Miracle-dz/UniFAR-FTUs huggingface dataset:
Sensory Innervation of the Human Soft Palate

The human soft palate plays an important role in respiration, swallowing, and speech. These motor activities depend on reflexes mediated by sensory nerve endings. To date, the details of human sensory innervation to the soft palate have not been demonstrated. In this study, eight adult human whole‐mount (soft palate‐tongue‐pharynx‐larynx‐upper esophagus) specimens were obtained from autopsy. Each specimen was bisected in the midline, forming two equal and symmetrical halves. Eight hemi‐specimens were processed with Sihler's stain, a whole‐mount nerve staining technique. The remaining eight hemi‐soft palates were used for immunohistochemical study. The soft palatal mucosa was dissected from the oral and nasal sides and prepared for neurofilament staining. Our results showed that the sensory nerve fibers formed a dense nerve plexus in the lamina propria of the soft palatal mucosa. There was a significant difference in the innervation density between both sides. Specifically, the oral side had higher density of sensory nerve fibers than the nasal side of the soft palate. The mean number and percent area of the sensory nerve fibers in the mucosa of the nasal side was 78% and 72% of those in the mucosa of the oral side, respectively (P < 0.0001). The data presented here could be helpful for further investigating the morphological and quantitative alterations in the sensory nerves in certain upper airway disorders involving the soft palate such as obstructive sleep apnea (OSA) and for designing effective therapeutic strategies to treat OSA. Anat Rec, 301:1861–1870, 2018. © 2018 Wiley Periodicals, Inc.
Physical Properties, Cytocompatibility and Sealability of HealApex (a Novel Premixed Biosealer)

Introduction: The objective of this study was to evaluate the physical properties, cytotoxicity and sealing ability of HealApex _a new premixed calcium-silicate-phosphate-based biosealer_ in comparison with AH-26. Methods and Materials: Setting time, working time, film thickness, flow and radiopacity evaluation were performed according to ISO 6876 specification. L929 fibroblasts were incubated with the extracts of sealers and cytotoxicity was then evaluated using MTT assay. Thirty intact extracted human premolars were instrumented using step-back technique. The specimens were obturated with gutta-percha and experimental sealers employing lateral condensation technique. Sealing ability of sealers was investigated for up to one month using fluid filtration method. Data were statistically analyzed by t-test and ANOVA. Results: Physical properties of both sealers conformed to ISO specification. AH-26 exhibited significantly higher flow, higher radiopacity and lower film thickness; whereas HealApex showed lower setting time (P<0.05). HealApex represented high cell viability (P<0.05); however, AH-26 demonstrated significantly lower cell viability compared with the negative control group (P<0.05). There was no significant difference in microleakage between the sealers after 1 and 7 days; however, after 30 days, HealApex displayed better sealing ability (P<0.05). Conclusions: In this in vitro study, HealApex revealed acceptable physical properties, biocompatibility and good sealing ability as an endodontic sealer. Obtained results showed the new sealer had acceptable physical properties and good biocompatibility. In short term, the sealing ability of HealApex was comparable with AH-26 whilst in long term, HealApex’s sealing ability was better than the epoxy resin-based sealer.
Extending Detection with Privileged Information via Generalized Distillation

Detection systems based on machine learning models are essential tools for system and enterprise defense. These systems construct models of attacks (or non-attacks) from past observations (i.e., features) using a training algorithm. After that, the detection systems use that model for detection at run-time. In this way, the detection system recognizes when the environmental state becomes—at least probabilistically—dangerous. A limitation of this traditional model of detection is that model training is limited to features available at run-time. However, many features are either too expensive to collect in real-time or only available after the fact. In traditional detection, such features are ignored for the purpose of detection. In this paper, we consider an alternative detection model learning approach, generalized distillation, that trains models using privileged information—features available at training time but not at run-time-to improve the accuracy of detection systems. We use a deep neural network to implement generalized distillation for the training of detection models and making predictions. Our empirical study shows that detection with privileged information via generalized distillation increases precision and recall in systems of user face authentication, fast-flux bot detection, and malware classification over systems with no privileged information.
Generation meets recommendation: proposing novel items for groups of users

Consider a movie studio aiming to produce a set of new movies for summer release: What types of movies it should produce? Who would the movies appeal to? How many movies should it make? Similar issues are encountered by a variety of organizations, e.g., mobile-phone manufacturers and online magazines, who have to create new (non-existent) items to satisfy groups of users with different preferences. In this paper, we present a joint problem formalization of these interrelated issues, and propose generative methods that address these questions simultaneously. Specifically, we leverage on the latent space obtained by training a deep generative model---the Variational Autoencoder (VAE)---via a loss function that incorporates both rating performance and item reconstruction terms. We use a greedy search algorithm that utilize this learned latent space to jointly obtain K plausible new items, and user groups that would find the items appealing. An evaluation of our methods on a synthetic dataset indicates that our approach is able to generate novel items similar to highly-desirable unobserved items. As case studies on real-world data, we applied our method on the MART abstract art and Movielens Tag Genome datasets, which resulted in promising results: small and diverse sets of novel items.
Distributed Receding Horizon Control of Constrained Networked Leader–Follower Formations Subject to Packet Dropouts

A novel receding horizon control scheme is presented in this paper to manage networked leader–follower formation control problems in the presence of time-varying delay and data loss (measurement and feedback packet dropouts) occurrences on the communication and feedback channels. One of the main novelties of the proposed paradigm relies on the use of reachability analysis concepts involving the capability to combine sequences of predecessor sets with polyhedral piecewise state-space partitions. The predecessor sets are here derived offline by taking into account the prescribed state and input constraints, the interconnected nature of the system and the time delay induced by the network latency. They are used online to compute admissible and optimal control actions via a distributed receding horizon computational paradigm. Feasibility, uniformly ultimate boundedness and constraints satisfaction are formally proved regardless of any bounded time delays.
Teaching Industrial Automation Concepts with the use of Virtual/Augmented Reality - The IEC 61499 Case

We propose a framework for teaching complex industrial automation concepts with the help of Virtual/Augmented reality. Initially we construct the needed 3D models like conveyors belts, and liquid tanks to be used as testbed examples for the proposed approach and afterwards with the use of the appropriate engine and the Virtual Reality headset we create the enriched virtual and augmented reality use cases to support higher education students better understand complex industrial automation concepts. As this is an initial framework proposal, an extended literature review providing the needed background information on the status and progress of the Virtual and Augmented reality for educational and not only purposes is provided. The proposed system’s architecture is in detail explained and use cases where the proposed approach can be used are presented. An overview of the IEC 61499 framework is also presented since it is the underlying framework for supporting the industrial automation concepts.
Optimizing Space-Air-Ground Integrated Networks by Artificial Intelligence

It is widely acknowledged that the development of traditional terrestrial communication technologies cannot provide all users with fair and high quality services due to scarce network resources and limited coverage areas. To complement the terrestrial connection, especially for users in rural, disaster-stricken, or other difficult-to-serve areas, satellites, UAVs, and balloons have been utilized to relay communication signals. On this basis, SAGINs have been proposed to improve the users' QoE. However, compared with existing networks such as ad hoc networks and cellular networks, SAGINs are much more complex due to the various characteristics of three network segments. To improve the performance of SAGINs, researchers are facing many unprecedented challenges. In this article, we propose the AI technique to optimize SAGINs, as the AI technique has shown its predominant advantages in many applications. We first analyze several main challenges of SAGINs and explain how these problems can be solved by AI. Then, we consider the satellite traffic balance as an example and propose a deep learning based method to improve traffic control performance. Simulation results evaluate that the deep learning technique can be an efficient tool to improve the performance of SAGINs.
Alcohol Metabolizing Polygenic Risk for Alcohol Consumption in European American College Students.

OBJECTIVE Evidence suggests that the nature and magnitude of some genetic effects on alcohol use vary by age. We tested for moderation in the effect of an alcohol metabolizing polygenic score by time across the college years. METHOD Participants (total n = 2,214) were drawn from three cohorts of undergraduate college students, who were assessed annually for up to 4 years starting in their freshman year. Polygenic risk scores (PRSs) were calculated from genes involved in the metabolism of alcohol, as many of these markers are among the best replicated in association studies examining alcohol use phenotypes. Linear mixed effects models were fit by maximum likelihood to test the main effects of time and the PRS on alcohol consumption, as well as moderation of the PRS effect on alcohol consumption by time. RESULTS In the main effects model, the fixed effects for time and the PRS were positively associated with alcohol consumption. The interaction term testing moderation of the PRS effect by time reached statistical significance and remained statistically significant after other relevant interaction effects were controlled for. The main effect of the PRS accounted for 0.2% of the variance in alcohol consumption, whereas the interaction of PRS effect and time accounted for 0.05%. CONCLUSIONS Alcohol metabolizing genetic effects on alcohol use appear to be more influential in later years of college than in earlier years. Shifting environmental contexts, such as increased access to alcohol as individuals approach the legal age to purchase alcohol, may account for this association.
Severe obstructive sleep apnea treatment with mandibular advancement device: A case report

Mandibular advancement device (MAD) has been described as an alternative treatment to the severe obstructive sleep apnea (OSA), once it is not as effective as the continuous positive airway pressure therapy (CPAP) in reducing the apnea and hypopnea index (AHI). The objective of this study is to report a case using a MAD in a CPAP-intolerant patient suffering from severe OSA. Polysomnography exams were performed before and after treatment. Five months after fitting and titrating the MAD, the AHI was reduced from 80.5 events/hour to 14.6 events/hour and the minimum oxyhemoglobin saturation (SpO 2 ) increased from 46% to 83%. A two-year assessment of therapy revealed an AHI of 8 events/hour and SpO 2 of 85%.
Multi-Level Common Space Learning for Person Re-Identification

Matching people in different cameras, commonly referred to as person re-identification, is a challenging task. The challenges come from the drastic appearance variation across different camera views caused by changes in pose, lighting condition, occlusion, background, and so on. Instead of matching images in the original feature space, many existing methods learn distance metrics or feature transformations to improve the matching accuracy. For example, data from different camera views can be projected onto their common space to mitigate the feature gap caused by view discrepancy. However, a single-step mapping may not be sufficient to close this gap as the view difference can be significant between different cameras. To overcome this limitation, we propose a multi-level common space learning framework, which can gradually minimize the data discrepancy between different views in an iterative manner. At each intermediate level, synthetic data are generated using the automatically discovered grouping information, and these synthetic data can be viewed as a transitional state between the original camera views. The matching is performed by mapping the probe and gallery onto their common space in multiple steps. We evaluate the proposed method on two widely used person re-identification data sets. The results show that the proposed multi-level scheme significantly outperforms single-level mapping. In addition, competitive results are achieved as compared with over 20 state-of-the-art techniques.
Computing Optical Properties of Photonic Crystals by Using Multilayer Perceptron and Extreme Learning Machine

In this paper, dispersion relations (DRs) of photonic crystals (PhCs) are computed by multilayer perceptron (MLP) and extreme learning machine (ELM) artificial neural networks (ANNs). Bi- and tri-dimensional optimized structures presenting distinct DRs and photonic band gaps (PBGs) were selected for case studies. Optical properties of a set of PhCs with similar geometries and different dimensions were calculated by an electromagnetic solver in order to provide input data for ANN training and testing. We demonstrate that simple- and fast-training ANN models are capable of providing accurate DRs’ curves in a very short time.
Effect of a Diabetes Self-Efficacy Enhancing Program on Older Adults With Type 2 Diabetes: A Randomized Controlled Trial

This randomized controlled trial examined the effect of a diabetes self-efficacy enhancing program (DSEEP) on older adults with type 2 diabetes. The 8-week DSEEP consisted of a guidebook on diabetes self-care, a 1-day workshop, and fortnightly follow-up telephone calls. In total, 113 participants (56 in intervention group and 57 in control group) completed the study. Data were collected at baseline and at 8 weeks from the baseline. Outcome measures included self-efficacy, diabetes self-care activities, health-related quality of life, glycated hemoglobin (HbA1c) and unplanned health care service usage. Compared with participants in the control group, those who received DSEEP had significantly higher increase in self-efficacy and diabetes self-care activities, lower HbA1c, and lesser unplanned health service usage. However, there was no significant difference in health-related quality of life between the two groups. The DSEEP increased self-efficacy, which successfully enhanced self-care activities and reduced HbA1c.
Nanodevices Based on Proteins: the Emerging Science of Proteotronics

Different scientific disciplines, from biochemistry to electronics, are converging toward the investigation of nanodevices useful for biological, medical and electronic applications. The strategies to attempt this aim are therefore more and more interconnected and innovative so that it is legitimate to announce the born of a new discipline, the proteotronics. Proteotronics has the main objective to propose and develop innovative electronic devices, based on the selective action of specific proteins.
For-Profit Competition From Nonhealthcare Companies: Is Nursing Ready for the Challenge?

Recently, 3 industry giants, Amazon, Berkshire-Hathaway, andJPMorgan, announced their intention to form a nonprofit consortium to manage costs and quality of healthcare. Can this market-based effort succeed where others have failed to address these issues? One key problem that is widely discussed, but rarely defined, is the actual costs of healthcare. What are these costs and why do they continue to rise more quickly than overall inflation? Where does nursing care fit in the overall cost of healthcare? The starting point for discussion is to understand costs of healthcare from 3 perspectives:
Ameliorative Effect of Silymarin on Scopolamine-induced Dementia in Rats

AIM: This study aims to elucidate the possible ameliorative effect of silymarin on scopolamine-induced dementia using the object recognition test (ORT) in rats. METHODS: The study was extended to demonstrate the role of cholinergic activity, oxidative stress, neuroinflammation, brain neurotransmitters and histopathological changes in the anti-amnestic effect of silymarin in demented rats. Wistar rats were pre-treated with silymarin (200, 400, 800 mg/kg) or donepezil (10 mg/kg) orally for 14 consecutive days. Dementia was induced after the last drug administration by a single intraperitoneal dose of scopolamine (16 mg/kg). Then behavioural, biochemical, histopathological, and immunohistochemical analyses were then performed. RESULTS: Rats pre-treated with silymarin counteracted scopolamine-induced non-spatial working memory impairment in the ORT and decreased acetylcholinesterase (AChE) activity, reduced malondialdehyde (MDA), elevated reduced glutathione (GSH), restored gamma-aminobutyric acid (GABA) and dopamine (DA) contents in the cortical and hippocampal brain homogenates. Silymarin reversed scopolamine-induced histopathological changes. Immunohistochemical analysis showed that silymarin mitigated protein expression of the glial fibrillary acidic protein (GFAP) and nuclear factor kappa-B (NF-κB) in the brain cortex and hippocampus. All these effects of silymarin were similar to that of the standard anti-amnestic drug, donepezil. CONCLUSION: This study reveals that the ameliorative effect of silymarin on scopolamine-induced dementia in rats using the ORT maybe in part mediated by, enhancement of cholinergic activity, anti-oxidant and anti-inflammatory activities as well as mitigation in brain neurotransmitters and histopathological changes.
Rethinking Misalignment to Raise the Bar for Heap Pointer Corruption

Heap layout randomization renders a good portion of heap vulnerabilities unexploitable. However, some remnants of the vulnerabilities are still exploitable even under the randomized layout. According to our analysis, such heap exploits often abuse pointer-width allocation granularity to spray crafted pointers. To address this problem, we explore the efficacy of byte-granularity (the most fine-grained) heap randomization. Heap randomization, in general, has been a well-trodden area; however, the efficacy of byte-granularity randomization has never been fully explored as \emph{misalignment} raises various concerns. This paper unravels the pros and cons of byte-granularity heap randomization by conducting comprehensive analysis in three folds: (i) security effectiveness, (ii) performance impact, and (iii) compatibility analysis to measure deployment cost. Security discussion based on 20 CVE case studies suggests that byte-granularity heap randomization raises the bar against heap exploits more than we initially expected; as pointer spraying approach is becoming prevalent in modern heap exploits. Afterward, to demystify the skeptical concerns regarding misalignment, we conduct cycle-level microbenchmarks and report that the performance cost is highly concentrated to edge cases depending on L1-cache line. Based on such observations, we design and implement an allocator suited to optimize the performance cost of byte-granularity heap randomization; then evaluate the performance with the memory-intensive benchmark (SPEC2006). Finally, we discuss compatibility issues using Coreutils, Nginx, and ChakraCore.
Speculating Incident Zone System on Local Area Networks

Triage process in the incident handling lacks the ability to assess overall risks to modern cyber attacks. Zoning of local area networks by measuring internal network traffic in response to such risks is important. Therefore, we propose a SPeculating INcident Zone (SPINZ) system for supporting the triage process. The SPINZ analyzes internal network flows and outputs an incident zone, which is composed of devices related to the incident. We evaluate the performance of the SPINZ through simulations using incident flow datasets generated from internal traffic open data and lateral movement traffic. As a result, we confirm that the SPINZ has the capability to detect an incident zone, but removing unrelated devices from an incident zone is an issue to be further investigated.
Reactivating the Dormant Motor Cortex After Spinal Cord Injury With EEG Neurofeedback: A Case Study With a Chronic, Complete C4 Patient

Chronic spinal cord injury (SCI) patients present poor motor cortex activation during movement attempts. The reactivation of this brain region can be beneficial for them, for instance, allowing them to use brain-machine interfaces for motor rehabilitation or restoration. These brain-machine interfacess generally use electroencephalography (EEG) to measure the cortical activation during the attempts of movement, quantifying it as the event-related desynchronization (ERD) of the alpha/mu rhythm. Based on previous evidence showing that higher tonic EEG alpha power is associated with higher ERD, we hypothesized that artificially increasing the alpha power over the motor cortex of these patients could enhance their ERD (ie, motor cortical activation) during movement attempts. We used EEG neurofeedback (NF) to enhance the tonic EEG alpha power, providing real-time visual feedback of the alpha oscillations measured over the motor cortex. This approach was evaluated in a C4, ASIA A, SCI patient (9 months after the injury) who did not present ERD during the movement attempts of his paralyzed hands. The patient performed 4 NF sessions (in 4 consecutive days) and screenings of his EEG activity before and after each session. After the intervention, the patient presented a significant increase in the alpha power over the motor cortex, and a significant enhancement of the mu ERD in the contralateral motor cortex when he attempted to close the assessed right hand. As a proof of concept investigation, this article shows how a short NF intervention might be used to enhance the motor cortical activation in patients with chronic tetraplegia.