Miracle-dz/UniFAR-FTUs huggingface dataset:
Serial dependence in numerosity perception

Our conscious experience of the external world is remarkably stable and seamless, despite the intrinsically discontinuous and noisy nature of sensory information. Serial dependencies in visual perception—reflecting attractive biases making a current stimulus to appear more similar to previous ones—have been recently hypothesized to be involved in perceptual continuity. However, while these effects have been observed across a variety of visual features and at the neural level, several aspects of serial dependence and how it generalizes across visual dimensions is still unknown. Here we explore the behavioral signature of serial dependence in numerosity perception by assessing how the perceived numerosity of dot-array stimuli is biased by a task-irrelevant “inducer” stimulus presented before task-relevant stimuli. First, although prior work suggests that numerosity perception starts in the subcortex, the current study rules out a possible involvement of subcortical processing in serial dependence, confirming that the effect likely starts in the visual cortex. Second, we show that the effect is coarsely spatially localized to the position of the inducer stimulus. Third, we demonstrate that the effect is present even with a stimulus presentation procedure minimizing the involvement of post-perceptual processes, but only when participants actively pay attention to the inducer stimulus. Overall, these results provide a comprehensive characterization of serial dependencies in numerosity perception, demonstrating that attractive biases occur by means of spatially localized attentional modulations of early sensory activity.
Prevalence and resource utilization for vocal fold paralysis/paresis after esophagectomy

Vocal fold paralysis/paresis (VFP) is an uncommon but serious complication following esophagectomy. The objectives of this study were to: 1) identify the prevalence of VFP and associated complications after esophagectomy in the United States, and 2) determine the utilization and otolaryngology‐head and neck surgery/speech‐language pathology (OHNS/SLP) and predictors of such utilization in the management of these patients.
Inhibition of glial glutamate transporter GLT1 in the nucleus of the solitary tract attenuates baroreflex control of sympathetic nerve activity and heart rate

The astrocytic glutamate transporter (GLT1) plays an important role in the maintenance of extracellular glutamate concentration below neurotoxic levels in brain. However, the functional role of GLT1 within the nucleus of the solitary tract (NTS) in the regulation of cardiovascular function remains unclear. We examined the effect of inhibiting GLT1 in the subpostremal NTS on mean arterial pressure (MAP), renal sympathetic nerve activity (RSNA) and heart rate (HR) in anesthetized, artificially ventilated rats. It was found that dihydrokainate (DHK; inhibitor of GLT1, 5 mmol/L, 100 nL) injections into the NTS (n = 6) decreased MAP (50 ± 10 mmHg, mean ± SD), RSNA (89 ± 14%) and HR (37 ± 6 bpm). Pretreatment with kynurenate (KYN; glutamate receptor antagonist, 5 mmol/L, 30 μL) topically applied to the dorsal surface of the brainstem (n = 4) attenuated the responses to NTS injections of DHK (P < 0.01). The effect of DHK on arterial baroreflex function was examined using i.v. infusions of phenylephrine and nitroprusside. DHK reduced baroreflex response range (maximum−minimum) of RSNA by 91 ± 2% and HR by 83 ± 5% (n = 6, P < 0.001). These results indicate that inhibition of GLT1 within the NTS decreases MAP, RSNA, and HR by the activation of ionotropic glutamate receptors. As a result, baroreflex control of RSNA and HR was dramatically attenuated. The astrocytic glutamate transporter in the NTS plays an important role in the maintenance and regulation of cardiovascular function.
Linear and Non-Linear Classifiers for Clinical Risk Factor Analysis of Cancer Patients

Cancer is a common disease condition, which is growing rapidly in the modern society. Different features such as tumor size, age, number of genomic alterations and non-synonymous mutations etc. have been considered in diagnosing and treating cancer patients. Linear and non-linear models can be utilized in analyzing the risk factors of cancer patients. Regression analysis is one particular machine learning technique, which could be perfectly utilized to identify and characterize relationships between multiple risk factors of cancer. As the insights of relationships among the clinical risk factors of patients can be useful in diagnosis and treatments of cancer, our main objective is to identify the relationships among their clinical risk factors using simple and multiple regression analysis as the machine learning techniques and find the appropriateness of the results with the previous experimentally proved results. A significant (p-value >0.05) relationship could be observed among the overall survival months and the age of merged cohort of Lower Grade Glioma and Glioblastoma brain tumor patients. Further, the relationship between the percent aneuploidy and the age of these brain cancer patients is observed as significant. Moreover, a significant relationship can be seen between the overall survival months and the age of Acute Myeloid Leukemia patients. Further, we analyzed the brain cancer dataset using regression trees as it can provide a solution model using overall survival months and percent aneuploidy over age and found a non-linear solution.
Transcriptional fates of human-specific segmental duplications in brain

Despite the importance of duplicate genes for evolutionary adaptation, accurate gene annotation is often incomplete, incorrect, or lacking in regions of segmental duplication. We developed an approach combining long-read sequencing and hybridization capture to yield full-length transcript information and confidently distinguish between nearly identical genes/paralogs. We used biotinylated probes to enrich for full-length cDNA from duplicated regions, which were then amplified, size-fractionated, and sequenced using single-molecule, long-read sequencing technology, permitting us to distinguish between highly identical genes by virtue of multiple paralogous sequence variants. We examined 19 gene families as expressed in developing and adult human brain, selected for their high sequence identity (average >99%) and overlap with human-specific segmental duplications (SDs). We characterized the transcriptional differences between related paralogs to better understand the birth–death process of duplicate genes and particularly how the process leads to gene innovation. In 48% of the cases, we find that the expressed duplicates have changed substantially from their ancestral models due to novel sites of transcription initiation, splicing, and polyadenylation, as well as fusion transcripts that connect duplication-derived exons with neighboring genes. We detect unannotated open reading frames in genes currently annotated as pseudogenes, while relegating other duplicates to nonfunctional status. Our method significantly improves gene annotation, specifically defining full-length transcripts, isoforms, and open reading frames for new genes in highly identical SDs. The approach will be more broadly applicable to genes in structurally complex regions of other genomes where the duplication process creates novel genes important for adaptive traits.
Training-Free Human Vitality Monitoring Using Commodity Wi-Fi Devices

Device-free sensing using ubiquitous Wi-Fi signals has recently attracted lots of attention. Among the sensed information, two important basic contexts are (i) whether a target is still or not and (ii) where the target is located. Continuous monitoring of these contexts provides us with rich datasets to obtain important high-level semantics of the target such as living habits, physical conditions and emotions. However, even to obtain these two basic contexts, offline training and calibration are needed in traditional methods, limiting the real-life adoption of the proposed sensing systems. In this paper, using the commodity Wi-Fi infrastructure, we propose a training-free human vitality sensing platform, WiVit. It could capture these two contexts together with the target's movements speed information in real-time without any human effort in offline training or calibration. Based on our extensive experiments in three typical indoor environments, the precision of activity detection is higher than 98% and the area detection accuracy is close to 100%. Moreover, we implement a short-term activity recognition system on our platform to recognize 4 types of actions, and we can reach an average accuracy of 94.2%. We also take a feasibility study of monitoring long-term activities of daily living to show our platform's potential applications in practice.
Production of Low-Potassium Content Melon Through Hydroponic Nutrient Management Using Perlite Substrate

Chronic kidney disease patients are restricted to foods with high potassium content but our daily diets including melon are rich in potassium. Therefore, we investigated the production of low-potassium melon through hydroponic nutrient management in soilless culture using perlite substrate during autumn season of 2012, 2014 and spring season of 2016. In the first study, melon plants were supplied with 50% standard ‘Enshi’ nutrient solution until first 2 weeks of culture. In 3rd and 4th week, amount of applied potassium was 50, 75, 100, and 125% of required potassium nitrate for each plant per week (based on our previous study). It was found that, melon plants grown with 50% of its required potassium nitrate produced fruits with about 53% low-potassium compared to control. In the following study, four cultivars viz. Panna, Miyabi shunjuukei, Miyabi akifuyu412, and Miyabi soushun banshun309 were evaluated for their relative suitability of low-potassium melon production. Results showed insignificant difference in fruit potassium content among the cultivars used. Source of potassium fertilizer as potassium nitrate and potassium sulfate and their restriction (from 1 or 2 weeks after anthesis) were also studied. There were no influences on fruit potassium content and yield due to sources of potassium fertilizer and restriction timings. In our previous studies, it was evident that potassium can be translocated from leaves to fruits at maturity when it was supplied nutrient without potassium. Thus, we also studied total number of leaves per plant (23, 24, 25, 26, and 27 leaves per plant). It was evident that fruit potassium, yield, and quality were not influenced significantly due to differences in number of leaves per plant. These studies showed that restriction of potassium nitrate in the culture solution from anthesis to harvest could produce melon fruits with low-potassium (>20%) content compared to potassium content of greenhouse grown melon (340 mg/100 g FW). Quality testing and clinical validation of low-potassium melon also showed positive responses compared to greenhouse grown melon.
Development and preliminary evaluation of a new screening instrument for atypical odontalgia and persistent dentoalveolar pain disorder

AIM To develop and preliminarily evaluate a new screening instrument for atypical odontalgia (AO) or persistent dentoalveolar pain disorder (PDAP). To evaluate the instrument's performance in detecting AO/PDAP amongst a heterogeneous group of orofacial pain conditions and pain-free controls and empirically compare its performance with an established neuropathic screening instrument (S-LANSS), which is the best available standard. METHODS The study design was cross-sectional; subjects recruited included a convenience sample of pain-free controls (n = 21) and four groups of orofacial pain conditions: AO/PDAP (n = 22); trigeminal neuralgia (n = 21); temporomandibular disorder (n = 41); and acute dental pain (n = 41). The instrument's internal reliability and factor structure were examined alongside its sensitivity and specificity and ROC-determined threshold score. RESULTS The 9 AO/PDAP-specific items were found to moderately correlate with the S-LANSS (r = 0.58; P < 0.01). The 14-items of the full instrument were examined using exploratory factor analysis and reduced to ten items in a two-factor structure that explained 96% of the variance. This 10-item final instrument had a ROC area of 0.77 (95% CI: 0.67; 0.88), sensitivity of 77% (95% CI: 55; 92%), and specificity of 69% (95% CI: 60; 77%) with an intentionally higher false-positive rate than false-negative rate. In contrast, the S-LANSS exhibited sensitivity of 32% (95% CI: 14;55%) and specificity of 78% (95% CI: 70;85%) with less optimal false-positive versus false-negative rates. CONCLUSION This preliminary study confirms the new screening instrument for AO/PDAP merits progression to field testing.
Multi-dimensional player skill progression modelling for procedural content generation

Procedural Content Generation (PCG), i.e. how game content can be created algorithmically, is an increasingly important area and currently one of the most active topics within the software games industry and game research. One of the crucial aspects of PCG is the capacity to maintain the player engaged and in flow. In this work, we explore how player skill progression could be used by PCG to create more appropriate challenges for each player and propose a model for content adaptation that takes this concept as its core feature. Our approach introduces the player as an active element in the adaptation process and assumes both the player and the game should have an equal and active role in this process. Our adaptation explores how modelling the evolution of multiple dimensions of a same challenge while the game is played helps creating a better game experience for the player. To evaluate our approach, we present a novel validation process embedded in the game itself, with the purpose of providing a more direct and seamless way to analyse player preference. The results of the evaluation of our approach in the context of an endless running side-scrolling platformer game revealed that players have consistent and specific preferences regarding how difficulty should evolve over the course of a game, which should be taken into account when designing an engaging game progression.
Multi-stage filtering for single rainy image enhancement

Rain image enhancement is important for outdoor computer vision applications. In this study, the authors propose a multi-stage filtering method for single rainy image enhancement. It is based on their new rainy image model, and consists of two main operations: rain streaks removal and rain fog removal. For rain streaks removal, based on one key observation that the low-pass version of a rainy image and that of a non-rainy image of the same scene are almost the same after appropriate low-pass filtering, they remove rain streaks from rainy images by decomposing an input rainy image (or a rainy component image) into the low-frequency (LF) part and the high-frequency (HF) part via an LF smooth filter, i.e. the traditional Gaussian filter with a simple subtraction operation in multiple different stages. After rain streaks removal, dark channel prior-based method was employed for rain fog removal. Experimental results show that the proposed algorithm generated comparable outputs with most of the state-of-the-art algorithms with low computation cost.
Cerebrospinal Fluid Inflammatory Cytokine Aberrations in Alzheimer's Disease, Parkinson's Disease and Amyotrophic Lateral Sclerosis: A Systematic Review and Meta-Analysis

It has been suggested that cytokine-mediated inflammation plays a key role for the onset and/or development of neurodegenerative diseases including Alzheimer's disease (AD), Parkinson's disease (PD) and Amyotrophic lateral sclerosis (ALS). However, clinical studies have yielded inconsistent results for the aberrant cytokine levels in circulation of patients with AD, PD, and ALS. Previous studies have used meta-analysis to address the inconsistent data for blood cytokine levels in the patients with AD, PD, and ALS. Here, we performed a systemic review of cerebrospinal fluid inflammatory cytokine data in patients with AD, PD and ALS, and sought to quantitatively summarize the CSF inflammatory cytokine data with a meta-analytical technique. The systematic search from Pubmed and Web of Science identified 71 articles with 2629 patients and 2049 controls for the meta-analysis. Random-effects meta-analysis demonstrated that CSF TGF-β, MCP-1, and YKL-40 levels were significantly elevated in AD patients when compared with controls. In addition, patients with PD had heightened levels of TGF-β1, IL-6, and IL-1β in CSF. Furthermore, G-CSF, IL-2, IL-15, IL-17, MCP-1, MIP-1α, TNF-α, and VEGF levels were significantly increased in patients with ALS as compared with controls. Taken together, these results not only strengthen the clinical evidence that neurodegenerative diseases are accompanied by the increased inflammatory response, but also reveal the unique inflammatory response profile in the central nervous system of patients with AD, PD and ALS. Given the robust associations between some cytokines and neurodegenerative diseases found in this meta-analysis, CSF inflammatory cytokines may be used as biomarkers for these diseases in the future.
UPEQ: ubisoft perceived experience questionnaire: a self-determination evaluation tool for video games

In order to appeal to a growing market, game developers are offering a wide variety of activities. It is becoming necessary to understand which psychological need each activity caters for. The purpose of this paper is to demonstrate the development and evaluation of an instrument to assess which basic psychological needs are satisfied by different video games. This work is part of a growing effort in HCI to develop surveys able to capture subtle nuances of the player experience. This model, UPEQ, was developed by transforming a self-determination theory questionnaire into a video game specific survey. UPEQ consists of three subscale of Autonomy, Competence and Relatedness, which, through two studies focusing on development and validation of the model showed significant correlations with other self-reported measures of sense of transportation to the game as well as enjoyment of and engagement with the game. Regression with in-game behavior of players tracked by game engine also confirmed that each subscale of UPEQ independently predicts playtime, money spent on the game and playing as a group.
Fostering creativity in the mixed-initiative evolutionary dungeon designer

Mixed-initiative systems highlight the collaboration between humans and computers in fostering the generation of more interesting content in game design. In light of the ever-increasing cost of game development, providing mixed-initiative tools can not only significantly reduce the cost but also encourage more creativity amongst game designers. The Evolutionary Dungeon Designer (EDD) [3] is a mixed-initiative tool with a focus on using evolutionary computation to procedurally generate content that adhere to game design patterns. As part of an ongoing project, feedback from a user study on EDD's capabilities as a mixed-initiative design tool pointed out the need for improvement on the tool's functionalities [4]. In this paper we present a review of the principles of the mixed-initiative model, as well as the existing approaches that implement it. The outcome of this analysis allows us to address the appointed needs for improvement by shaping a new version of EDD that we describe here. Finally, we also present the results from a user study carried out with professional game developers, in order to assess EDD's new functionalities. Results show an overall positive evaluation of the tool's intuitiveness and capabilities for empowering game developers' creative skills during the design process of dungeons for adventure games. They also allow us to identify upcoming challenges pattern-based mixed-initiative tools could benefit from.
Risk-driven security metrics development for software-defined networking

Introduction of SDN (Software-Defined Networking) into the network management turns the formerly quite rigid networks to programmatically reconfigurable, dynamic and high-performing entities, which are managed remotely. At the same time, introduction of the new interfaces evidently widens the attack surface, and new kind of attack vectors are introduced threatening the QoS even critically. Thus, there is need for a security architecture, drawing from the SDN management and monitoring capabilities, and eventually covering the threats posed by the SDN evolution. For efficient security-architecture implementation, we analyze the security risks of SDN and based on that propose heuristic security objectives. Further, we decompose the objectives for effective security control implementation and security metrics definition to support informed security decision-making and continuous security improvement.
Validating MAS analysis models with the ASEME methodology

When designing agent-oriented software, engineers should consider performance-related non-functional requirements. To this end, performance engineering practices provide a useful toolbox. In particular, simulation of the system's processes appears eminently suitable. Agent-oriented software engineering methodologies are not directly linked to process simulation features. This paper extends an AOSE methodology for transforming agent roles models to process models, and for streamlining the transformation process towards simulation. Our method allows process model generation, aiming to support the process simulation, and was integrated into a model-driven engineering methodology. We used an established process modelling notation (BPMN) as the target language for the process model, and we are able to deliver a ready-to-simulate model. Through simulation, an analyst can validate specific system requirements and test scenarios of how the system scales beyond the current requirements. Furthermore, because of process models familiarity within the business domain, engineers, managers and stakeholders can seamlessly communicate system designs.
Resilience attenuates the association between neurocognitive functioning and everyday functioning in individuals aging with HIV in the Deep South

Adults aging with HIV are at risk for poorer neurocognitive and daily functioning. Identifying factors to protect such outcomes is a significant research priority. The aim of this study was to explore the role of resilience in cognitive and everyday functioning in a largely African American and low socioeconomic status sample of adults and older adults with HIV in the Deep South.
A Survey on Deep Learning

The field of machine learning is witnessing its golden era as deep learning slowly becomes the leader in this domain. Deep learning uses multiple layers to represent the abstractions of data to build computational models. Some key enabler deep learning algorithms such as generative adversarial networks, convolutional neural networks, and model transfers have completely changed our perception of information processing. However, there exists an aperture of understanding behind this tremendously fast-paced domain, because it was never previously represented from a multiscope perspective. The lack of core understanding renders these powerful methods as black-box machines that inhibit development at a fundamental level. Moreover, deep learning has repeatedly been perceived as a silver bullet to all stumbling blocks in machine learning, which is far from the truth. This article presents a comprehensive review of historical and recent state-of-the-art approaches in visual, audio, and text processing; social network analysis; and natural language processing, followed by the in-depth analysis on pivoting and groundbreaking advances in deep learning applications. It was also undertaken to review the issues faced in deep learning such as unsupervised learning, black-box models, and online learning and to illustrate how these challenges can be transformed into prolific future research avenues.