Body condition scoring (BCS) is a farm-management tool for estimating dairy cows' energy reserves. Today, BCS is performed manually by experts. This paper presents a 3-dimensional algorithm that provides a topographical understanding of the cow's body to estimate BCS. An automatic BCS system consisting of a Kinect camera (Microsoft Corp., Redmond, WA) triggered by a passive infrared motion detector was designed and implemented. Image processing and regression algorithms were developed and included the following steps: (1) image restoration, the removal of noise; (2) object recognition and separation, identification and separation of the cows; (3) movie and image selection, selection of movies and frames that include the relevant data; (4) image rotation, alignment of the cow parallel to the x-axis; and (5) image cropping and normalization, removal of irrelevant data, setting the image size to 150×200 pixels, and normalizing image values. All steps were performed automatically, including image selection and classification. Fourteen individual features per cow, derived from the cows' topography, were automatically extracted from the movies and from the farm's herd-management records. These features appear to be measurable in a commercial farm. Manual BCS was performed by a trained expert and compared with the output of the training set. A regression model was developed, correlating the features with the manual BCS references. Data were acquired for 4 d, resulting in a database of 422 movies of 101 cows. Movies containing cows' back ends were automatically selected (389 movies). The data were divided into a training set of 81 cows and a test set of 20 cows; both sets included the identical full range of BCS classes. Accuracy tests gave a mean absolute error of 0.26, median absolute error of 0.19, and coefficient of determination of 0.75, with 100% correct classification within 1 step and 91% correct classification within a half step for BCS classes. Results indicated good repeatability, with all standard deviations under 0.33. The algorithm is independent of the background and requires 10 cows for training with approximately 30 movies of 4 s each.

Melanoma is among the top 10 new cancer diagnoses for both American men and women. Nationally, melanoma incidence has increased 2.4% annually in the last decade.¹ Research and public education efforts have focused on melanoma prevention in white populations because of their higher risk of developing melanoma. Improved secondary prevention measures with earlier detection of thin (early-stage) melanoma likely account for the improved survival among whites from 68% in the early 1970s to 92% in recent years.¹ Such advances, however, have not occurred in other racial and ethnic groups in the United States. Emerging data call attention to disparity in melanoma diagnosis and survival in minorities such as Hispanics and blacks. Multiple reports found that US blacks have more advanced melanoma in association with worse survival rates^2-7; however, melanoma disparity among Hispanics is less recognized. The dearth of studies on melanoma among Hispanics partly reflects the small number of cases in many areas of the United States, as well as limitations of ethnicity information in cancer registries.^8,9 In fact, the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) program and most other cancer registries did not begin classifying data for “Hispanic” until the late 1990s.¹ As a result, few studies included data regarding Hispanics.¹⁰

A biomarker that will enable the identification of patients at high-risk for developing post-injury epilepsy is critically required. Microvascular pathology and related blood–brain barrier dysfunction and neuroinflammation were shown to be associated with epileptogenesis after injury. Here we used prospective, longitudinal magnetic resonance imaging to quantitatively follow blood–brain barrier pathology in rats following status epilepticus, late electrocorticography to identify epileptic animals and post-mortem immunohistochemistry to confirm blood–brain barrier dysfunction and neuroinflammation. Finally, to test the pharmacodynamic relevance of the proposed biomarker, two anti-epileptogenic interventions were used; isoflurane anaesthesia and losartan. Our results show that early blood–brain barrier pathology in the piriform network is a sensitive and specific predictor (area under the curve of 0.96, P < 0.0001) for epilepsy, while diffused pathology is associated with a lower risk. Early treatments with either isoflurane anaesthesia or losartan prevented early microvascular damage and late epilepsy. We suggest quantitative assessment of blood–brain barrier pathology as a clinically relevant predictive, diagnostic and pharmaco!dynamics biomarker for acquired epilepsy.

Background . The extent to which the upper-limb flexor synergy constrains or compensates for arm motor impairment during reaching is controversial. This synergy can be quantified with a minimal marker set describing movements of the arm-plane. Objectives . To determine whether and how ( a ) upper-limb flexor synergy in patients with chronic stroke contributes to reaching movements to different arm workspace locations and ( b ) reaching deficits can be characterized by arm-plane motion. Methods . Sixteen post-stroke and 8 healthy control subjects made unrestrained reaching movements to targets located in ipsilateral, central, and contralateral arm workspaces. Arm-plane, arm, and trunk motion, and their temporal and spatial linkages were analyzed. Results . Individuals with moderate/severe stroke used greater arm-plane movement and compensatory trunk movement compared to those with mild stroke and control subjects. Arm-plane and trunk movements were more temporally coupled in stroke compared with controls. Reaching accuracy was related to different segment and joint combinations for each target and group: arm-plane movement in controls and mild stroke subjects, and trunk and elbow movements in moderate/severe stroke subjects. Arm-plane movement increased with time since stroke and when combined with trunk rotation, discriminated between different subject groups for reaching the central and contralateral targets. Trunk movement and arm-plane angle during target reaches predicted the subject group. Conclusions . The upper-limb flexor synergy was used adaptively for reaching accuracy by patients with mild, but not moderate/severe stroke. The flexor synergy, as parameterized by the amount of arm-plane motion, can be used by clinicians to identify levels of motor recovery in patients with stroke.