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SUMMARY: Brassica carinata (BBCC) commonly referred to as Ethiopian mustard is a natural allotetraploid containing the genomes of Brassica nigra (BB) and Brassica oleracea (CC). It is an oilseed crop endemic to the northeastern regions of Africa. Although it is under limited cultivation, B. carinata is valuable as it is resistant/highly tolerant to most of the pathogens affecting widely cultivated Brassica species of the U's triangle. We report a chromosome‐scale genome assembly of B. carinata accession HC20 using long‐read Oxford Nanopore sequencing and Bionano optical maps. The assembly has a scaffold N50 of ~39.8 Mb and covers ~1.11 Gb of the genome. We compared the long‐read genome assemblies of the U's triangle species and found extensive gene collinearity between the diploids and allopolyploids with no evidence of major gene losses. Therefore, B. juncea (AABB), B. napus (AACC), and B. carinata can be regarded as strict allopolyploids. We cataloged the nucleotide‐binding and leucine‐rich repeat immune receptor (NLR) repertoire of B. carinata and, identified 465 NLRs, and compared these with the NLRs in the other Brassica species. We investigated the extent and nature of early‐generation genomic interactions between the constituent genomes of B. carinata and B. juncea in interspecific crosses between the two species. Besides the expected recombination between the constituent B genomes, extensive homoeologous exchanges were observed between the A and C genomes. Interspecific crosses, therefore, can be used for transferring disease resistance from B. carinata to B. juncea and broadening the genetic base of the two allotetraploid species. Significance Statement: We report a highly contiguous genome assembly of allotetraploid mustard Brassica carinata HC20 (BBCC) which contains resistance to many diseases affecting the other extensively cultivated Brassica species of the U's triangle. Comparative analyses of the long‐read assemblies of the six Brassica species revealed high gene collinearity between the three diploid and their allotetraploid species. We tracked the early‐generation genomic interactions between the subgenomes of B. carinata (BBCC) and B. juncea (AABB) in their interspecific crosses using molecular karyotyping and proposed strategies to utilize this information for genomic introgressions. [ABSTRACT FROM AUTHOR]

Handwritten signatures in biometric authentication leverage unique individual characteristics for identification, offering high specificity through dynamic and static properties. However, this modality faces significant challenges from sophisticated forgery attempts, underscoring the need for enhanced security measures in common applications. To address forgery in signature-based biometric systems, integrating a forgery-resistant modality, namely, noninvasive electroencephalography (EEG), which captures unique brain activity patterns, can significantly enhance system robustness by leveraging multimodality's strengths. By combining EEG, a physiological modality, with handwritten signatures, a behavioral modality, our approach capitalizes on the strengths of both, significantly fortifying the robustness of biometric systems through this multimodal integration. In addition, EEG's resistance to replication offers a high-security level, making it a robust addition to user identification and verification. This study presents a new multimodal SignEEG v1.0 dataset based on EEG and hand-drawn signatures from 70 subjects. EEG signals and hand-drawn signatures have been collected with Emotiv Insight and Wacom One sensors, respectively. The multimodal data consists of three paradigms based on mental, & motor imagery, and physical execution: i) thinking of the signature's image, (ii) drawing the signature mentally, and (iii) drawing a signature physically. Extensive experiments have been conducted to establish a baseline with machine learning classifiers. The results demonstrate that multimodality in biometric systems significantly enhances robustness, achieving high reliability even with limited sample sizes. We release the raw, pre-processed data and easy-to-follow implementation details. [ABSTRACT FROM AUTHOR]

Aims: Out-of-hospital cardiac arrest (OHCA) is a major health concern worldwide. Although one-third of all patients achieve a return of spontaneous circulation and may undergo a difficult period in the intensive care unit, only 1 in 10 survive. This study aims to improve our previously developed machine learning model for early prognostication of survival in OHCA., Methods and Results: We studied all cases registered in the Swedish Cardiopulmonary Resuscitation Registry during 2010 and 2020 ( n = 55 615). We compared the predictive performance of extreme gradient boosting (XGB), light gradient boosting machine (LightGBM), logistic regression, CatBoost, random forest, and TabNet. For each framework, we developed models that optimized (i) a weighted F1 score to penalize models that yielded more false negatives and (ii) a precision-recall area under the curve (PR AUC). LightGBM assigned higher importance values to a larger set of variables, while XGB made predictions using fewer predictors. The area under the curve receiver operating characteristic (AUC ROC) scores for LightGBM were 0.958 (optimized for weighted F1) and 0.961 (optimized for a PR AUC), while for XGB, the scores were 0.958 and 0.960, respectively. The calibration plots showed a subtle underestimation of survival for LightGBM, contrasting with a mild overestimation for XGB models. In the crucial range of 0-10% likelihood of survival, the XGB model, optimized with the PR AUC, emerged as a clinically safe model., Conclusion: We improved our previous prediction model by creating a parsimonious model with an AUC ROC at 0.96, with excellent calibration and no apparent risk of underestimating survival in the critical probability range (0-10%). The model is available at www.gocares.se., Competing Interests: Conflict of interest: none declared., (© The Author(s) 2024. Published by Oxford University Press on behalf of the European Society of Cardiology.)