Human Neuromedin S ELISA Kit

Background: Drug-induced movement disorders (DIMDs) are commonly encountered, but an often-under-reported subgroup of movement disorders. Objectives: We aimed to highlight the spectrum of DIMDs in patients taking different groups of drugs at our movement disorder center. Methods: It is a cross-sectional descriptive study including 97 consecutive DIMDs patients diagnosed over the past two years (2017-2019). Results: The mean +/- standard deviation (SD) age of our study population was 35.89 +/- 17.8 years (Range-2-80 years). There were 51 males and 46 females. Different DIMDs observed included tardive dystonia (n = 41; 42.2%), postural tremor (n = 38; 39.2%), parkinsonism (n = 32; 33%), tardive dyskinesia (n = 21; 21.6%), acute dystonia (n = 10; 10.3%), neuroleptic malignant syndrome (NMS) (n = 2; 2.1%), and others [(n = 10; 10.30%) including chorea and stereotypy each in 3; acute dyskinesia in 2; and myoclonic jerks and acute akathisia each in 1 patient]. Of these 97 patients, 49 had more than one type of DIMDs while 48 had a single type of DIMDs. In our study 37 (38%) patients had received non-dopamine receptor blocking agents (non-DRBA), 30 (31%) patients had received dopamine receptor blocking agents (DRBA), and 30 (31%) patients had received both DRBA and non-DRBA. Conclusions: Tardive dystonia was the most common DIMDs observed in our study. Our DIMDs patients were younger than other reported studies. We observed a significant number of non-DRBA drugs causing DIMD in our study as compared to previous studies. Drug-induced parkinsonism (DIP) was the most common DIMDs in the DRBA group. Tardive dystonia was the most common DIMDs seen in DRBA + non-DRBA group and the second most common in the DRBA and non-DRBA group. The postural tremor was the most common DIMDs in the non-DRBA group.

For satellite remote sensing image, ship detection is affected by factors such as cloud, weather and sea clutter, and there are problems such as high false alarm rate and missed detection rate. A deep neural network (DT-YOLO) for real-time detection of surface ships in unmanned aerial vehicle (UAV) aerial photography is proposed. DT-YOLO firstly improves the traditional YOLOv3 algorithm by constructing a deep neural network using densely connected modules and transition modules to extract ship features and designs five different scale convolution feature maps. After upsampling, they are merged with the corresponding scale feature maps to form a multi-scale feature pyramid to perform ship prediction tasks. Then the k-means algorithm is used to cluster the target frame dimensions, determine the target frame parameters, and improve the positioning accuracy of the model; In the detection network, the non-maximum suppression algorithm (NMS) is optimized by attenuating the confidence score linearly, which effectively mitigates the problem of mutual occlusion detection of ships. Finally, the self-constructed sea surface ship dataset is established to test the performance of the algorithm in different scenes by using multi-scale training and data enhancement strategies. The results show that the proposed method improves the detection accuracy under the premise of ensuring real-time performance, especially in the detection of larger targets and mutually occluded ships.