Linguistic Priming and Learning Adjacent and Non-Adjacent Dependencies in Serial Reaction Time Tasks

Although syntactic priming is well studied and commonly assumed to involve implicit learning, the mechanisms behind this phenomenon are still under debate. We tested whether implicit learning of adjacent and non-adjacent sequences occurs in a non-linguistic, finger sequence task (Serial Reaction Time task), and if so, whether these implicitly-learned dependencies can cause syntactic priming in the linguistic domain. We followed the logic that exposure to statistical patterns in the SRT task may influence language users’ relative clause (RC) attachment biases, and trained participants on SRT sequences with adjacent or non-adjacent dependencies. Participants then wrote completions to relative clause fragments in a situation where they could opt for adjacent or non-adjacent linguistic structures. Participants successfully learned the adjacent and non-adjacent dependency implicitly during the SRT task, but, strikingly, their RC continuations did not exhibit priming effects. Implications for theories of syntactic priming and its relations to implicit learning are discussed

Learning Non-Adjacent Dependencies in Continuous Presentation of an Artificial Language

Many grammatical dependencies in natural language involve elements that are not adjacent, such as between the subject and verb in the child always runs. To date, most experiments showing evidence of learning non- adjacent dependencies have used artificial languages in which the to-be-learned dependencies are presented in isolation by presenting the minimal sequences that contain the dependent elements. However, dependencies in natural language are not typically isolated in this way. In this study we exposed learners to non-adjacent dependencies in long sequences of words. We accelerated the speed of presentation and learners showed evidence for learning of non-adjacent dependencies. The previous pause-based positional mechanisms for learning of non-adjacent dependency are challenged

Grammatical Bracketing Determines Learning of Non-adjacent Dependencies

Grammatical dependencies often involve elements that are not adjacent. However, most experiments in which non-adjacent dependencies are learned bracketed the dependent material with pauses, which is not how dependencies appear in natural language. Here we report successful learning of embedded NAD without pause bracketing. Instead, we induce learners to compute structure in an artificial language by entraining them through processing English sentences. We also found that learning becomes difficult when grammatical entrainment causes learners to compute boundaries that are misaligned with NAD structures. In sum, we demonstrated that grammatical entrainment can induce boundaries that can carry over to reveal structures in novel language materials, and this effect can be used to induce learning of non-adjacent dependencies

Impact of the Chlorination of Lithium Argyrodites on the Electrolyte/Cathode Interface in Solid‐State Batteries

Lithium argyrodite-type electrolytes are regarded as promising electrolytes due to their high ionic conductivity and good processability. Chemical modifications to increase ionic conductivity have already been demonstrated, but the influence of these modifications on interfacial stability remains so far unknown. In this work, we study Li6PS5Cl and Li5.5PS4.5Cl1.5 to investigate the influence of halogenation on the electrochemical decomposition of the solid electrolyte and the chemical degradation mechanism at the cathode interface in depth. Electrochemical measurements, gas analysis and time-of-flight secondary ion mass spectrometry indicate that the Li5.5PS4.5Cl1.5 shows pronounced electrochemical decomposition at lower potentials. The chemical reaction at higher voltages leads to more gaseous degradation products, but a lower fraction of solid oxygenated phosphorous and sulfur species. This in turn leads to a decreased interfacial resistance and thus a higher cell performance

DRAC: Diabetic Retinopathy Analysis Challenge with Ultra-Wide Optical Coherence Tomography Angiography Images

Computer-assisted automatic analysis of diabetic retinopathy (DR) is of great importance in reducing the risks of vision loss and even blindness. Ultra-wide optical coherence tomography angiography (UW-OCTA) is a non-invasive and safe imaging modality in DR diagnosis system, but there is a lack of publicly available benchmarks for model development and evaluation. To promote further research and scientific benchmarking for diabetic retinopathy analysis using UW-OCTA images, we organized a challenge named "DRAC - Diabetic Retinopathy Analysis Challenge" in conjunction with the 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2022). The challenge consists of three tasks: segmentation of DR lesions, image quality assessment and DR grading. The scientific community responded positively to the challenge, with 11, 12, and 13 teams from geographically diverse institutes submitting different solutions in these three tasks, respectively. This paper presents a summary and analysis of the top-performing solutions and results for each task of the challenge. The obtained results from top algorithms indicate the importance of data augmentation, model architecture and ensemble of networks in improving the performance of deep learning models. These findings have the potential to enable new developments in diabetic retinopathy analysis. The challenge remains open for post-challenge registrations and submissions for benchmarking future methodology developments

Definitions of disease burden across the spectrum of metastatic castration-sensitive prostate cancer: comparison by disease outcomes and genomics

BACKGROUND: Several definitions have attempted to stratify metastatic castrate-sensitive prostate cancer (mCSPC) into low and high-volume states. However, at this time, comparison of these definitions is limited. Here we aim to compare definitions of metastatic volume in mCSPC with respect to clinical outcomes and mutational profiles. METHODS: We performed a retrospective review of patients with biochemically recurrent or mCSPC whose tumors underwent somatic targeted sequencing. 294 patients were included with median follow-up of 58.3 months. Patients were classified into low and high-volume disease per CHAARTED, STAMPEDE, and two numeric (≤3 and ≤5) definitions. Endpoints including radiographic progression-free survival (rPFS), time to development of castration resistance (tdCRPC), and overall survival (OS) were evaluated with Kaplan-Meier survival curves and log-rank test. The incidence of driver mutations between definitions were compared. RESULTS: Median OS and tdCRPC were shorter for high-volume than low-volume disease for all four definitions. In the majority of patients (84.7%) metastatic volume classification did not change across all four definitions. High volume disease was significantly associated with worse OS for all four definitions (CHAARTED: HR 2.89; p < 0.01, STAMPEDE: HR 3.82; p < 0.01, numeric ≤3: HR 4.67; p < 0.01, numeric ≤5: HR 3.76; p < 0.01) however, were similar for high (p = 0.95) and low volume (p = 0.79) disease across all four definitions. Those with discordant classification tended to have more aggressive clinical behavior and mutational profiles. Patients with low-volume disease and TP53 mutation experienced a more aggressive course with rPFS more closely mirroring high-volume disease. CONCLUSIONS: The spectrum of mCSPC was confirmed across four different metastatic definitions for clinical endpoints and genetics. All definitions were generally similar in classification of patients, outcomes, and genetic makeup. Given these findings, the simplicity of numerical definitions might be preferred, especially when integrating metastasis directed therapy. Incorporation of tumor genetics may allow further refinement of current metastatic definitions

Genome-Wide Association Mapping and Genomic Prediction Analyses Reveal the Genetic Architecture of Grain Yield and Flowering Time Under Drought and Heat Stress Conditions in Maize

Drought stress (DS) is a major constraint to maize yield production. Heat stress (HS) alone and in combination with DS are likely to become the increasing constraints. Association mapping and genomic prediction (GP) analyses were conducted in a collection of 300 tropical and subtropical maize inbred lines to reveal the genetic architecture of grain yield and flowering time under well-watered (WW), DS, HS, and combined DS and HS conditions. Out of the 381,165 genotyping-by-sequencing SNPs, 1549 SNPs were significantly associated with all the 12 trait-environment combinations, the average PVE (phenotypic variation explained) by these SNPs was 4.33%, and 541 of them had a PVE value greater than 5%. These significant associations were clustered into 446 genomic regions with a window size of 20 Mb per region, and 673 candidate genes containing the significantly associated SNPs were identified. In addition, 33 hotspots were identified for 12 trait-environment combinations and most were located on chromosomes 1 and 8. Compared with single SNP-based association mapping, the haplotype-based associated mapping detected fewer number of significant associations and candidate genes with higher PVE values. All the 688 candidate genes were enriched into 15 gene ontology terms, and 46 candidate genes showed significant differential expression under the WW and DS conditions. Association mapping results identified few overlapped significant markers and candidate genes for the same traits evaluated under different managements, indicating the genetic divergence between the individual stress tolerance and the combined drought and HS tolerance. The GP accuracies obtained from the marker-trait associated SNPs were relatively higher than those obtained from the genome-wide SNPs for most of the target traits. The genetic architecture information of the grain yield and flowering time revealed in this study, and the genomic regions identified for the different trait-environment combinations are useful in accelerating the efforts on rapid development of the stress-tolerant maize germplasm through marker-assisted selection and/or genomic selection

Parent-of-origin-specific allelic associations among 106 genomic loci for age at menarche.

Age at menarche is a marker of timing of puberty in females. It varies widely between individuals, is a heritable trait and is associated with risks for obesity, type 2 diabetes, cardiovascular disease, breast cancer and all-cause mortality. Studies of rare human disorders of puberty and animal models point to a complex hypothalamic-pituitary-hormonal regulation, but the mechanisms that determine pubertal timing and underlie its links to disease risk remain unclear. Here, using genome-wide and custom-genotyping arrays in up to 182,416 women of European descent from 57 studies, we found robust evidence (P < 5 × 10(-8)) for 123 signals at 106 genomic loci associated with age at menarche. Many loci were associated with other pubertal traits in both sexes, and there was substantial overlap with genes implicated in body mass index and various diseases, including rare disorders of puberty. Menarche signals were enriched in imprinted regions, with three loci (DLK1-WDR25, MKRN3-MAGEL2 and KCNK9) demonstrating parent-of-origin-specific associations concordant with known parental expression patterns. Pathway analyses implicated nuclear hormone receptors, particularly retinoic acid and γ-aminobutyric acid-B2 receptor signalling, among novel mechanisms that regulate pubertal timing in humans. Our findings suggest a genetic architecture involving at least hundreds of common variants in the coordinated timing of the pubertal transition

The 2nd Workshop on Maritime Computer Vision (MaCVi) 2024

The 2nd Workshop on Maritime Computer Vision (MaCVi) 2024 addresses maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicles (USV). Three challenges categories are considered: (i) UAV-based Maritime Object Tracking with Re-identification, (ii) USV-based Maritime Obstacle Segmentation and Detection, (iii) USV-based Maritime Boat Tracking. The USV-based Maritime Obstacle Segmentation and Detection features three sub-challenges, including a new embedded challenge addressing efficicent inference on real-world embedded devices. This report offers a comprehensive overview of the findings from the challenges. We provide both statistical and qualitative analyses, evaluating trends from over 195 submissions. All datasets, evaluation code, and the leaderboard are available to the public at https://macvi.org/workshop/macvi24.Comment: Part of 2nd Workshop on Maritime Computer Vision (MaCVi) 2024 IEEE Xplore submission as part of WACV 202