Rapid and accurate recognition of lesions masquerading as acute stroke is important. Any incorrect or delayed diagnosis of stroke mimics will not only increase the risk of being exposed to unnecessary and possibly dangerous interventional therapies, but will also delay proper treatment. In this article, written from a neuroradiologist's perspective, we classified these lesions masquerading as acute stroke into three groups: lesions that may have "normal imaging," lesions that are "symptom mimics" but on imaging clearly not a stroke, and lesions that are "symptom and imaging mimics" with imaging findings similar to stroke. We focused the review on neuroimaging findings of the latter two groups ending with a suggestion for a diagnostic approach in the form of an algorithm.

Additional assessment of program components is necessary to produce clinically significant outcomes. Caregiver PI and GI scores may serve as objective measures of their oral hygiene skills, which can be improved with comprehensive instructions.

Organic/amorphous silicon (a-Si) hybrid tandem solar cells have the potential to provide a highly efficient low-cost photovoltaic technology using abundant elements, and the technology is adaptable to large-scale processes. With their high open-circuit voltage (V oc ) and adaptability to a broad solar spectrum, organic/a-Si tandem devices offer significantly improved performance. We have shown that organic/a-Si hybrid tandem solar cells with a complementary organic absorber can exhibit a power conversion efficiency (PCE) of up to 7.5%, with a fill factor (FF) of 72.3% and a V oc almost equivalent to the sum of the sub-cells under standard air mass (AM) 1.5 illumination. The high performance of the device results from the complementary absorption spectra of two sub-cells and well-matched energy levels of the intermediate layer. This study provides an effective design strategy for organic/a-Si hybrid tandem solar cells of improved efficiency.

Electroencephalography (EEG) records the electrical activity of the brain, which is an important tool for the automatic detection of epileptic seizures. It is certainly a very heavy burden to only recognize EEG epilepsy manually, so the method of computer-assisted treatment is of great importance. This paper presents a seizure detection algorithm based on variational modal decomposition (VMD) and a deep forest (DF) model. Variational modal decomposition is performed on EEG recordings, and the first three variational modal functions (VMFs) are selected to construct the time–frequency distribution of the EEG signals. Then, the log−Euclidean covariance matrix (LECM) is computed to represent the EEG properties and form EEG features. The deep forest model is applied to complete the EEG signal classification, which is a non-neural network deep model with a cascade structure that performs feature learning through the forest. In addition, to improve the classification accuracy, postprocessing techniques are performed to generate the discriminant results by moving average filtering and adaptive collar expansion. The algorithm was evaluated on the Bonn EEG dataset and the Freiburg long−term EEG dataset, and the former achieved a sensitivity and specificity of 99.32% and 99.31%, respectively. The mean sensitivity and specificity of this method for the 21 patients in the Freiburg dataset were 95.2% and 98.56%, respectively, with a false detection rate of 0.36/h. These results demonstrate the superior performance advantage of our algorithm and indicate its great research potential in epilepsy detection.

Background:Combining training or sensory stimulation with non-invasive brain stimulation has shown to improve performance in healthy subjects and improve brain function in patients after brain injury. However, the plasticity mechanisms and the optimal parameters to induce long-term and sustainable enhanced performance remain unknown. Objective: This work was designed to identify the protocols of which combining sensory stimulation with repetitive transcranial magnetic stimulation (rTMS) will facilitate the greatest changes in fMRI activation maps in the rat's primary somatosensory cortex (S1). Methods: Several protocols of combining forepaw electrical stimulation with rTMS were tested, including a single stimulation session compared to multiple, daily stimulation sessions, as well as synchronous and asynchronous delivery of both modalities. High-resolution fMRI was used to determine how pairing sensory stimulation with rTMS induced short and long-term plasticity in the rat S1. Results: All groups that received a single session of rTMS showed short-term increases in S1 activity, but these increases did not last three days after the session. The group that received a stimulation protocol of 10 Hz forepaw stimulation that was delivered simultaneously with 10 Hz rTMS for five consecutive days demonstrated the greatest increases in the extent of the evoked fMRI responses compared to groups that received other stimulation protocols. Conclusions: Our results provide direct indication that pairing peripheral stimulation with rTMS induces long-term plasticity, and this phenomenon appears to follow a time-dependent plasticity mechanism. These results will be important to lead the design of new training and rehabilitation paradigms and training towards achieving maximal performance in healthy subjects.