Miracle-dz/UniFAR-FTUs huggingface dataset:
Music and Speech Elicit Similar Subcortical Responses in Human Listeners

Music and speech are two sounds that are unique to human beings and encountered in daily life. Both are transformed by the auditory pathway from an initial acoustical encoding to higher level cognition. Most studies of speech and music processing are focused on the cortex, and the subcortical response to natural, polyphonic music is essentially unstudied. This study was aimed to compare the subcortical encoding of music and speech using the auditory brainstem response (ABR). While several methods have recently been developed to derive the ABR to continuous speech, they are either not applicable to music or give poor results. In this study, we explored deriving the ABR through deconvolution using three regressors: 1) the half-wave rectified stimulus waveform, 2) the modeled inner hair cell potential, and 3) the auditory nerve model firing rate (ANM), where the latter two were generated from a computational auditory periphery model. We found the ANM regressor yields robust and interpretable ABR waveforms to diverse genres of music and multiple types of speech. We then used the ANM-derived ABRs to compare the subcortical responses to music and speech and found that they are highly similar in morphology. We further investigated cortical responses using the same deconvolution method, and found the responses there were also quite similar, which was unexpected based on previous studies. We conclude that when using our proposed deconvolution regressor that accounts for acoustical differences’ nonlinear effects on peripheral encoding, the derived brainstem and cortical responses to music and speech are highly correlated.
A Flow-Level Wi-Fi Model for Large Scale Network Simulation

Wi-Fi networks are extensively used to provide Internet access to end-users and to deploy applications at the edge. By playing a major role in modern networking, Wi-Fi networks are getting bigger and denser. However, studying their performance at large-scale and in a reproducible manner remains a challenging task. Current solutions include real experiments and simulations. While the size of experiments is limited by their financial cost and potential disturbance of commercial networks, the simulations also lack scalability due to their models' granularity and computational runtime. In this paper, we introduce a new Wi-Fi model for large-scale simulations. This model, based on flow-level simulation, requires fewer computations than state-of-the-art models to estimate bandwidth sharing over a wireless medium, leading to better scalability. Comparing our model to the already existing Wi-Fi implementation of ns-3, we show that our approach yields to close performance evaluations while improving the runtime of simulations by several orders of magnitude. Using this kind of model could allow researchers to obtain reproducible results for networks composed of thousands of nodes much faster than previously.
Multi-step Prediction of Worker Resource Usage at the Extreme Edge

Democratizing the edge by leveraging the prolific yet underutilized computational resources of end devices, referred to as Extreme Edge Devices (EEDs), can open a new edge computing tech market that is people-owned, democratically managed, and accessible/lucrative to all. Parallel computing at EEDs can also move the computing service much closer to end-users, which can help satisfy the stringent Quality-of-Service (QoS) requirements of delay-critical and/or data-intensive IoT applications. However, EEDs are heterogeneous user-owned devices, and are thus subject to a highly dynamic user access behavior (i.e., dynamic resource usage). This makes the process of determining the computational capability of EEDs increasingly challenging. Estimating the dynamic resource usage of EEDs (i.e., workers) has been mostly overlooked. The complexity of Machine Learning (ML)-based models renders them impractical for deployment at the edge for the purpose of such estimations. In this paper, we propose the Resource Usage Multi-step Prediction (RUMP) scheme to estimate the dynamic resource usage of workers over multiple steps ahead in a computationally efficient way while providing a relatively high prediction accuracy. Towards that end, RUMP exploits the use of the Hierarchical Dirichlet Process-Hidden Semi-Markov Model (HDP-HSMM) to estimate the dynamic resource usage of workers in EED-based computing paradigms. Extensive evaluations on a real testbed of heterogeneous workers for multi-step sizes show an 87.5% prediction accuracy for the starting point of 2-steps and coming to as little as a 16% average difference in prediction error compared to a representative of state-of-the-art ML-based schemes.
Blender: Toward Practical Simulation Framework for BLE Neighbor Discovery

For the widely used Bluetooth Low-Energy (BLE) neighbor discovery, the parameter configuration of neighbor discovery directly decides the results of the trade-off between discovery latency and power consumption. Therefore, it requires evaluating whether any given parameter configuration meets the demands. The existing solutions, however, are far from satisfactory due to unsolved issues. In this paper, we propose Blender, a simulation framework that produces a determined and full probabilistic distribution of discovery latency for a given parameter configuration. To capture the key features in practice, Blender provides adaption to the stochastic factors such as the channel collision and the random behavior of the advertiser. Evaluation results show that, compared with the state-of-art simulators, Blender converges closer to the traces from the Android-based realistic estimations. Blender can be used to guide parameter configuration for BLE neighbor discovery systems where the trade-off between discovery latency and power consumption is of critical importance.
Optimizing Cell Sizes for Ultra-Reliable Low-Latency Communications in 5G Wireless Networks

The millimeter-wave (mmWave) band with large antenna arrays and dense base station deployments has become the prime candidate for 5G mobile systems and key enabler for ultra-reliable low-latency communications (URLLC). In this paper, we propose an approach to estimating the optimal cell sizes of 5G networks that support URLLC services by combining both physical and data link layers, leveraging concepts from stochastic geometry and queuing theory. Furthermore, the impacts of the densification of base stations on the average blocking probability, which are of practical interest, are investigated with numerical results. The results show that the signal-to-noise-and-interference ratio (SINR) coverage probability and the average blocking probability achieve optimal values at different cell sizes. Moreover, the differences between the two types of optimal values become more significant with higher SINR thresholds. Our results suggest that traditional SINR-based approach for cell sizing will cause over-provisioning of base stations and significantly higher costs. Specifically, we share the insight that the interactions between SINR at physical layer and retransmission at link layer contribute to varying cost saving.
A separable convolutional neural network-based fast recognition method for AR-P300

Augmented reality-based brain–computer interface (AR–BCI) has a low signal-to-noise ratio (SNR) and high real-time requirements. Classical machine learning algorithms that improve the recognition accuracy through multiple averaging significantly affect the information transfer rate (ITR) of the AR–SSVEP system. In this study, a fast recognition method based on a separable convolutional neural network (SepCNN) was developed for an AR-based P300 component (AR–P300). SepCNN achieved single extraction of AR–P300 features and improved the recognition speed. A nine-target AR–P300 single-stimulus paradigm was designed to be administered with AR holographic glasses to verify the effectiveness of SepCNN. Compared with four classical algorithms, SepCNN significantly improved the average target recognition accuracy (81.1%) and information transmission rate (57.90 bits/min) of AR–P300 single extraction. SepCNN with single extraction also attained better results than classical algorithms with multiple averaging.
Construction and validation of a nomogram for predicting survival in elderly patients with cardiac surgery

Background In recent years, the number of elderly patients undergoing cardiac surgery has rapidly increased and is associated with poor outcomes. However, there is still a lack of adequate models for predicting the risk of death after cardiac surgery in elderly patients. This study sought to identify independent risk factors for 1-year all-cause mortality in elderly patients after cardiac surgery and to develop a predictive model. Methods A total of 3,752 elderly patients with cardiac surgery were enrolled from the Medical Information Mart for Intensive Care III (MIMIC-III) dataset and randomly divided into training and validation sets. The primary outcome was the all-cause mortality at 1 year. The Least absolute shrinkage and selection operator (LASSO) regression was used to decrease data dimensionality and select features. Multivariate logistic regression was used to establish the prediction model. The concordance index (C-index), receiver operating characteristic curve (ROC), and decision curve analysis (DCA) were used to measure the predictive performance of the nomogram. Results Our results demonstrated that age, sex, Sequential Organ Failure Assessment (SOFA), respiratory rate (RR), creatinine, glucose, and RBC transfusion (red blood cell) were independent factors for elderly patient mortality after cardiac surgery. The C-index of the training and validation sets was 0.744 (95%CI: 0.707–0.781) and 0.751 (95%CI: 0.709–0.794), respectively. The area under the curve (AUC) and decision curve analysis (DCA) results substantiated that the nomogram yielded an excellent performance predicting the 1-year all-cause mortality after cardiac surgery. Conclusions We developed a novel nomogram model for predicting the 1-year all-cause mortality for elderly patients after cardiac surgery, which could be an effective and useful clinical tool for clinicians for tailored therapy and prognosis prediction.
Automatic classification of gastric lesions in gastroscopic images using a lightweight deep learning model with attention mechanism and cost-sensitive learning

Magnification endoscopy with narrow-band imaging (ME-NBI) technology is widely used in the early diagnosis of precancerous lesions and gastric cancer, which is critical to reducing the incidence of gastric cancer and improving the survival rate of gastric cancer patients. The diagnosis based on ME-NBI image is mostly in manual way in clinics. In order to improve its objectivity and efficiency, here we proposed a lightweight attention mechanism deep learning model to automatically classify ME-NBI images for artificial intelligence (AI) diagnosis of early gastric cancer, low-grade intraepithelial neoplasia, and non-neoplasm. We collected 4,098 images from 990 patients for model training and validation and evaluated the performance of our model by comparisons with that of other models, such as the benchmark model. An additional 587 images from 296 patients were collected as an independent test set to further evaluate our method’s performance. The validation set showed that the overall accuracy, recall, precision, F1 score, and the area under the curve of our method were higher than those of other methods. The independent test set showed that our method achieved state-of-the-art classification for low-grade intraepithelial neoplasia (accuracy = 93.9%, sensitivity = 92.6%). Our method displayed the advantages of lightweight and high effectiveness in classifying effectiveness, which is the potential for AI diagnosis of early gastric cancer, low-grade intraepithelial neoplasia, and non-neoplasm.
Multimodal magnetic resonance imaging of youth sport-related concussion reveals acute changes in the cerebellum, basal ganglia, and corpus callosum that resolve with recovery

Magnetic resonance imaging (MRI) can provide a number of measurements relevant to sport-related concussion (SRC) symptoms; however, most studies to date have used a single MRI modality and whole-brain exploratory analyses in attempts to localize concussion injury. This has resulted in highly variable findings across studies due to wide ranging symptomology, severity and nature of injury within studies. A multimodal MRI, symptom-guided region-of-interest (ROI) approach is likely to yield more consistent results. The functions of the cerebellum and basal ganglia transcend many common concussion symptoms, and thus these regions, plus the white matter tracts that connect or project from them, constitute plausible ROIs for MRI analysis. We performed diffusion tensor imaging (DTI), resting-state functional MRI, quantitative susceptibility mapping (QSM), and cerebral blood flow (CBF) imaging using arterial spin labeling (ASL), in youth aged 12-18 years following SRC, with a focus on the cerebellum, basal ganglia and white matter tracts. Compared to controls similar in age, sex and sport (N = 20), recent SRC youth (N = 29; MRI at 8 ± 3 days post injury) exhibited increased susceptibility in the cerebellum (p = 0.032), decreased functional connectivity between the caudate and each of the pallidum (p = 0.035) and thalamus (p = 0.021), and decreased diffusivity in the mid-posterior corpus callosum (p < 0.038); no changes were observed in recovered asymptomatic youth (N = 16; 41 ± 16 days post injury). For recent symptomatic-only SRC youth (N = 24), symptom severity was associated with increased susceptibility in the superior cerebellar peduncles (p = 0.011) and reduced activity in the cerebellum (p = 0.013). Fewer days between injury and MRI were associated with reduced cerebellar-parietal functional connectivity (p < 0.014), reduced activity of the pallidum (p = 0.002), increased CBF in the caudate (p = 0.005), and reduced diffusivity in the central corpus callosum (p < 0.05). Youth SRC is associated with acute cerebellar inflammation accompanied by reduced cerebellar activity and cerebellar-parietal connectivity, as well as structural changes of the middle regions of the corpus callosum accompanied by functional changes of the caudate, all of which resolve with recovery. Early MRI post-injury is important to establish objective MRI-based indicators for concussion diagnosis, recovery assessment and prediction of outcome.
The impact of myocardial fibrosis biomarkers in a heart failure population with atrial fibrillation—The HARVEST-Malmö study

Background Several studies suggest that circulating biomarkers of myocardial fibrosis are associated with worse prognosis in subjects with atrial fibrillation (AF). Here, we aimed to explore associations between fibrosis biomarkers, prevalent AF, and left atrial volume (LAV) enlargement in subjects with heart failure (HF). Additionally, we evaluated the prognostic impact of fibrotic biomarkers in HF with co-existing AF. Materials and methods Patients hospitalized for HF (n = 316, mean age 75 years; 30% women) were screened for AF. Seven proteins previously associated with myocardial fibrosis [metalloproteinase inhibitor 4 (TIMP-4), suppression of tumorigenicity 2 (ST-2), galectin-3 (GAL-3), growth/differentiation factor-15 (GDF-15), and matrix metalloproteinase 2, 3, and 9 (MMP-3, MMP-3, and MMP-9, respectively)] were analyzed using a proximity extension assay. Proteins with significant Bonferroni-corrected associations with mortality and re-hospitalization risk were taken forward to multivariable Cox regression analyses. Further, Bonferroni-corrected multivariable logistic regression models were used to study associations between protein plasma levels, prevalent AF, and severely enlarged left atrial volume index (LAVI ≥ 48 ml/m2). Results Prevalent AF was observed in 194 patients at the hospitalization of whom 178 (92%) were re-hospitalized and 111 (57%) died during the follow-up period. In multivariable logistic regression models, increased plasma levels of TIMP-4, GDF-15, and ST-2 were associated with the prevalence of AF, whereas none of the seven proteins showed any significant association with severely enlarged LAVI. Increased plasma levels of five proteins yielded significant associations with all-cause mortality in patients with co-existing AF; TIMP-4 (HR 1.33; CI95% 1.07–1.66; p = 0.010), GDF-15 (HR 1.30; CI95% 1.05–1.62; p = 0.017), GAL-3 (HR 1.29; CI95% 1.03–1.61; p = 0.029), ST-2 (HR 1.48; CI95% 1.18–1.85; p < 0.001), and MMP-3 (HR 1.33; CI95% 1.09–1.63; p = 0.006). None of the proteins showed any significant association with re-hospitalization risk. Conclusion In this study, we were able to demonstrate that elevated levels of three plasma proteins previously linked to myocardial fibrosis are associated with prevalent AF in a HF population. Additionally, higher levels of five plasma proteins yielded an increased risk of mortality in the HF population with or without co-existing AF.
Biosensors toward behavior detection in diagnosis of alzheimer’s disease

In recent years, a huge number of individuals all over the world, elderly people, in particular, have been suffering from Alzheimer’s disease (AD), which has had a significant negative impact on their quality of life. To intervene early in the progression of the disease, accurate, convenient, and low-cost detection technologies are gaining increased attention. As a result of their multiple merits in the detection and assessment of AD, biosensors are being frequently utilized in this field. Behavioral detection is a prospective way to diagnose AD at an early stage, which is a more objective and quantitative approach than conventional neuropsychological scales. Furthermore, it provides a safer and more comfortable environment than those invasive methods (such as blood and cerebrospinal fluid tests) and is more economical than neuroimaging tests. Behavior detection is gaining increasing attention in AD diagnosis. In this review, cutting-edge biosensor-based devices for AD diagnosis together with their measurement parameters and diagnostic effectiveness have been discussed in four application subtopics: body movement behavior detection, eye movement behavior detection, speech behavior detection, and multi-behavior detection. Finally, the characteristics of behavior detection sensors in various application scenarios are summarized and the prospects of their application in AD diagnostics are presented as well.
Dihydropyridine calcium blockers do not interfere with non-rapid eye movement sleep

Non-rapid eye movement (NREM) sleep is tightly homeostatically regulated and essential for survival. In the electroencephalogram (EEG), oscillations in the delta (0.5–4 Hz) range are prominent during NREM sleep. These delta oscillations are, to date, the best indicator for homeostatic sleep regulation; they are increased after prolonged waking and fade during NREM sleep. The precise mechanisms underlying sleep homeostasis and the generation of EEG delta oscillations are still being investigated. Activity-dependent neuronal calcium influx has been hypothesized to play an important role in generating delta oscillations and might be involved in downstream signaling that mediates sleep function. Dihydropyridine blockers of L-type voltage-gated calcium channels (VGCCs) are in wide clinical use to treat hypertension and other cardiovascular disorders and are readily blood-brain-barrier penetrant. We therefore, wanted to investigate their potential effects on EEG delta oscillation and homeostatic NREM sleep regulation in freely behaving mice. In vivo two-photon imaging of cortical neurons showed larger spontaneous calcium transients in NREM sleep compared to waking. Application of the dihydropyridine calcium blocker nicardipine significantly reduced cortical calcium transients without affecting the generation of delta oscillations. Nicardipine also did not affect EEG delta oscillations over 24 h following application. The time spent in NREM sleep and NREM episode duration was also not affected. Thus, acute block of calcium entry through L-type VGCCs does not interfere with EEG delta oscillations or their homeostatic regulation, despite prior evidence from calcium channel knockout mice.
Platelets and platelet extracellular vesicles in drug delivery therapy: A review of the current status and future prospects

Platelets are blood cells that are primarily produced by the shedding of megakaryocytes in the bone marrow. Platelets participate in a variety of physiological and pathological processes in vivo, including hemostasis, thrombosis, immune-inflammation, tumor progression, and metastasis. Platelets have been widely used for targeted drug delivery therapies for treating various inflammatory and tumor-related diseases. Compared to other drug-loaded treatments, drug-loaded platelets have better targeting, superior biocompatibility, and lower immunogenicity. Drug-loaded platelet therapies include platelet membrane coating, platelet engineering, and biomimetic platelets. Recent studies have indicated that platelet extracellular vesicles (PEVs) may have more advantages compared with traditional drug-loaded platelets. PEVs are the most abundant vesicles in the blood and exhibit many of the functional characteristics of platelets. Notably, PEVs have excellent biological efficacy, which facilitates the therapeutic benefits of targeted drug delivery. This article provides a summary of platelet and PEVs biology and discusses their relationships with diseases. In addition, we describe the preparation, drug-loaded methods, and specific advantages of platelets and PEVs targeted drug delivery therapies for treating inflammation and tumors. We summarize the hot spots analysis of scientific articles on PEVs and provide a research trend, which aims to give a unique insight into the development of PEVs research focus.
Kind-defining relative clauses in the diachrony of Italian

We will show that the postcopular kind-defining relative clause belongs to a larger class of kind-defining relatives, in which other kind of verbs incorporate a stage level predicate. We will consider their properties in varieties of Italian and English, and their evolution in the history of Italian. The copular structures whose predicate can be the antecedent of a kind-defining relative belong to the predicational (canonical, extensional) type, while the identificational (inverse, intensional) ones are excluded. The relatives that appear in this context, namely those modifying the nominal predicate of a copular sentence, display a number of special properties that set them apart from ordinary restrictive relative clauses and render them partly similar to non-restrictive relatives. We will try to relate these special properties to the fact that such relatives concur to define a kind, and more specifically to the fact that (in opposition to restrictives) the relative clause content is not presupposed as true, and the DP in predicate position is not truly referential.