Net water uptake within the ischemic penumbra predicts the presence of the midline shift in patients with acute ischemic stroke

ObjectiveThe study aimed to explore the association between midline shift (MLS) and net water uptake (NWU) within the ischemic penumbra in acute ischemic stroke patients.MethodsThis was a retrospective cohort study that examined patients with anterior circulation stroke. Net water uptake within the acute ischemic core and penumbra was calculated using data from admission multimodal CT scans. The primary outcome was severe cerebral edema measured by the presence of MLS on 24 to 48 h follow-up CT scans. The presence of a significant MLS was defined by a deviation of the septum pellucidum from the midline on follow-up CT scans of at least 3 mm or greater due to the mass effect of ischemic edema. The net water uptake was compared between patients with and without MLS, followed by logistic regression analyses and receiver operating characteristics (ROCs) to assess the predictive power of net water uptake in MLS.ResultsA total of 133 patients were analyzed: 50 patients (37.6%) with MLS and 83 patients (62.4%) without. Compared to patients without MLS, patients with MLS had higher net water uptake within the core [6.8 (3.2–10.4) vs. 4.9 (2.2–8.1), P = 0.048] and higher net water uptake within the ischemic penumbra [2.9 (1.8–4.3) vs. 0.2 (−2.5–2.7), P < 0.001]. Penumbral net water uptake had higher predictive performance than net water uptake of the core in MLS [area under the curve: 0.708 vs. 0.603, p < 0.001]. Moreover, the penumbral net water uptake predicted MLS in the multivariate regression model, adjusting for age, sex, admission National Institutes of Health Stroke Scale (NIHSS), diabetes mellitus, atrial fibrillation, ischemic core volume, and poor collateral vessel status (OR = 1.165; 95% CI = 1.002–1.356; P = 0.047). No significant prediction was found for the net water uptake of the core in the multivariate regression model.ConclusionNet water uptake measured acutely within the ischemic penumbra could predict severe cerebral edema at 24–48 h

Quantitative assessment of collateral time on perfusion computed tomography in acute ischemic stroke patients

Background and aimGood collateral circulation is recognized to maintain perfusion and contribute to favorable clinical outcomes in acute ischemic stroke. This study aimed to derive and validate an optimal collateral time measurement on perfusion computed tomography imaging for patients with acute ischemic stroke.MethodsThis study included 106 acute ischemic stroke patients with complete large vessel occlusions. In deriving cohort of 23 patients, the parasagittal region of the ischemic hemisphere was divided into six pial arterial zones according to pial branches of the middle cerebral artery. Within the 85 arterial zones with collateral vessels, the receiver operating characteristic analysis was performed to derive the optimal collateral time threshold for fast collateral flow on perfusion computed tomography. The reference for fast collateral flow was the peak contrast delay on the collateral vessels within each ischemic arterial zone compared to its contralateral normal arterial zone on dynamic computed tomography angiography. The optimal perfusion collateral time threshold was then tested in predicting poor clinical outcomes (modified Rankin score of 5–6) and final infarct volume in the validation cohort of 83 patients.ResultsFor the derivation cohort of 85 arterial zones, the optimal collateral time threshold for fast collateral flow on perfusion computed tomography was a delay time of 4.04 s [area under the curve = 0.78 (0.67, 0.89), sensitivity = 73%, and specificity = 77%]. Therefore, the delay time of 4 s was used to define the perfusion collateral time. In the validation cohort, the perfusion collateral time showed a slightly higher predicting power than dynamic computed tomography angiography collateral time in poor clinical outcomes (area under the curve = 0.72 vs. 0.67; P < 0.001). Compared to dynamic computed tomography angiography collateral time, the perfusion collateral time also had better performance in predicting final infarct volume (R-squared values = 0.55 vs. 0.23; P < 0.001).ConclusionOur results indicate that perfusion computed tomography can accurately quantify the collateral time after acute ischemic stroke

I4U Submission to NIST SRE 2018: Leveraging from a Decade of Shared Experiences

The I4U consortium was established to facilitate a joint entry to NIST speaker recognition evaluations (SRE). The latest edition of such joint submission was in SRE 2018, in which the I4U submission was among the best-performing systems. SRE'18 also marks the 10-year anniversary of I4U consortium into NIST SRE series of evaluation. The primary objective of the current paper is to summarize the results and lessons learned based on the twelve sub-systems and their fusion submitted to SRE'18. It is also our intention to present a shared view on the advancements, progresses, and major paradigm shifts that we have witnessed as an SRE participant in the past decade from SRE'08 to SRE'18. In this regard, we have seen, among others, a paradigm shift from supervector representation to deep speaker embedding, and a switch of research challenge from channel compensation to domain adaptation.Comment: 5 page

I4U Submission to NIST SRE 2018: Leveraging from a Decade of Shared Experiences

The I4U consortium was established to facilitate a joint entry to NIST speaker recognition evaluations (SRE). The latest edition of such joint submission was in SRE 2018, in which the I4U submission was among the best-performing systems. SRE'18 also marks the 10-year anniversary of I4U consortium into NIST SRE series of evaluation. The primary objective of the current paper is to summarize the results and lessons learned based on the twelve subsystems and their fusion submitted to SRE'18. It is also our intention to present a shared view on the advancements, progresses, and major paradigm shifts that we have witnessed as an SRE participant in the past decade from SRE'08 to SRE'18. In this regard, we have seen, among others , a paradigm shift from supervector representation to deep speaker embedding, and a switch of research challenge from channel compensation to domain adaptation

I4U Submission to NIST SRE 2018: Leveraging from a Decade of Shared Experiences

International audienceThe I4U consortium was established to facilitate a joint entry to NIST speaker recognition evaluations (SRE). The latest edition of such joint submission was in SRE 2018, in which the I4U submission was among the best-performing systems. SRE'18 also marks the 10-year anniversary of I4U consortium into NIST SRE series of evaluation. The primary objective of the current paper is to summarize the results and lessons learned based on the twelve subsystems and their fusion submitted to SRE'18. It is also our intention to present a shared view on the advancements, progresses, and major paradigm shifts that we have witnessed as an SRE participant in the past decade from SRE'08 to SRE'18. In this regard, we have seen, among others , a paradigm shift from supervector representation to deep speaker embedding, and a switch of research challenge from channel compensation to domain adaptation

A robust zero-watermarking scheme based on non-negative matrix factorization for audio protection.

The copyright problem of digital products is becoming more and more prominent. In this case, digital watermarking technology has attracted the attention of many experts and scholars in the field of information security. Among the proposed technologies, zero-watermarking technology has been favored greatly with its excellent imperceptibility. In this paper, a novel robust audio zero-watermarking scheme is designed by applying non-negative matrix decomposition algorithm to zero-watermarking technology. Firstly, the proposed scheme divides the input audio signal into fixed frames, then applies fast Fourier transform(FFT) and non-negative matrix factorization(NMF) algorithm to extract the feature vector of the original audio signal. Finally, XOR the feature vector and the digital watermark sequence to achieve the embedding of zero-watermarking. The experimental results show that the proposed scheme performs more effectively in resisting common and frame-desynchronization attacks than the existing zero-watermarking schemes

A zero‐watermarking technique based on i‐vector model for audio copyright protection

Abstract Audio zero‐watermarking is a promising technology for audio copyright protection. It does not modify the content of the original carrier and has good imperceptibility. Considering that i‐vector model can map audio signals of different lengths into a vector space of fixed length, which has good robustness against desynchronization attacks, a robust audio zero‐watermarking technique based on i‐vector model is proposed here. The proposed scheme extracts the i‐vector of the input signal to identify its inherent stability features and then generates a key by XOR operation on the watermark and encoded feature sequences. Experimental results show that this scheme can effectively resist common attacks and desynchronization attacks

Human action recognition based on kinematic similarity in real time.

Human action recognition using 3D pose data has gained a growing interest in the field of computer robotic interfaces and pattern recognition since the availability of hardware to capture human pose. In this paper, we propose a fast, simple, and powerful method of human action recognition based on human kinematic similarity. The key to this method is that the action descriptor consists of joints position, angular velocity and angular acceleration, which can meet the different individual sizes and eliminate the complex normalization. The angular parameters of joints within a short sliding time window (approximately 5 frames) around the current frame are used to express each pose frame of human action sequence. Moreover, three modified KNN (k-nearest-neighbors algorithm) classifiers are employed in our method: one for achieving the confidence of every frame in the training step, one for estimating the frame label of each descriptor, and one for classifying actions. Additional estimating of the frame's time label makes it possible to address single input frames. This approach can be used on difficult, unsegmented sequences. The proposed method is efficient and can be run in real time. The research shows that many public datasets are irregularly segmented, and a simple method is provided to regularize the datasets. The approach is tested on some challenging datasets such as MSR-Action3D, MSRDailyActivity3D, and UTD-MHAD. The results indicate our method achieves a higher accuracy

Human pose estimation method based on single depth image

Many of current human pose estimation methods based on depth images require training stage. However, the training stage costs huge work on making samples. And many methods for human pose occlusion condition cannot work well. In this study, a novel approach to estimate human pose with a depth image called model‐based recursive matching (MRM) is introduced. A human skeleton model with customised parameters is created based on T‐pose to fit different body types. The authors use depth image and 3D point cloud corresponding to input. In contrast to previous work, the proposed method avoids training step and can give an accurate estimation in the case of the human occlusion condition. They demonstrate the method by comparing to the method Kinect offered by using random forest on 20 human poses. And the ground truth of coordinates of pose joint is made by the motion capture system. The result shows that the proposed method not only works well on the general human pose but also can deal with human occlusion better. And the authors’ method can be also applied to the disabled people and other creatures