AXON Neuroscience (Slovakia)

For English as a Second Language (ESL) learners, unknown words significantly hinder reading fluency. We present EyeLingo, a novel method that detects unknown words in real-time by integrating user gaze trajectories with pre-trained language models (PLMs). Unlike prior gaze-only methods whose performance is highly sensitive to eye-tracking accuracy, EyeLingo leverages the linguistic context captured by RoBERTa to compensate for noisy gaze data. Our system achieves 97.6% accuracy (F1-score 71.1%) with professional eye trackers and maintains 97.3% accuracy (F1-score 65.1%) using commodity webcams.

災害発生後, 地すべりや斜面崩壊の調査は, リモートセンシングによって行われるようになった。近年では航空機によるレーザ計測を行うことで, 過去の計測データとの差分を解析し変動を抽出して地表面変動を明らかにした研究が行われている。特に, 表層崩壊が明瞭に発生しない並進地すべりにおいては, この手法を活用することで今後の活動の予察や被害想定を行うことも可能になる。そこで, 本研究は, 能登半島地震における新第三紀堆積岩分布域である八太郎峠西方で発生した深層並進地すべりを対象として, 変動ベクトル解析を用いて変動特性を解明した。具体的には, 第一に地すべり領域全体の変動特性の把握, 第二に, 局所的な変動特性を有するブロック区分を行った。この結果, 変動を示すブロックは3つに区分することができ, 中央ブロックで最大となる平均変動ベクトル量4.6m, 0.6度の回転量が認められ, 北側で沈下, 南側で隆起する特性を把握できた。

Current understanding of how immunodeficiencies impact protective responses against viral infections and vaccination is primarily derived from adult cohorts that may not accurately reflect the pediatric, adolescent, and young adult population. This cross-sectional study aimed to evaluate immune responses in this underrepresented population affected by various immunodeficiencies after SARS-CoV-2 infection, two doses of the mRNA BNT162b2 vaccine, or after a combination of both. We analyzed blood samples from 102 immunocompromised patients (IC) (5-25 years) categorized into groups of primary immunodeficiencies (PID, n=17), bronchial asthma and allergic rhinitis (BA-AR, n=39), rheumatoid diseases (RD, n=21), and individuals who had undergone hematopoietic stem cell transplantation (HSCT, n=28), as well as 30 healthy individuals (9-26 years). We measured titres of Spike-specific IgM, IgA, and IgG antibody classes (including IgG subclasses) in plasma using ELISA and evaluated their inhibitory potential in a Spike-ACE2 cell-based internalization assay. Spike-specific CD4 T-cells were examined using a flow cytometry-based proliferation assay (FASCIA). In the IC group, all participants except eight generated detectable levels of IgG antibodies. The IgG titres induced by vaccination (Geometric mean titre (GMTvac) = 205023, 95% CI: 116074-362136) and a combination of vaccination and infection (GMThyb = 172819, 95% CI: 33133-901403) were higher than after infection (GMTinf = 3323, 95% CI: 578-19109, Pvac/inf = .006 and Phyb/inf = .001). On the other hand, the hybrid immunity induced the highest IgA titres (GMThyb = 2672, 95% CI: 566-12623) compared to vaccination (GMPvac = 275, 95% CI: 97-777, Phyb/vac = .016) and infection (GMTinf = 60, 95% CI: 13-280, Phyb/inf = .002). The IgG titres in vaccinated and hybrid immunity groups strongly correlated (rSpearman = 0.86, P <.0001) with the levels of antibodies inhibiting the internalization of Spike protein (S protein) in a cell-based assay. Most IC patients (except five) also developed above-threshold Spike-specific CD4 T-cell responses, which were not statistically different from the responses in the healthy control group. Our data show that infection and vaccination can induce protective humoral or cellular responses against SARS-CoV-2 in IC patients. The activated cellular response in patients with agammaglobulinemia may assist them in overcoming viral infections.

The transcellular propagation of the aberrantly modified protein tau along the functional brain network is a key hallmark of Alzheimer's disease and related tauopathies. Inoculation-based tau propagation models can recapitulate the stereotypical spread of tau and reproduce various types of tau inclusions linked to specific tauopathy, albeit with varying degrees of fidelity. With this systematic review, we underscore the significance of judicious selection and meticulous functional, biochemical, and biophysical characterization of various tau inocula. Furthermore, we highlight the necessity of choosing suitable animal models and inoculation sites, along with the critical need for validation of fibrillary pathology using confirmatory staining, to accurately recapitulate disease-specific inclusions. As a practical guide, we put forth a framework for establishing a benchmark of inoculation-based tau propagation models that holds promise for use in preclinical testing of disease-modifying drugs.

Abstract A new Slovak speech database EWA-DB was created for research purposes aimed at early detection of neurodegenerative diseases from speech. It contains 1649 speakers performing various speech and language tasks, such as sustained vowel phonation, diadochokinesis, naming and picture description. The sample of speakers consists of individuals with Alzheimer’s disease, mild cognitive impairment, Parkinson’s disease, and healthy controls. In this article we describe the EWA-DB development process, the language and speech task selection, patient and healthy control recruitment, as well as the testing and recording protocol. The structure and content of the database and file formats are described in detail. We assume that the presented database could be suitable for the development of automatic systems predicting the diagnoses of Alzheimer’s disease, mild cognitive impairment, and Parkinson’s disease from language and speech features.

Alzheimer's disease (AD) is an incurable neurodegenerative disease that primarily affects the mental and linguistic abilities of the sufferer. The disease affects to a large extent not only the quality of life of the patients themselves, but also of their immediate environment. Diagnosing AD is a complex process that requires multiple resources, facilities and, above all, qualified experts. The whole process is time-consuming. In the early stages, such as mild cognitive impairment (MCI), it is difficult to diagnose the disease because of the very mild manifestations of the disease. To help medical specialists and researchers detect MCI and AD disease, we have developed a machine learning based approach. The patient is asked to describe in spoken word the offered images, either in a single word or a sentence description. This description is captured as an audio recording in a mobile app. The dataset of such recordings consists of more than 1500 recordings of different individuals, with approximately 9.49% of them having been diagnosed with MCI or AD. The dataset imbalanced nature calls for use of metrics such as Mathews correlation coefficient (MCC), sensitivity-specificity and ROC curve to evaluate the used inbtelligent algorithms. The case study resulted in a machine learning model with 79.07% (54.35%87.4%) MCC.

The present report on Dissemination and Communication is prepared within the Dissemination, Communication and Exploitation of Results Work Package (WP8) of the SECANT project. This document is a deliverable, entitled D8.3: Initial report on dissemination, communication, and exploitation, to be submitted to the European Commission (EC) by Month 12 (August 2022) of the SECANT Project. It is a reviewed and updated version of deliverable 8.2 entitled “Plans for dissemination, communication and exploitation” that was submitted in Month 02. In particular, it is associated with the tasks T8.1 “Dissemination and communication”, T8.3 “Contributions to standards” and T8.4 “Liaison and interaction with relevant stakeholders”. The SECANT project has defined a plan for disseminating and communicating the project’s results which was outlined in detail in Deliverable 8.2 submitted in M02 of the project’s lifetime. As a followup, the present deliverable reports all dissemination and communication activities for the first 12 months of the project’s lifetime (including metrics around web and social media campaign) as well as un updated outline of the calendar for all dissemination activities to be undertaken during the second year. This dissemination report D8.3 will detail how dissemination has been achieved to date and also how this plan will be revised and refined in line with work package progress project outcomes. It also includes an audit of the channels currently available to all partners and the identification of their relationships with key academic and industrial players. The KPIs of the dissemination activities outlined in deliverable D8.2 are reported in this deliverable for the first year while the KPIs for the second year will also be revised and outlined. Finally, this document reports the consortium’s initial standardization efforts that kicked off in Month 06. The document structure serves to revisit and update: • the objectives to be fulfilled by the project and in particular by the dissemination and communication activities,<br> • the general guidebook providing a set of rules as well as the strategy for the dissemination and communication actions,<br> • the dissemination and communication activities of the first year at a more detailed level,<br> • the possible target audience group for any outreach action,<br> • the various channels that will be used,<br> • a clear and unique strategy for the dissemination and communication of the project with a precise definition of targets, directed messages and possible communication channels and tools in order to achieve the maximum impact,<br> • the plan to coordinate with external partners and create liaisons with them,<br> • the strategic impact on Standardisation. This is a living document, able to accommodate any required customisation. The dissemination and communication are expected to change based on the needs and developments of the project. This document will thus be constantly reviewed and updated during the course of the project through an annual follow-up (which will constitute the deliverable D8.4: Interim report on dissemination and<br> communication) in order to ensure that its objectives are met and adjusted, if necessary. This deliverable iteration will be concluded with the final dissemination and communication report which will be delivered at the end of the project (M36)