Miracle-dz/UniFAR-FTUs huggingface dataset:
Is there a relationship between the prevalence of autoimmune thyroid disease and diabetic kidney disease?

Abstract Autoimmune thyroid disease (AITD) is more common among diabetes mellitus (DM) patients and may impact its microvascular complications. The present study aimed to assess the relationship between AITD and the prevalence of diabetic kidney disease (DKD) in patients with diabetes mellitus type 1 (DM1). Anthropometric parameters, parameters of metabolic control of DM, thyreometabolic status, and the UACR were assessed. DKD was diagnosed if patients’ UACR level was ≥30 mg/g or eGFR level was <60 mL/min. This study involved 144 patients with DM1 aged 36.2 ± 11.7 years: 49 men and 95 women. Significant differences in creatinine, eGFR, and UACR levels were found in patients with DKD. fT3 concentration was significantly lower among DKD patients. A significantly higher probability of DKD was found in DM1 patients with lower fT3 levels. Patients with DM1 and AITD had significantly lower creatinine levels than the control group. However, the study did not show any significant relationship between AITD and the occurrence of DKD in patients with DM1. Significantly lower fT3 concentrations in DKD patients may be caused by metabolic disorders in the course of DKD and require further cohort studies in a larger population of patients with DM1 and AITD.
Potency boost of a Mycobacterium tuberculosis dihydrofolate reductase inhibitor by multienzyme F420H2-dependent reduction

Significance Bacterial metabolism can cause intrinsic drug resistance but can also convert inactive parent drugs into bioactive derivatives, as is the case for several antimycobacterial prodrugs. Here, we show that the intrabacterial metabolism of a Mtb dihydrofolate reductase (DHFR) inhibitor with moderate affinity for its target boosts its on-target activity by two orders of magnitude. This is a “prodrug-like” antimycobacterial that possesses baseline activity in the absence of intracellular bioactivation. By elucidating the metabolic enhancement mechanism, we have provided the basis for the rational optimization of a class of DHFR inhibitors and uncovered an antibacterial drug discovery concept. Triaza-coumarin (TA-C) is a Mycobacterium tuberculosis (Mtb) dihydrofolate reductase (DHFR) inhibitor with an IC50 (half maximal inhibitory concentration) of ∼1 µM against the enzyme. Despite this moderate target inhibition, TA-C shows exquisite antimycobacterial activity (MIC50, concentration inhibiting growth by 50% = 10 to 20 nM). Here, we investigated the mechanism underlying this potency disconnect. To confirm that TA-C targets DHFR and investigate its unusual potency pattern, we focused on resistance mechanisms. In Mtb, resistance to DHFR inhibitors is frequently associated with mutations in thymidylate synthase thyA, which sensitizes Mtb to DHFR inhibition, rather than in DHFR itself. We observed thyA mutations, consistent with TA-C interfering with the folate pathway. A second resistance mechanism involved biosynthesis of the redox coenzyme F420. Thus, we hypothesized that TA-C may be metabolized by Mtb F420–dependent oxidoreductases (FDORs). By chemically blocking the putative site of FDOR-mediated reduction in TA-C, we reproduced the F420-dependent resistance phenotype, suggesting that F420H2-dependent reduction is required for TA-C to exert its potent antibacterial activity. Indeed, chemically synthesized TA-C-Acid, the putative product of TA-C reduction, displayed a 100-fold lower IC50 against DHFR. Screening seven recombinant Mtb FDORs revealed that at least two of these enzymes reduce TA-C. This redundancy in activation explains why no mutations in the activating enzymes were identified in the resistance screen. Analysis of the reaction products confirmed that FDORs reduce TA-C at the predicted site, yielding TA-C-Acid. This work demonstrates that intrabacterial metabolism converts TA-C, a moderately active “prodrug,” into a 100-fold-more-potent DHFR inhibitor, thus explaining the disconnect between enzymatic and whole-cell activity.
Information-theoretic measures of superconductivity in a two-dimensional doped Mott insulator

Significance Quantum information can provide an overarching perspective on phases of matter in interacting quantum systems. We use tools of quantum information to characterize the entanglement-related properties of unconventional superconductivity in a doped Mott insulator—a phase of matter realized in cuprates and one that could soon be emulated with ultracold atoms. Using the canonical model of interacting quantum particles, we predict how key measures of correlations—thermodynamic entropy, local entropy, and total mutual information—detect the superconducting phase in a 2D doped Mott insulator. The local entropy reflects the source of condensation energy. In the superconducting phase, the thermodynamic entropy is suppressed, and the total mutual information is amplified. Our work provides a route to studying superconductivity with information-theoretic measures. A key open issue in condensed-matter physics is how quantum and classical correlations emerge in an unconventional superconductor from the underlying normal state. We study this problem in a doped Mott insulator with information-theory tools on the two-dimensional (2D) Hubbard model at finite temperature with cluster dynamical mean-field theory. We find that the local entropy detects the superconducting state and that the difference in the local entropy between the superconducting and normal states follows the same difference in the potential energy. We find that the thermodynamic entropy is suppressed in the superconducting state and monotonically decreases with decreasing doping. The maximum in entropy found in the normal state above the overdoped region of the superconducting dome is obliterated by superconductivity. The total mutual information, which quantifies quantum and classical correlations, is amplified in the superconducting state of the doped Mott insulator for all doping levels and shows a broad peak versus doping, as a result of competing quantum and classical effects.
Affect-Aware Learning for Social Robots

Robots designed to collaborate with human partners should be able to implicitly anticipate and adapt to their needs. To do so, robots need a framework supporting cognition and mutual understanding in social settings. We posit that even basic cognitive processes, such as learning, could benefit from considering the social and affective dimensions. In this direction, we propose a recently developed scenario, based on a competitive game, as a tool to steer the development of socially-aware competitive reinforcement learning (RL).
Hydrogen Production via Glycerol Steam Reforming over Ni/Al2O3: Influence of Nickel Precursors

This paper describes an investigation regarding the influence of Ni precursors on catalytic performances of Ni/Al2O3 catalysts in glycerol steam reforming. A series of Ni/Al2O3 is synthesized using four different precursors, nickel nitrate, nickel chloride, nickel acetate, and nickel acetylacetonate. Characterization results based on N2 adsorption–desorption, X-ray diffraction, H2 temperature-programmed reduction, H2 chemisorption, transmission electron microscopy, and thermogravimetric analysis show that reduction degrees of nickel, nickel dispersion, and particle sizes of Ni/Al2O3 catalysts are closely dependent on the anion size and nature of the nickel precursors. Ni/Al2O3 prepared by nickel acetate possesses the moderate Ni reduction degree, high Ni dispersion, and small nickel particle size, which possesses the highest H2 yield. Reaction parameters are also examined, and 550 °C and a steam-to-carbon ratio of 3 are optimized. Moreover, coke deposition, mainly graphite species, leads to the deactivati.
On the conditions for integrating deep learning into the study of visual politics

Traditional methods to study visual politics have been limited in geographical, media and temporal coverage. Recent advances in deep learning have the potential to dramatically extend the scope of the field especially with respect to making sense of the contemporary social and political developments in digital media. While some early adopters may be tempted to take the new computational tools at face value, others see their black-box character as cause for concern. This paper argues that the integration of deep learning into the study of visual politics must be approached still more critically and boldly. On the one hand, the complexity of visual political themes requires a more substantial human involvement if compared with other applications of deep neural networks. Therefore, a question is how the scientist and the network should best interact. On the other hand, it is important to acknowledge that a deep learning tool will never simply replace specific tasks inside a research process: its adoption has implications for the broader process from the delineation of the object of analysis, to data collection, to the interpretation and communication of results. We examine the conditions of integrating a deep learning tool for image classification into the large-scale study of visual politics in digital and social media along these two dimensions.
The Illusion of Control in Privacy Trade-Offs: Does Familiarity Play a Role?

Contemporary legislative and scientific trends stress the importance of control as an instrument to manage informational privacy. Still, privacy decision-making remains far from optimal as there is a discrepancy between privacy attitudes and privacy behaviours. To interpret this gap, this study builds upon the systematic biases found in behavioural economics theory, more specifically the illusion of control. This study examines the effects of the illusion of control through stimulus familiarity on privacy behaviour. More specifically, we compared the participants’ willingness to provide personal data between a very familiar web store and a web store unknown to them. The results from a sample of 171 students in the Netherlands indicate that, even though the level of perceived control and the amount of data disclosure are higher in the familiar condition, stimulus familiarity does not induce an illusion of control in privacy trade-offs. Moreover, this relationship is slightly weaker for sensitive disclosure. However, this study did find evidence of gender differences in sensitive disclosure: in this sample, women disclosed significantly less sensitive information than men, possibly due to risk-aversion.
Toward Benchmarking in Catalysis Science: Best Practices, Challenges, and Opportunities

Benchmarking is a community-based and (preferably) community-driven activity involving consensus-based decisions on how to make reproducible, fair, and relevant assessments. In catalysis science, important catalyst performance metrics include activity, selectivity, and the deactivation profile, which enable comparisons between new and standard catalysts. Benchmarking also requires careful documentation, archiving, and sharing of methods and measurements, to ensure that the full value of research data can be realized. Beyond these goals, benchmarking presents unique opportunities to advance and accelerate understanding of complex reaction systems by combining and comparing experimental information from multiple, in situ and operando techniques with theoretical insights derived from calculations characterizing model systems. This Perspective describes the origins and uses of benchmarking and its applications in computational catalysis, heterogeneous catalysis, molecular catalysis, and electrocatalysis. It a.
Accelerated weathering of PP based nanocomposites: Effect of the presence of maleic anhydryde grafted polypropylene

Polymer nanocomposites are currently a topic of great interest. The increasing importance they are gaining also in the standpoint of industrial applications, is giving concerns regarding their environmental stability and, in general, their behaviour in outdoor applications, under direct solar irradiation. Papers available in the literature have highlighted the dif- ferent influences of different nanosized fillers, in particular clay-based nanofillers; however, few data are available regard- ing other nanosized fillers. Furthermore, the research on polymer nanocomposites has clearly pointed out that the use of compatibilizers is required to improve the mechanical performance and the dispersion of polar fillers inside apolar polymer matrices, especially when complex mechanisms such as intercalation and exfoliation, typical of clay-filled nanocomposites, are involved. In this work, the photo-oxidation behaviour of polypropylene/clay and polypropylene/calcium carbonate nanocomposites containing different amounts of maleic anhydride grafted polypropylene (PPgMA) and subjected to accel- erated weathering, was investigated. The results showed significant differences between the two nanofillers and different degradation behaviours in presence of the compatibilizer. In particular, PPgMA modified the dispersion of the nanofillers but, on the other hand, higher amounts proved to lead to the formation of some heterogeneities. Furthermore, PPgMA proved to positively affect the photo-oxidation behaviour by decreasing the rate of formation of the degradation products.
CCCC: Corralling Cookies into Categories with CookieMonster

Browser cookies are ubiquitous in the web ecosystem today. Although these cookies were initially introduced to preserve user-specific state in browsers, they have now been used for numerous other purposes, including user profiling and tracking across multiple websites. This paper sets out to understand and quantify the different uses for cookies, and in particular, the extent to which targeting and advertising, performance analytics and other uses which only serve the website and not the user add to overall cookie volumes. We start with 31 million cookies collected in Cookiepedia, which is currently the most comprehensive database of cookies on the Web. Cookiepedia provides a useful four-part categorisation of cookies into strictly necessary, performance, functionality and targeting/advertising cookies, as suggested by the UK International Chamber of Commerce. Unfortunately, we found that, Cookiepedia data can categorise less than 22% of the cookies used by Alexa Top20K websites and less than 15% of the cookies set in the browsers of a set of real users. These results point to an acute problem with the coverage of current cookie categorisation techniques. Consequently, we developed CookieMonster, a novel machine learning-driven framework which can categorise a cookie into one of the aforementioned four categories with more than 94% F1 score and less than 1.5 ms latency. We demonstrate the utility of our framework by classifying cookies in the wild. Our investigation revealed that in Alexa Top20K websites necessary and functional cookies constitute only 13.05% and 9.52% of all cookies respectively. We also apply our framework to quantify the effectiveness of tracking countermeasures such as privacy legislation and ad blockers. Our results identify a way to significantly improve coverage of cookies classification today as well as identify new patterns in the usage of cookies in the wild.
An Analysis on the Feasibility of a Low-cost Fall Detection System: Feasibility study on threshold-based fall detection system that is realized using ADXL 345 tri-axial accelerometer which is considered as a low-cost sensor

There are various fall detection algorithms that have been developed as the solution to the increase on the risk of fall on elderly. A fall detection system which is low-cost is certainly favored, but that pose another question on whether the system is accurate enough by using low-cost components. This paper will discuss the analysis on the feasibility of a threshold-based fall detection system that was realized with ADXL 345 sensor, which is considered as a low-cost accelerometer sensor. A simulated fall study and simulated Activities of Daily Living (ADL) study was conducted so that the accuracy of the realized system can be known. The result of this experimental study shows that the realized system has a satisfactory accuracy comparable to other systems which use more advanced sensor(s), but further study which involves elderly as subjects is needed to give a more accurate insight about the accuracy of the system.
FollowUpAR: enabling follow-up effects in mobile AR applications

Existing smartphone-based Augmented Reality (AR) systems are able to render virtual effects on static anchors. However, today's solutions lack the ability to render follow-up effects attached to moving anchors since they fail to track the 6 degrees of freedom (6-DoF) poses of them. We find an opportunity to accomplish the task by leveraging sensors capable of generating sparse point clouds on smartphones and fusing them with vision-based technologies. However, realizing this vision is non-trivial due to challenges in modeling radar error distributions and fusing heterogeneous sensor data. This study proposes FollowUpAR, a framework that integrates vision and sparse measurements to track object 6-DoF pose on smartphones. We derive a physical-level theoretical radar error distribution model based on an in-depth understanding of its hardware-level working principles and design a novel factor graph competent in fusing heterogeneous data. By doing so, FollowUpAR enables mobile devices to track anchor's pose accurately. We implement FollowUpAR on commodity smartphones and validate its performance with 800,000 frames in a total duration of 15 hours. The results show that FollowUpAR achieves a remarkable rotation tracking accuracy of 2.3° with a translation accuracy of 2.9mm, outperforming most existing tracking systems and comparable to state-of-the-art learning-based solutions. FollowUpAR can be integrated into ARCore and enable smartphones to render follow-up AR effects to moving objects.
Improving Reactions to Rejection in Crowdsourcing Through Self-Reflection

In popular crowdsourcing marketplaces like Amazon Mechanical Turk, crowd workers complete tasks posted by requesters in return for monetary rewards. Task requesters are solely responsible for deciding whether to accept or reject submitted work. Rejecting work can directly affect the monetary reward of corresponding workers, and indirectly influence worker qualifications and their future work opportunities in the marketplace. Unexpected or unwarranted rejections therefore result in negative emotions and reactions among workers. Despite the high prevalence of rejections in crowdsourcing marketplaces, little research has explored ways to mitigate the negative emotional repercussions of rejections on crowd workers. Addressing this important research gap, we investigate whether introducing self-reflection at different stages after task execution can alleviate the emotional toll of rejection decisions on crowd workers. Our work is inspired by prior studies in psychology that have shown that self-reflection on negative personal experiences can positively affect one’s emotion. To this end, we carried out an experimental study investigating the impact of explicit self-reflection on the emotions of rejected crowd workers. Results show that allowing workers to self-reflect on their delivered work, especially before receiving a rejection, has a significantly positive impact on their self-reported emotions in terms of valence and dominance. Our findings reveal that introducing a self-reflection stage before workers receive acceptance or rejection decisions on submitted work, can help in positively influencing the emotions of a worker. These findings have important design implications towards fostering a healthier requester-worker relationship and contributing towards the sustainability of the crowdsourcing marketplace.
Pedagogy vs. Technology: Challenges in Developing Online Courses in Social Work Education

ABSTRACT Similar to all academic disciplines, the use of information and communication technology (ICT) in social work has grown exponentially in both scope and volume during recent decades. Until the COVID 19 crisis, main driving forces were the growing demand for higher education, constant evolving of innovative technologies, cost effectiveness, and accessibility that it offers to those who are not able to benefit from traditional face to face classes because of their geographic locations, responsibilities or disabilities. The Covid19 pandemic drove social work education to become almost exclusively and universally taught online. This required a quick conversion of tradition courses and development of new courses to be taught online. This article discusses and illustrates issues, challenges and strategies related to the interaction between the faculty and technology experts in the context of developing online courses and points to directions for future research.
Interactions of Silicon With Essential and Beneficial Elements in Plants

Silicon (Si) is not classified as an essential element for plants, but numerous studies have demonstrated its beneficial effects in a variety of species and environmental conditions, including low nutrient availability. Application of Si shows the potential to increase nutrient availability in the rhizosphere and root uptake through complex mechanisms, which still remain unclear. Silicon-mediated transcriptional regulation of element transporters for both root acquisition and tissue homeostasis has recently been suggested as an important strategy, varying in detail depending on plant species and nutritional status. Here, we summarize evidence of Si-mediated acquisition, uptake and translocation of nutrients: nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), sulfur (S), iron (Fe), zinc (Zn), manganese (Mn), copper (Cu), boron (B), chlorine (Cl), and nickel (Ni) under both deficiency and excess conditions. In addition, we discuss interactions of Si-with beneficial elements: aluminum (Al), sodium (Na), and selenium (Se). This review also highlights further research needed to improve understanding of Si-mediated acquisition and utilization of nutrients and vice versa nutrient status-mediated Si acquisition and transport, both processes which are of high importance for agronomic practice (e.g., reduced use of fertilizers and pesticides).