Bluetongue disease: impact of the 2008 vaccination on fertility in supervised dairy herds

In June 2008 the compulsary nationwide vaccination against BTV-8 (Bluetongue virus serotype 8) was started. After a short time, several owners complained about undesirable effects of the vaccination on fertility and milk quality. Data from 47 dairy farms, regularly supervised by herd health practitioners, were analysed in order to clarify a possible connection between vaccination and fertility. Both vaccinations given each cow for basic immunization were evaluated according to their effects on conception rate and pregnancy. In model calculations the first vaccination had no significant effect on the first service conception rate (FCR), the all service conception rate (ACR) and on the abortion rate. The second vaccination led to a significantly reduced FCR when the cow was inseminated within 20 days of being vaccinated and to a significantly worse ACR when inseminated 10 days before or after vaccination. However, these individually established reductions of the insemination rate had only little influence on overall data.

Long-term effects of supervised exercise training on walking capacity and quality of life in patients with intermittent claudication

Supervised exercise training has been shown to improve walking capacity in several studies of patients with intermittent claudication. However, data on long-term outcome are quite limited. The aim of this prospective study was to evaluate long-term effects of supervised exercise training on walking capacity and quality of life in patients with intermittent claudication. Patients and methods: Sixty-seven consecutive patients with intermittent claudication who completed a supervised 12-week exercise training program were asked for follow up evaluation 39 +/- 20 months after program completion. Pain-free walking distance (PWD) and maximum walking distances (MWD) were assessed by treadmill test and several questionnaires. Results: Forty (60%) patients agreed to participate, 22 (33%) refused participation, and 5 (7%) died during follow-up. PWD and MWD significantly improved at completion of 12-weeks supervised exercise training as compared to baseline (PWD 114 +/- 100 vs. 235 +/- 248, p = 0.002; MWD 297 +/- 273 vs. 474 +/- 359, p = 0.001). Improvement of PWD and MWD could be maintained at follow up (197 +/- 254, p = 0.014; 390 +/- 324, p = 0.035, respectively) with non-smokers showing significantly better sustained PWD and MWD improvement as compared to baseline. Overall, walking capacity correlated with functional status of quality of life. Conclusions: Major findings of this investigation were that improvement in walking capacity is sustained after completion of supervised exercise training program with best results in patients who quitted or never smoked. Improved walking capacity is associated with increased functional status of quality of life.

Effect of 6-month supervised exercise on low-density lipoprotein apolipoprotein B kinetics in patients with type 2 diabetes mellitus

Although low-density lipoprotein (LDL) cholesterol is often normal in patients with type 2 diabetes mellitus, there is evidence for a reduced fractional catabolic rate and consequently an increased mean residence time (MRT), which can increase atherogenic risk. The dyslipidemia and insulin resistance of type 2 diabetes mellitus can be improved by aerobic exercise, but effects on LDL kinetics are unknown. The effect of 6-month supervised exercise on LDL apolipoprotein B kinetics was studied in a group of 17 patients with type 2 diabetes mellitus (mean age, 56.8 years; range, 38-68 years). Patients were randomized into a supervised group, who had a weekly training session, and an unsupervised group. LDL kinetics were measured with an infusion of 1-(13)C leucine at baseline in all groups and after 6 months of exercise in the patients. Eight body mass index-matched nondiabetic controls (mean age, 50.3 years; range, 40-67 years) were also studied at baseline only. At baseline, LDL MRT was significantly longer in the diabetic patients, whereas LDL production rate and fractional clearance rates were significantly lower than in controls. Percentage of glycated hemoglobin A(1c), body mass index, insulin sensitivity measured by the homeostasis model assessment, and very low-density lipoprotein triglyceride decreased (P < .02) in the supervised group, with no change in the unsupervised group. After 6 months, LDL cholesterol did not change in either the supervised or unsupervised group; but there was a significant change in LDL MRT between groups (P < .05) that correlated positively with very low-density lipoprotein triglyceride (r = 0.51, P < .04) and negatively with maximal oxygen uptake, a measure of fitness (r = -0.51, P = .035), in all patients. The LDL production and clearance rates did not change in either group. This study suggests that a supervised exercise program can reduce deleterious changes in LDL MRT.

Evaluating retraining rules for semi-supervised learning in neural network based cursive word recognition

Training a system to recognize handwritten words is a task that requires a large amount of data with their correct transcription. However, the creation of such a training set, including the generation of the ground truth, is tedious and costly. One way of reducing the high cost of labeled training data acquisition is to exploit unlabeled data, which can be gathered easily. Making use of both labeled and unlabeled data is known as semi-supervised learning. One of the most general versions of semi-supervised learning is self-training, where a recognizer iteratively retrains itself on its own output on new, unlabeled data. In this paper we propose to apply semi-supervised learning, and in particular self-training, to the problem of cursive, handwritten word recognition. The special focus of the paper is on retraining rules that define what data are actually being used in the retraining phase. In a series of experiments it is shown that the performance of a neural network based recognizer can be significantly improved through the use of unlabeled data and self-training if appropriate retraining rules are applied.

Caries-preventive effectiveness of fluoride varnish as adjunct to oral health promotion and supervised tooth brushing in preschool children: a double-blind randomized controlled trial.

OBJECTIVES To evaluate the effect of biannual fluoride varnish applications in preschool children as an adjunct to school-based oral health promotion and supervised tooth brushing with 1000ppm fluoride toothpaste. METHODS 424 preschool children, 2-5 year of age, from 10 different pre schools in Athens were invited to this double-blind randomized controlled trial and 328 children completed the 2-year programme. All children received oral health education with hygiene instructions twice yearly and attended supervised tooth brushing once daily. The test group was treated with fluoride varnish (0.9% diflurosilane) biannually while the control group had placebo applications. The primary endpoints were caries prevalence and increment; secondary outcomes were gingival health, mutans streptococci growth and salivary buffer capacity. RESULTS The groups were balanced at baseline and no significant differences in caries prevalence or increment were displayed between the groups after 1 and 2 years, respectively. There was a reduced number of new pre-cavitated enamel lesions during the second year of the study (p=0.05) but the decrease was not statistically significant. The secondary endpoints were unaffected by the varnish treatments. CONCLUSIONS Under the present conditions, biannual fluoride varnish applications in preschool children did not show significant caries-preventive benefits when provided as an adjunct to school-based supervised tooth brushing with 1000ppm fluoride toothpaste. CLINICAL SIGNIFICANCE In community based, caries prevention programmes, for high caries risk preschool children, a fluoride varnish may add little to caries prevention, when 1000ppm fluoride toothpaste is used daily.

Patient-Specific Semi-Supervised Learning for Postoperative Brain Tumor Segmentation

In contrast to preoperative brain tumor segmentation, the problem of postoperative brain tumor segmentation has been rarely approached so far. We present a fully-automatic segmentation method using multimodal magnetic resonance image data and patient-specific semi-supervised learning. The idea behind our semi-supervised approach is to effectively fuse information from both pre- and postoperative image data of the same patient to improve segmentation of the postoperative image. We pose image segmentation as a classification problem and solve it by adopting a semi-supervised decision forest. The method is evaluated on a cohort of 10 high-grade glioma patients, with segmentation performance and computation time comparable or superior to a state-of-the-art brain tumor segmentation method. Moreover, our results confirm that the inclusion of preoperative MR images lead to a better performance regarding postoperative brain tumor segmentation.

Facial Nerve Image Enhancement from CBCT using Supervised Learning Technique

Facial nerve segmentation plays an important role in surgical planning of cochlear implantation. Clinically available CBCT images are used for surgical planning. However, its relatively low resolution renders the identification of the facial nerve difficult. In this work, we present a supervised learning approach to enhance facial nerve image information from CBCT. A supervised learning approach based on multi-output random forest was employed to learn the mapping between CBCT and micro-CT images. Evaluation was performed qualitatively and quantitatively by using the predicted image as input for a previously published dedicated facial nerve segmentation, and cochlear implantation surgical planning software, OtoPlan. Results show the potential of the proposed approach to improve facial nerve image quality as imaged by CBCT and to leverage its segmentation using OtoPlan.

Prediction of Trabecular Bone Anisotropy from Quantitative Computed Tomography using Supervised Learning and a Novel Morphometric Feature Descriptor

Patient-specific biomechanical models including local bone mineral density and anisotropy have gained importance for assessing musculoskeletal disorders. However the trabecular bone anisotropy captured by high-resolution imaging is only available at the peripheral skeleton in clinical practice. In this work, we propose a supervised learning approach to predict trabecular bone anisotropy that builds on a novel set of pose invariant feature descriptors. The statistical relationship between trabecular bone anisotropy and feature descriptors were learned from a database of pairs of high resolution QCT and clinical QCT reconstructions. On a set of leave-one-out experiments, we compared the accuracy of the proposed approach to previous ones, and report a mean prediction error of 6% for the tensor norm, 6% for the degree of anisotropy and 19◦ for the principal tensor direction. These findings show the potential of the proposed approach to predict trabecular bone anisotropy from clinically available QCT images.

Fast Prediction of Femoral Biomechanics Using Supervised Machine Learning and Statistical Shape Modeling

Finite element (FE) analysis is an important computational tool in biomechanics. However, its adoption into clinical practice has been hampered by its computational complexity and required high technical competences for clinicians. In this paper we propose a supervised learning approach to predict the outcome of the FE analysis. We demonstrate our approach on clinical CT and X-ray femur images for FE predictions ( FEP), with features extracted, respectively, from a statistical shape model and from 2D-based morphometric and density information. Using leave-one-out experiments and sensitivity analysis, comprising a database of 89 clinical cases, our method is capable of predicting the distribution of stress values for a walking loading condition with an average correlation coefficient of 0.984 and 0.976, for CT and X-ray images, respectively. These findings suggest that supervised learning approaches have the potential to leverage the clinical integration of mechanical simulations for the treatment of musculoskeletal conditions.