Multimodal Measures Research Articles

Functional connectivity trajectories in genetic frontotemporal dementia

AbstractBackgroundCharacterizing disease trajectories in frontotemporal lobar degeneration (FTLD) is essential to determine the optimal timing for initiating therapeutic interventions, and there is an urgent need for novel and sensitive disease biomarkers. Intrinsic connectivity network (ICN) mapping using task‐free fMRI is a promising biomarker which reveals early abnormalities even before symptomatic onset (Dopper et al. 2014; Lee et al. 2016). Yet, ICN trajectories from the presymptomatic to symptomatic stages in FTLD mutation carriers remain unclear.MethodIn this cross‐sectional study, we used UCSF and ALLFTD Consortia data to study 129 carriers with variants in MAPT (presymptomatic/prodromal/symptomatic: 34/4/7), C9orf72 (30/9/12) and GRN (22/4/7). Using cross‐sectional data to construct ICN trajectories by genotype, we built separate generalized additive models for each genetic group to estimate non‐linear relationships between standardized connectivity changes and disease age across clinical stages. ICN connectivity was based on seed‐based analyses for networks associated with common clinical syndromes for each genetic group (i.e., salience and default mode [MAPT/C9orf72/GRN], corticobasal syndrome [MAPT/GRN], progressive supranuclear palsy syndrome [MAPT], sensorimotor and medial pulvinar‐related [C9orf72], and non‐fluent variant primary progressive aphasia (nfvPPA) syndrome networks [GRN]). We included 127 healthy controls as a reference to calculate ICN z‐scores for each carrier. Each carrier’s disease age was defined as the difference between the carrier’s chronological age and estimated age of onset based on multimodal measures of structural imaging, fluid biomarker and clinical assessments (Staffaroni et al. 2022).ResultIn MAPT+, presymptomatic carriers showed declining salience network connectivity starting approximately 30 years before estimated onset, which plateaued about 20 years before onset. In C9orf72+, salience network hyperconnectivity emerged about 30 years before onset and declined throughout the presymptomatic and symptomatic stages. For GRN+, connectivity changes as a function of disease age were found within the salience and nfvPPA networks, with connectivity stable until around 10 years before estimated onset, followed by decline starting in the late presymptomatic phase and continuing through the prodromal and symptomatic stages.ConclusionThese findings demonstrate that FTLD genes show distinct ICN trajectories throughout the clinical spectrum. These results help pave the way for determining the optimal timing to initiate therapeutic interventions.

Functional connectivity trajectories in genetic frontotemporal dementia

AbstractBackgroundCharacterizing disease trajectories in frontotemporal lobar degeneration (FTLD) is essential to determine the optimal timing for initiating therapeutic interventions, and there is an urgent need for novel and sensitive disease biomarkers. Intrinsic connectivity network (ICN) mapping using task‐free fMRI is a promising biomarker which reveals early abnormalities even before symptomatic onset (Dopper et al. 2014; Lee et al. 2016). Yet, ICN trajectories from the presymptomatic to symptomatic stages in FTLD mutation carriers remain unclear.MethodIn this cross‐sectional study, we used UCSF and ALLFTD Consortia data to study 129 carriers with variants in MAPT (presymptomatic/prodromal/symptomatic: 34/4/7), C9orf72 (30/9/12) and GRN (22/4/7). Using cross‐sectional data to construct ICN trajectories by genotype, we built separate generalized additive models for each genetic group to estimate non‐linear relationships between standardized connectivity changes and disease age across clinical stages. ICN connectivity was based on seed‐based analyses for networks associated with common clinical syndromes for each genetic group (i.e., salience and default mode [MAPT/C9orf72/GRN], corticobasal syndrome [MAPT/GRN], progressive supranuclear palsy syndrome [MAPT], sensorimotor and medial pulvinar‐related [C9orf72], and non‐fluent variant primary progressive aphasia (nfvPPA) syndrome networks [GRN]). We included 127 healthy controls as a reference to calculate ICN z‐scores for each carrier. Each carrier’s disease age was defined as the difference between the carrier’s chronological age and estimated age of onset based on multimodal measures of structural imaging, fluid biomarker and clinical assessments (Staffaroni et al. 2022).ResultIn MAPT+, presymptomatic carriers showed declining salience network connectivity starting approximately 30 years before estimated onset, which plateaued about 20 years before onset. In C9orf72+, salience network hyperconnectivity emerged about 30 years before onset and declined throughout the presymptomatic and symptomatic stages. For GRN+, connectivity changes as a function of disease age were found within the salience and nfvPPA networks, with connectivity stable until around 10 years before estimated onset, followed by decline starting in the late presymptomatic phase and continuing through the prodromal and symptomatic stages.ConclusionThese findings demonstrate that FTLD genes show distinct ICN trajectories throughout the clinical spectrum. These results help pave the way for determining the optimal timing to initiate therapeutic interventions.

Photochromic ceramics for multimode detection of UV-VIS radiation dose

Electromagnetic radiation in the ultraviolet-to-visible (UV-VIS) band is closely related to human life, and therefore, accurate measurement of UV-VIS radiation dose is essential. However, there are disadvantages to existing radiation dose equipment: it is cumbersome to operate, takes time to analyze, and lacks multimodal measurements. In this study, a photochromic ceramic with rare earth ions Ho3+ and Yb3+ co-doped Ca2SnO4 host was developed. It produces rapid color switching between light yellow and black under UV and VIS radiation. The XPS and EPR results indicate that oxygen vacancies are important in coloring. Based on the photochromic effect, the intensity of down and up-conversion luminescence can be regulated. The maximum regulation rate is 95 %, higher than most photochromic materials of the same type. A multimode UV-VIS radiation detection method is proposed based on photochromism, down-conversion, and up-conversion luminescence light intensity modulation. The results show that the ceramics respond to a wide spectral range of electromagnetic radiation from UV-VIS with varying degrees, and the best response is to UVB (Ultraviolet Radiation B). A convenient scheme for on-site radiation dosimetry by colorimetry and changes in luminescence intensity was designed to verify the method's reliability. Stability tests have shown that these ceramics have good cyclic stability, making them an ideal detection material for accurate UV-VIS radiation dose detection.

Label-free multimodal electro-thermo-mechanical (ETM) phenotyping as a novel biomarker to differentiate between normal, benign, and cancerous breast biopsy tissues.

Technologies for quick and label-free diagnosis of malignancies from breast tissues have the potential to be a significant adjunct to routine diagnostics. The biophysical phenotypes of breast tissues, such as its electrical, thermal, and mechanical properties (ETM), have the potential to serve as novel markers to differentiate between normal, benign, and malignant tissue. We report a system-of-biochips (SoB) integrated into a semi-automated mechatronic system that can characterize breast biopsy tissues using electro-thermo-mechanical sensing. The SoB, fabricated on silicon using microfabrication techniques, can measure the electrical impedance (Z), thermal conductivity (K), mechanical stiffness (k), and viscoelastic stress relaxation (%R) of the samples. The key sensing elements of the biochips include interdigitated electrodes, resistance temperature detectors, microheaters, and a micromachined diaphragm with piezoresistive bridges. Multi-modal ETM measurements performed on formalin-fixed tumour and adjacent normal breast biopsy samples from N = 14 subjects were able to differentiate between invasive ductal carcinoma (malignant), fibroadenoma (benign), and adjacent normal (healthy) tissues with a root mean square error of 0.2419 using a Gaussian process classifier. Carcinoma tissues were observed to have the highest mean impedance (110018.8 ± 20293.8 Ω) and stiffness (0.076 ± 0.009 kNm-1) and the lowest thermal conductivity (0.189 ± 0.019 Wm-1K-1) amongst the three groups, while the fibroadenoma samples had the highest percentage relaxation in normalized load (47.8 ± 5.12%). The work presents a novel strategy to characterize the multi-modal biophysical phenotype of breast biopsy tissues to aid in cancer diagnosis from small-sized tumour samples. The methodology envisions to supplement the existing technology gap in the analysis of breast tissue samples in the pathology laboratories to aid the diagnostic workflow.

Field-based longitudinal evaluation of multimodal worker fatigue assessments in offshore shiftwork

Fatigue in offshore environments is a critical safety hazard, yet the utility of daily fatigue assessments has not been longitudinally examined in these environments. The aim of this exploratory longitudinal field study across two drillships in the Gulf of Mexico was to determine the changes in subjective, performance-based, and physiological fatigue measures over time across different shift types (day, night, and swing) and to identify correlations between these multimodal fatigue assessments. Repeated measures correlation analyses of daily fatigue data from seventy offshore workers revealed that while total sleep time remained unaffected by time on rig, workers’ performances on the psychomotor vigilance test (PVT) deteriorated over time across all shift types. Several correlations between the various multimodal measures were consistent with the extant literature on worker fatigue symptoms and perceptual and physiological manifestations. These findings emphasize the utility of PVT and single item self-reports to capture worker fatigue in offshore shiftwork.

Deep learning-based grading of white matter hyperintensities enables identification of potential markers in multi-sequence MRI data

BackgroundWhite matter hyperintensities (WMHs) are widely-seen in the aging population, which are associated with cerebrovascular risk factors and age-related cognitive decline. At present, structural atrophy and functional alterations coexisted with WMHs lacks comprehensive investigation. This study developed a WMHs risk prediction model to evaluate WHMs according to Fazekas scales, and to locate potential regions with high risks across the entire brain. MethodsWe developed a WMHs risk prediction model, which consisted of the following steps: T2 fluid attenuated inversion recovery (T2-FLAIR) image of each participant was firstly segmented into 1000 tiles with the size of 32 × 32 × 1, features from the tiles were extracted using the ResNet18-based feature extractor, and then a 1D convolutional neural network (CNN) was used to score all tiles based on the extracted features. Finally, a multi-layer perceptron (MLP) was constructed to predict the Fazekas scales based on the tile scores. The proposed model was trained using T2-FLAIR images, we selected tiles with abnormal scores in the test set after prediction, and evaluated their corresponding gray matter (GM) volume, white matter (WM) volume, fractional anisotropy (FA), mean diffusivity (MD), and cerebral blood flow (CBF) via longitudinal and multi-sequence Magnetic Resonance Imaging (MRI) data analysis. ResultsThe proposed WMHs risk prediction model could accurately predict the Fazekas ratings based on the tile scores from T2-FLAIR MRI images with accuracy of 0.656, 0.621 in training data set and test set, respectively. The longitudinal MRI validation revealed that most of the high-risk tiles predicted by the WMHs risk prediction model in the baseline images had WMHs in the corresponding positions in the longitudinal images. The validation on multi-sequence MRI demonstrated that WMHs were associated with GM and WM atrophies, WM micro-structural and perfusion alterations in high-risk tiles, and multi-modal MRI measures of most high-risk tiles showed significant associations with Mini Mental State Examination (MMSE) score. ConclusionOur proposed WMHs risk prediction model can not only accurately evaluate WMH severities according to Fazekas scales, but can also uncover potential markers of WMHs across modalities. The WMHs risk prediction model has the potential to be used for the early detection of WMH-related alterations in the entire brain and WMH-induced cognitive decline.

In-situ measurement of dynamic micro X-ray CT and dynamic mechanical analysis for rubber materials

Observing the internal state that takes place during deformation is essential for comprehending the mechanical properties of polymeric materials like rubber. For the effective utilization of rubber in damping applications, it is imperative to comprehend the relationship between the loss factor and microstructure. In an earlier study, we developed a dynamic X-ray CT technique tailored for damping materials subjected to tensile amplitude and proposed a method to directly evaluate the internal deformation behavior under dynamic conditions. However, owing to the technical constraints of the shaker employed for dynamic X-ray computed tomography, key variables such as vibration frequency and amplitude could not be incorporated into the analysis. Additionally, it has not been possible to simultaneously measure dynamic viscoelasticity and dynamic micro X-ray CT in situ.In this study, we developed an experimental system that enables simultaneous measurement of dynamic mechanical properties and dynamic X-ray computed tomography (CT). This system facilitates in-situ investigation of internal deformation behavior and the loss factor in rubber materials, allowing for the assessment of variations in loss factors at the micro-scale. We examined the differences between styrene butadiene rubber (SBR) and natural rubber (NR) as base materials and investigated the influence of shape, and particle size of ZnO on the dynamic behavior of SBR composites. Dynamic X-ray computed tomography (CT) was conducted at excitation frequencies of 1 and 2 Hz, excitation amplitudes corresponding to 1% and 1.25% of the inter-chuck distance of the specimens, and a spatial resolution of 0.5 µm. Local strains were extracted from the 3D reconstructed images, and both the local strain amplitudes and their histograms were assessed as characteristics of dynamic behavior. The results indicated that the effect of varying the excitation frequency between 1 and 2 Hz was minimal for the SBR. Additionally, the median value of local strain amplitudes shifted in relation to the magnitude of the excitation amplitude. When comparing base materials (SBR and NR) with significantly different loss factors, no discernible differences were observed in the histograms of local strain amplitudes. The histograms did vary based on the formulation conditions of the SBR microparticle composite, although the loss factor remained unchanged. This lack of change in the loss factor can be attributed to the minimal impact of the particulate composites on the inherent loss factor of pure SBR. Importantly, the study successfully achieved multimodal and simultaneous measurements using dynamic mechanical analysis and dynamic micro X-ray CT.

Debiased momentum contrastive learning for multimodal video similarity measures

The growing potential of multimodal short videos has contributed to a new type of recommendation. It depends on effectively measuring the similarities between the short video pairs, which consist of video frames and descriptions. Previous studies proposed using contrastive learning with in-batch negative sampling. However, such a strategy would take other semantically similar samples in the batch as negatives, leading to a biased issue. This paper proposes a debiased momentum contrastive learning (DMCL) on the unified multimodal Transformer model (VideoSim) for video similarity measures. The proposed DMCL alleviates the bias issue of negative sampling by introducing implicit knowledge of the model itself as soft labels. Instead of simply taking other samples in the batch as negatives, the proposed DMCL applies soft labels as supervision from the ground truth and the contextual semantic similarities between video-text pairs to alleviate the bias caused by negative sampling. In addition, DMCL expands the negative samples by a momentum queue strategy, which allows storing more multimodal representations to contrast more negatives. The experimental results of measuring multimodal video similarity show that the proposed DMCL outperforms all baselines in terms of Spearman’s rank correlation. Ablation studies and extensive analysis further demonstrate the effectiveness of the proposed method.

Neural substrates underlying REM sleep duration in patients with major depressive disorder: A longitudinal study combining multimodal MRI data

IntroductionPrior studies have discussed rapid eye movement (REM) sleep disturbance as a potential endophenotype of major depressive disorder (MDD). However, the neural substrates underlying the percentage of REM sleep duration (REM%) and its association with disease progression in MDD remain unclear. MethodsOne hundred and fourteen MDD patients and 74 healthy controls (HCs) underwent resting-state functional and perfusion magnetic resonance imaging (MRI) scans as well as overnight polysomnography examination to assess brain function and REM%, with 48 patients completing follow-up visits. Correlation and mediation analyses were conducted to investigate the associations among baseline REM%, multimodal brain imaging measures, and the improvement of depressive symptoms at follow-up in MDD. ResultsWe found voxel-wise correlations between baseline REM% and multimodal brain imaging metrics in many brain regions involved in sensorimotor, visual processing, emotion, and cognition in patients with MDD. Moreover, the baseline REM% was correlated with the improvement of depressive symptoms from acute to remitted status in patients through regulating brain activity in the left inferior temporal gyrus and cerebral blood flow in the bilateral paracentral lobule. ConclusionOur findings help to identify the neural underpinnings of REM% in depression and highlight REM% as a potential prognostic biomarker to predict disease progression. These may inform future novel interventions of MDD from the perspective of regulating REM sleep.

Using multi-modal neuroimaging to characterise social brain specialisation in infants.

The specialised regional functionality of the mature human cortex partly emerges through experience-dependent specialisation during early development. Our existing understanding of functional specialisation in the infant brain is based on evidence from unitary imaging modalities and has thus focused on isolated estimates of spatial or temporal selectivity of neural or haemodynamic activation, giving an incomplete picture. We speculate that functional specialisation will be underpinned by better coordinated haemodynamic and metabolic changes in a broadly orchestrated physiological response. To enable researchers to track this process through development, we develop new tools that allow the simultaneous measurement of coordinated neural activity (EEG), metabolic rate, and oxygenated blood supply (broadband near-infrared spectroscopy) in the awake infant. In 4- to 7-month-old infants, we use these new tools to show that social processing is accompanied by spatially and temporally specific increases in coupled activation in the temporal-parietal junction, a core hub region of the adult social brain. During non-social processing, coupled activation decreased in the same region, indicating specificity to social processing. Coupling was strongest with high-frequency brain activity (beta and gamma), consistent with the greater energetic requirements and more localised action of high-frequency brain activity. The development of simultaneous multimodal neural measures will enable future researchers to open new vistas in understanding functional specialisation of the brain.

SHAP-based Prediction of Mother's History of Depression to Understand the Influence on Child Behavior.

Depression strongly impacts parents' behavior. Does parents' depression strongly afect the behavior of their children as well? To investigate this question, we compared dyadic interactions between 73 depressed and 75 non-depressed mothers and their adolescent child. Families were of low income and 84% were white. Child behavior was measured from audio-video recordings using manual annotation of verbal and nonverbal behavior by expert coders and by multimodal computational measures of facial expression, face and head dynamics, prosody, speech behavior, and linguistics. For both sets of measures, we used Support Vector Machines. For computational measures, we investigated the relative contribution of single versus multiple modalities using a novel approach to SHapley Additive exPlanations (SHAP). Computational measures outperformed manual ratings by human experts. Among individual computational measures, prosody was the most informative. SHAP reduction resulted in a four-fold decrease in the number of features and highest performance (77% accuracy; positive and negative agreements at 75% and 76%, respectively). These fndings suggest that maternal depression strongly impacts the behavior of adolescent children; diferences are most revealed in prosody; multimodal features together with SHAP reduction are most powerful.

Basics for the Use of Andexanet

For life-threatening or uncontrollable bleeding in association with the thrombin inhibitor dabigatran, the monoclonal antibody fragment idarucizumab is available, and for bleeding in association with the direct factor Xa inhibitors rivaroxaban or apixaban, the modified recombinant FXa protein andexanet is available for reversal. These antidotes represent emergency drugs that are typically used only after performing guideline-compliant multimodal measures. An interdisciplinary group of experienced experts in the fields of angiology, hematology, internal medicine, clinical pharmacology, laboratory medicine, transfusion medicine, anesthesiology, intensive care, and hemostaseology developed recommendations relevant to daily clinical practice based on the current scientific evidence. Reversal of oral anticoagulants should be considered for severe bleeding in the following situations: (1) life-threatening bleeding or refractory hemorrhagic shock, (2) intracerebral bleeding, or (3) endoscopically unstoppable gastrointestinal bleeding. After successful hemostasis, anticoagulation (e.g., direct oral anticoagulant, vitamin K antagonist, and heparin) should be resumed promptly, taking into account individual bleeding and thromboembolic risk. This article aims to facilitate the management of patients with andexanet by all medical disciplines involved, thereby ensuring optimal care of patients during bleeding episodes.

Robust Localization for Secure Navigation of UAV Formations Under GNSS Spoofing Attack

Nowadays, aerial formations are frequently employed in outdoor scenarios to cooperatively explore and monitor wide areas of interest. In these applications, the vehicles are often exposed to relevant security vulnerabilities, as, for instance, the alteration of navigation signals from an attacker with map counterfeiting (if not even hijacking) purposes. In this work, we focus on an Unmanned Aerial Vehicle (UAV) formation that monitors an area, wherein navigation spoofing attacks may occur. Letting the UAVs cooperate and exploiting the redundancy in the available sensing information, a distributed procedure is proposed to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$i)$</tex-math> </inline-formula> detect spoofing attacks, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$ii)$</tex-math> </inline-formula> support the navigation in adverse conditions. The validity of the designed approach is confirmed by numerical results. Aerial vehicles for outdoor operation are generally endowed with inertial measurements, relative ranging, and GNSS sensing capability. In this work, two cascaded estimation algorithms for concurrent GNSS spoofing detection and localization in a multi-UAV scenario is proposed, to attain robust navigation in areas subject to GNSS spoofing attacks. The attack detection leverages on information theoretic tools to provide a practical threshold test by checking the multimodal measurement consistency. The localization procedures exploit a decision logic relying on measurement reliability to combine information sources that are different in nature, for UAV self-localization in both safe and under-attack conditions. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Aerial vehicles for outdoor operation are generally endowed with inertial measurements, relative ranging, and GNSS sensing capability. In this work, two cascaded estimation algorithms for concurrent GNSS spoofing detection and localization in a multi-UAV scenario is proposed, to attain robust navigation in areas subject to GNSS spoofing attacks. The attack detection leverages on information theoretic tools to provide a practical threshold test by checking the multimodal measurement consistency. The localization procedures exploit a decision logic relying on measurement reliability to combine information sources that are different in nature, for UAV self-localization in both safe and under-attack conditions.

Does a Soft Actuated Back Exosuit Influence Multimodal Physiological Measurements and User Perception During an Industry Inspired Task?

Back support soft exosuits are promising solutions to reduce risk of musculoskeletal injuries at workplaces resulting from physically demanding and repetitive lifting tasks. Design of novel active exosuits address the impact on the muscle activity and metabolic costs but do not consider other critical aspects such as comfort and user perception during the intended tasks. Thus, in this study, we describe a novel soft active exosuit in line with its impact on physiological and subjective measures during lifting. We tested four healthy participants who performed repetitive lifting tasks with and without this exosuit. The exosuit provided assistance proportional to the lumbar flexion angle measured using an inertial measurement unit. We measured the participant's multimodal physiological measures including surface electromyography, metabolic cost, heart rate, and skin temperature. We also measured subjective scores on user exertion, task load, and device acceptability. All participants perceived a reduction in task load when using the exosuit. Three participants showed reduction of muscle activity for the erector spinae muscles. The metabolic costs and heart rate reserve reduced for two participants, with similar trends for skin temperature. For future development of workplace exosuits, we recommend incorporating assessments of both physiological and subjective measures, considering the user-dependent response to the exosuit.

Intravitreal fluocinolone acetonide 0.19 mg (ILUVIEN®) in patients with non-infectious uveitis: real-world effectiveness and safety outcomes at 12 months

PurposeThis study assessed the effectiveness of the 0.19-mg fluocinolone acetonide (FAc) implant by multimodal measurements in patients with non-infectious uveitis (NIU) in a real-world setting in Spain.MethodsA prospective study of patients who had NIU including uveitic macular oedema (UME) with ≥ 12 months follow-up was done. Exclusion criteria include infectious uveitis and uncontrolled glaucoma or ocular hypertension requiring more than 2 medications. Effectiveness was assessed using a multicomponent outcome measure that included nine outcomes. Effectiveness was defined as all components being met at every timepoint. Secondary outcome measures were onset or progression of glaucoma and investigator-reported adverse events.ResultsTwenty-six eyes from 22 patients were included, with 96.2% having an indication including UME. During the 12-month study, the FAc implant was effective in 15 (57.7%) eyes, reaching effectiveness as soon as 2 weeks post-implantation. Mean best-corrected visual acuity and mean central macular thickness (CMT) were significantly improved vs. baseline at all timepoints (all comparisons p < 0.01). During the 12-month study, inflammation markers (anterior chamber cells and vitreous haze) had also significantly declined. Factors predicting effectiveness at month 12 were systemic corticosteroid dose pre-FAc, higher immunomodulatory therapy (IMT) load at baseline and thicker retinal nerve fibre layer (RNFL) at baseline (all p < 0.05). Factors predicting failure were male gender, thinner RNFL at baseline and treatment ineffectiveness at 1 month (all p < 0.05). In parallel, corticosteroid and IMT use also declined significantly. No significant increase in IOP was detected.ConclusionThe FAc implant is safe and effective at treating NIU over 12 months in a real-world setting in Spain.

End-to-end algorithm for the automatic detection of the neural canal opening in OCT images based on a multi-task deep learning model

Neural canal opening (NCO) are important landmarks of the retinal pigment epithelium layer in the optic nerve head region. Conventional NCO detection employs multimodal measurements and feature engineering, which is usually suitable for one specific task. In this study, we proposed an end-to-end deep learning scenario for NCO detection based on single-modality features (OCT). The proposed method contains two visual tasks: one is to verify the existence of NCO points as a binary classification, and the other is to locate the NCO points as a coordinate regression. The feature representation of OCT images, extracted by a MobileNetV2 architecture, was evaluated under new testing data, with an average Euclidean distance error of 5.68 ± 4.45 pixels and an average intersection over union of 0.90 ± 0.03. This suggests that data-driven scenarios have the opportunity to provide a universal and efficient solution to various visual tasks from OCT images.

Design and validation of the 1-week memory battery for assessing episodic memory and accelerated long-term forgetting in cognitively unimpaired subjects.

Subtle decline in memory is thought to arise in the preclinical phase of Alzheimer's disease (AD). However, detecting these initial cognitive difficulties cross-sectionally has been challenging, and the exact nature of the decline is still debated. Accelerated long-term forgetting (ALF) has been recently suggested as one of the earliest and most sensitive indicators of memory dysfunction in subjects at risk of developing AD. The objective of this study was to design and validate the 1-week memory battery (1WMB) for assessing episodic memory and ALF in cognitively unimpaired individuals. The 1WMB is unique in that it assesses multimodal memory and measures recall at both short delay (20 min) and at long term (1 week). Forty-five cognitively unimpaired subjects were assessed with 1WMB and standardized neuropsychological tests. Subjective cognitive decline (SCD), levels of anxiety and depression, and cognitive reserve were also measured. The tests of 1WMB showed a high internal consistency, and concurrent validity was observed with standard tests of episodic memory and executive functions. The analysis revealed a greater loss of information at 1 week compared to short-term forgetting (20 min). Performance in the 1WMB was affected by age and educational level, but was not associated with levels of anxiety and depression. Unlike standard tests, performance in the 1WMB correlated with measures of SCD. Our findings indicate that the 1WMB has good psychometric properties, and future studies are needed to explore its potential usefulness to assess cognitively unimpaired subjects at increased risk of developing AD. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Operationalising Pluriliteracies in a Lesson Planning Tool for Vocational CLIL Teachers

Whilst researchers agree that the integration of content and language goals is the operative principle behind any successful CLIL programme, there is an ongoing debate over how we can best conceptualise this integration for practical use. A promising way of integrating content and language goals in CLIL teaching is foregrounding the subject/pluriliteracies skills relevant to the respective CLIL subjects, and using them as a guide to offer targeted language support and multimodal scaffolding measures. Yet the content teachers typically involved in CLIL programmes in Austria are seldom trained in subject literacy education or foreign language teaching methodology, which makes the task of translating a content and language integrated model into CLIL practice especially demanding. This becomes even more challenging at Austrian technical colleges, where CLIL programmes typically focus on highly specialised technical content subjects in which cognitive demand is high in terms of both content and language. To support teachers at technical colleges in implementing an integrated CLIL model, this project aims at developing a CLIL lesson planning tool that operationalises subject literacy for the context of technical colleges. Following a design-based research (DBR) approach, the tool is designed in collaboration with two novice CLIL teachers and tested in their respective subjects recycling technology and control engineering. Both the process and the outcome of this project offer new insights into CLIL lesson planning and vocational CLIL in general. The lesson planning tool itself holds the potential to improve CLIL practice in the often-neglected context of vocational CLIL, thus bridging the gap between theory and practice.

Projecting RNA measurements onto single cell atlases to extract cell type-specific expression profiles using scProjection

Multi-modal single cell RNA assays capture RNA content as well as other data modalities, such as spatial cell position or the electrophysiological properties of cells. Compared to dedicated scRNA-seq assays however, they may unintentionally capture RNA from multiple adjacent cells, exhibit lower RNA sequencing depth compared to scRNA-seq, or lack genome-wide RNA measurements. We present scProjection, a method for mapping individual multi-modal RNA measurements to deeply sequenced scRNA-seq atlases to extract cell type-specific, single cell gene expression profiles. We demonstrate several use cases of scProjection, including identifying spatial motifs from spatial transcriptome assays, distinguishing RNA contributions from neighboring cells in both spatial and multi-modal single cell assays, and imputing expression measurements of un-measured genes from gene markers. scProjection therefore combines the advantages of both multi-modal and scRNA-seq assays to yield precise multi-modal measurements of single cells.

Product Styling Cognition Based on Kansei Engineering Theory and Implicit Measurement

Effective product styling designs must increasingly address users’ emotional requirements. This study introduces a product styling design method combining electroencephalography (EEG) and eye tracking for multimodal measurement based on the Kansei engineering theory. The feasibility of determining a target image using a similarity calculation model is verified. An experimental paradigm based on implicit measures is presented for product styling cognition research. This paradigm involves determining the target image, sample selection, target image matching experiments, and product styling cognition experiments. Based on the combined EEG and eye-tracking measurements, insights into product-form cognition are deduced to provide a scientific basis for product-form innovation design. Notably, variations in event-related potential during user cognition of product styling are more evident in the product-styling perception phase than in the evaluation phase. In the styling perception phase, samples with “high match” with the target image elicit more pronounced EEG responses than those with “low match”. These findings demonstrate the viability of understanding product-form cognition through multimodal implicit measurements, addressing issues such as the pronounced subjectivity inherent in traditional methods. Furthermore, this approach provides a pioneering technique for Kansei engineering research and offers a methodology for multimodal implicit measurements of product innovation design.