐local effects of human activity on the spatial distribution of migratory wildlife in Serengeti National Park, Tanzania

Abstract Human activities are transforming landscapes and altering the structure and functioning of ecosystems worldwide and often result in sharp contrasts between human‐dominated landscapes and adjacent natural habitats that lead to the creation of hard edges and artificial boundaries. The configuration of these boundaries could influence local biotic interactions and animal behaviours. Here, we investigate whether boundaries of different degrees of ‘hardness’ affect space utilization by migratory species in Serengeti National Park, Tanzania. We deployed camera traps along transects perpendicular to the national park boundary at three different locales. The transects were located in areas that consisted of two types of human–wildlife interface: a sudden transition from the national park into agro‐pastoral land use (termed a ‘hard’ boundary) and a more gradual transition mediated by a shared usage area (termed a ‘soft’ boundary). Camera traps were placed at 2 km intervals along each 10 km transect from the edge towards the core of the park and were programmed to collect images hourly between dawn and dusk between June 2016 and March 2019. We used a deep neural network to detect the presence of wildlife within images and then used a Bayesian model with diffuse priors to estimate parameters of a generalized linear model with a Bernoulli likelihood. We explored the binomial probability of either wildebeest or zebra presence as a function of distance to the boundary, the rate of grass greening or drying (dNDVI) and the concentration of grass protein. There was a strong negative effect of distance to boundary on the probability of detecting wildebeest or zebra; however, this was only observed where the transition from human‐dominated landscape to protected areas was sudden. Conversely, soft boundaries had little to no effect on the probability of detecting wildebeest or zebra. The results suggest that boundary type affects migratory species occurrence. The implications of these findings suggest that hard boundaries reduce the effective size of conservation areas; for many species, the area used by wildlife is likely less than the gazetted area under protection. The impacts may be severe especially for narrow protected areas or dispersal corridors.

Detecting exoplanets with the false inclusion probability

Context.It is common practice to claim the detection of a signal if, for a certain statistical significance metric, the signal significance exceeds a certain threshold fixed in advance. In the context of exoplanet searches in radial velocity data, the most common statistical significance metrics are the Bayes factor and the false alarm probability (FAP). Both criteria have proved useful, but do not directly address whether an exoplanet detection should be claimed. Furthermore, it is unclear which detection threshold should be taken and how robust the detections are to model misspecification.Aims.The aim of the present work is to define a detection criterion that conveys as precisely as possible the information needed to claim an exoplanet detection, as well as efficient numerical methods to compute it. We compare this new criterion to existing ones in terms of sensitivity and robustness to a change in the model.Methods.We define a general detection criterion called the false inclusion probability (FIP). In the context of exoplanet detections it provides the posterior probability of presence of a planet with a period in a certain interval. Posterior distributions are computed with the nested sampling packagePOLYCHORD. We show that for FIP and Bayes factor calculations, defining priors on linear parameters as Gaussian mixture models can significantly speed up computations. The performance of the FAP, Bayes factor, and FIP are studied via simulations and analytical arguments. We compare the methods assuming the model is correct, then evaluate their sensitivity to the prior and likelihood choices.Results.Among other properties, the FIP offers ways to test the reliability of the significance levels; it is a particularly efficient way to account for aliasing, and it allows the presence of planets to be excluded with a certain confidence. In our simulations, we find that the FIP outperforms existing detection metrics. We show that low amplitude planet detections are sensitive to priors on period and semi-amplitude, which will require further attention for the detection of Earth-like planets. We recommend to let the parameters of the noise model free in the analysis, rather than fixing a noise model based on a fit to ancillary indicators.

A species distribution model of the Antarctic minke whale (Balaenoptera bonaerensis)

The Antarctic minke whale (Balaenoptera bonaerensis) is regarded a Southern Hemisphere endemic found throughout the Southern Hemisphere, generally south of 60°S in austral summer. Here they have been routinely observed in highest densities adjacent to and inside the sea ice edge, and where they feed predominantly on krill. Detecting abundance trends regarding this species by employing visual monitoring is problematic. Partly this is because the whales are frequently sighted within sea ice where navigational safety concerns prevent ships from surveying. In this respect species-habitat models are increasingly recognized as valuable tools to predict the probability of cetacean presence, relative abundance or density throughout an area of interest and to gain insight into the ecological processes affecting these patterns. The objective of this study was to provide this background information for the above research needs and in a broader context use species distribution models (SDMs) to establish a current habitat suitability description for the species and to identify the main environmental covariates related to its distribution. We used filtered 464 occurrences to generate the SDMs. We selected eight predictor variables with reduced collinearity for constructing the models: mean annuals of the surface temperature (ºC), salinity (PSS), current velocity (m/s), sea ice concentration (fraction, %), chlorophyll-a concentration (mg/m³), primary productivity (g/m3/day), cloud cover (%), and bathymetry (m). Six modelling algorithms were tested and the Bayesian additive regression trees (BART) model demonstrated the best performance. Based on variable importance, those that best explained the environmental requirements of the species were sea ice concentration, chlorophyll-a concentration and topography of the sea floor (bathymetry), explaining in sum around 62% of the variance. Using the BART model, habitat preferences have been interpreted from patterns in partial dependence plots. Areas where the AMW have particularly high likelihood of occurrence are East Antarctica, NE of the Weddell Sea, areas around the northern tip of the Antarctica Peninsula, areas bordering the Scotia–Weddell Confluence. Given the association of AMWs with sea ice, the pagophilic character of their biology makes them particularly vulnerable to climate change and a near-perfect biological indicator for tracking these changes.

Multivariate Hawkes processes on inhomogeneous random graphs

We consider a population of N interacting neurons, represented by a multivariate Hawkes process: The firing rate of each neuron depends on the history of the connected neurons. Contrary to the mean-field framework where the interaction occurs on the complete graph, the connectivity between particles is given by a random possibly diluted and inhomogeneous graph where the probability of presence of each edge depends on the spatial position of its vertices. We address the well-posedness of this system and Law of Large Numbers results as N→∞. A crucial issue will be to understand how spatial inhomogeneity influences the large time behavior of the system.

Hazel dormouse in managed woodland select for young, dense, and species-rich tree stands

In fragmented forest landscapes, population persistence of arboreal species with limited dispersal ability may strongly depend on the quality of the remaining forest habitat. Using the hazel dormouse (Muscardinus avellanarius) as a model species, we studied habitat selection at two spatial scales (home range and within home range) in intensely managed woodlands at its northern distributional range in Denmark. We modelled selection at home range level as the conditional probability of occupancy of 588 nest boxes and nest tubes in 15 managed forests relative to habitat variables measured within 25 m radius. Habitat selection within home ranges was modelled by comparing habitat variables within 3 m radius of triangulated locations by 19 radio-tracked individuals (12 M, 7 F) when active at night with regularly distributed available locations within their home ranges.At both spatial scales, hazel dormice strongly selected sites with high abundance-weighted species richness and high vegetation density of woody plants. On home range level, they furthermore selected for young tree vegetation, while they within home ranges selected for intermediate aged tree stands (maximum trunk circumference: 1.50 m). The predicted probability of presence in nest boxes or nest tubes varied from less than 1% to more than 99% as a combined function of three habitat variables. From May to October, selection for abundance-weighted species richness of woody plants of radio-tagged individuals decreased with date and body weight, suggesting that a diverse food base is particularly important early in their season of activity and for lean and small (growing) individuals. Selection for dense vegetation increased with body mass and mean available vegetation density within home ranges, indicating behavioural variability related to changes in energy expenditure and need for safety among individuals.The study demonstrates that the hazel dormouse has specific habitat requirements related to food and safety that can be accommodated with relatively simple means in managed forests.

Mapping the global distribution of Strongyloides stercoralis and hookworms by ecological niche modeling

BackgroundThe WHO has established a control strategy for Strongyloides stercoralis in school-aged children as well as targets and to maintain control programs for Ascaris lumbricoides, Trichuris trichiura and hookworms. For an efficient development of control programs, it is necessary to know the target countries around the world, as well as the areas within each country where efforts should be focused. Therefore, maps that provide information on the areas at risk for soil-transmitted helminth (STH) infections on a national and sub-national scale would allow for a better allocation of resources.MethodsWe used the ecological niche models MaxEnt and Kuenm R library to estimate the global distribution of S. stercoralis and hookworms. We used occurrence points of both species extracted from surveys of two literature reviews and from the Global Atlas of Helminth Infection database, together with 14 raster maps of environmental variables.ResultsWe obtained two raster maps with the presence probability of S. stercoralis and hookworm infections at a global level and then estimated the global population at risk to be 2.6 and 3.4 billion, respectively. The population at risk was also estimated at the country level using estimations for areas as small as 25 km2. A relationship was found between the probability of the presence of S. stercoralis and its prevalence, and a raster map was generated. Annual precipitation, annual temperature, soil carbon content and land cover were the main associated environmental variables. The ecological niches of Strongyloides stercoralis and hookworms had an overlap of 68%.ConclusionsHere we provide information that can be used for developing more efficient and integrated control strategies for S. stercoralis and hookworm infections. This information can be annexed to the study of other risk factors or even other diseases to assess the health status of a community.Graphical Abstarct Supplementary InformationThe online version contains supplementary material available at 10.1186/s13071-022-05284-w.

Feature-guided regularization parameter selection in sparse de-noising for fault diagnosis

Using the sparse and redundant representation model to de-noise vibration signals is an effective method for mechanical fault diagnosis. The selection of the regularization parameter in the sparse de-noising algorithm plays a vital role in reconstructing fault features. The current choices, however, are primarily based on fixed-form parameters derived under the assumption of additive white noise or based on the “trial and error” method. As a result, the extracted de-noised signals often fail to show fault features. To solve this difficulty, a self-adaptive regularization parameter selection strategy is proposed, which directly measures the significance of fault features in sparse de-noised signals. Firstly, the entire solution path is obtained by the computationally efficient least angle regression (LARS) algorithm. Besides, a feature presence probability index (FP) is well designed to measure the significance of fault features in the reconstructed signal envelope demodulation spectrum. Accordingly, with the help of the maximal FP indicator, the optimal regularization parameter and LARS solution is determined. The proposed method is not limited by noise probability distributions; therefore, it shows the superiority of extracting fault features in both the simulation and experimental data analysis.

A Poisson-multinomial spatial model for simultaneous outbreaks with application to arboviral diseases.

Dengue, Zika, and chikungunya are arboviral diseases (AVD) transmitted mainly by Aedes aegypti. Rio de Janeiro city, Brazil, has been endemic for dengue for over 30 years, and experienced the first joint epidemic of the three diseases between 2015-2016. They present similar symptoms and only a small proportion of cases are laboratory-confirmed. These facts lead to potential misdiagnosis and, consequently, uncertainty in the registration of the cases. We have available the number of cases of each disease for the neighborhoods of Rio de Janeiro. We propose a Poisson model for the total number of cases of Aedes-borne diseases and, conditioned on the total, we assume a multinomial model for the allocation of the number of cases of each of the diseases across the neighborhoods. This provides simultaneously the estimation of the associations of the relative risk of the total cases of AVD with environmental and socioeconomic variables; and the estimation of the probability of presence of each disease as a function of available covariates. Our findings suggest that a one standard deviation increase in the social development index decreases the relative risk of the total cases of AVD by 28%. Neighborhoods with smaller proportion of green area had greater odds of having chikungunya in comparison to dengue and Zika. A one standard deviation increase in population density decreases the odds of a neighborhood having Zika instead of dengue by 18% but increases the odds of chikungunya in comparison to dengue by 18% and by 43% in comparison to Zika.

How Parental Predictors Jointly Affect the Risk of Offspring Congenital Heart Disease: A Nationwide Multicenter Study Based on the China Birth Cohort.

ObjectiveCongenital heart disease (CHD) is complex in its etiology. Its genetic causes have been investigated, whereas the non-genetic factor related studies are still limited. We aimed to identify dominant parental predictors and develop a predictive model and nomogram for the risk of offspring CHD.MethodsThis was a retrospective study from November 2017 to December 2021 covering 44,578 participants, of which those from 4 hospitals in eastern China were assigned to the development cohort and those from 5 hospitals in central and western China were used as the external validation cohort. Univariable and multivariable analyses were used to select the dominant predictors of CHD among demographic characteristics, lifestyle behaviors, environmental pollution, maternal disease history, and the current pregnancy information. Multivariable logistic regression analysis was used to construct the model and nomogram using the selected predictors. The predictive model and the nomogram were both validated internally and externally. A web-based nomogram was developed to predict patient-specific probability for CHD.ResultsDominant risk factors for offspring CHD included increased maternal age [odds ratio (OR): 1.14, 95% CI: 1.10–1.19], increased paternal age (1.05, 95% CI: 1.02–1.09), maternal secondhand smoke exposure (2.89, 95% CI: 2.22–3.76), paternal drinking (1.41, 95% CI: 1.08–1.84), maternal pre-pregnancy diabetes (3.39, 95% CI: 1.95–5.87), maternal fever (3.35, 95% CI: 2.49–4.50), assisted reproductive technology (2.89, 95% CI: 2.13–3.94), and environmental pollution (1.61, 95% CI: 1.18–2.20). A higher household annual income (100,000–400,000 CNY: 0.47, 95% CI: 0.34–0.63; > 400,000 CNY: 0.23, 95% CI: 0.15–0.36), higher maternal education level (13–16 years: 0.68, 95% CI: 0.50–0.93; ≥ 17 years: 0.87, 95% CI: 0.55–1.37), maternal folic acid (0.21, 95% CI: 0.16–0.27), and multivitamin supplementation (0.33, 95% CI: 0.26–0.42) were protective factors. The nomogram showed good discrimination in both internal [area under the receiver-operating-characteristic curve (AUC): 0.843] and external validations (development cohort AUC: 0.849, external validation cohort AUC: 0.837). The calibration curves showed good agreement between the nomogram-predicted probability and actual presence of CHD.ConclusionWe revealed dominant parental predictors and presented a web-based nomogram for the risk of offspring CHD, which could be utilized as an effective tool for quantifying the individual risk of CHD and promptly identifying high-risk population.

A machine learning method for estimating the probability of presence using presence-background data.

Estimating the prevalence or the absolute probability of the presence of a species from presence‐background data has become a controversial topic in species distribution modelling. In this paper, we propose a new method by combining both statistics and machine learning algorithms that helps overcome some of the known existing problems. We have also revisited the popular but highly controversial Lele and Keim (LK) method by evaluating its performance and assessing the RSPF condition it relies on. Simulations show that the LK method with the RSPF assumptions would render fragile estimation/prediction of the desired probabilities. Rather, we propose the local knowledge condition, which relaxes the predetermined population prevalence condition that has so often been used in much of the existing literature. Simulations demonstrate the performance of the new method utilizing the local knowledge assumption to successfully estimate the probability of presence. The local knowledge extends the local certainty or the prototypical presence location assumption, and has significant implications for demonstrating the necessary condition for identifying absolute (rather than relative) probability of presence from presence background without absence data in species distribution modelling.

Distribution and Roost Site Selection of Eastern Small-Footed Bats (Myotis leibii) in Mountains of West-Central Arkansas

Myotis leibii (Eastern Small-footed Bat) is a rare species in eastern North America that is threatened by habitat loss and white-nose syndrome. Although rare, this species cannot be adequately evaluated for listing on the Endangered Species Act because of data deficiencies, including about its distribution and roost habitat. Our objectives were to document the distribution of Eastern Small-footed Bats and determine landscape and local characteristics that influence their probability of presence in the mountains of west-central Arkansas. Using acoustic monitoring, visual searches of rock formations, and mist-netting, we found that presence of Eastern Small-footed Bats was more likely in regions with talus slopes and nearby forest cover. These features may benefit the species by providing abundant options for roost switching, unique thermal properties, and short distances to foraging habitat.

Hawksbill presence and habitat suitability of a marine reserve in Honduras

Marine protected areas (MPAs) have been established to decrease the negative impacts of human activities on important marine habitats. Several sea turtle species reside within MPAs; however, analyses of habitat suitability for sea turtles within many MPAs is typically lacking. Habitat modeling and species mapping have become essential tools in determining areas suitable for species inhabitance. MaxEnt is a popular machine-learning program that uses presence-only occurrence data and is considered the best modeling method in discriminating between suitable and unsuitable habitats. Using MaxEnt, we created species distribution models (SDMs) from presence-only occurrence points for juvenile hawksbills and three environmental variables: boat traffic intensities, prey item distributions, and benthic characterizations, within the Sandy Bay West End Marine Reserve (SBWEMR) in Roatán, Honduras. Area under the receiving operator curve (AUC) values were greater than 0.75, indicating high model accuracy. Jackknife evaluations indicated algal prey item Dictyota spp., boat traffic intensity, and Geodia neptuni sponge abundance had the greatest influence on species distribution. Areas of suitability shifted based on time of day and ranged from a low probability of hawksbill presence (0.0) to a high probability of hawksbill presence (0.8). Regardless of time of day, the SBWEMR is classified as at least moderately suitable (>0.2–0.4), with least suitable habitat (0–0.2) prevalent in the most northeastern region of the reserve. The greatest suitability (>0.6–1) was evident within the mid-region and southern most regions of the reserve. Probability of hawksbill presence increased in accordance with increasing boat traffic, although there was no clear causative relationship between the two. Although the reserve overall is mostly moderately suitable, the SBWEMR remains an important local recruitment and foraging ground for juvenile hawksbills. Hawksbills have been considered resilient, continually functioning beneath their operational limits. However, with continued global depletion of essential habitats over time and lack of effective MPA management, areas of habitat suitable for hawksbill inhabitance may decrease below levels sufficient for hawksbill presence in protected areas of the Western Caribbean.

Diagnosis of Peritoneal Carcinomatosis of Colorectal Origin Based on an Innovative Fuzzy Logic Approach.

Colorectal cancer represents one of the most important causes worldwide of cancer related morbidity and mortality. One of the complications which can occur during cancer progression, is peritoneal carcinomatosis. In the majority of cases, it is diagnosed in late stages due to the lack of diagnostic tools capable of revealing the early-stage peritoneal burden. Therefore, still associates with poor prognosis and quality of life, despite recent therapeutic advances. The aim of the study was to develop a fuzzy logic approach to assess the probability of peritoneal carcinomatosis presence using routine blood test parameters as input data. The patient data was acquired retrospective from patients diagnosed between 2010–2021. The developed model focuses on the specific quantitative alteration of these parameters in the presence of peritoneal carcinomatosis, which is an innovative approach as regards the literature in the field and validates the feasibility of using a fuzzy logic approach in the noninvasive diagnosis of peritoneal carcinomatosis.

Evolving Hypothesis that Prostate/BPH Size Matters in Protection against Prostate Cancer

Benign prostatic hyperplasia (BPH) and prostate cancer (PCa) are the two predominant urologic diseases affecting aging men. As a matter of fact, 95–98% of elderly men with prostate as their “primary organ disease” for consulting urologists have either the clinical diagnosis of BPH, PCa or both. Other primary diagnoses of the prostate such as sarcoma, lymphoma or isolated bacterial prostatitis (not in the context of a systemic urinary tract infection) are rare entities. Although BPH and PCa are very common, their interaction is currently not well understood. Numerous clinical studies and meta-analysis reviews have demonstrated a negative correlation between prostate BPH size and the probability of PCa presence, but little is known to explain this clinical phenomenon. Recent histo-anatomical and magnetic resonance imaging studies have revealed data leading to a hypothesis indicating the expanding transition zone (TZ) in a growing BPH prostate causes mechanical stress within the peripheral zone (PZ) with secondary fibrosis and atrophy of the glands within the PZ. This dynamic interaction of the TZ against the PZ in a growing BPH-prostate could explain the negative correlation between BPH size and PCa incidence. The purpose of this review is to present and discuss the evolving hypothesis that prostate size may matter and be protective against prostate cancer.

Landscape configuration affects probability of apex predator presence and community structure in experimental metacommunities

Biodiversity is declining at an unprecedented rate, highlighting the urgent requirement for well-designed protected areas. Design tactics previously proposed to promote biodiversity include enhancing the number, connectivity, and heterogeneity of reserve patches. However, how the importance of these features changes depending on what the conservation objective is remains poorly understood. Here we use experimental landscapes containing ciliate protozoa to investigate how the number and heterogeneity in size of habitat patches, rates of dispersal between neighbouring patches, and mortality risk of dispersal across the non-habitat ‘matrix’ interact to affect a number of diversity measures. We show that increasing the number of patches significantly increases γ diversity and reduces the overall number of extinctions, whilst landscapes with heterogeneous patch sizes have significantly higher γ diversity than those with homogeneous patch sizes. Furthermore, the responses of predators depended on their feeding specialism, with generalist predator presence being highest in a single large patch, whilst specialist predator presence was highest in several-small patches with matrix dispersal. Our evidence emphasises the importance of considering multiple diversity measures to disentangle community responses to patch configuration.

Factors related to survival time in dogs with pulmonary hypertension secondary to degenerative mitral valve disease stage C

ABSTRACT Pulmonary hypertension (PH) is a common complication in dogs with degenerative mitral valve disease (DMVD). The aim of this study was to determine the survival time and to determine factors related to the survival time of dogs with PH secondary to DMVD stage C. A retrospective study was conducted in 37 dogs with PH secondary to DMVD stage C to analyse the median survival time and associated factors such as age, sex, breed, body weight, the presence of syncope, heart rate, systolic blood pressure, manifestation of congestive heart failure (CHF), vertebral heart score (VHS), the presence of left or right heart enlargement, probability of PH and medication. Data were analysed using the Log-Rank test and plotted with the Kaplan-Meier curve. The results showed that the median survival time of dogs with PH secondary to DMVD stage C was 368 days, and factors that shortened the median survival time of dogs with PH secondary to DMVD stage C were mixed breeds, VHS > 11.5, the presence of right heart enlargement, ascites and high probability of PH. Factors associated with an increased hazard of death were mixed breed dogs, dogs with right heart enlargement and ascites. These findings may be useful for the prognosis and management of dogs with PH secondary to DMVD stage C.