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Noninvasive electrocardiographic imaging (ECGI) is a promising tool for revealing crucial cardiac electrical events with diagnostic potential. We propose a novel nonparametric regression framework based on multivariate adaptive regression splines (MARS) for ECGI.

The inverse problem was solved by using the regression model trained with body surface potentials (BSP) and corresponding electrograms (EGM). Simulated data as well as experimental data from torso-tank experiments were used as to assess the performance of the proposed method. The robustness of the method to measurement noise and geometric errors were assessed in terms of electrogram reconstruction quality, activation time accuracy, and localization error metrics. The methods were compared with Tikhonov regularization and neural network (NN)-based methods. The resulting mapping functions between the BSPs and EGMs were also used to evaluate the most influential measurement leads.

MARS-based method outperformed Tikhonov regularization in terms of reconstruction accuracy and robustness to measurement noise. The effects of geometric errors were remedied to some extent by enriching the training set composition including model errors. The MARS-based method had a comparable performance with NN-based methods, which require the adjustment of many parameters.

MARS-based method successfully discovers the inverse mapping functions between the BSPs and EGMs yielding accurate reconstructions, and quantifies the contribution of each BSP lead.

MARS-based method is adaptive, requires fewer parameter adjustments than NN-based methods, and robust to errors. Thus, it can be a feasible data-driven approach for accurately solving inverse imaging problems.

MARS-based method is adaptive, requires fewer parameter adjustments than NN-based methods, and robust to errors. Thus, it can be a feasible data-driven approach for accurately solving inverse imaging problems.Electrical impedance tomography (EIT) is a noninvasive imaging technology used to reconstruct the conductivity distribution in objects and the human body. In recent years, numerous EIT systems and image reconstruction algorithms have been developed. However, most of these EIT systems require conventional electrodes with conductive gels (wet electrodes) and cannot be adapted to different body types, resulting in limited applicability. In this study, a wearable wireless EIT belt with dry electrodes was designed to enable EIT imaging of the human body without using wet electrodes. The specific design of the belt mechanism and dry electrodes provide the advantages of easy wear and adaptation to different body sizes. Danuglipron purchase Additionally, the GaussNewton method was used to optimize the EIT image. Finally, experiments were performed on the phantom and human body to validate the performance of the proposed EIT belt. The results demonstrate that the proposed system can provide accurate location information of the objects in the EIT image and the system can be successfully applied for noninvasive measurement of the human body.

Target identification in brain-computer interface (BCI) spellers refers to the electroencephalogram (EEG) classification for predicting the target character that the subject intends to spell. When the visual stimulus of each character is tagged with a distinct frequency, the EEG records steady-state visually evoked potentials (SSVEP) whose spectrum is dominated by the harmonics of the target frequency. In this setting, we address the target identification and propose a novel deep neural network (DNN) architecture.

The proposed DNN processes the multi-channel SSVEP with convolutions across the sub-bands of harmonics, channels, time, and classifies at the fully connected layer. We test with two publicly available large scale (the benchmark and BETA) datasets consisting of in total 105 subjects with 40 characters. Our first stage training learns a global model by exploiting the statistical commonalities among all subjects, and the second stage fine tunes to each subject separately by exploiting the individualities.

Our DNN achieves impressive information transfer rates (ITRs) on both datasets, 265.23 bits/min and 196.59 bits/min, respectively, with only 0.4 seconds of stimulation. link2 The code is available for reproducibility at https//github.com/osmanberke/Deep-SSVEP-BCI.

The presented DNN strongly outperforms the state-of-the-art techniques as our accuracy and ITR rates are the highest ever reported performance results on these datasets.

Due to its unprecedentedly high speller ITRs and flawless applicability to general SSVEP systems, our technique has great potential in various biomedical engineering settings of BCIs such as communication, rehabilitation and control.

Due to its unprecedentedly high speller ITRs and flawless applicability to general SSVEP systems, our technique has great potential in various biomedical engineering settings of BCIs such as communication, rehabilitation and control.Growing impact of poststroke upper extremity (UE) functional limitations entails newer dimensions in assessment methodologies. This has compelled researchers to think way beyond traditional stroke assessment scales during the out-patient rehabilitation phase. In concurrence with this, sensor-driven quantitative evaluation of poststroke UE functional limitations has become a fertile field of research. Here, we have emphasized an instrumented wearable for systematic monitoring of stroke patients with right-hemiparesis for evaluating their grasp abilities deploying intelligent algorithms. An instrumented glove housing 6 flex sensors, 3 force sensors, and a motion processing unit was developed to administer 19 activities of Action Research Arm Test (ARAT) while experimenting on 20 voluntarily participating subjects. After necessary signal conditioning, meaningful features were extracted, and subsequently the most appropriate ones were selected using the ReliefF algorithm. An optimally tuned support vector classifier was employed to classify patients with different degrees of disability and an accuracy of 92% was achieved supported by a high area under the receiver operating characteristic score. Furthermore, selected features could provide additional information that revealed the causes of grasp limitations. This would assist physicians in planning more effective poststroke rehabilitation strategies. Results of the one-way ANOVA test conducted on actual and predicted ARAT scores of the subjects indicated remarkable prospects of the proposed glove-based method in poststroke grasp ability assessment and rehabilitation.The novel, anaerobic, Gram-positive, rod-shaped bacterial strain, ResAG-91T, was isolated from a faecal sample of a male human volunteer. Analysis of the 16S rRNA gene sequence revealed that strain ResAG-91T showed high similarity to the type strains of Adlercreutzia equolifaciens subsp. equolifaciens and Adlercreutzia equolifaciens subsp. celatus. Analysis of the whole draft genome sequences, i.e. digital DNA-DNA hybridization (dDDH) and average nucleotide identity (ANI), of strain ResAG-91T and the type strains of Adlercreutzia species revealed that strain ResAG-91T represents a novel species of the genus Adlercreutzia. The genome size of strain ResAG-91T is 2.8 Mbp and the G+C content is 63.3 mol%. The major respiratory quinone of strain ResAG-91T was MMK-5 (methylmenaquinone). Major cellular fatty acids were C15  0 anteiso, C14  0 iso and C14  0 2-OH. Galactose and ribose were detected as major whole cell sugars. Furthermore, the peptidoglycan type of strain ResAG-91T was A1γ with meso-diaminopimelic acid. The polar lipids were phosphatidylglycerol, diphosphatidylglycerol, one unidentified lipid, three unidentified phospholipids and five unidentified glycolipids. Strain ResAG-91T was able to metabolize the stilbene resveratrol into dihydroresveratrol. On the basis of this polyphasic approach, including phenotypical, molecular (16S rRNA gene and whole genome sequencing) and biochemical (fatty acids, quinones, polar lipids, peptidoglycan, whole cell sugars, Rapid ID32A and API20A) analyses, we propose the novel species Adlercreutzia rubneri sp. nov. with the type and only strain ResAG-91T (=DSM 111416T=JCM 34176T=LMG 31897T).The 2014-15 Disneyland measles outbreak that began at the California theme park in December 2014 sparked an international conversation regarding measles, vaccine hesitancy, and vaccine policies. The outbreak capped a year with the highest number of measles cases reported in two decades and came amidst increasing trends in nonmedical vaccine exemptions in California and elsewhere. Because of its sensational story line and spread among unvaccinated populations, the outbreak received a high level of media coverage that focused on vaccine hesitancy as a primary driver of the outbreak. This media coverage and the ostensible public support for vaccines that followed led some to hypothesize that the outbreak might have a "Disneyland effect," or a positive influence on the uptake of pediatric measles vaccine. This article reviews the facts of the outbreak and its context, and explores the evidence for the Disneyland outbreak causing an influence on U.S. pediatric vaccine-related beliefs and behaviors.

Cerebral ischemia-reperfusion (CIR) injury is a severe disease, which may cause serious dysfunction of the brain. Most circular RNAs (circRNAs) have been demonstrated to play a significant role in CIR injury. link3 However, a novel circRNA, circ_0062166 (circ_BCL2L13) has not been investigated for CIR injury. Hence, we aim to disclose the role of circ_0062166 in CIR injury in this study.

Firstly, RT-qPCR was applied to examine the expression of circ_0062166 in oxygen-glucose deprivation and reoxygenation (OGD/R) cell model. Functional assays were conducted to detect the role of circ_0062166 in CIR injury. RNA pull down, RIP and luciferase reporter assays were implemented to probe into the regulatory mechanism of circ_0062166.

Circ_0062166 was significantly up-regulated in neuro-2A (N2A) neuroblastoma cells following OGD/R. Functionally, the silencing of circ_0062166 inhibited cell proliferation and promoted cell apoptosis under OGD/R condition. From the perspective of mechanism, circ_0062166 functioned as a competing endogenous RNA (ceRNA) for microRNA-526b-5p (miR-526b-5p) and regulated BCL2 like 13 (BCL2L13). Eventually, the promoting role of the circ_0062166/miR-526b-5p/BCL2L13 axis in the CIR injury was verified.

To sum up, the present study has demonstrated that circ_0062166/miR-526b-5p/BCL2L13 axis accelerated the progression of CIR injury, which might provide effective strategies for CIR injury therapy.

To sum up, the present study has demonstrated that circ_0062166/miR-526b-5p/BCL2L13 axis accelerated the progression of CIR injury, which might provide effective strategies for CIR injury therapy.University campuses could become leaders in developing alternatives to policing for managing public health and safety, yet, nearly all campuses rely on campus or local police to respond to mental health emergencies. Herein, we present the available evidence for campus mobile crisis intervention teams (MCITs) as an alternative to policing, consider what colleges and universities can learn from existing community MCIT models, and propose initial steps for the development and implementation of a campus MCIT.