Simultaneous bilateral hypertensive basal ganglia hemorrhage

Context Hypertension is the single most important risk factor for intracerebral hemorrhage (ICH) and often leads to solitary hematoma. Multiple spontaneous simultaneous ICH is not common, and bilateral hemorrhages occurred in symmetrical basal ganglia is extremely rare. Most reported cases accepted conservative treatment and suffered extremely poor outcome. Case report A 57-year-old male became unconscious when having supper and was transported to our emergency room immediately. Non-contract CT brain scanning showed simultaneous bilateral hypertensive basal ganglia hemorrhage; he was treated by stereotactic aspiration and thrombolysis for both sides, with subsequent thrombolysis and clot aspiration through hematoma-indwelling catheter. The hematomas were almost totally cleared within a week. His condition improved gradually. Nearly 10 months after onset, he could chow and swallow food, controlling bowels and bladder all by himself, but need some help when feeding and using toilet. Conclusion Simultaneous bilateral hypertensive basal ganglia hemorrhage is a devastating cerebrovascular disease with significant high morbidity and mortality. Stereotactic aspiration and thrombolysis is a safe and effective way to clear hematomas within short time, thus reducing the neurological impairment from hematoma mass effect and secondary brain injury, improving prognosis

Design Optimization of a Concrete Face Rock-Fill Dam by Using Genetic Algorithm

This paper combined with the adaptive principle to improve the genetic algorithms (GA) and applied it to optimal design of the shape of the concrete face rock-fill dam (CFRD). Based on the improved GA, a mathematical model was established for the design optimization of CFRD. CFRD utilizes dam cost as objective function and dam slope and geometries of the dam material partition as design variables. Dam stability, stress, displacement, and stress level are used as the main condition constraints. The calculation procedures were prepared, and the GA was used to optimize the design of Jishixia CFRD. Results show that the GA could solve the global optimal solution problem of complex optimization design, such as the high degree of nonlinearity and the recessiveness of constraint conditions, and using the GA to optimize the CFRD design can reduce the quantities of projects and engineering safety costs

Quantitative volumetric analysis of primary glioblastoma multiforme on MRI and 11C-methionine PET: initial study on five patients

To investigate the discrepancy between 11C-methionine (MET) positron emission tomography (PET) and MRI results in primary glioblastoma multiforme (GBM) through three-dimensional (3D) volumetric analysis, we retrospectively analysed patients with primary GBM who underwent preoperative 3D MRI and MET PET and were operated between June 2016 and January 2017. Tumour delineation and volumetric analysis were conducted using MRIcron software. Tumour volumes defined by MRI (VMRI) were manually drawn slice by slice in axial and sagittal or coronal images of enhanced T1 sequence, while metabolic tumour volumes were automatically segmented in MET PET (VMET) based on three (frontal, occipital and temporal) 3D reference volumes of interest (VOI). Discrepancies were evaluated in terms of both absolute volume and percentage on the combined images. MET PET contours contained and extended beyond MRI contours in all five patients; in a subset of cases, MET PET contours extended to the contralateral hemisphere. The discrepancy between MET uptake and MRI results was 27.67 cm3 (4.20–51.20 cm3), i.e. approximately 39.0% (17.4–64.3%) of the metabolic tumour volume was located outside the volumes of the Gd-enhanced area. Metabolic tumour volume is substantially underestimated by Gd-enhanced area in patients with primary GBM. Quantitative volumetric information derived from MET uptake is useful in defining tumour targets and designing individualised therapy strategies in primary GBM

IntentDial: An Intent Graph based Multi-Turn Dialogue System with Reasoning Path Visualization

Intent detection and identification from multi-turn dialogue has become a widely explored technique in conversational agents, for example, voice assistants and intelligent customer services. The conventional approaches typically cast the intent mining process as a classification task. Although neural classifiers have proven adept at such classification tasks, the issue of neural network models often impedes their practical deployment in real-world settings. We present a novel graph-based multi-turn dialogue system called , which identifies a user's intent by identifying intent elements and a standard query from a dynamically constructed and extensible intent graph using reinforcement learning. In addition, we provide visualization components to monitor the immediate reasoning path for each turn of a dialogue, which greatly facilitates further improvement of the system.Comment: 4pages, 5 figure