A retrospective study of pyometra at five RSPCA hospitals in the United Kingdom: 1,728 cases from 2006-2011

A retrospective cross-sectional study was used to analyse pyometra cases at five RSPCA Animal Hospitals across the UK from 2006 to 2011. A total of 1728 cases of pyometra were recovered from a female dog outpatient caseload of 78,469 animals, giving a total prevalence of 2.2 per cent over the study period. There was an annual increase in the incidence of pyometra within the population, while elective ovariohysterectomy caseload has declined. There were variations in breed and age at presentation. Bullmastiffs (P<0.0001), golden retrievers (P=0.001) and dogue de Bordeaux (P=0.008) were over-represented in the pyometra population when compared with the female dog outpatient caseload. Mean age at presentation was 7.7 years. Some breeds presented at a significantly lower age, including dogue de Bordeaux (mean age 3.3 years) and bullmastiffs (mean age 5.4 years), while others presented as older dogs, including Yorkshire terriers (mean age 9.4 years) and border collies (mean age 10.3 years). Surgical mortality rate at the Greater Manchester Animal Hospital was 3.2 per cent. Pyometra is of significant welfare concern, and also has cost implications, particularly in charity practice. These results serve to highlight this condition so that future change in charity practice caseload can be anticipated and strategies can be directed to improve animal welfare

Intraoperative Organ Motion Models with an Ensemble of Conditional Generative Adversarial Networks

In this paper, we describe how a patient-specific, ultrasound-probe-induced prostate motion model can be directly generated from a single preoperative MR image. Our motion model allows for sampling from the conditional distribution of dense displacement fields, is encoded by a generative neural network conditioned on a medical image, and accepts random noise as additional input. The generative network is trained by a minimax optimisation with a second discriminative neural network, tasked to distinguish generated samples from training motion data. In this work, we propose that 1) jointly optimising a third conditioning neural network that pre-processes the input image, can effectively extract patient-specific features for conditioning; and 2) combining multiple generative models trained separately with heuristically pre-disjointed training data sets can adequately mitigate the problem of mode collapse. Trained with diagnostic T2-weighted MR images from 143 real patients and 73,216 3D dense displacement fields from finite element simulations of intraoperative prostate motion due to transrectal ultrasound probe pressure, the proposed models produced physically-plausible patient-specific motion of prostate glands. The ability to capture biomechanically simulated motion was evaluated using two errors representing generalisability and specificity of the model. The median values, calculated from a 10-fold cross-validation, were 2.8+/-0.3 mm and 1.7+/-0.1 mm, respectively. We conclude that the introduced approach demonstrates the feasibility of applying state-of-the-art machine learning algorithms to generate organ motion models from patient images, and shows significant promise for future research.Comment: Accepted to MICCAI 201

Phenomenological Study of Fathers’ Parental Alienation Experiences

AbstractHigh-conflict divorce and resulting parental alienation (PA) impact both the children and the parent who has been the target of PA. This situation has been found to cause mental health issues and unhealthy coping skills in children. The purpose of this phenomenological study was to explore the lived experiences of nonresidential fathers who were alienated from their children in the context of a high-conflict separation or divorce. This study was rooted in Bowlby’s attachment theory. Data were collected from interviews with 10 adult participants. The software program Quirkos was used to review the data and discern thematic patterns and correlate the themes. Themes identified in the data where legal issues, physical ailments, mental health consequences, and financial consequences. Better understanding of the perspectives of fathers experienced PA increases awareness of the sequelae of high-conflict divorce. Such awareness can lead to positive social change by helping generate an understanding of what fathers may experience in high-conflict separation or divorce when the relationship with their children is strained or lost during high-conflict separation or divorce. The increased understanding from this study allows clinical professionals to target interventions and the legal system to address issues of PA in divorce proceedings, helping to create better outcomes for parents and children. This awareness allows clinical professionals and the legal system to address issues of PA in high-conflict divorces, helping to create better outcomes for parents and children

Adversarial Deformation Regularization for Training Image Registration Neural Networks

We describe an adversarial learning approach to constrain convolutional neural network training for image registration, replacing heuristic smoothness measures of displacement fields often used in these tasks. Using minimally-invasive prostate cancer intervention as an example application, we demonstrate the feasibility of utilizing biomechanical simulations to regularize a weakly-supervised anatomical-label-driven registration network for aligning pre-procedural magnetic resonance (MR) and 3D intra-procedural transrectal ultrasound (TRUS) images. A discriminator network is optimized to distinguish the registration-predicted displacement fields from the motion data simulated by finite element analysis. During training, the registration network simultaneously aims to maximize similarity between anatomical labels that drives image alignment and to minimize an adversarial generator loss that measures divergence between the predicted- and simulated deformation. The end-to-end trained network enables efficient and fully-automated registration that only requires an MR and TRUS image pair as input, without anatomical labels or simulated data during inference. 108 pairs of labelled MR and TRUS images from 76 prostate cancer patients and 71,500 nonlinear finite-element simulations from 143 different patients were used for this study. We show that, with only gland segmentation as training labels, the proposed method can help predict physically plausible deformation without any other smoothness penalty. Based on cross-validation experiments using 834 pairs of independent validation landmarks, the proposed adversarial-regularized registration achieved a target registration error of 6.3 mm that is significantly lower than those from several other regularization methods.Comment: Accepted to MICCAI 201

Label-driven weakly-supervised learning for multimodal deformable image registration

Spatially aligning medical images from different modalities remains a challenging task, especially for intraoperative applications that require fast and robust algorithms. We propose a weakly-supervised, label-driven formulation for learning 3D voxel correspondence from higher-level label correspondence, thereby bypassing classical intensity-based image similarity measures. During training, a convolutional neural network is optimised by outputting a dense displacement field (DDF) that warps a set of available anatomical labels from the moving image to match their corresponding counterparts in the fixed image. These label pairs, including solid organs, ducts, vessels, point landmarks and other ad hoc structures, are only required at training time and can be spatially aligned by minimising a cross-entropy function of the warped moving label and the fixed label. During inference, the trained network takes a new image pair to predict an optimal DDF, resulting in a fully-automatic, label-free, real-time and deformable registration. For interventional applications where large global transformation prevails, we also propose a neural network architecture to jointly optimise the global- and local displacements. Experiment results are presented based on cross-validating registrations of 111 pairs of T2-weighted magnetic resonance images and 3D transrectal ultrasound images from prostate cancer patients with a total of over 4000 anatomical labels, yielding a median target registration error of 4.2 mm on landmark centroids and a median Dice of 0.88 on prostate glands.Comment: Accepted to ISBI 201

Comparison of intestinal bacterial communities in asymptomatic and diseased Asian seabass (Lates calcarifer) with chronic enteritis and mixed bacterial infections

Asian seabass (Lates calcarifer) is a major aquaculture food fish species in Singapore. Farming of this species is increasingly threatened by frequent outbreaks of infectious diseases, resulting in mortality exceeding 50–70%. In this study, we investigated the comparative gut bacterial microbiota using 16S rRNA metasequencing between asymptomatic and diseased juvenile fish collected during a disease outbreak soon after stocking. Mild to severe chronic granulomatous enteritis was observed histopathologically in both asymptomatic and diseased fish. Kidneys of diseased fish tested PCR positive for the ‘big belly’ novel Vibrio spp., Streptococcus iniae and Vibrio harveyi. These bacteria were also readily detected by PCR in water samples corresponding to tanks fish were sampled from. Potentially beneficial microbes that promote gut health such as Firmicutes, Bacteroidota and Actinobacteriota were the dominant phyla in the intestinal microbiota of asymptomatic fish. Moreover, the bacteria with probiotic potential such as Lactobacillus only presented in asymptomatic fish, and Weissella was unique and prevalent (47.59%) in asymptomatic fish during the recovery phase of the disease outbreak, making them candidate biomarkers for monitoring health status of L. calcarifer. Conversely, diseased fish showed reduced diversity of their gut microbiome, with high abundance of members of the phylum Proteobacteria. Vibrio was the most dominant genus (87.3%) and Streptococcus iniae was only detected in diseased fish. These findings provide a baseline study for understanding changes in intestinal microbiota in newly stocked fish with mixed bacterial infection, biomarker assisted health monitoring, and future host-derived probiotics screening in L. calcarifer

Coloring Hypergraphs Induced by Dynamic Point Sets and Bottomless Rectangles

We consider a coloring problem on dynamic, one-dimensional point sets: points appearing and disappearing on a line at given times. We wish to color them with k colors so that at any time, any sequence of p(k) consecutive points, for some function p, contains at least one point of each color. We prove that no such function p(k) exists in general. However, in the restricted case in which points appear gradually, but never disappear, we give a coloring algorithm guaranteeing the property at any time with p(k)=3k-2. This can be interpreted as coloring point sets in R^2 with k colors such that any bottomless rectangle containing at least 3k-2 points contains at least one point of each color. Here a bottomless rectangle is an axis-aligned rectangle whose bottom edge is below the lowest point of the set. For this problem, we also prove a lower bound p(k)>ck, where c>1.67. Hence for every k there exists a point set, every k-coloring of which is such that there exists a bottomless rectangle containing ck points and missing at least one of the k colors. Chen et al. (2009) proved that no such function $p(k)$ exists in the case of general axis-aligned rectangles. Our result also complements recent results from Keszegh and Palvolgyi on cover-decomposability of octants (2011, 2012).Comment: A preliminary version was presented by a subset of the authors to the European Workshop on Computational Geometry, held in Assisi (Italy) on March 19-21, 201

Scotland Registry for Ankylosing Spondylitis (SIRAS) – Protocol

Funding SIRAS was funded by unrestricted grants from Pfizer and AbbVie. The project was reviewed by both companies, during the award process, for Scientific merit, to ensure that the design did not compromise patient safety, and to assess the global regulatory implications and any impact on regulatory strategy.Publisher PD

Tunneling Ionization Rates from Arbitrary Potential Wells

We present a practical numerical technique for calculating tunneling ionization rates from arbitrary 1-D potential wells in the presence of a linear external potential by determining the widths of the resonances in the spectral density, rho(E), adiabatically connected to the field-free bound states. While this technique applies to more general external potentials, we focus on the ionization of electrons from atoms and molecules by DC electric fields, as this has an important and immediate impact on the understanding of the multiphoton ionization of molecules in strong laser fields.Comment: 13 pages, 7 figures, LaTe