Miracle-dz/UniFAR-FTUs huggingface dataset:
Machine Learning models for detection and assessment of progression in Alzheimer’s disease based on blood and cerebrospinal fluid biomarkers

Machine-learning techniques were applied to human blood plasma and cerebrospinal fluid (CSF) biomarker data related to cognitive decline in Alzheimer’s Disease (AD) patients available via Alzheimer Disease Neuroimaging Initiative (ADNI) study. We observed the accuracy of AD diagnosis is greatest when protein biomarkers from cerebrospinal fluid are combined with plasma proteins using Support Vector Machines (SVM); this is not improved by adding age and sex. The area under the receiver operator characteristic (ROC) curve for our model of AD diagnosis based on a full (unbiased) set of plasma proteins was 0.94 in cross-validation and 0.82 on an external validation (test) set. Taking plasma in combination with CSF, the model reaches 0.98 area under the ROC curve on the test set. Accuracy of prediction of risk of mild cognitive impairment progressing to AD is the same for blood plasma biomarkers as for CSF and is not improved by combining them or adding age and sex as covariates.Clinical relevance— The identification of accurate and cost-effective biomarkers to screen for risk of developing AD and monitoring its progression is crucial for improved understanding of its causes and stratification of patients for treatments under development. This paper demonstrates the feasibility of AD detection and prognosis based on blood plasma biomarkers.
Different Cortical Activation of Facial Synkinesis Patients with Different Course of Disease: A Task fMRI Study

Facial synkinesis is a disease characterized by unintentional activation of facial muscles, which causes that the patients cannot control their facial expressions independently. Previous studies have shown that its pathogenesis is related to the reorganization of cerebral cortex, but it remains unclear what brain changes the patients have at different stage of the disease. For this study, we recruited 30 patients with facial synkinesis and 19 healthy control subjects from Shanghai Huashan Hospital. All participants completed bilateral blinking and grinning tasks while functional magnetic resonance imaging (fMRI) data was collected. We measured the brain activation strength of each task and observed the activation similarity of the ipsilateral tasks. Then we explored the correlation between activation pattern and clinical scale. Results showed different activation pattern along the courses of disease for blinking and grinning task, which may be due to the inconsistent process of cortical reorganization. The late stage group activated more in blinking task, but the least in grinning tasks, especially on the affected side (p<0.001 at voxel level, p<0.05 at cluster level, FWE corrected). Compared with healthy controls, the activation of patients between tasks on the affected side is more similar(p<0.05). There was a negative correlation in right postcentral gyrus between activation similarity and scale scores (symmetry of voluntary movement scores: R = -0.469, p = 0.009). This could be attributed to the rearrangement of the nervous system following facial nerve injury, leading to incorrect connections between nerves and muscles. Our study may be helpful for understanding mechanism of facial synkinesis and provide basis for the stage-dependent diagnosis and treatment.
Multi-View Stereo with Learnable Cost Metric

In this paper, we present LCM-MVSNet, a novel multi-view stereo (MVS) network with learnable cost metric (LCM) for more accurate and complete depth estimation and dense point cloud reconstruction. To adapt to the scene variation and improve the reconstruction quality in non-Lambertian low-textured scenes, we propose LCM to adaptively aggregate multi-view matching similarity into the 3D cost volume by leveraging sparse points hints. The proposed LCM benefits the MVS approaches in four folds, including depth estimation enhancement, reconstruction quality improvement, memory footprint reduction, and computational burden alleviation, allowing the depth inference for high-resolution images to achieve more accurate and complete reconstruction. Moreover, we improve the depth estimation by enhancing the propagation of shallow features via a bottom-up path and strengthen the end-to-end supervision by adapting the focal loss to reduce ambiguity caused by sample imbalance. Extensive experiments on two benchmark datasets show that our network achieves state-of-the-art performance on the DTU dataset and exhibits strong generalization ability with a competitive performance on the Tanks and Temples benchmark. Furthermore, we deploy our LCM-MVSNet into the real-world application for large-scale 3D reconstruction based on multi-view aerial images collected by self-developed UAV, demonstrating the robustness and scalability of our method. More detailed results are available in the Appendix11shorturl.at/rBG28
Interpolation Accuracy Evaluation for 3D Radio Environment Maps Construction

The characterization of the wireless spectrum in modern and future communications has been established as an important field of research, particularly in view of the increasing densification of terrestrial and aerial network nodes. Radio Environment Maps (REMs) have been prominently utilized for volumetric spectrum occupancy analysis and the design of algorithms for resource allocation but their construction relies on interpolation methods due to the limited availability of measurements. This paper evaluates four such estimators for two datasets collected in real-world environments in three-dimensional (3D) space - indoor through an automated measuring tool, and outdoor through a UAV-based sensor. The process of calibration of the interpolation methods is described, and their performance is assessed, reaching a minimum error of −7 dB. The influence of the distance between the measurement points and the estimated ones, on the accuracy, is analyzed. The findings in this work facilitate the further analysis in 3D signal propagation and REM prediction.
Coupling Machine Learning Models with Innovative Technology-based Screening Tool for Identifying Psychological Distress among Aboriginal Perinatal Mothers

Aboriginal perinatal mothers are at a significant risk of experiencing mental health problems, which can have profound negative impacts, despite their overall resilience. This work aimed to build prediction models for identifying high psychological distress among Aboriginal perinatal mothers by coupling machine learning models with an innovative and culturally-safe screening tool. The original dataset of 179 Aboriginal mothers with 337 variables was obtained from twelve perinatal health settings at Perth metropolitan and regional centers in Western Australia between July and September 2022, using a specifically designed web-based rubric for the perinatal mental health assessment. After data preprocessing and feature selection, 23 variables related to emotional manifestations, the problematic partner, worries about daily living, and the need for follow-up wraparound support were identified as significant predictors for the high risk of psychological distress measured by the Kessler 5 plus adaptation. The selected predictors were used to train prediction models, and most of the chosen machine learning models achieved satisfactory results, with Random Forest and Support Vector Machine yielding the highest AUC of over 0.95, accuracy over 0.86, and F1 score above 0.87. This study demonstrates the potential of using machine learning-based models in clinical decision-making to facilitate healthcare and social and emotional well-being for Aboriginal families.
An Online Caching Scheme for 360-Degree Videos at the Edge

360-degree videos have gained considerable popularity by offering immersive experiences to viewers. However, they consume significantly high bandwidth and demand for specialized caching schemes at the edge network. Previous caching schemes are limited as they either rely on complete historical data or neglect the viewer-varying characteristics in 360-degree videos. In this paper, we present an online caching scheme for 360-degree videos that leverages feedback from sequentially arriving viewers at the network edge. Our scheme consists of two components: an online tile popularity prediction component that accurately predicts the popularity of the tiles with the PopPred algorithm, and an online tile-bitrate caching optimization component that optimizes caching decisions to enhance viewers’ quality of experience (QoE) with the CacheOpt algorithm. We prove that both algorithms have sublinear regret, i.e., $O(\sqrt K ),$ where K is the number of viewers. We also conduct comprehensive experiments using real-world data to show that our caching scheme achieves better performance with lower regrets, higher QoE, and higher hit ratios compared with existing algorithms.
Discovering Prototypical Sound Speed Profiles with Deep Learning and Self-Organizing Maps

Sound speed profiles (SSPs), which measure the speed of sound underwater as a function of depth, are valuable for understanding acoustic characteristics such as transmission loss in particular regions of the ocean. As such, it can be advantageous to apply unsupervised learning techniques to SSPs in order to group regions of the ocean by their acoustic characteristics, called provincing. However, the variable length of SSPs (as ocean depth is not constant) makes tasks like this difficult. We demonstrate that a ResNet-based regression model that is trained to predict surface temperature from variable-length SSPs can be leveraged as a powerful fixed-length feature extractor for SSPs. We verify the effectiveness of this approach, referred to as ResNetSSP, by using self-organizing maps to find prototype SSPs in features extracted from a public CTD (conductivity, temperature, and depth) dataset.
Near-infrared photodetectors based on graphene embedded between amorphous and crystalline silicon

In last years, the introduction of new materials such as graphene has led to advances in the performances of silicon-based photonic devices. Here, we present an innovative type of photodetector based on graphene embedded between hydrogenated amorphous and crystalline silicon. More in detail, with the aim to increase the graphene optical absorption in the near-infrared spectrum, a photodetector based on low-finesse Fabry-Pérot optical microcavity has been investigated demonstrating responsivities of 26 mA/W around 1550nm. Furthermore, a photodetector integrated into a hybrid waveguide able to absorb the full incoming radiation has been numerically studied.
Multi-Loss U-Net Reformulation as an Efficient Solution to the Colony-Forming Unit Counting Problem

U-Net is undoubtedly the most cited and popularized deep learning architecture in the biomedical domain. Starting with image, volume, or video segmentation in numerous practical applications, such as digital pathology, and continuing to Colony-Forming Unit (CFU) segmentation, new emerging areas require an additional U-Net reformulation to solve some inherent inefficiencies of a simple segmentation-tailored loss function, such as the Dice Similarity Coefficient, being applied at the training step. One of such areas is segmentation-driven CFU counting, where after receiving a segmentation output map one should count all distinct segmented regions belonging to different detected microbial colonies. This problem can be a challenging endeavor, as a pure segmentation objective tends to produce many irrelevant artifacts or flipped pixels. Our novel multi-loss U-Net reformulation proposes an efficient solution to the aforementioned problem. It introduces an additional loss term at the bottom-most U-Net level, which focuses on providing an auxiliary signal of where to look for distinct CFUs. In general, our experiments show that all probed multi-loss U-Net architectures consistently outperform their single-loss counterparts, which embark on the Dice Similarity Coefficient and Cross-Entropy training losses alone.
Impacts of Cortical Regions on EEG-based Classification of Lexical Tones and Vowels in Spoken Speech

Speech impairment is one of the most serious problems for patients with communication disorders, e.g., stroke survivors. The brain-computer interface (BCI) systems have shown the potential to alternatively control or rehabilitate the neurological damages in speech production. The effects of different cortical regions in speech-based BCI systems are essential to be studied, which are favorable for improving the performance of speech-based BCI systems. This work aimed to explore the impacts of different speech-related cortical regions in the electroencephalogram (EEG) based classification of seventy spoken Mandarin monosyllables carrying four vowels and four lexical tones. Seven audible speech production-related cortical regions were studied, involving Broca’s and Wernicke’s areas, auditory cortex, motor cortex, prefrontal cortex, sensory cortex, left brain, right brain, and whole brain. Following the previous studies in which EEG signals were collected from ten subjects during Mandarin speech production, the features of EEG signals were extracted by the Riemannian manifold method, and a linear discriminant analysis (LDA) was regarded as a classifier to classify different vowels and lexical tones. The results showed that when using electrodes from whole brain, the classifier reached the best performances, which were 48.5% for lexical tones and 70.0% for vowels, respectively. The vowel classification results under Broca’s and Wernicke’s areas, auditory cortex, or prefrontal cortex were higher than those under the motor cortex or sensory cortex. No such differences were observed in the lexical tone classification task.
Automatic elasticity measurement of single cells using a microfluidic system with real-time image processing*

The mechanical properties of cells are closely related to their physiological states and functions. Due to the limitations of conventional cell elasticity measurement technologies such as low throughput, cell-invasiveness, and high cost, microfluidic systems are emerging as powerful tools for high-throughput cell mechanical property studies. This paper introduces a microfluidic system to automatically measure the elastic modulus of single cells in real time. The system integrated a microfluidic chip with a microchannel for cell constriction, a pressure pump, a precision differential pressure sensor, and a program for online analysis of cell deformation. The program used a fast U-net to segment cell images and measure protrusion length during cell deformation. Subsequently, the cell elasticity was determined in real-time based on the deformation and required pressure using the power law rheological model. Finally, Young's modulus of BMSCs, Huh-7 cells, EMSCs, and K562 cells was measured as 25.13 ± 15.19 Pa, 69.74 ± 92.01 Pa, 54.50 ± 59.31 Pa and 58.43 ± 27.27 Pa, respectively. The microfluidic system has significant application potential in the automated evaluation of cell mechanical properties.Clinical Relevance—The technique in this paper may be used for the automatic and high throughput study of the stiffness of cells, such as stem cells and cancer cells. The stiffness data may contribute to stem cell therapy and cancer research.
Risk assessment of diabetic retinopathy with machine and deep learning models with PPG signals and PWV

Retinopathy is one of the most common micro vascular impairments in diabetic subjects. Elevated blood glucose leads to capillary occlusion, provoking the uncontrolled increase in local growth of new vessels in the retina. When left untreated, it can lead to blindness. Traditional approaches for retinopathy detection require expensive devices and high specialized personnel. Being a microvascular complication, the retinopathy could be detected using the photoplethysmography (PPG) technology. In this paper we investigate the predictive value of the pulse wave velocity and PPG signal analysis with machine and deep learning approaches to detect retinopathy in diabetic subjects. PPG signals and pulse wave velocity (PWV) showed promising results in assessing the diabetic retinopathy. The best performances were scored by a LightGBM based model trained over a subset of the available dataset obtaining 80% of specificity and sensitivity.Clinical relevance— PPG based retinopathy detection could make the retinopathy detection more accessible since it does not need neither expensive devices for signal acquisition nor highly specialized personnel to be interpreted.
Spontaneous alpha-band oscillations reflect individual differences in audiovisual temporal perception

Light, and sound are persistently out of sync for subjective temporal perception called point of subjective simultaneity (PSS). It is stable within individuals but variable among individuals. Previous studies found that spontaneous alpha power, functioning in attention-related brain states, predicts individual PSS in the temporal order judgment (TOJ) task. However, the neural mechanisms underlying individual differences in audiovisual PSS have not been elucidated in the simultaneity judgment (SJ) task. A hypothesis that the spontaneous alpha band power might reflect the individual subjective temporal bias was proposed. We designed an SJ task EEG experiment where subjects judged whether the beep-flash stimuli are synchronous to test the above hypothesis. We primarily explored the correlation between the alpha-band power differences (visual- and auditory-leading conditions) with individual PSS. We used the V50A (~50% proportion of synchronous responses) to represent visual-leading conditions while A50V represents auditory-leading ones. We found the higher alpha power difference (V50A – A50V) predicted larger individual PSS. This study extends previous results and found that individual difference effects in the alpha band power also exist in the SJ task. The results suggested that alpha power might be associated with a spontaneous attentional state and reflect individuals’ subjective temporal bias.
Soil and Nutrient Losses in a Flat Land‐Reclamation District of Central Italy

Erosion and nutrient losses represent two of the most important environmental impacts of agriculture. In a field experiment, we compared the impact and productivity of three continuous maize‐cropping systems—conventional, low input and environmentally protective—on two contrasting soils (peat and loam soil) within a flat land‐reclamation district of Central Italy. This area is characterized by intensive agriculture and the presence of a receiving eutrophic lake. Cumulative runoff, sediment, dissolved and particulate nutrient losses were discontinuously measured over 2 years by means of 2‐m2 runoff traps. The effect of cropping systems on erosion was influenced by soil type. Conventional showed the highest losses on the loam soil and low input on the peat soil, whereas environmentally protective ensured the best soil protection at both sites.
News Consumption Helps Readers Identify Model-Generated News

Text-generation to produce journalistic content is increasing. In parallel, there is public concern about the misuse of language generation to disseminate disinformation. In this focused evaluation study, we report on an IRB-approved experiment carried out online with non-expert news audiences to study their perception of English and Spanish news-like content generated by GPT-3 versus news stories written by journalists. We explore how readers assessed news quality in terms of linguistic coherence/readability, journalistic expertise, and credibility when they perceived articles to be model-generated vs. journalist-written. We also reveal how news consumption may impact readers’ capacity to identify generated text.
The Relationship between Nociceptive Detection Thresholds and Pressure- and Electrical Pain Thresholds: An Explorative Study in Rheumatoid Arthritis Patients

Recently, methods have been developed enabling the characterization of the nociceptive function at the detection threshold level by measuring nociceptive detection thresholds (NDTs), rather than at the level of the pain threshold via pain threshold (PT) measurements. Both NDT and PT measurements aim to characterize (parts of) the nociceptive system. To date it is unclear if, and if so to what extent, the two outcomes relate to one another. In this study, the primary aim is to explore the relationship between the two measures in patients with rheumatoid arthritis (RA). As secondary aim, we explore differences in NDT between these RA patients with age- and sex-matched healthy controls (HC) from a readily existing dataset. In total 46 RA patients have been recruited, whereby the pressure- (PPT; bilaterally at two locations) and electrical (EPT) pain threshold were evaluated, as well as the NDTs. Significant, positive correlations were found between the EPT and PPT (R=0.54-0.60), but not with the NDTs (R≤0.25). As compared to HC, higher NDTs were found in the RA group. As the presence of a statistically significant weak relationship can only be evaluated using a larger sample size, our results indicate that there is no moderate or stronger relation between PT and NDT outcomes. This implicates that the two outcomes are not strongly driven by the same (nociceptive) mechanism(s). Future research into NDTs and what factors and/or mechanisms affect the outcome, could yield relevant insights into how to use and interpret the results of this relatively new method.Clinical Relevance — The evaluation of nociceptive detection thresholds, in isolation or together with conventionally evaluated pain thresholds, might provide valuable and complementary insights into nociceptive (dis)function in man.
Hierarchical Classification Strategy for Mitigating the Impact of The Presence of Pain in fNIRS-based BCIs

Brain computer interfaces (BCIs) can find applications in assistive systems for patients who experience conditions that impede their motor abilities. A BCI uses signals acquired from the brain to control external devices. As physical pain influences cortical signals, the presence of pain can negatively impact the performance of the BCI. In this work, we propose a strategy to mitigate this negative impact. Cortical signals are acquired from test subjects while they performed two mental arithmetic tasks, in the presence and the absence of painful stimuli. The task of the BCI is to reliably classify the two mental arithmetic tasks from the cortical recordings, irrespective of the presence or the absence of pain. We propose to do this classification, hierarchically, in two levels. In the first level, the data is classified into those captured in the presence and the absence of pain. Depending on the results of the classification from the first level, in the second level, the BCI performs the classification of tasks using a classifier trained either in the presence or the absence of pain. A 1-dimensional convolutional neural network (1D-CNN) is used for classification at both levels. It is observed that using this hierarchical strategy, the BCI is able to classify the tasks with an accuracy greater than 90%, irrespective of the presence or the absence of pain. Given that the presence of physical pain has shown previously to reduce the classification accuracy of a BCI to almost chance levels, this mitigation strategy will be a significant step towards enhancing the performance of BCIs when they are used in assistive systems for patients.