13,217 research outputs found
Hashimoto’s encephalopathy cases: Chinese experience
BACKGROUND: Hashimoto’s encephalopathy is a poorly understood syndrome consisting of heterogeneous neurological symptoms and high serum antithyroid antibody titers, typically responding to steroids. More clinical series studies are required to characterize the clinical, laboratory and imaging features, and outcomes, especially in the Chinese population. METHODS: We analyzed the clinical, laboratory, and imaging features and outcomes of thirteen consecutive patients with Hashimoto’s encephalopathy diagnosed in Xuan Wu Hospital, Beijing from 2005 to 2010 retrospectively. RESULTS: Cognitive impairment (84.6%) and psychiatric symptoms (38.5%) were the most frequent symptoms. Seizures (30.8%) and myoclonus (7.7%) were less common than previously described. Three (23.1%) patients showed abnormal signals in hippocampus or temporal lobe, which were believed related to their memory disorders or seizures. MRI changes showed resolution paralleling clinical improvement in one patient. Among eight patients who received steroid therapy, five patients recovered, one patient improved with residual deficits, and two patients relapsed or had no effect. Among five non-steroid treated patients, three patients experienced stable remission with antiepileptic drugs or general neurotrophic therapy, and two patients experienced continuous deterioration. CONCLUSIONS: Most patients with Hashimoto’s encephalopathy showed good response to steroids. Some patients improved without steroid therapy. Considering its reversible course, we recommend that Hashimoto’s encephalopathy should always be in the differential diagnosis while evaluating disorders of the central nervous system
Recommended from our members
Liquid biopsy genotyping in lung cancer: ready for clinical utility?
Liquid biopsy is a blood test that detects evidence of cancer cells or tumor DNA in the circulation. Despite complicated collection methods and the requirement for technique-dependent platforms, it has generated substantial interest due, in part, to its potential to detect driver oncogenes such as epidermal growth factor receptor (EGFR) mutants in lung cancer. This technology is advancing rapidly and is being incorporated into numerous EGFR tyrosine kinase inhibitor (EGFR-TKI) development programs. It appears ready for integration into clinical care. Recent studies have demonstrated that biological fluids such as saliva and urine can also be used for detecting EGFR mutant DNA through application other user-friendly techniques. This review focuses on the clinical application of liquid biopsies to lung cancer genotyping, including EGFR and other targets of genotype-directed therapy and compares multiple platforms used for liquid biopsy
Probing onset of strong localization and electron-electron interactions with the presence of direct insulator-quantum Hall transition
We have performed low-temperature transport measurements on a disordered
two-dimensional electron system (2DES). Features of the strong localization
leading to the quantum Hall effect are observed after the 2DES undergoes a
direct insulator-quantum Hall transition with increasing the perpendicular
magnetic field. However, such a transition does not correspond to the onset of
strong localization. The temperature dependences of the Hall resistivity and
Hall conductivity reveal the importance of the electron-electron interaction
effects to the observed transition in our study.Comment: 9 pages, 4 figure
Integral Human Pose Regression
State-of-the-art human pose estimation methods are based on heat map
representation. In spite of the good performance, the representation has a few
issues in nature, such as not differentiable and quantization error. This work
shows that a simple integral operation relates and unifies the heat map
representation and joint regression, thus avoiding the above issues. It is
differentiable, efficient, and compatible with any heat map based methods. Its
effectiveness is convincingly validated via comprehensive ablation experiments
under various settings, specifically on 3D pose estimation, for the first time
Engaging Diverse Secondary Students in International Collaborative Networked Learning
This study examines the processes of engaging a group of highly diverse 7th and 8th grade students in the utilization of information and communication technology (ICT) for collaborative activities through a global networked learning environment called APEC Cyber Academy. Thirty-six middle school students went through a nine-week project-based learning program that aimed at improving ICT skills and promoting international peer learning. Although students showed strong interest in learning about ICT, the findings suggest that students need to be coached in collaboration skills, given appropriate roles to ensure proper division of labor, and supervised closely to ensure the completion of tasks
Measuring the Accuracy of Object Detectors and Trackers
The accuracy of object detectors and trackers is most commonly evaluated by
the Intersection over Union (IoU) criterion. To date, most approaches are
restricted to axis-aligned or oriented boxes and, as a consequence, many
datasets are only labeled with boxes. Nevertheless, axis-aligned or oriented
boxes cannot accurately capture an object's shape. To address this, a number of
densely segmented datasets has started to emerge in both the object detection
and the object tracking communities. However, evaluating the accuracy of object
detectors and trackers that are restricted to boxes on densely segmented data
is not straightforward. To close this gap, we introduce the relative
Intersection over Union (rIoU) accuracy measure. The measure normalizes the IoU
with the optimal box for the segmentation to generate an accuracy measure that
ranges between 0 and 1 and allows a more precise measurement of accuracies.
Furthermore, it enables an efficient and easy way to understand scenes and the
strengths and weaknesses of an object detection or tracking approach. We
display how the new measure can be efficiently calculated and present an
easy-to-use evaluation framework. The framework is tested on the DAVIS and the
VOT2016 segmentations and has been made available to the community.Comment: 10 pages, 7 Figure
Recursive solutions for Laplacian spectra and eigenvectors of a class of growing treelike networks
The complete knowledge of Laplacian eigenvalues and eigenvectors of complex
networks plays an outstanding role in understanding various dynamical processes
running on them; however, determining analytically Laplacian eigenvalues and
eigenvectors is a theoretical challenge. In this paper, we study the Laplacian
spectra and their corresponding eigenvectors of a class of deterministically
growing treelike networks. The two interesting quantities are determined
through the recurrence relations derived from the structure of the networks.
Beginning from the rigorous relations one can obtain the complete eigenvalues
and eigenvectors for the networks of arbitrary size. The analytical method
opens the way to analytically compute the eigenvalues and eigenvectors of some
other deterministic networks, making it possible to accurately calculate their
spectral characteristics.Comment: Definitive version accepted for publication in Physical Reivew
Deep Regionlets for Object Detection
In this paper, we propose a novel object detection framework named "Deep
Regionlets" by establishing a bridge between deep neural networks and
conventional detection schema for accurate generic object detection. Motivated
by the abilities of regionlets for modeling object deformation and multiple
aspect ratios, we incorporate regionlets into an end-to-end trainable deep
learning framework. The deep regionlets framework consists of a region
selection network and a deep regionlet learning module. Specifically, given a
detection bounding box proposal, the region selection network provides guidance
on where to select regions to learn the features from. The regionlet learning
module focuses on local feature selection and transformation to alleviate local
variations. To this end, we first realize non-rectangular region selection
within the detection framework to accommodate variations in object appearance.
Moreover, we design a "gating network" within the regionlet leaning module to
enable soft regionlet selection and pooling. The Deep Regionlets framework is
trained end-to-end without additional efforts. We perform ablation studies and
conduct extensive experiments on the PASCAL VOC and Microsoft COCO datasets.
The proposed framework outperforms state-of-the-art algorithms, such as
RetinaNet and Mask R-CNN, even without additional segmentation labels.Comment: Accepted to ECCV 201
On the existence and uniqueness of solutions to stochastic differential equations driven by G-Brownian motion with integral-Lipschitz coefficients
In this paper, we study the existence and uniqueness of solutions to
stochastic differential equations driven by G-Brownian motion (GSDEs) with
integral-Lipschitz conditions on their coefficients
- …