A Case of Central Cord Syndrome Related Status Epilepticus - A Case Report -

Central cord syndrome (CCS) is extremely rare as a direct consequence of generalized epileptic seizure. CCS is associated with hyperextension of the spinal cord and has characteristic radiologic findings including posterior ligamentous injury and prevertebral hyperintensity following magnetic resonance imaging (MRI). We experienced the case of a 25-year-old man who suffered CCS after status epilepticus. Cervical spinal MRI revealed high signal intensity at the C1 level but with no signal or structural changes in other sites. After rehabilitation management, the patient significantly improved on the ASIA (American Spinal Injury Association) motor scale and bladder function. We proposed that epilepsy related CCS may be caused by muscle contractions during generalized seizure, which can induce traction injury of the spinal cord or relative narrowing of spinal canal via transient herniated nucleus pulposus or transient subluxation of vertebra. We also suggest CCS without radiologic findings of trauma has good prognosis compared with other CCS

A convolutional neural network for segmentation of yeast cells without manual training annotations

MOTIVATION: Single-cell time-lapse microscopy is a ubiquitous tool for studying the dynamics of complex cellular processes. While imaging can be automated to generate very large volumes of data, the processing of the resulting movies to extract high-quality single-cell information remains a challenging task. The development of software tools that automatically identify and track cells is essential for realizing the full potential of time-lapse microscopy data. Convolutional neural networks (CNNs) are ideally suited for such applications, but require great amounts of manually annotated data for training, a time-consuming and tedious process. RESULTS: We developed a new approach to CNN training for yeast cell segmentation based on synthetic data and present (i) a software tool for the generation of synthetic images mimicking brightfield images of budding yeast cells and (ii) a convolutional neural network (Mask R-CNN) for yeast segmentation that was trained on a fully synthetic dataset. The Mask R-CNN performed excellently on segmenting actual microscopy images of budding yeast cells, and a density-based spatial clustering algorithm (DBSCAN) was able to track the detected cells across the frames of microscopy movies. Our synthetic data creation tool completely bypassed the laborious generation of manually annotated training datasets, and can be easily adjusted to produce images with many different features. The incorporation of synthetic data creation into the development pipeline of CNN-based tools for budding yeast microscopy is a critical step toward the generation of more powerful, widely applicable and user-friendly image processing tools for this microorganism. AVAILABILITY AND IMPLEMENTATION: The synthetic data generation code can be found at https://github.com/prhbrt/synthetic-yeast-cells. The Mask R-CNN as well as the tuning and benchmarking scripts can be found at https://github.com/ymzayek/yeastcells-detection-maskrcnn. We also provide Google Colab scripts that reproduce all the results of this work. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online

Active Learning for Classifying Political Tweets

We examine methods for improving models for automatically labeling social media data. In particular we evaluate active learning: a method for selecting candidate training data whose labeling the classification model would benefit most of. We show that this approach requires careful ex-periment design, when it is combined with language modelin

Determining the Function of Political Tweets

We study the discursive practices of politicians and journalists on social media. For this we need more annotated data than we currently have but the annotation process is time-consuming and costly. In this paper we examine machine learning methods for automatically annotating unseen tweetsbased on a small set of manually annotated tweets. Forimproving the performance of the learner, we focus onmethods related to training data expansion, like artificialtraining data, active learning and incorporating languagemodels developed from unannotated text

Segmented Shape-Symbolic Time Series Representation

Abstract. This paper introduces a symbolic time series representation using monotonic sub-sequences and bottom up segmentation. The representation min-imizes the square error between the segments and their monotonic approximations. The representation can robustly classify the direction of a segment and is scale in-variant with respect to the time and value dimensions. This paper describes two experiments. The first shows how accurately the monotonic functions are able to discriminate between different segments. The second tests how well the segmenta-tion technique recognizes segments and classifies them with correct symbols. Fi-nally this paper illustrates the new representation on real-world data.

Association between central sensitization and gait in chronic low back pain:Insights from a machine learning approach

BACKGROUND: Central sensitization (CS) is often present in patients with chronic low back pain (CLBP). Gait impairments due to CLBP have been extensively reported; however, the association between CS and gait is unknown. The present study examined the association between CS and CLBP on gait during activities of daily living. METHOD: Forty-two patients with CLBP were included. CS was assessed through the Central Sensitization Inventory (CSI), and patients were divided in a low and high CS group (23 CLBP- and 19 CLBP+, respectively). Patients wore a tri-axial accelerometer device for one week. From the acceleration signals, gait cycles were extracted and 36 gait outcomes representing quantitative and qualitative characteristics of gait were calculated. A Random Forest was trained to classify CLBP- and CLBP + based on the gait outcomes. The maximum Youden index was computed to measure the diagnostic test's ability and SHapley Additive exPlanations (SHAP) indexed the gait outcomes' importance to the classification model. RESULTS: The Random Forest accurately (84.4%) classified the CLBP- and CLBP+. Youden index was 0.65, and SHAP revealed that the gait outcomes' important to the classification model were related to gait smoothness, stride frequency variability, stride length variability, stride regularity, predictability, and stability. CONCLUSIONS: CLBP- and CLBP + patients had different motor control strategies. Patients in the CLBP- group presented with a more "loose control", with higher gait smoothness and stability, while CLBP + patients presented with a "tight control", with a more regular, less variable, and more predictable gait pattern

Structural similarity analysis of midfacial fractures:a feasibility study

The structural similarity index metric is used to measure the similarity between two images. The aim here was to study the feasibility of this metric to measure the structural similarity and fracture characteristics of midfacial fractures in computed tomography (CT) datasets following radiation dose reduction, iterative reconstruction (IR) and deep learning reconstruction. Zygomaticomaxillary fractures were inflicted on four human cadaver specimen and scanned with standard and low dose CT protocols. Datasets were reconstructed using varying strengths of IR and the subsequently applying the PixelShine™ deep learning algorithm as post processing. Individual small and non-dislocated fractures were selected for the data analysis. After attenuating the osseous anatomy of interest, registration was performed to superimpose the datasets and subsequently to measure by structural image quality. Changes to the fracture characteristics were measured by comparing each fracture to the mirrored contralateral anatomy. Twelve fracture locations were included in the data analysis. The most structural image quality changes occurred with radiation dose reduction (0.980036±0.011904), whilst the effects of IR strength (0.995399±0.001059) and the deep learning algorithm (0.999996±0.000002) were small. Radiation dose reduction and IR strength tended to affect the fracture characteristics. Both the structural image quality and fracture characteristics were not affected by the use of the deep learning algorithm. In conclusion, evidence is provided for the feasibility of using the structural similarity index metric for the analysis of structural image quality and fracture characteristics