We evaluated the influence of time of recanalization or degree of initial leptomeningeal collateral blood flow in cardioembolic or arterio-arterial middle cerebral artery (MCA) occlusion on infarct size and clinical outcome in a series of 34 consecutive acute stroke patients with main stem (N = 31) or major branch (N = 3) occlusions using CT, initial cerebral arteriography (N = 21), repetitive close-meshed transcranial Doppler ultrasonography, and a neurologic stroke scale. We treated 15 patients with tissue plasminogen activator intravenously within the first 6 hours. The type and size of infarction depended on the location of the occluding lesions within the MCA trunk. Proximal MCA occlusion always led to infarction involving the striatum and internal capsule. Sixty-five percent of patients showed recanalization of the occluded MCA within 1 week. Following MCA recanalization, hyperperfusion was present in 38 to 44% of cases. There was a marginally significant relation between size of infarction on CT and recanalization time within the first 24 hours. The more rapidly recanalization occurred, the smaller the size of the infarct. When recanalization time was greater than 8 hours, the lesions always extended to the cortex. An additional good leptomeningeal collateral blood flow significantly reduced the size of the infarct and improved clinical outcome after 17 days and after 10 months. Early recanalization of embolic MCA occlusions within up to 8 hours, in conjunction with good transcortical collateralization, has a favorable impact on infarct size and outcome and may constitute the therapeutic window of opportunity.

To determine how early and how reliably ischaemic brain infarcts can be detected on CT within 6 h of the onset of cerebral hemisphere strokes, 44 such studies were interpreted by an experienced neuroradiologist blinded to clinical signs, but aware that the cohort was a stroke population. He was asked to detect and localise an area of parenchymal low density and/or focal brain swelling. A follow-up study showing the definite infarct served as a reference in each case. In 38 patients areas of slightly low density were seen, and in 36 follow-up CT confirmed infarcts in the locations indicated. In 2 patients the reading was false positive. In 6 patients no low density focus could be detected. In these 8 patients examined by CT within 180 min of the stroke, no low density could be identified, even in retrospect with the knowledge of the findings on follow-up. Thus, 42 readings (95%) were true positive or true negative; 2 were false positive; and none was a false negative. CT within 6 h of the onset of symptoms has a mean sensitivity of 82% (36/44) for ischaemic cerebral hemisphere infarcts. By contrast, its sensitivity to ischaemic parenchymal low density is low during the initial 2 h. The early development of hemispheric infarcts can be detected reliably if the radiologist is familiar with the signs.

The monitoring of farm animals and the automatic recognition of deviant behavior have recently become increasingly important in farm animal science research and in practical agriculture. The aim of this study was to develop an approach to automatically predict behavior and posture of sows by using a 2D image-based deep neural network (DNN) for the detection and localization of relevant sow and pen features, followed by a hierarchical conditional statement based on human expert knowledge for behavior/posture classification. The automatic detection of sow body parts and pen equipment was trained using an object detection algorithm (YOLO V3). The algorithm achieved an Average Precision (AP) of 0.97 (straw rack), 0.97 (head), 0.95 (feeding trough), 0.86 (jute bag), 0.78 (tail), 0.75 (legs) and 0.66 (teats). The conditional statement, which classifies and automatically generates a posture or behavior of the sow under consideration of context, temporal and geometric values of the detected features, classified 59.6% of the postures (lying lateral, lying ventral, standing, sitting) and behaviors (interaction with pen equipment) correctly. In conclusion, the results indicate the potential of DNN toward automatic behavior classification from 2D videos as potential basis for an automatic farrowing monitoring system.

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