Search Results (299)

Accurate knowledge of a patient’s anatomy during every treatment fraction in proton therapy is an important prerequisite to ensure a correct dose deposition in the target volume. Adaptive proton therapy aims to detect those changes and adjust the treatment plan accordingly. One way to trigger a daily re-planning of the treatment is to take a proton radiograph from the beam’s-eye view before the treatment to check for possible changes in the water equivalent thickness (WET) along the path due to daily changes in the patient’s anatomy. In this paper, the Two-Plane Imaging System (TPIS) is presented, comprising two ATLAS IBL silicon pixel-detector modules developed for the tracking detector of the ATLAS experiment at CERN. The prototype of the TPIS is described in detail, and proof-of-principle WET images are presented, of two-step phantoms and more complex phantoms with bone-like inlays (WET 10 to 40 mm). This study shows the capability of the TPIS to measure WET images with high precision. In addition, the potential of the TPIS to accurately determine WET changes over time down to 1 mm between subsequently taken WET images of a changing phantom is shown. This demonstrates the possible application of the TPIS and ATLAS IBL pixel-detector module in adaptive proton therapy. Full article

To address the challenges of radar multi-target detection in marine environments, this paper proposes an information geometry (IG)-based, two-stage track-before-detect (TBD) framework. Specifically, multi-target measurements are first modeled on the manifold, leveraging its geometric properties for enhanced detection. The designed scoring function incorporates both the feature dissimilarity between targets and clutter, as well as the precise inter-target path associations. Consequently, a novel merit function combining feature dissimilarity and transition cost is derived to mitigate the mutual interference between adjacent targets. Subsequently, to overcome the integrated merit function expansion phenomenon, a two-stage integration strategy combining dynamic programming (DP) and greedy integration (GI) algorithms was adopted. To tackle the challenges of unknown target numbers and computationally infeasible multi-hypothesis testing, a target cancellation detection scheme is proposed. Furthermore, by exploiting the independence of multi-target motions, an efficient implementation method for the detector is developed. Experimental results demonstrate that the proposed algorithm inherits the superior clutter discrimination capability of IG detectors in sea clutter environments while effectively resolving track mismatches between neighboring targets. Finally, the effectiveness of the proposed method was validated using real-recorded sea clutter data, showing significant improvements over conventional approaches, and the signal-to-clutter ratio was improved by at least 2 dB. Full article

Cloud cover can delay landslide detection in optical satellite imagery for weeks, complicating disaster response. Synthetic Aperture Radar (SAR) backscatter imagery, which is widely used for monitoring floods and avalanches, remains underutilised for landslide detection due to a limited understanding of landslide signatures in SAR data. We developed a conceptual model of landslide expression in SAR backscatter (σ°) change images through iterative investigation of over 1000 landslides across 30 diverse study areas. Using multi-temporal composites and dense time series Sentinel-1 C-band SAR data, we identified characteristic patterns linked to land cover, terrain, and landslide material. The results showed either increased or decreased backscatter depending on environmental conditions, with reduced visibility in urban or mixed vegetation areas. Detection was also hindered by geometric distortions and snow cover. The diversity of landslide expression illustrates the need to consider local variability and multi-track (ascending and descending) satellite data in designing representative training datasets for automated detection models. The conceptual model was applied to three recent disaster events using the first post-event Sentinel-1 image, successfully identifying previously unknown landslides before optical imagery became available in two cases. This study provides a theoretical foundation for interpreting landslides in SAR imagery and demonstrates its utility for rapid landslide detection. The findings support further exploration of rapid landslides in SAR backscatter data and future development of automated detection models, offering a valuable tool for disaster response. Full article

Effective means are urgently needed to identify non-cooperative targets intruding on airport clearance zones for the safety of low-altitude flights. Passive radars are an ideal means of low-altitude airspace surveillance for their low costs in terms of hardware and operation. However, non-ideal signals transmitted by third-party illuminators challenge feature extraction and target recognition in such radars. To tackle this problem, we propose a light-weight recognition-before-tracking method based on a beam constraint for passive radars. Under the background of sparse targets, the proposed method utilizes the continuity of target motion to identify the same target from the same array beam. Then, with its peaks detected in range-Doppler maps, a feature vector based on the biased radar cross-section is constructed for recognition. Meanwhile, to use the local scattering characteristics of targets for dynamic recognition, we introduce a parameter named normalized bistatic velocity to characterize the attitude of the target relative to the receiving station. With the proposed light-weight metric, the similarity of feature vectors between the unknown target and standard targets is measured to determine the target type. The feasibility and effectiveness of the proposed method are validated by the simulated and measured data. Full article

Background: Torque Teno Virus (TTV) viremia has been proposed as a marker for infection risk in kidney transplant (KT) recipients. This study aimed to evaluate the prognostic value of TTV levels for predicting infections post-KT. Methods: A cohort of 82 KT patients was analyzed. TTV loads were measured before KT and at the time of cutoff analysis (mean time since KT: 20.2 ± 10.3 months). Infections were tracked within six months following the time of cutoff analysis. Univariable analyses and a supervised machine learning approach (logistic regression with leave-one-out cross-validation) were conducted to rigorously assess TTV’s predictive ability for post-transplant infection. Results: Seventy-two patients (87.8%) had detectable TTV before KT. Of these, 30.5% developed infections, predominantly viral. TTV loads increased significantly from 3.35 ± 1.67 log₁₀ cp/mL before KT to 4.53 ± 1.93 log₁₀ cp/mL at the time of cutoff analysis. Infected patients had significantly higher TTV loads (5.39 ± 1.68 log₁₀ vs. 4.16 ± 1.94 log₁₀ cp/mL, p = 0.0057). The optimal TTV threshold for predicting infection at the time of cutoff analysis was 5.16 log₁₀ cp/mL, with 60% sensitivity and 81% specificity. Machine learning models improved performance, with sensitivity and specificity 0.805 and 0.735, respectively. Conclusions: TTV viremia may serve as a biomarker for infection risk, particularly when used with other clinical variables. The identified TTV threshold of 5.16 log₁₀ cp/mL offers a practical tool for clinical decision-making, particularly when integrated with a machine learning model. Further studies with larger cohorts are needed to validate these findings and refine clinical applications. Full article

Physical rehabilitation is crucial for injury recovery, offering pain relief and faster healing. However, traditional methods rely heavily on in-person professional feedback, which can be time-consuming, expensive, and prone to human error, limiting accessibility and effectiveness. As a result, patients are often encouraged to perform exercises at home; however, due to the lack of professional guidance, motivation dwindles and adherence becomes a challenge. To address this, this paper proposes a smartphone-based solution that enables patients to receive exercise feedback independently. This paper reviews current Computer Vision systems for assessing rehabilitation exercises and introduces an intelligent system designed to assist patients in their recovery. Our proposed system uses motion tracking based on Computer Vision, analyzing videos recorded with a smartphone. With accessibility as a priority, the system is evaluated against the advanced Qualysis Motion Capture System using a dataset labeled by expert physicians. The framework focuses on human pose detection and movement quality assessment, aiming to reduce recovery times, minimize human error, and make rehabilitation more accessible. This proof-of-concept study was conducted as a pilot evaluation involving 15 participants, consistent with earlier work in the field, and serves to assess feasibility before scaling to larger datasets. This innovative approach has the potential to transform rehabilitation, providing accurate feedback and support to patients without the need for in-person supervision or specialized equipment. Full article

Distributed target tracking in multi-agent networks plays a critical role in cooperative sensing and autonomous navigation. However, it faces significant challenges in highly dynamic and adversarial setups. This study aims to enhance the resilience of decentralized target tracking algorithms against measurement faults and cyber–physical threats, especially false data injection attacks. We propose a consensus-based estimation algorithm that integrates a nearly constant velocity model with saturation-based filtering to suppress impulsive measurement variations and promote robust, distributed state estimation. To counteract adversarial conditions, we incorporate a dynamic false data injection detection and isolation mechanism that uses innovation thresholds to identify and disregard suspicious measurements before they can degrade the global estimate. The effectiveness of the proposed algorithms is demonstrated through a series of simulation-based case studies under both benign and adversarial conditions. The results show that increased network connectivity and higher consensus iteration rates improve estimation accuracy and convergence speed, while properly tuned saturation filters achieve a practical balance between fault suppression and accurate estimation. Furthermore, under localized, coordinated, and transient false data injection attacks, the detection mechanism successfully identifies compromised agents and prevents their data from corrupting the distributed global estimate. Overall, this study illustrates that the proposed algorithm provides a simplified fault-tolerant solution that significantly enhances the accuracy and resilience of distributed target tracking without imposing excessive communication or computational burdens. Full article

Inflammatory bowel disease (IBD) is a relapsing–remitting condition resulting in chronic inflammation of the gastrointestinal tract. Present methods are either inadequate or not viable for continuous tracking of disease progression in individuals. In this study, we present the development towards an implantable biosensor for detecting interleukin-6 (IL-6), an important cytokine implicated in IBD. The optimised sensor design includes a gold surface functionalised with a known IL-6-specific aptamer, integrating a recognition sequence and an electrochemical redox probe. The IL-6 aptasensor demonstrated a sensitivity of up to 40% and selectivity up to 10% to the IL-6 target in vitro. Sensors were found to degrade over 7 days when exposed to recombinant IL-6, with the degradation rate rapidly increasing when exposed to intestinal mucosa. A feasibility in vivo experiment with a newly designed implantable gut sensor array confirmed rapid degradation over a 5-h implantation period. We achieved up to a 93% reduction in sensor degradation rates, with a polyvinyl alcohol–methyl acrylate hydrogel coating that aimed to reduce nonspecific interactions in complex analytes compared to uncoated sensors. Degradation was linked to desorption of the monolayer leading to breakage of gold thiol bonds. While there are key challenges to be resolved before a stable implantable IBD sensor is realised, this work highlights the potential of aptamer-based biosensors as effective tools for long-term diagnostic monitoring in IBD. Full article

Background: This systematic review examines artificial intelligence (AI) applications in neuroimaging for autism spectrum disorder (ASD), addressing six research questions regarding biomarker optimization, modality integration, social function prediction, developmental trajectories, clinical translation challenges, and multimodal data enhancement for earlier detection and improved outcomes. Methods: Following PRISMA guidelines, we conducted a comprehensive literature search across 8 databases, yielding 146 studies from an initial 1872 records. These studies were systematically analyzed to address key questions regarding AI neuroimaging approaches in ASD detection and prognosis. Results: Neuroimaging combined with AI algorithms demonstrated significant potential for early ASD detection, with electroencephalography (EEG) showing promise. Machine learning classifiers achieved high diagnostic accuracy (85–99%) using features derived from neural oscillatory patterns, connectivity measures, and signal complexity metrics. Studies of infant populations have identified the 9–12-month developmental window as critical for biomarker detection and the onset of behavioral symptoms. Multimodal approaches that integrate various imaging techniques have substantially enhanced predictive capabilities, while longitudinal analyses have shown potential for tracking developmental trajectories and treatment responses. Conclusions: AI-driven neuroimaging biomarkers represent a promising frontier in ASD research, potentially enabling the detection of symptoms before they manifest behaviorally and providing objective measures of intervention efficacy. While technical and methodological challenges remain, advancements in standardization, diverse sampling, and clinical validation could facilitate the translation of findings into practice, ultimately supporting earlier intervention during critical developmental periods and improving outcomes for individuals with ASD. Future research should prioritize large-scale validation studies and standardized protocols to realize the full potential of precision medicine in ASD. Full article

Background/Objectives: While machine learning has made substantial strides in pronunciation detection in recent years, there remains a notable gap in the literature regarding research on improvements in the acquisition of speech sounds following a training intervention, especially in the domain of perception. This study addresses this gap by developing a deep learning algorithm designed to identify perceptual gains resulting from second language (L2) phonetic training. Methods: The participants underwent multiple sessions of high-variability phonetic training, focusing on discriminating challenging L2 vowel contrasts. The deep learning model was trained on perceptual data collected before and after the intervention. Results: The results demonstrated good model performance across a range of metrics, confirming that learners’ gains in phonetic training could be effectively detected by the algorithm. Conclusions: This research underscores the potential of deep learning techniques to track improvements in phonetic training, offering a promising and practical approach for evaluating language learning outcomes and paving the way for more personalized, adaptive language learning solutions. Deep learning enables the automatic extraction of complex patterns in learner behavior that might be missed by traditional methods. This makes it especially valuable in educational contexts where subtle improvements need to be captured and assessed objectively. Full article

The detection of weak moving targets in heterogeneous clutter backgrounds is a significant challenge in radar systems. In this paper, we propose a track-before-detect (TBD) method based on information geometry (IG) theory applied to range-azimuth measurements, which extends the IG detectors to multi-frame detection through inter-frame information integration. The approach capitalizes on the distinctive benefits of the information geometry detection framework in scenarios with strong clutter, while enhancing the integration of information across multiple frames within the TBD approach. Specifically, target and clutter trajectories in multi-frame range-azimuth measurements are modeled on the Hermitian positive definite (HPD) and power spectrum (PS) manifolds. A scoring function based on information geometry, which uses Kullback–Leibler (KL) divergence as a geometric metric, is then devised to assess these motion trajectories. Moreover, this study devises a solution framework employing dynamic programming (DP) with constraints on state transitions, culminating in an integrated merit function. This algorithm identifies target trajectories by maximizing the integrated merit function. Experimental validation using real-recorded sea clutter datasets showcases the effectiveness of the proposed algorithm, yielding a minimum 3 dB enhancement in signal-to-clutter ratio (SCR) compared to traditional approaches. Full article

Studies in animals and humans suggested that the tonic dopamine inhibition of prolactin release may be estimated by submaximal prolactin stimulation by thyrotropin-releasing hormone (TRH), the mini-TRH test. Because patients with schizophrenia may be more vulnerable to stress-induced elevations of prolactin, great care was taken to avoid stress-induced increases in prolactin, including applying local anaesthesia before blood extraction in our psychotic patients. Basal prolactin levels were in the reference range in all psychotic patients studied by us and were not higher in male patients than in normal men. Results of the mini-TRH test suggested that in acute patients with non-affective psychoses, everyday memory problems, non-paranoid delusions, and first-rank symptoms, but not other Comprehensive Psychopathological Rating Scale (CPRS) positive symptoms, could correlate with decreasing dopamine transmission in lactotrophs. In acute patients with first-episode schizophrenia, increasing negative disorganisation symptoms might correlate with increasing dopamine transmission. In first-episode patients, a hypersensitivity of the TRH response was detected, which could indicate that variability in the basal prolactin levels may confound the interpretation of the mini-TRH response. To avoid that, a smaller dose of TRH was recommended in first-episode patients. Studies using other estimates of basal dopamine release suggested that striatal dopamine transmission reflected delusions and hallucinations but not other Positive and Negative Symptom Scale (PANSS) positive symptoms. Including a wide range of symptoms in the PANSS positive scale may reduce its specificity for assessing basal dopamine transmission, although the scale remains useful for tracking treatment response. Full article

In the realm of underwater acoustic signal processing, challenges such as random missing measurements due to low signal-to-noise ratios, merging–splitting contacts in the measurement space, and prolonged trajectory losses due to target interference pose significant difficulties for passive sonar tracking. Conventional tracking methods often struggle with tracking losses or association errors in these scenarios. However, particle filter (PF)-based track-before-detect (TBD) methods have demonstrated significant advantages in avoiding association challenges. The PF-TBD method calculates the posterior density distribution using the energy accumulation of multiple pings along the particle trajectories, thereby circumventing the association problem between measurements. Consequently, this method is less sensitive to missing measurements but relies on trajectory continuity. When a weak target crosses paths with a strong one, it can be submerged by strong interference for an extended period, leading to discontinuities in the tracking results. To address these issues, this study proposes a TBD algorithm based on particle states and band features. The algorithm employs frequency-band adaptive matching for each tracking target to enhance the continuity of the target trajectories. This joint processing improves tracking outcomes for weak targets, particularly in crossing scenarios processed by PF-TBD. The effectiveness of the algorithm is validated using experimental data obtained at sea. The proposed algorithm demonstrates superior performance in terms of tracking accuracy and trajectory continuity compared to existing methods, making it a valuable addition to the field of underwater target tracking. Full article

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that impairs cognitive function, making early detection crucial for timely intervention. This study proposes a novel AD detection framework integrating gaze and head movement analysis via a dual-pathway convolutional neural network (CNN). Unlike conventional methods relying on linguistic, speech, or neuroimaging data, our approach leverages non-invasive video-based tracking, offering a more accessible and cost-effective solution to early AD detection. To enhance feature representation, we introduce GazeMap, a novel transformation converting 1D gaze and head pose time-series data into 2D spatial representations, effectively capturing both short- and long-term temporal interactions while mitigating missing or noisy data. The dual-pathway CNN processes gaze and head movement features separately before fusing them to improve diagnostic accuracy. We validated our framework using a clinical dataset (112 participants) from Konkuk University Hospital and an out-of-distribution dataset from senior centers and nursing homes. Our method achieved 91.09% accuracy on in-distribution data collected under controlled clinical settings, and 83.33% on out-of-distribution data from real-world scenarios, outperforming several time-series baseline models. Model performance was validated through cross-validation on in-distribution data and tested on an independent out-of-distribution dataset. Additionally, our gaze-saliency maps provide interpretable visualizations, revealing distinct AD-related gaze patterns. Full article

Background/Objectives: Transcranial Direct Current Stimulation (tDCS) has proven to be a promising intervention for major depressive disorder (MDD). Even so, the specific neurophysiological mechanisms underlying its therapeutic effects, particularly regarding frontal EEG markers, remain insufficiently understood. This pilot study investigated both the clinical efficacy and neurophysiological impact of frontal tDCS in individuals with mild to severe depression, with particular focus on mood changes and alterations in Frontal Alpha Asymmetry (FAA), Beta Symmetry, and Theta/Alpha Ratios at the F3 and F4 electrode sites. Methods: A total of thirty–one participants were enrolled and completed a standardized Flow Neuroscience tDCS protocol targeting the dorsolateral prefrontal cortex using a bilateral F3/F4 montage. The intervention included an active phase of five stimulations per week for three weeks, followed by a Strengthening Phase with two stimulations per week. Clinical outcomes were assessed using the Montgomery–Åsberg Depression Rating Scale (MADRS), while neurophysiological changes were evaluated via standardized quantitative EEG (QEEG) recordings obtained before and after the treatment course. Among the participants, fourteen individuals had a baseline MADRS score of ≥20, indicating moderate to severe depressive symptoms. Results: Following tDCS treatment, significant reductions in MADRS scores were observed across the cohort, with clinical response rates notably higher in the moderate/severe group (71.4%) compared to the mild depression group (20.0%). Neurophysiological effects were modest: no significant changes were detected in FAA or Beta Symmetry measures. However, a substantial reduction in the Theta/Alpha Ratio at F4 was found in participants with moderate to severe depression (p = 0.018, Cohen’s d = −0.72), suggesting enhanced frontal cortical activation associated with clinical improvement. Conclusions: These findings indicate that frontal tDCS is effective in reducing depressive symptoms, particularly in cases of moderate to severe depression. While improvements in FAA and Beta Symmetry were not significant, changes in the Theta/Alpha Ratio at F4 point toward dynamic neurophysiological reorganization potentially linked to therapeutic outcomes. The Theta/Alpha Ratio may serve as a promising biomarker for tracking tDCS response, whereas other EEG metrics might represent more stable trait characteristics. Future research should prioritize individualized stimulation protocols and incorporate more sensitive neurophysiological assessments, including functional connectivity analyses and task-evoked EEG paradigms, to understand the mechanisms underlying clinical improvements. Full article