Miracle-dz/UniFAR-FTUs huggingface dataset:
Implant placement in fresh extraction sockets

Recent systematic reviews have shown that the survival rate of immediate implant placement is similar to those with a delayed approach. However, preclinical models and human studies have shown that immediate implant placement per se does not preserve the anatomy of the alveolus, mainly at the buccal bone crest, leading to bony dehiscences and subsequently to soft-tissue recession, with a great impact on esthetic outcomes. On the other hand, preclinical and human studies have identified factors that may prevent bone resorption after immediate implant placement, such as anatomical/biological (alveolus, gingival biotype, periapical/periodontal pathology) and surgical/restorative ones (implant diameter and positioning, flap/flapless, bone and connective tissue grafts, immediate loading/provisionalization, antibiotics). Taking these factors together and with a critical treatment plan made by an expert professional, the immediate treatment approach could be possible and beneficial for the patient.
Overexpression of lncRNA GAS5 suppresses prostatic epithelial cell proliferation by regulating COX-2 in chronic non-bacterial prostatitis

ABSTRACT Chronic non-bacterial prostatitis (CNP) is a common urologic disease that is linked to the development of prostate cancer. Long non-coding RNA (lncRNA) GAS5 has been identified to mediate cell proliferation in prostate cancer, although its role in CNP is still unclear. Human prostate epithelial cell line RWPE-1 was induced by lipopolysaccharide (LPS) to mimic CNP model in vitro. Real-time PCR was performed to determine the expression of GAS5 and COX-2, while western blotting was used to evaluate the protein expression of COX-2. The interaction between GAS5 and COX-2 was determined using RNA pull-down and RNA immunoprecipitation (RIP). Cell proliferation was determined using MTT assay. The expression of GAS5 was decreased, while COX-2 was increased in prostatitis tissues and in LPS-induced RWPE-1 cells. The overexpression of GAS5 suppressed the protein level of COX-2, and inhibited cell proliferation of LPS-induced RWPE-1 cells and HPECs, which was rescued by the co-transfection with pcDNA-GAS5 and pcDNA-COX-2. GAS5 was confirmed to promote the ubiquitination of COX-2, and the in vivo GAS5-overexpressed CNP rat model decreased the motor scores, the volume of prostate tissues, the average number of inflammatory cells, prostatic proliferation, and COX-2 expression. Our findings revealed that overexpression of GAS5 inhibited cell proliferation via negatively regulating the expression of COX-2, thus alleviating the progression of CNP.
Analgesic effects of a capacitive-resistive monopolar radiofrequency in patients with myofascial chronic neck pain: a pilot randomized controlled trial.

BACKGROUND To date, there is a lack of prior studies on the use of capacitive resistive monopolar radiofrequency (RF) to treat neck pain. The objective of this study was to investigate the immediate effect of capacitive resistive monopolar radiofrequency (RF=448 kHz), in comparison with a placebo, on (1) reducing neck pain intensity at myofascial trigger points (MTrP), (2) decreasing neck disability and (3) improving cervical range of motion (CROM). METHODS A randomized, double-blind, placebo-controlled trial (NCT02353195) was carried out. Patients with myofascial chronic neck pain (N=24) with active MTrP in one upper trapezius muscle were randomly divided into two groups: a radio-frequency group, which received eight sessions of a monopolar capacitive resistive radio-frequency application over the upper trapezius muscle, and a placebo group (PG), which received eight sessions of placebo radio-frequency over the same muscle. Visual analog scale (VAS), CROM and Neck Disability Index (NDI) were evaluated after the first session and after the eight sessions. RESULTS The Wilcoxon test for VAS showed statistically significant differences between baseline, immediately after the first session and after eight sessions (p<.001). No significant differences for PG were found. No differences were observed between groups. NDI improved in both groups after eight sessions, but no differences were found between groups (p<.05). ANOVA for time factor showed statistically significant changes in the right cervical rotation in both groups (F=4.112; p=.026) after eight sessions. CONCLUSIONS Even though there were no differences between both groups, the monopolar capacitive, resistive RF could have a potential effect on pain intensity.
Prenatal and perinatal risk factors for eating disorders in women: A population cohort study.

OBJECTIVE The fetal programming model hypothesizes that developmental programming in utero and in early life induces adaptations that predetermine the adult phenotype. This study investigated whether prenatal/perinatal complications are associated with lifetime eating disorders in women. METHOD Participants included 46,373 adult women enrolled in the Norwegian Mother and Child Cohort Study (den norske Mor & barn-undersøkelsen [MoBa]). MoBa mothers and their mothers (MoBa grandmothers) were the focus of the current study. MoBa mothers with lifetime eating disorders were compared to a referent group. RESULTS MoBa mothers who weighed more at birth (birth weight, adjusted odds ratio [OR] = 1.14; 95% confidence interval [CI]: 1.10-1.19) or were born large-for-gestational-age (adjusted OR = 1.39; 95% CI: 1.27-1.52) were more likely to develop binge-eating disorder in later life. MoBa mothers who weighed less at birth were more likely to develop anorexia nervosa (birth weight, adjusted OR = 0.88; 95% CI: 0.81-0.95). Bulimia nervosa and purging disorder (PD) were not significantly predicted by the prenatal and perinatal factors examined. DISCUSSION Results of this study, which include the first known investigation of prenatal and perinatal factors in binge-eating disorder and PD, suggest that fetal programming may be relevant to the development of anorexia nervosa and binge-eating disorder. Future genetically informative research is needed to help disentangle whether these associations are a function of genetic influences or a true environmental fetal programming effect.
Iterative Joint Image Demosaicking and Denoising Using a Residual Denoising Network

Modern digital cameras rely on the sequential execution of separate image processing steps to produce realistic images. The first two steps are usually related to denoising and demosaicking, where the former aims to reduce noise from the sensor and the latter converts a series of light intensity readings to color images. Modern approaches try to jointly solve these problems, i.e., joint denoising–demosaicking, which is an inherently ill-posed problem given that two-thirds of the intensity information is missing and the rest is perturbed by noise. While there are several machine learning systems that have been recently introduced to solve this problem, the majority of them rely on generic network architectures, which do not explicitly consider the physical image model. In this paper, we propose a novel algorithm that is inspired by powerful classical image regularization methods, large-scale optimization, and deep learning techniques. Consequently, our derived iterative optimization algorithm, which involves a trainable denoising network, has a transparent and clear interpretation compared with other black-box data driven approaches. Our extensive experimentation line demonstrates that our proposed method outperforms any previous approaches for both noisy and noise-free data across many different datasets. This improvement in reconstruction quality is attributed to the rigorous derivation of an iterative solution and the principled way we design our denoising network architecture, which as a result requires fewer trainable parameters than the current state-of-the-art solution, and furthermore can be efficiently trained by using a significantly smaller number of training data than existing deep demosaicking networks.
Effect of two post-surgical cleansing protocols on early periodontal wound healing and cytokine levels following osseous resective surgery: A randomized controlled study.

OBJECTIVES The aim of this RCT study was to compare early wound healing and gingival crevicular fluid cytokine levels of patients treated with two different post-surgical cleansing protocols. METHODS A total of 30 chronic periodontitis patients scheduled for osseous resective surgery with fibre retention technique were randomly assigned to follow one of two post-surgical protocols. Patients assigned to the test protocol (n = 15) were instructed to brush the surgical area with a sonic toothbrush starting the day after surgery in addition to 0.12% chlorhexidine (CHX) rinsing, while patients following the control protocol (n = 15) rinsed only with 0.12% CHX solution and resumed mechanical cleansing with a manual toothbrush on day 14 after surgery. Interleukin (IL)-1β and IL-8 levels were assessed before and 14 days post-operatively in gingival crevicular fluid. Patients were recalled on day 7, 14, 21 and 28 after surgery for clinical assessment. Pain was self-reported by a visual analogue scale. RESULTS Lower early wound healing scores, higher bacterial plaque reduction and milder inflammatory response were observed at the surgical sites in the test group on day 7, 14 and 28 when compared to the control group (P < 0.01). The faster wound healing process was modulated by a statistically significant decrease in IL-1β and IL-8 levels on day 14 in the sonic group. The intensity of pain was similar between groups. CONCLUSIONS The introduction of sonic toothbrush on the first post-operative day as an adjunct of daily CHX rinsing would seem to accelerate early wound healing.
Pgc suppresses the zygotically acting RNA decay pathway to protect germ plasm RNAs in the Drosophila embryo

ABSTRACT Specification of germ cells is pivotal to ensure continuation of animal species. In many animal embryos, germ cell specification depends on maternally supplied determinants in the germ plasm. Drosophila polar granule component (pgc) mRNA is a component of the germ plasm. pgc encodes a small protein that is transiently expressed in newly formed pole cells, the germline progenitors, where it globally represses mRNA transcription. pgc is also required for pole cell survival, but the mechanism linking transcriptional repression to pole cell survival remains elusive. We report that pole cells lacking pgc show premature loss of germ plasm mRNAs, including the germ cell survival factor nanos, and undergo apoptosis. We found that pgc– pole cells misexpress multiple miRNA genes. Reduction of miRNA pathway activity in pgc– embryos partially suppressed germ plasm mRNA degradation and pole cell death, suggesting that Pgc represses zygotic miRNA transcription in pole cells to protect germ plasm mRNAs. Interestingly, germ plasm mRNAs are protected from miRNA-mediated degradation in vertebrates, albeit by a different mechanism. Thus, independently evolved mechanisms are used to silence miRNAs during germ cell specification. Summary: The Drosophila Pgc protein ensures proper primordial germ cell development by repressing transcription of miRNA genes and contributing to the maintenance of germ plasm integrity during the establishment of the germ cell fate.
Are There Gender, Racial, or Religious Denominational Differences in Religiosity’s Effect on Alcohol Use and Binge Drinking Among Youth in the United States? A Propensity Score Weighting Approach

Abstract Background: Alcohol use/misuse is a prevalent health issue among youth and may lead to adverse consequences. Religiosity has been identified as a protective factor against alcohol use/misuse among youth. Identifying moderators in the religiosity-alcohol relationship has important implications for intervention development. Objective: This study aims to examine whether gender, race, or religious denomination moderate the religiosity-alcohol relationship. Method: This study overcame previous studies’ limitations by using longitudinal data from a nationally representative sample and robust analytical methods (N = 1969). This study used the propensity score weighting method to control for a large number of confounders. Propensity score weights were estimated using Generalized Boosted Models. Results: Findings indicate that gender and religious denomination might not moderate the religiosity-alcohol relationship, whereas racial difference was present. Among White youth, religiosity was found to have a buffering effect against alcohol use (average treatment effect [ATE] = –0.57, CI.95 = –0.83, –0.32) and binge drinking (ATE = 0.54, CI.95 = 0.38, 0.71). However, among non-White youth, religiosity was not found to have an effect on alcohol use (ATE = 0.08, CI.95 = –0.31, 0.47) or binge drinking (ATE = 1.07, CI.95 = 0.68, 1.64). Conclusions: Findings suggest that preventions/interventions of youth alcohol involvement that are religiously based may not need to adapt their efforts based on youth’s gender or religious denominations. More importantly, when addressing alcohol use/misuse issue among White American youth, religious and faith-based organizations, youth religious group leaders, and clergy should be included in the prevention/intervention efforts.
Differentially Private Consensus With an Event-Triggered Mechanism

This paper studies the differentially private consensus problem of multiagent networks by employing a distributed event-triggered mechanism such that not only agents can protect the privacy of their initial states from information disclosure, but the execution efficiency of the whole network can be improved. First, we propose a distributed event-triggered mechanism for a differentially private consensus algorithm such that frequent real-time communication and controller updates can be avoided. Second, we propose a distributed event-triggering condition that only depends on local information and local parameters, which can effectively avoid global information collection. Third, the convergence analysis of the mean-square average consensus is given to explain the sufficiency of the proposed event-triggered mechanism and event-triggering condition. Furthermore, we establish the statistic properties of the convergent accuracy that the expectation of the convergence point converges to the average value of all agents’ initial states exactly and the disturbance variance is bounded with an explicit expression. In addition, we further give the differential privacy analysis that each agent can flexibly select its own privacy level to prevent information disclosure. Finally, simulation results are given to illustrate the effectiveness of the proposed mechanism and the correctness of the theoretical results.
The Lonely Selfie King: Selfies and the Conspicuous Prosumption of Gender and Race

In this article, we provide a sociological analysis of selfies, interpreting them as a social tool that can be used in producing and consuming racial and gender identities. To do this, we share the results of a study we conducted that considered the attitudes and experiences associated with producing and consuming selfies among millennials in New York and Texas. 2 Though we interviewed a relatively small number of participants—40 in total—we feel that the trends uncovered in this study warrant scholarly exploration. Our analysis focuses on both the production and consumption of selfies and on personal experiences associated with taking selfies. Here, we follow the lead of Ritzer and Rey (2013), who argue that rather than existing as a dichotomy, production and consumption always co-exist; there is always a
Scalable Digital Neuromorphic Architecture for Large-Scale Biophysically Meaningful Neural Network With Multi-Compartment Neurons

Multicompartment emulation is an essential step to enhance the biological realism of neuromorphic systems and to further understand the computational power of neurons. In this paper, we present a hardware efficient, scalable, and real-time computing strategy for the implementation of large-scale biologically meaningful neural networks with one million multi-compartment neurons (CMNs). The hardware platform uses four Altera Stratix III field-programmable gate arrays, and both the cellular and the network levels are considered, which provides an efficient implementation of a large-scale spiking neural network with biophysically plausible dynamics. At the cellular level, a cost-efficient multi-CMN model is presented, which can reproduce the detailed neuronal dynamics with representative neuronal morphology. A set of efficient neuromorphic techniques for single-CMN implementation are presented with all the hardware cost of memory and multiplier resources removed and with hardware performance of computational speed enhanced by 56.59% in comparison with the classical digital implementation method. At the network level, a scalable network-on-chip (NoC) architecture is proposed with a novel routing algorithm to enhance the NoC performance including throughput and computational latency, leading to higher computational efficiency and capability in comparison with state-of-the-art projects. The experimental results demonstrate that the proposed work can provide an efficient model and architecture for large-scale biologically meaningful networks, while the hardware synthesis results demonstrate low area utilization and high computational speed that supports the scalability of the approach.
A Microscopic Model for Lane-Less Traffic

In this paper, a new model is introduced for traffic on broad roads, where the drivers do not follow lane-discipline. For both longitudinal and lateral motions, the driver reactions are assumed to be influenced by possibly a number of vehicles, obstacles, and unmodeled entities in visibility cones to the front and to the sides of each vehicle. The network of influences and the resultant interaction is modeled by “influence graphs. ” In congested traffic situations, it is assumed that the influence structure is time invariant and all drivers are forced to behave homogeneously. Then, the collection converges to a layered formation with fixed intervehicle distances. In sparse and heterogeneous traffic, the velocity and intervehicle separations in the set of modeled vehicles, though can oscillate continuously, are uniformly bounded. These model-based predictions are verified experimentally. Videos of typical traffic on a sample road in Mumbai city, India, are recorded. Detailed motion information of groups of cars is extracted through image processing techniques. The proposed model is initialized with the extracted data and the computed trajectories are compared with the actual ones calculated from the images. It is verified that the proposed model, in addition to macroscopic patterns, can also accurately predict complex maneuvers, such as overtaking, sideways movements and avoiding collisions with slower moving vehicles.
Design and Evaluation of Visualization Techniques of Off-Screen and Occluded Targets in Virtual Reality Environments

This research explores the design and evaluation of visualization techniques of targets that reside outside of users’ view and/or are occluded by other elements within a virtual reality environment (VE). We first compare four techniques (3DWedge, 3DArrow, 3DMinimap, and Radar) that use different types of visual elements to provide direction and distance information of targets. To give structure to their evaluation, we also develop a framework of four tasks (one for direction and three for distance) and their assessment criteria. The results of the first study show that 3DWedge is the best-performing and most usable technique. However, all techniques, including 3DWedge, have poor performance in dense scenarios with a large number of targets. To improve support in dense scenarios, a fifth technique, 3DWedge+, is developed by using 3DWedge as its foundation and including additional visual elements that are derived from the other three techniques which are shown to be useful. A second study is conducted to evaluate the performance of 3DWedge+ in relation to the other techniques. The results show that both 3DWedge and 3DWedge+ are significantly better in distinguishing user-to-target distance and that 3DWedge+ is particularly suitable for dense scenarios. Based on these results, we provide a set of recommendations for the design of visualization techniques of off-screen and occluded targets in 3D VE.
An updated review on pathophysiology and management of burning mouth syndrome with endocrinological, psychological and neuropathic perspectives

Burning mouth syndrome (BMS) is a chronic oro-facial pain disorder of unknown cause. It is more common in peri- and post-menopausal women, and sex hormone dysregulation is believed to be an important causative factor. Psychosocial events often trigger or exacerbate symptoms, and persons with BMS appear to be predisposed towards anxiety and depression. Atrophy of small nerve fibres in the tongue epithelium has been reported, and potential neuropathic mechanisms for BMS are now widely investigated. Historically, BMS was thought to comprise endocrinological, psychosocial and neuropathic components. Neuroprotective steroids and glial cell line-derived neurotrophic factor family ligands may have pivotal roles in the peripheral mechanisms associated with atrophy of small nerve fibres. Denervation of chorda tympani nerve fibres that innervate fungiform buds leads to alternative trigeminal innervation, which results in dysgeusia and burning pain when eating hot foods. With regard to the central mechanism of BMS, depletion of neuroprotective steroids alters the brain network-related mood and pain modulation. Peripheral mechanistic studies support the use of topical clonazepam and capsaicin for the management of BMS, and some evidence supports the use of cognitive behavioural therapy. Hormone replacement therapy may address the causes of BMS, although adverse effects prevent its use as a first-line treatment. Selective serotonin reuptake inhibitors (SSRIs) and serotonin and noradrenaline reuptake inhibitors (SNRIs) may have important benefits, and well-designed controlled studies are expected. Other treatment options to be investigated include brain stimulation and TSPO (translocator protein 18 kDa) ligands.
Learning Gentle Object Manipulation with Curiosity-Driven Deep Reinforcement Learning

Robots must know how to be gentle when they need to interact with fragile objects, or when the robot itself is prone to wear and tear. We propose an approach that enables deep reinforcement learning to train policies that are gentle, both during exploration and task execution. In a reward-based learning environment, a natural approach involves augmenting the (task) reward with a penalty for non-gentleness, which can be defined as excessive impact force. However, augmenting with only this penalty impairs learning: policies get stuck in a local optimum which avoids all contact with the environment. Prior research has shown that combining auxiliary tasks or intrinsic rewards can be beneficial for stabilizing and accelerating learning in sparse-reward domains, and indeed we find that introducing a surprise-based intrinsic reward does avoid the no-contact failure case. However, we show that a simple dynamics-based surprise is not as effective as penalty-based surprise. Penalty-based surprise, based on predicting forceful contacts, has a further benefit: it encourages exploration which is contact-rich yet gentle. We demonstrate the effectiveness of the approach using a complex, tendon-powered robot hand with tactile sensors. Videos are available at this http URL.