Optical fiber-to-chip assembly process for ultra-low loss photonic devices based on silicon nitride for space applications

[EN] In this work, we demonstrate an efficient fiber array-to-chip assembly process with a high number of input/output ports. The proposed approach is based on using a pre-alignment coupling structure to separately align the input and output ports. The assembling process has been experimentally validated in photonic integrated circuits fabricated with an ultra-low-loss waveguide technology based on silicon nitride, which features propagation losses as low as 9.5 dB/m. The developed technology is expected to extend the use of integrated photonics for space applicationsThis work was supported by EU-funded H2020 project RETINA under grant agreement n° 821943Brimont, ACJ.; Zurita Herranz, D.; Duarte, VC.; Mengual, T.; Chmielak, B.; Suckow, S.; Giesecke, A.... (2020). Optical fiber-to-chip assembly process for ultra-low loss photonic devices based on silicon nitride for space applications. 1-3. http://hdl.handle.net/10251/1786581

In vitro neuroprotective potential of four medicinal plants against rotenone-induced toxicity in SH-SY5Y neuroblastoma cells

BACKGROUND: Lannea schweinfurthii, Zanthoxylum capense, Scadoxus puniceus and Crinum bulbispermum are used traditionally to treat neurological disorders. The aim of this study was to evaluate the cytoprotective potential of the four plants, after induction of toxicity using rotenone, in SH-SY5Y neuroblastoma cells. METHODS: Cytotoxicity of the plant extracts and rotenone was assessed using the sulforhodamine B (SRB) assay. Fluorometry was used to measure intracellular redox state (reactive oxygen species (ROS) and intracellular glutathione content), mitochondrial membrane potential (MMP) and caspase-3 activity, as a marker of apoptotic cell death. RESULTS: Of the tested plants, the methanol extract of Z. capense was the least cytotoxic; LC(50) 121.3 ± 6.97 μg/ml, while S. puniceus methanol extract was the most cytotoxic; LC(50) 20.75 ± 1.47 μg/ml. Rotenone reduced intracellular ROS levels after 24 h exposure. Pre-treating cells with S. puniceus and C. bulbispermum extracts reversed the effects of rotenone on intracellular ROS levels. Rotenone exposure also decreased intracellular glutathione levels, which was counteracted by pre-treatment with any one of the extracts. MMP was reduced by rotenone, which was neutralized by pre-treatment with C. bulbispermum ethyl acetate extract. All extracts inhibited rotenone-induced activation of caspase-3. CONCLUSION: The studied plants demonstrated anti-apoptotic activity and restored intracellular glutathione content following rotenone treatment, suggesting that they may possess neuroprotective properties

Quotient supermanifolds

A necessary and sufficient condition for the existence of a supermanifold structure on a quotient defined by an equivalence relation is established. Furthermore, we show that an equivalence relation it on a Berezin-Leites-Kostant supermanifold X determines a quotient supermanifold X/R if and only if the restriction Ro of R to the underlying smooth manifold Xo of X determines a quotient smooth manifold Xo/Ro

Flipping the classroom to improve learning with MOOCs technology

The use of Massive Open Online Courses (MOOCs) is increasing worldwide and brings a revolution in education. The application of MOOCs has technological but also pedagogical implications. MOOCs are usually driven by short video lessons, automatic correction exercises, and the technological platforms can implement gamification or learning analytics techniques. However, much more analysis is required about the success or failure of these initiatives in order to know if this new MOOCs paradigm is appropriate for different learning situations. This work aims at analyzing and reporting whether the introduction of MOOCs technology was good or not in a case study with the Khan Academy platform at our university with students in a remedial Physics course in engineering education. Results show that students improved their grades significantly when using MOOCs technology, student satisfaction was high regarding the experience and for most of the different provided features, and there were good levels of interaction with the platform (e.g., number of completed videos or proficient exercises), and also the activity distribution for the different topics and types of activities was appropriate. © 2016 Wiley Periodicals, Inc. Comput Appl Eng Educ 25:15–25, 2017; View this article online at wileyonlinelibrary.com/journal/cae; DOI 10.1002/cae.21774

High-efficiency grating coupler for an ultralow-loss Si3N4-based platform

[EN] Integrated silicon nitride waveguides of 100¿nm height can achieve ultralow propagation losses below 0.1¿dB/cm at the 1550¿nm wavelength band but lack the scattering strength to form efficient grating couplers. An enhanced grating coupler design based on an amorphous silicon layer on top of silicon nitride is proposed and demonstrated to improve the directionality of the coupler. The fabrication process is optimized for a self-alignment process between the amorphous silicon and silicon nitride layers without increasing waveguide losses. Experimental coupling losses of 5¿dB and a 3¿dB bandwidth of 75¿nm are achieved with both regular and focusing designs.Horizon 2020 Framework Programme (grant agreement n degrees 821943, RETINA project)Chmielak, B.; Suckow, S.; Parra Gómez, J.; Duarte, VC.; Mengual Chulia, T.; Piqueras Ruipérez, MÁ.; Giesecke, AL.... (2022). High-efficiency grating coupler for an ultralow-loss Si3N4-based platform. Optics Letters. 47(10):2498-2501. https://doi.org/10.1364/OL.455078S24982501471

Machine learning of electrophysiological signals for the prediction of ventricular arrhythmias: systematic review and examination of heterogeneity between studies

Background: Ventricular arrhythmia (VA) precipitating sudden cardiac arrest (SCD) is among the most frequent causes of death and pose a high burden on public health systems worldwide. The increasing availability of electrophysiological signals collected through conventional methods (e.g. electrocardiography (ECG)) and digital health technologies (e.g. wearable devices) in combination with novel predictive analytics using machine learning (ML) and deep learning (DL) hold potential for personalised predictions of arrhythmic events. Methods: This systematic review and exploratory meta-analysis assesses the state-of-the-art of ML/DL models of electrophysiological signals for personalised prediction of malignant VA or SCD, and studies potential causes of bias (PROSPERO, reference: CRD42021283464). Five electronic databases were searched to identify eligible studies. Pooled estimates of the diagnostic odds ratio (DOR) and summary area under the curve (AUROC) were calculated. Meta-analyses were performed separately for studies using publicly available, ad-hoc datasets, versus targeted clinical data acquisition. Studies were scored on risk of bias by the PROBAST tool. Findings: 2194 studies were identified of which 46 were included in the systematic review and 32 in the meta-analysis. Pooling of individual models demonstrated a summary AUROC of 0.856 (95% CI 0.755–0.909) for short-term (time-to-event up to 72 h) prediction and AUROC of 0.876 (95% CI 0.642–0.980) for long-term prediction (time-to-event up to years). While models developed on ad-hoc sets had higher pooled performance (AUROC 0.919, 95% CI 0.867–0.952), they had a high risk of bias related to the re-use and overlap of small ad-hoc datasets, choices of ML tool and a lack of external model validation. Interpretation: ML and DL models appear to accurately predict malignant VA and SCD. However, wide heterogeneity between studies, in part due to small ad-hoc datasets and choice of ML model, may reduce the ability to generalise and should be addressed in future studies. Funding: This publication is part of the project DEEP RISK ICD (with project number 452019308) of the research programme Rubicon which is (partly) financed by the Dutch Research Council ( NWO). This research is partly funded by the Amsterdam Cardiovascular Sciences (personal grant F.V.Y.T)

Novel Domain Knowledge-Encoding Algorithm Enables Label-Efficient Deep Learning for Cardiac CT Segmentation to Guide Atrial Fibrillation Treatment in a Pilot Dataset

Background: Segmenting computed tomography (CT) is crucial in various clinical applications, such as tailoring personalized cardiac ablation for managing cardiac arrhythmias. Automating segmentation through machine learning (ML) is hindered by the necessity for large, labeled training data, which can be challenging to obtain. This article proposes a novel approach for automated, robust labeling using domain knowledge to achieve high-performance segmentation by ML from a small training set. The approach, the domain knowledge-encoding (DOKEN) algorithm, reduces the reliance on large training datasets by encoding cardiac geometry while automatically labeling the training set. The method was validated in a hold-out dataset of CT results from an atrial fibrillation (AF) ablation study. Methods: The DOKEN algorithm parses left atrial (LA) structures, extracts “anatomical knowledge” by leveraging digital LA models (available publicly), and then applies this knowledge to achieve high ML segmentation performance with a small number of training samples. The DOKEN-labeled training set was used to train a nnU-Net deep neural network (DNN) model for segmenting cardiac CT in N = 20 patients. Subsequently, the method was tested in a hold-out set with N = 100 patients (five times larger than training set) who underwent AF ablation. Results: The DOKEN algorithm integrated with the nn-Unet model achieved high segmentation performance with few training samples, with a training to test ratio of 1:5. The Dice score of the DOKEN-enhanced model was 96.7% (IQR: 95.3% to 97.7%), with a median error in surface distance of boundaries of 1.51 mm (IQR: 0.72 to 3.12) and a mean centroid–boundary distance of 1.16 mm (95% CI: −4.57 to 6.89), similar to expert results (r = 0.99; p p Conclusions: The proposed novel domain knowledge-encoding algorithm was able to perform the segmentation of six substructures of the LA, reducing the need for large training data sets. The combination of domain knowledge encoding and a machine learning approach could reduce the dependence of ML on large training datasets and could potentially be applied to AF ablation procedures and extended in the future to other imaging, 3D printing, and data science applications