Miracle-dz/UniFAR-FTUs huggingface dataset:
cGMP Imaging in Brain Slices Reveals Brain Region-Specific Activity of NO-Sensitive Guanylyl Cyclases (NO-GCs) and NO-GC Stimulators

Impaired NO-cGMP signaling has been linked to several neurological disorders. NO-sensitive guanylyl cyclase (NO-GC), of which two isoforms—NO-GC1 and NO-GC2—are known, represents a promising drug target to increase cGMP in the brain. Drug-like small molecules have been discovered that work synergistically with NO to stimulate NO-GC activity. However, the effects of NO-GC stimulators in the brain are not well understood. In the present study, we used Förster/fluorescence resonance energy transfer (FRET)-based real-time imaging of cGMP in acute brain slices and primary neurons of cGMP sensor mice to comparatively assess the activity of two structurally different NO-GC stimulators, IWP-051 and BAY 41-2272, in the cerebellum, striatum and hippocampus. BAY 41-2272 potentiated an elevation of cGMP induced by the NO donor DEA/NO in all tested brain regions. Interestingly, IWP-051 potentiated DEA/NO-induced cGMP increases in the cerebellum and striatum, but not in the hippocampal CA1 area or primary hippocampal neurons. The brain-region-selective activity of IWP-051 suggested that it might act in a NO-GC isoform-selective manner. Results of mRNA in situ hybridization indicated that the cerebellum and striatum express NO-GC1 and NO-GC2, while the hippocampal CA1 area expresses mainly NO-GC2. IWP-051-potentiated DEA/NO-induced cGMP signals in the striatum of NO-GC2 knockout mice but was ineffective in the striatum of NO-GC1 knockout mice. These results indicate that IWP-051 preferentially stimulates NO-GC1 signaling in brain slices. Interestingly, no evidence for an isoform-specific effect of IWP-051 was observed when the cGMP-forming activity of whole brain homogenates was measured. This apparent discrepancy suggests that the method and conditions of cGMP measurement can influence results with NO-GC stimulators. Nevertheless, it is clear that NO-GC stimulators enhance cGMP signaling in the brain and should be further developed for the treatment of neurological diseases.
Privacy in Social Media: Identification, Mitigation and Applications

The increasing popularity of social media has attracted a huge number of people to participate in numerous activities on a daily basis. This results in tremendous amounts of rich user-generated data. This data provides opportunities for researchers and service providers to study and better understand users' behaviors and further improve the quality of the personalized services. Publishing user-generated data risks exposing individuals' privacy. Users privacy in social media is an emerging task and has attracted increasing attention in recent years. These works study privacy issues in social media from the two different points of views: identification of vulnerabilities, and mitigation of privacy risks. Recent research has shown the vulnerability of user-generated data against the two general types of attacks, identity disclosure and attribute disclosure. These privacy issues mandate social media data publishers to protect users' privacy by sanitizing user-generated data before publishing it. Consequently, various protection techniques have been proposed to anonymize user-generated social media data. There is a vast literature on privacy of users in social media from many perspectives. In this survey, we review the key achievements of user privacy in social media. In particular, we review and compare the state-of-the-art algorithms in terms of the privacy leakage attacks and anonymization algorithms. We overview the privacy risks from different aspects of social media and categorize the relevant works into five groups 1) graph data anonymization and de-anonymization, 2) author identification, 3) profile attribute disclosure, 4) user location and privacy, and 5) recommender systems and privacy issues. We also discuss open problems and future research directions for user privacy issues in social media.
Expression of programmed death ligand-1 and programmed death-1 in samples of invasive ductal carcinoma of the breast and its correlation with prognosis

The aim of the current study is to investigate programmed death-1 (PD-1) and programmed death ligand-1 (PD-L1) expressions and to analyze the relationship between the expression of PD-L1 and PD-1 proteins and the molecular type, clinicopathological factors, and prognosis of invasive ductal carcinoma. We enrolled 136 patients with invasive ductal carcinoma of the breast. The expression of PD-L1 in tumor cells and that of PD-1 on paratumor-infiltrating immune cells was detected by immunohistochemistry, and the data were analyzed using SPSS software. The positive expression rates of PD-L1 and PD-1 in triple-negative breast cancer (TNBC) were 47.8 and 43.5%, which were higher than those of other subtypes (P<0.05). The expression of PD-L1 in tumor cells was correlated with the expression of estrogen receptor, progesterone receptor, and Ki-67 (P<0.05). The expression of PD-1 in the tumor-infiltrating immune cells was correlated with the expression of estrogen receptor, progesterone receptor, and Ki-67 and the histological grade (P<0.05). The expression of PD-L1 in tumor cells was correlated with the expression of PD-1 in paratumor-infiltrating immune cells (P<0.001). The expression of PD-L1 in tumor cells was found to be an independent prognostic risk factor with the progression-free survival rate for breast invasive ductal carcinoma (P=0.003). These results indicate that PD-L1 and PD-1 were highly expressed in TNBC which suggests that patients with TNBC may benefit from targeted immune therapies to a greater degree than patients with other subtypes. PD-L1 expression is an independent risk factor for breast invasive ductal carcinoma and expression of PD-L1 is expected to be a prognostic factor for breast cancer.
Associations Between Peritoneal Glucose Exposure, Glucose Degradation Product Exposure, and Peritoneal Membrane Transport Characteristics in Peritoneal Dialysis Patients: Secondary Analysis of the balANZ Trial

Background: Glucose is the most commonly used osmotic medium in peritoneal dialysis (PD) solutions, and its use has been associated with both local and systemic adverse effects. Previous, single-center, observational cohort studies have reported conflicting findings regarding whether a relationship exists between peritoneal glucose exposure and peritoneal small solute transport rate. Methods: In this secondary analysis of the balANZ multicenter, multinational, randomized controlled trial of a neutral pH, ultra-low glucose degradation product (biocompatible) versus conventional PD solutions over a 2-year period, the relationship between time varying peritoneal glucose exposure and change in peritoneal solute transport rate, (measured as dialysate to plasma creatinine ratio at 4 hours [D:PCr4h]), was evaluated using multivariable, multilevel linear regression. Baseline peritoneal glucose exposure was also assessed as either a continuous or categorical variable. Results: The study included 165 patients (age 58.1 ± 14.2 years, 55% male, 33% diabetic). Peritoneal glucose exposure increased over time (coefficient 1.49, 95% confidence interval [CI] 1.07 – 1.92 and was not significantly associated with change in D:PCr4h (coefficient 0.00004, 95% CI -0.0001 – 0.0002, p = 0.68). Similar results were found when peritoneal glucose exposure was examined as a baseline continuous or categorical variable. A significant 2-way interaction was observed with PD solution type, whereby a progressive increase in D:PCr4h was seen in the patients receiving conventional PD solution, but not in those receiving biocompatible solution. Conclusions: Increases in peritoneal solute transport rate in PD patients over time were not associated with peritoneal glucose exposure, although a strong and positive association with PD solution glucose degradation product content was identified. Peritoneal glucose exposure may be a less important consideration than peritoneal glucose degradation product exposure with respect to peritoneal membrane function over time.
Paying Attention to Attention: Highlighting Influential Samples in Sequential Analysis

In (Yang et al. 2016), a hierarchical attention network (HAN) is created for document classification. The attention layer can be used to visualize text influential in classifying the document, thereby explaining the model's prediction. We successfully applied HAN to a sequential analysis task in the form of real-time monitoring of turn taking in conversations. However, we discovered instances where the attention weights were uniform at the stopping point (indicating all turns were equivalently influential to the classifier), preventing meaningful visualization for real-time human review or classifier improvement. We observed that attention weights for turns fluctuated as the conversations progressed, indicating turns had varying influence based on conversation state. Leveraging this observation, we develop a method to create more informative real-time visuals (as confirmed by human reviewers) in cases of uniform attention weights using the changes in turn importance as a conversation progresses over time.
Efficient and Reliable Cluster-Based Data Transmission for Vehicular Ad Hoc Networks

Vehicular ad hoc network (VANET) is an emerging technology for the future intelligent transportation systems (ITSs). The current researches are intensely focusing on the problems of routing protocol reliability and scalability across the urban VANETs. Vehicle clustering is testified to be a promising approach to improve routing reliability and scalability by grouping vehicles together to serve as the foundation for ITS applications. However, some prominent characteristics, like high mobility and uneven spatial distribution of vehicles, may affect the clustering performance. Therefore, how to establish and maintain stable clusters has become a challenging problem in VANETs. This paper proposes a link reliability-based clustering algorithm (LRCA) to provide efficient and reliable data transmission in VANETs. Before clustering, a novel link lifetime-based (LLT-based) neighbor sampling strategy is put forward to filter out the redundant unstable neighbors. The proposed clustering scheme mainly composes of three parts: cluster head selection, cluster formation, and cluster maintenance. Furthermore, we propose a routing protocol of LRCA to serve the infotainment applications in VANET. To make routing decisions appropriate, we nominate special nodes at intersections to evaluate the network condition by assigning weights to the road segments. Routes with the lowest weights are then selected as the optimal data forwarding paths. We evaluate clustering stability and routing performance of the proposed approach by comparing with some existing schemes. The extensive simulation results show that our approach outperforms in both cluster stability and data transmission.
MatchBench: An Evaluation of Feature Matchers

Feature matching is one of the most fundamental and active research areas in computer vision. A comprehensive evaluation of feature matchers is necessary, since it would advance both the development of this field and also high-level applications such as Structure-from-Motion or Visual SLAM. However, to the best of our knowledge, no previous work targets the evaluation of feature matchers while they only focus on evaluating feature detectors and descriptors. This leads to a critical absence in this field that there is no standard datasets and evaluation metrics to evaluate different feature matchers fairly. To this end, we present the first uniform feature matching benchmark to facilitate the evaluation of feature matchers. In the proposed benchmark, matchers are evaluated in different aspects, involving matching ability, correspondence sufficiency, and efficiency. Also, their performances are investigated in different scenes and in different matching types. Subsequently, we carry out an extensive evaluation of different state-of-the-art matchers on the benchmark and make in-depth analyses based on the reported results. This can be used to design practical matching systems in real applications and also advocates the potential future research directions in the field of feature matching.
Insights on Car Relocation Operations in One-Way Carsharing Systems

One-way carsharing system is a mobility service that offers short-time car rental service for its users in an urban area. This kind of service is attractive since users can pick up a car from a station and return it to any other station unlike round-trip carsharing systems where users have to return the car to the same station of departure. Nevertheless, uneven users’ demands for cars and for parking places throughout the day poses a challenge on the carsharing operator to rebalance the cars in stations to satisfy the maximum number of users’ requests. We refer to a rebalancing operation by car relocation. These operations increase the cost of operating the carsharing system. As a result, optimizing these operations is crucial in order to reduce the cost of the operator. In this paper, the problem is modeled as an Integer Linear Programming model (ILP). Then we present three different car relocation policies that we implement in a greedy search algorithm. The comparison between the three policies shows that car relocation operations that do not consider future demands do not effectively decrease rejected demands. On the contrary, they can generate more rejected demands. Results prove that solutions provided by our greedy algorithm when using a good policy, are competitive with CPLEX solutions. Furthermore, adding stochastic modification on the input data proves that the results of the two presented approaches are highly affected by the input demand even after adding threshold values constraints.
Venture Capital and Earnings Management in IPOs

We investigate earnings management (EM) in IPOs and the role of private equity/venture capital (PEVC) in hampering such practice. We show that when analyzing EM, PEVC and non-PEVC-sponsored firms should be treated as different samples: if one splits the sample, R-squared increases drastically for both subsamples. For PEVC-sponsored IPOs EM is marginal, mostly related to firms’ characteristics and little related to the phases of the IPOs. Differently, for non-PEVC-sponsored IPOs EM is significant, mostly related to the phases of the IPO and little related to firms’ characteristics. Finally, the reputation of the auditor is important only for PEVC-sponsored IPOs, suggesting that the choice of auditor is more meaningful for PEVC-sponsored firm, i.e, the choice of reputed auditor represents a compromise not to manage earnings.
From Gamification to Gameful Design and Gameful Experience in Learning

Abstract Learning is a goal driven social activity determined by motivational factors. To be able to efficiently gamify learning for improved student motivation and engagement, the educators have to understand the related aspects studied in games, motivational psychology and pedagogy. This will help them to identify the factors that drive and explain desired learning behaviors. This paper presents a survey of the main approaches employed in gamification and the emerging new directions in the context of the relevant motivational psychology and pedagogy. The focus is on the motivational factors that impact learning and understanding of behavior change. The purpose of the paper is two-fold: on one side, to provide analysis and guide to relevant works related to gamification along with outlining the emerging trends, and on the other, to provide foundation for evaluation and identification of the areas of possible improvements.
Homophone Identification and Merging for Code-switched Speech Recognition

Code-switching or mixing is the use of multiple languages in a single utterance or conversation. Borrowing occurs when a word from a foreign language becomes part of the vocabulary of a language. In multilingual societies, switching/mixing and borrowing are not always clearly distinguishable. Due to this, transcription of code-switched and borrowed words is often not standardized, and leads to the presence of homophones in the training data. In this work, we automatically identify and disambiguate homophones in code-switched data to improve recognition of code-switched speech. We use a WX-based common pronunciation scheme for both languages being mixed and unify the homophones during training, which results in a lower word error rate for systems built using this data. We also extend this framework to propose a metric for code-switched speech recognition that takes into account homophones in both languages while calculating WER, which can help provide a more accurate picture of errors the ASR system makes on code-switched speech.
Air pollution and emergency admissions for cardiorespiratory diseases in Lisbon (Portugal)

Daily records of hospital admissions due to cardiorespiratory diseases and levels of PM10, SO2, CO, NO, NO2, and O3 were collected from 1999-2004 to evaluate the relationship between air pollution and morbidity in Lisbon. Generalised additive Poisson regression models were adopted, controlling for temperature, humidity, and both short and long-term seasonality. Significant positive associations, lagged by 1 or 2 days, were found between markers of traffic-related pollution (CO and NO2) and cardiocirculatory diseases in all age groups. Increased childhood emergency admissions for respiratory illness were significantly correlated with the 1-day lagged SO2 levels coming from industrial activities.
Team Interactions and Health IT Use during Hospital Multidisciplinary Rounds

Multidisciplinary rounds (MDRs) are an important form of team-based process in health care. Team members coordinate patient care and make decisions on care plans jointly. Health IT used during MDRs should be designed to facilitate the team interactions given the specific context or work system where MDRs occur. This study examines and compares team interactions and health IT use in three hospital services (contexts): hospitalist, cardiology and critical care. There were few differences in team interactions and health IT use across services. However, health IT use was different for different rounding tasks and team members. Therefore, health IT should be designed to support team interactions and users’ needs during MDRs in different contexts.
Modeling Corporate Bond Default Risk: A MultipleTime Series Approach

t A multiple time series approach is used to forecast the short-term u.s. corporate bond default level. These time series have two auxiliary economic variables: U.S. price inflation and U.S. GNP growth rate. Actual U.S. data from the turn of the century to the present are used to estimate the parameters of multivariate time series model. Diagnostic checks are performed to examine adequacy of the model. The model's forecast for the aggregate U.S. bond default level in 2000-2001 are 0.42% and 0.56%, respectively, while the forecast for the speculative-grade default rate in 2000 is 3.6%, which is more pessimistic than some other forecasts available in the market.
The Enriching Interplay between Openness and Interest: A Theoretical Elaboration of the OFCI Model and a First Empirical Test

The Openness-Fluid-Crystallized-Intelligence (OFCI) model posits long-term relations between Openness and cognitive abilities and has been successfully tested with longitudinal data. However, research on the developmental interplay between cognitive abilities and personality exists only sparsely. The current paper focuses on a theoretical development of the OFCI model which suggests micro-level mechanisms underlying the long-term development. Specifically, within-situation relations between Openness, interests, situational perception, cognitive abilities, and emotions are proposed to explain longitudinal relations between Openness and cognitive abilities. Using experience sampling, selected parts of this elaboration were empirically scrutinized in a first test of the proposed ideas. Openness and specific interest both varied substantially across situations and covaried systematically. In interaction with an indicator of fluid intelligence, this covariation was related to an indicator of crystallized intelligence. The paper contributes to theorizing the intertwined development of personality and cognitive abilities, and highlights the importance of within-situation research for explaining long-term development.
Lagrange–Galerkin methods for the incompressible Navier-Stokes equations: a review

Abstract We review in this paper the development of Lagrange-Galerkin (LG) methods to integrate the incompressible Navier-Stokes equations (NSEs) for engineering applications. These methods were introduced in the computational fluid dynamics community in the early eighties of the past century, and at that time they were considered good methods for both their theoretical stability properties and the way of dealing with the nonlinear terms of the equations; however, the numerical experience gained with the application of LG methods to different problems has identified drawbacks of them, such as the calculation of specific integrals that arise in their formulation and the calculation of the ow trajectories, which somehow have hampered the applicability of LG methods. In this paper, we focus on these issues and summarize the convergence results of LG methods; furthermore, we shall briefly introduce a new stabilized LG method suitable for high Reynolds numbers.
Experimental and numerical study of wall layer development in a tribocharged fluidized bed

The effects of triboelectricity in a small-scale fluidized bed of polyethylene particles were investigated by imaging the particle layer in the vicinity of the column wall and by measuring the pressure drop across the bed. The average charge on the particles was altered by changing the relative humidity of the gas. A triboelectric charging model coupled with a computational fluid dynamics–discrete element method (CFD-DEM) model was utilized to simulate gas–particle flow in the bed. The electrostatic forces were evaluated based on a particle–particle particle–mesh method, accounting for the surface charge on the insulating walls. It was found that simulations with fixed and uniform charge distribution among the particles capture remarkably well both the agglomeration of the particles on the wall and the associated decrease in the pressure drop across the bed. With a dynamic tribocharging model, the charging rate had to be accelerated to render the computations affordable. Such simulations with an artificial acceleration significantly over-predict charge segregation and the wall becomes rapidly sheeted with a single layer of strongly charged particles.