Necessary for seizure forecasting outcome metrics: seizure frequency and benchmark model.

Work is ongoing to advance seizure forecasting, but the performance metrics used to evaluate model effectiveness can sometimes lead to misleading outcomes. For example, some metrics improve when tested on patients with a particular range of seizure frequencies (SF). This study illustrates the connection between SF and metrics. Additionally, we compared benchmarks for testing performance: a moving average (MA) or the commonly used permutation benchmark. Three data sets were used for the evaluations: (1) Self-reported seizure diaries of 3,994 Seizure Tracker patients; (2) Automatically detected (and sometimes manually reported or edited) generalized tonic-clonic seizures from 2,350 Empatica Embrace 2 and Mate App seizure diary users, and (3) Simulated datasets with varying SFs. Metrics of calibration and discrimination were computed for each dataset, comparing MA and permutation performance across SF values. Most metrics were found to depend on SF. The MA model outperformed or matched the permutation model in all cases. The findings highlight SF's role in seizure forecasting accuracy and the MA model's suitability as a benchmark. This underscores the need for considering patient SF in forecasting studies and suggests the MA model may provide a better standard for evaluating future seizure forecasting models.

Large Language Model Performance on Practice Epilepsy Board Examinations.

This diagnostic study examines whether large language models are able to pass practice licensing examinations for epilepsy.

Development of an Adaptive Artifact Subspace Reconstruction Based on Hebbian/Anti-Hebbian Learning Networks for Enhancing BCI Performance.

Brain-computer interface (BCI) actively translates the brain signals into executable actions by establishing direct communication between the human brain and external devices. Recording brain activity through electroencephalography (EEG) is generally contaminated with both physiological and nonphysiological artifacts, which significantly hinders the BCI performance. Artifact subspace reconstruction (ASR) is a well-known statistical technique that automatically removes artifact components by determining the rejection threshold based on the initial reference EEG segment in multichannel EEG recordings. In real-world applications, the fixed threshold may limit the efficacy of the artifact correction, especially when the quality of the reference data is poor. This study proposes an adaptive online ASR technique by integrating the Hebbian/anti-Hebbian neural networks into the ASR algorithm, namely, principle subspace projection ASR (PSP-ASR) and principal subspace whitening ASR (PSW-ASR) that segmentwise self-organize the artifact subspace by updating the synaptic weights according to the Hebbian and anti-Hebbian learning rules. The effectiveness of the proposed algorithm is compared to the conventional ASR approaches on benchmark EEG dataset and three BCI frameworks, including steady-state visual evoked potential (SSVEP), rapid serial visual presentation (RSVP), and motor imagery (MI) by evaluating the root-mean-square error (RMSE), the signal-to-noise ratio (SNR), the Pearson correlation, and classification accuracy. The results demonstrated that the PSW-ASR algorithm effectively removed the EEG artifacts and retained the activity-specific brain signals compared to the PSP-ASR, standard ASR (Init-ASR), and moving-window ASR (MW-ASR) methods, thereby enhancing the SSVEP, RSVP, and MI BCI performances. Finally, our empirical results from the PSW-ASR algorithm suggested the choice of an aggressive cutoff range of c = 1-10 for activity-specific BCI applications and a moderate range of for the benchmark dataset and general BCI applications.

Rapid prototyping-assisted tooth autotransplantation is associated with a reduced root canal treatment rate: a retrospective cohort study.

BACKGROUND: Autotransplantation is a beneficial treatment with a high success rate for young patients. However, most adult patients require root canal treatment (RCT) of the donor teeth after the autotransplantation procedure, which causes a prolonged treatment time and additional expenses and increases the rate of future tooth fracture. Rapid prototyping (RP)-assisted autotransplantation shortens the extra-alveolar time and enables a superior clinical outcome. However, no cohort studies of the application of this method on adult populations have been reported. METHODS: This study is a retrospective cohort study. All patients underwent autotransplantation from 2012 to 2020 in the Kaohsiung and Chia-Yi branches of Chang Gung Memorial Hospital, and the procedure and clinical outcomes were analysed. Differences in clinical outcomes, age, sex, extra-alveolar time, fixation method, and RCT rate were compared between the two groups. RESULTS: We enrolled 21 patients, 13 treated using the conventional method and 8 treated using the RP-based technique. The RCT rates of the conventional group and RP group were 92.3% and 59%, respectively. The mean age of the two groups was significantly different (28.8 ± 10 vs. 21.6 ± 2.1); after performing subgroup analysis by excluding all of the patients aged > 40 years, we found that the RCT rates were still significantly different (91.0% vs. 50%). The mean extra-alveolar time was 43 s in the RP group, and the autotransplantation survival rate in both groups was 100%. CONCLUSIONS: Rapid prototyping-assisted autotransplantation was successfully adopted for all patients in our study population. By shortening the extra-alveolar time, only 50% of the patients required a root canal treatment with a 100% autotransplantation survival rate. TRIAL REGISTRATION: Retrospectively registered.

Facile Fabrication of Self-Assembly Functionalized Polythiophene Hole Transporting Layer for High Performance Perovskite Solar Cells.

Crystallinity and crystal orientation have a predominant impact on a materials' semiconducting properties, thus it is essential to manipulate the microstructure arrangements for desired semiconducting device performance. Here, ultra-uniform hole-transporting material (HTM) by self-assembling COOH-functionalized P3HT (P3HT-COOH) is fabricated, on which near single crystal quality perovskite thin film can be grown. In particular, the self-assembly approach facilitates the P3HT-COOH molecules to form an ordered and homogeneous monolayer on top of the indium tin oxide (ITO) electrode facilitate the perovskite crystalline film growth with high quality and preferred orientations. After detailed spectroscopy and device characterizations, it is found that the carboxylic acid anchoring groups can down-shift the work function and passivate the ITO surface, retarding the interface carrier recombination. As a result, the device made with the self-assembled HTM show high open-circuit voltage over 1.10 V and extend the lifetime over 4,300 h when storing at 30% relative humidity. Moreover, the cell works efficiently under much reduced light power, making it useful as power source under dim-light conditions. The demonstration suggests a new facile way of fabricating monolayer HTM for high efficiency perovskite devices, as well as the interconnecting layer needed for tandem cell.

Evaluation of Artifact Subspace Reconstruction for Automatic Artifact Components Removal in Multi-Channel EEG Recordings.

OBJECTIVE: Artifact subspace reconstruction (ASR) is an automatic, online-capable, component-based method that can effectively remove transient or large-amplitude artifacts contaminating electroencephalographic (EEG) data. However, the effectiveness of ASR and the optimal choice of its parameter have not been systematically evaluated and reported, especially on actual EEG data. METHODS: This paper systematically evaluates ASR on 20 EEG recordings taken during simulated driving experiments. Independent component analysis (ICA) and an independent component classifier are applied to separate artifacts from brain signals to quantitatively assess the effectiveness of the ASR. RESULTS: ASR removes more eye and muscle components than brain components. Even though some eye and muscle components retain after ASR cleaning, the power of their temporal activities is reduced. Study results also showed that ASR cleaning improved the quality of a subsequent ICA decomposition. CONCLUSIONS: Empirical results show that the optimal ASR parameter is between 20 and 30, balancing between removing non-brain signals and retaining brain activities. SIGNIFICANCE: With an appropriate choice of parameter, ASR can be a powerful and automatic artifact removal approach for offline data analysis or online real-time EEG applications such as clinical monitoring and brain-computer interfaces.

Electrically Pumped White-Light-Emitting Diodes Based on Histidine-Doped MoS2 Quantum Dots.

MoS2 quantum dots (QDs)-based white-light-emitting diodes (QD-WLEDs) are designed, fabricated, and demonstrated. The highly luminescent, histidine-doped MoS2 QDs synthesized by microwave induced fragmentation of 2D MoS2 nanoflakes possess a wide distribution of available electronic states as inferred from the pronounced excitation-wavelength-dependent emission properties. Notably, the histidine-doped MoS2 QDs show a very strong emission intensity, which exceeds seven times of magnitude larger than that of pristine MoS2 QDs. The strongly enhanced emission is mainly attributed to nitrogen acceptor bound excitons and passivation of defects by histidine-doping, which can enhance the radiative recombination drastically. The enabled electroluminescence (EL) spectra of the QD-WLEDs with the main peak around 500 nm are found to be consistent with the photoluminescence spectra of the histidine-doped MoS2 QDs. The enhanced intensity of EL spectra with the current increase shows the stability of histidine-doped MoS2 based QD-WLEDs. The typical EL spectrum of the novel QD-WLEDs has a Commission Internationale de l'Eclairage chromaticity coordinate of (0.30, 0.36) exhibiting an intrinsic broadband white-light emission. The unprecedented and low-toxicity QD-WLEDs based on a single light-emitting material can serve as an excellent alternative for using transition metal dichalcogenides QDs as next generation optoelectronic devices.

Hybrid Optical/Electric Memristor for Light-Based Logic and Communication.

Light-based information processing has the potential to increase speed, security, and scalability of electronic devices if issues in the device complexity could be resolved. We here demonstrate an integrated nanoelectronic device that can combine, store, and manipulate optical and electronic information. Employing a mechanically flexible and multilayered structure, a device is realized that shows memristive behavior. Illumination is shown to control the device operation in several unique ways. First, the device produces photocurrent that allows us to read out the device state in a self-powered manner. More importantly, a varying light intensity modulates the switching transition in a proportional manner that is akin to a neuron with variable plasticity and which can be taught and queried using either light or electrical inputs. This behavior enables a multilevel light-controlled logic and teaching schemes that can be applied to light-based communication devices and provides a route toward ubiquitous and low-cost sensors for future internet of things applications.

Online Automatic Artifact Rejection using the Real-time EEG Source-mapping Toolbox (REST).

Non-brain contributions to electroencephalographic (EEG) signals, often referred to as artifacts, can hamper the analysis of scalp EEG recordings. This is especially true when artifacts have large amplitudes (e.g., movement artifacts), or occur continuously (like eye-movement artifacts). Offline automated pipelines can detect and reduce artifact in EEG data, but no good solution exists for online processing of EEG data in near real time. Here, we propose the combined use of online artifact subspace reconstruction (ASR) to remove large amplitude transients, and online recursive independent component analysis (ORICA) combined with an independent component (IC) classifier to compute, classify, and remove artifact ICs. We demonstrate the efficacy of the proposed pipeline using 2 EEG recordings containing series of (1) movement and muscle artifacts, and (2) cued blinks and saccades. This pipeline is freely available in the Real-time EEG Sourcemapping Toolbox (REST) for MATLAB (The Mathworks, Inc.).

Evaluation of Artifact Subspace Reconstruction for Automatic EEG Artifact Removal.

One of the greatest challenges that hinder the decoding and application of electroencephalography (EEG) is that EEG recordings almost always contain artifacts - non-brain signals. Among existing automatic artifact-removal methods, artifact subspace reconstruction (ASR) is an online and realtime capable, component-based method that can effectively remove transient or large-amplitude artifacts. However, the effectiveness of ASR and the optimal choice of its parameter have not been evaluated and reported, especially on real EEG data. This study systematically validates ASR on ten EEG recordings in a simulated driving experiment. Independent component analysis (ICA) is applied to separate artifacts from brain signals to allow a quantitative assessment of ASR's effectiveness in removing various types of artifacts and preserving brain activities. Empirical results show that the optimal ASR parameter is between 10 and 100, which is small enough to remove activities from artifacts and eye-related components and large enough to retain signals from brain-related components. With the appropriate choice of the parameter, ASR can be a powerful and automatic artifact removal approach for offline data analysis or online real-time EEG applications such as clinical monitoring and brain-computer interfaces.

siRNA Delivery with Chitosan: Influence of Chitosan Molecular Weight, Degree of Deacetylation, and Amine to Phosphate Ratio on in Vitro Silencing Efficiency, Hemocompatibility, Biodistribution, and in Vivo Efficacy.

Chitosan (CS) shows in vitro and in vivo efficacy for siRNA delivery but with contradictory findings for incompletely characterized systems. For understanding which parameters produce effective delivery, a library of precisely characterized chitosans was produced at different degrees of deacetylation (DDAs) and average molecular weights (Mn). Encapsulation and transfection efficiencies were characterized in vitro. Formulations were selected to examine the influence of Mn and N:P ratio on nanoparticle uptake, metabolic activity, genotoxicity, and in vitro transfection. Hemocompatibility and in vivo biodistribution were then investigated for different Mn, N:P ratios, and doses. Nanoparticle uptake and gene silencing correlated with increased surface charge, which was obtained at high DDA and high Mn. A minimum polymer length of ∼60-70 monomers (∼10 kDa) was required for stability and knockdown. In vitro knockdown was equivalent to lipid control with no metabolic or genotoxicity. An inhibitory effect of serum on biological performance was dependent on DDA, Mn, and N:P. In vivo biodistribution in mice show accumulation of nanoparticles in kidney with 40-50% functional knockdown.

Spt4 is selectively required for transcription of extended trinucleotide repeats.

Lengthy trinucleotide repeats encoding polyglutamine (polyQ) stretches characterize the variant proteins of Huntington's disease and certain other inherited neurological disorders. Using a phenotypic screen to identify events that restore functionality to polyQ proteins in S. cerevisiae, we discovered that transcription elongation factor Spt4 is required to transcribe long trinucleotide repeats located either in ORFs or nonprotein-coding regions of DNA templates. Mutation of SPT4 selectively decreased synthesis of and restored enzymatic activity to expanded polyQ protein without affecting protein lacking long-polyQ stretches. RNA-seq analysis revealed limited effects of Spt4 on overall gene expression. Inhibition of Supt4h, the mammalian ortholog of Spt4, reduced mutant huntingtin protein in neuronal cells and decreased its aggregation and toxicity while not altering overall cellular mRNA synthesis. Our findings identify a cellular mechanism for transcription through repeated trinucleotides and a potential target for countermeasures against neurological disorders attributable to expanded trinucleotide regions.