Miracle-dz/UniFAR-FTUs huggingface dataset:
Real-world evidence for the long-term effect of allergen immunotherapy: current status on database-derived studies

Background: Randomized controlled trials (RCTs) are the gold-standard for benefit-risk assessments during drug approval processes. Real-word data (RWD) and the resulting real-world evidence (RWE) are becoming increasingly important for assessing the effectiveness of drug products after marketing authorization showing how RCT results are transferred into real life care. The effectiveness of allergen immunotherapy (AIT) has been assessed in several RWE studies based on large prescription databases. Methods: We performed a literature search for retrospective cohort assessments of prescription databases in Europe to provide an overview on the methodology, long-term effectiveness outcomes and adherence to AIT. Results: 13 respective publications were selected. AIT was more effective in reducing the progression of allergic rhinitis (AR) compared to a non-AIT control group receiving only symptomatic treatment for AR for up to 6 years. The development and progression of asthma was hampered for most endpoints in patients treated with most preparations compared to the non-AIT group, receiving only anti-asthmatic medication. The results for “time to onset” of asthma were inconsistent. Adherence to AIT decreased during the recommended 3-years treatment period, however in most studies higher adherence to subcutaneous than to sublingual AIT was shown. Conclusion: The analysis of long-term effectiveness outcomes of the RWE studies based on prescription databases confirms the long-term efficacy of AIT demonstrated in RCTs. Progression of rhinitis and asthma symptoms as well as delayed onset of asthma triggered by different allergens, real life adherence to the treatment shows differences in particular application routes. Real-world evidence for the long-term effect of allergen immunotherapy: current status on database-derived studies Short title: Narrative Review of Real-World Evidence in AIT Christian Vogelberg (ORCID ID 0000-0002-4881-8368)1, Ludger Klimek (ORCID ID 0000-0002-2455-0192) 2, Bernd Brüggenjürgen (ORCID ID 0000-0002-8866-0809)3, Marek Jutel (ORCID ID 0000-0003-1555-9379) 4,5 Author affiliations 1Department of Pediatric Pneumology and Allergology, University Hospital Carl Gustav Carus, Technical University Dresden, Dresden, Germany 2Center for Rhinology and Allergology, Wiesbaden, Germany
Integration of Transcriptome and MicroRNA Profile Analysis of iMSCs Defines Their Rejuvenated State and Conveys Them into a Novel Resource for Cell Therapy in Osteoarthritis

Although MSCs grant pronounced potential for cell therapies, several factors, such as their heterogeneity restrict their use. To overcome these limitations, iMSCs (MSCs derived from induced pluripotent stem cells (iPSCs) have attracted attention. Here, we analyzed the transcriptome of MSCs, iPSCs and iMSCs derived from healthy individuals and osteoarthritis (OA) patients and explored miRNA-mRNA interactions during these transitions. We performed RNA-seq and gene expression comparisons and Protein-Protein-Interaction analysis followed by GO enrichment and KEGG pathway analyses. MicroRNAs’ (miRNA) expression profile using miRarrays and differentially expressed miRNA’s impact on regulating iMSCs gene expression was also explored. Our analyses revealed that iMSCs derivation from iPSCs favors the expression of genes conferring high proliferation, differentiation, and migration properties, all of which contribute to a rejuvenated state of iMSCs compared to primary MSCs. Additionally, our exploration of the involvement of miRNAs in this rejuvenated iMSCs transcriptome concluded in twenty-six miRNAs that, as our analysis showed, are implicated in pluripotency. Notably, the identified here interactions between hsa-let7b/i, hsa-miR-221/222-3p, hsa-miR-302c, hsa-miR-181a, hsa-miR-331 with target genes HMGA2, IGF2BP3, STARD4, and APOL6 could prove to be the necessary tools that will convey iMSCs into the ideal mean for cell therapy in osteoarthritis.
Modern pillories: Overdue debts of the poor

We model the consequences of the soft budget constraint in the context of retail borrowers. While János Kornai formulated the term of “soft budget constraint” mainly for organizations (firms, banks, municipalities, NGOs, etc.), we show that it can be applied to individual borrowers as well. We derive the feasibility conditions for private and public debt relief programs in a utilitarian framework and find that lenders have no interest in offering payment reductions if non-performing borrowers are few, have small debts, and are difficult to reach – precisely the characteristics of the poor. In this situation, poor debtors serve better as deterrents, similarly if we put them into a pillory. We calibrate the model parameters to survey data on poor households struggling with overdue debts in small villages in a disadvantaged rural region in Hungary. We find that in normal economic circumstances, private debt relief programs are not feasible. State intervention can be justified by positive externalities and moral considerations.
Scalp EEG-based Classification of Disorder of Consciousness States using Machine Learning Techniques

Disorders of consciousness (DOC) described by impaired wakefulness and awareness, can be categorized into Coma, Unresponsive Wakefulness Syndrome (UWS), and Minimally Conscious State (MCS). Resting-state EEG-based differentiation of these classes acts as a helping hand or even more to the conventional behavioral assessment methods in the diagnosis and prognosis of DOC patients. In this paper, multi-class classification of DOC patients using different machine learning models was performed and the results were analyzed using features like sample entropy, permutation entropy, and absolute and relative power extracted from resting state EEG data. The one-way ANOVA method determined the discriminative ability of the features with a post hoc Least Significant Difference (LSD) test. All four features showed significant differences (p < 0.05) in delta, alpha, and beta bands between the groups. The feature significance was also measured across the different brain regions as well. The classification results showed that the Random Forest classifier best classified the group with an accuracy of 78% and a precision of 88%.
Using Hobbies as Proxy for Gamification Player Types

Many gamification approaches use a one size fits all approach. Different gamification player types and associated preferences have been proposed, such as the gamification player type Hexad. According to the Hexad, people prefer different gameful activities, such as exploration, social activities, or competition. Similarly in hobbies, different things motivate and interest different people, such as some finding board games interesting, while others like hiking and going on camping trips. We propose that a person’s hobbies and interests provide an insight into what type of person they are, indicating what they might like, and could have a connection to gameful system preferences. In this paper, we explore the relationship between free-time activities and gamification player types, to enable earlier and faster detection of the player types and their preferences. The research was conducted by building a survey to measure the free-time activities of people and also measure the Hexad player type as defined by Marczewski and Tondello et al. Significant relationships were found between free-time activity variables “Active physical exerciser”, “Audience of cultural events”, and the player type Philanthropist. Overall, these results indicate that free-time activities can have correlations with player types and reported hobbies can be used as a proxy when selecting personalized gamification approaches.
Multiperson Respiratory Monitoring Using Single-Channel Continuous-Wave Radar With Time Modulated Array

Radio frequency (RF) signals are widely used in respiratory detection due to their non-contact, remote sensing advantages. In order to satisfy multiperson monitoring, the existing RF sensing system greatly increases the complexity of the feeder network, which will affect the stability of the system and have high maintenance costs. In this study, we propose a single-channel, low-complexity multiperson respiratory monitoring system. It uses a time-modulated array (TMA) to detect the position and respiration rate of multiple targets. The TMA is reconstructed into a conventional multichannel array by the harmonic recovery method. Beamforming is used to scan the scene horizontally. At the same time, the beamformed signal is converted to the frequency domain to construct the angle of arrival-frequency map (AFM). By forming a beam pointing to the target, the time domain respiration signal of each target can be acquired. The experimental results show that the maximum respiratory rate (RR) estimation errors of the proposed system in single-person, two-person, and three-person scenarios are 0.77, 0.96, and 1.32 bmp, respectively. In addition, the Bland-Altman analysis shows that our system has good consistency with the reference sensor.
GENETIC ANALYSIS FOR EARLINESS AND YIELD TRAITS IN MAIZE

Five white kernel maize inbred lines with distinct genetic make-up were crossed in a 5 × 5 complete diallel fashion during spring season 2011 at Cereal Crops Research Institute (CCRI), Pirsabak Nowshera, Pakistan. The resulting 20 F1 hybrids, their five inbred lines and two checks hybrids (OPV 'Jalal' and 'Pioneer hybrid 30K08') were further evaluated during subsequent summer season 2011 at four locations. Present research was designed to study the genetic mechanisms controlling various earliness and yield traits through Hayman’s diallel approach. Genotypes, locations and genotype by environment interactions (GEI) showed significant (p≤0.01) differences for all the traits studied. Significant genotypic differences for various traits justified to carryout the Hayman's genetic analysis. For adequacy, the additive-dominance model was adequate / partially adequate for various traits at all the locations. According to genetic analysis, the key components of genetic variances i.e., additive (D) and dominance components (H1, H2) and average degree of dominance revealed that dominance components were predominant and overdominance type of gene action played an important role in the inheritance of all the traits at different locations. Genetic analysis further revealed unequal proportion of positive (U) and negative (V) alleles in the loci (H2<H1) with asymmetrical distribution of genes in the parental genotypes (H2/4H1 < 0.25) for majority of the traits. Broad sense heritability values were higher for days to 50% tasseling (0.89 to 0.97), days to 50% silking (0.91 to 0.97), ear length (0.86 to 0.99), 1000-grain weight (0.92 to 0.97) and grain yield (0.98 to 0.99), respectively at all the locations. Narrow sense heritability for above traits was low to medium ranging from 0.12 to 0.23, 0.17 to 0.33, 0.13 to 0.36, 0.10 to 0.51 and 0.07 to 0.11, respectively at all the locations. Desirable high genetic gain values were observed for yield traits while for earliness the values were moderate. Due to non-additive genes controlling various traits and high broad sense heritability estimates, the promising F1 hybrids could be developed in future breeding programs for production of early maturing and high yielding maize hybrids and cultivars through selection from later segregating generations.
9.9 V High-Voltage Switch in a standard 180 nm CMOS technology for MEMS Inclinometers

In this paper a high-voltage switch, capable of delivering a voltage of 9.9 V to a MEMS actuator is presented. This circuit is implemented in a standard 180 nm CMOS technology with a nominal voltage of 3.3 V for the I/O transistors. The switch is implemented following the stacked transistors principle, to ensure that none of the devices is stressed beyond acceptable limits. Moreover, all the auxiliary circuitry such as the pre-driver and voltage buffers required to generate the intermediate voltage levels are described in detail. Compared to other designs shown in the literature, the implementation presented here enables the largest tuning of the output range, from 5 V to 9.9 V. Post-layout simulation results validate the successful operation of the proposed architecture.
Evaluating Learners’ Reactions to an Online Training Programme: A Case Study of Maldives National University

In this paper the first level of KirkPatrick’s model is used to evaluate the reaction of learners on the online training program conducted at the Maldives National University (MNU). A measurement instrument assessing learners’ engagement, satisfaction and relevance of the content was developed and evaluated. A sample of 46 university teachers involved in the training program was used to evaluate the measurement instrument. The results revealed strong positive correlation between all three research constructs. Detailed analysis of participant’s responses showed that COVID-19 related lockdown has played a significant role in participants’ perception of their engagement and satisfaction with the training program.
Geographic dispersion and earnings management q

Using a sample of U.S. listed firms in the period from 1994 to 2011, we examine how the geographic dispersion of a firm affects the earnings management’s choice between accrual based management and real activities management. We show that geographically dispersed firms have lower accrual based management but higher real earnings management, when compared to geographically concentrated firms. These patterns remain robust to various proxies for geographic dispersion and are not likely to be driven by the firm’s endogenous choice. We further show that our results are consistent with the investor recognition explanation as in Merton (1987) and Garcia and Norli (2012). 2015 Elsevier Inc. All rights reserved.
Effects of Uniaxial Bianisotropic Media on Full-Polarimetric GPR Signatures

The polarimetric response of full-polarimetric ground-penetrating radar (FP-GPR) from anisotropic media is a pressing issue to be investigated. It can improve the detection accuracy of the targets in the anisotropic media and can also provide effective ways to detect anisotropic targets. In this article, we focus on the FP-GPR signals from uniaxial bianisotropic media, i.e., the complete case that both the permittivity and the conductivity of the background media are anisotropic. Based on the propagation characteristics of electromagnetic waves in the anisotropic media and the transmission coefficients on the surface of the media, we construct a mathematical relation between the measured scattering matrix affected by anisotropies and the real scattering matrix, derive a coefficient to characterize the polarization rotation (PR) effects, and proposed a diagonal matrix for target decomposition analyses. The computation results indicate that <inline-formula> <tex-math notation="LaTeX">$\varepsilon _{z}/\varepsilon _{x}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$\sigma _{z}/\sigma _{x}$ </tex-math></inline-formula>, and antenna interval have complex effects on the PR coefficient and the H-Alpha decomposition results of three types of typical targets. Multiple 3-D FP-GPR simulations on a series of uniaxial bianisotropic models verified the results.
Detecting JavaScript libraries using identifiers and hashes

The web is a widespread platform for data exchange and service delivery on which many depend. Due to the high demand for website creation, methods are offered that provide more time-efficient design and programming. One of them is using JavaScript libraries, which can introduce vulnerabilities into web applications. Because of the sheer number of websites, as well as libraries and their vulnerabilities, it is difficult to maintain website security. There is a need to create tools that will automatically and proactively search for vulnerable libraries and enable timely remediation. This paper deals with the aforementioned problem and provides an insight into the implemented solution. The paper describes how JavaScript libraries are obtained and used, and proposes multiple methods for detecting them. It also presents the technical side of the implementation, as well as the results obtained by detecting libraries on a set of web pages from the Croatian web space. Finally, we discuss the observed distribution of popular libraries and their vulnerabilities, as well as the limitations of the proposed implementation, and offer potential solutions with the aim of improving the project.
The Challenges to Information Security in Croatian Chamber of Economy during COVID-19 Pandemic

The paper deals with analysis of both challenges to information security Croatian Chamber of Economy prior and after COVID-19 pandemic and the results of the measures that were taken in order to counter fought with those challenges by means of state-of-the-art Information and Communication Technology solutions. COVID-19 pandemic has rapidly and significantly changed ecosystem for social and business activities. Croatian Chamber of Economy, as legal entity with public authorities, has been affected by numerous limitation which have directly impacted effectiveness of the business processes. The analysis of the measures and activities undertaken by Croatian Chamber of Economy in order to keep a high level of information security during COVID-19 pandemic and experience from that time period is described in the paper. Pre-emption of possible events with negative influence on business activities, as well as risk management and crisis management plan, are some of the most important prerequisites for a successful continuation of business activities during a crisis such COVID-19 pandemic has been. Beside the solutions based on ICT technology, permanent education of the employees is the activity that must not be omitted.
Finance and Economics Discussion Series Divisions of Research & Statistics and Monetary Affairs Federal Reserve Board , Washington , D . C . Bank Capital Ratios and the Structure of Nonfinancial Industries

We exploit variation in commercial bank capital ratios across states to identify the impact of commercial bank balance sheet pressures manifested through changes in capital ratios on employment in the manufacturing sector. For industries dependent on external finance, we find that an increase in the capital ratio has no statistically significant effect on net firm creation, but has an economically significant impact on average firm size, as measured in the number of employees. Our findings indicate a lack of substitutes for bank funding both in the short and long run. This lack of substitutes implies a notable adverse impact of balance sheet pressures on employment in industries dependent on external sources of funding. Our results highlight the potential effects that bank balance sheet pressures, for example, from tightening capital adequacy standards, such as Basel III, may have on nonfinancial firm dynamics. JEL Classification: G21, G28, G30, J20, L25
Ten Facts on Declining Business Dynamism and Lessons from Endogenous Growth Theory

In this paper, we review the literature on declining business dynamism and its implications in the United States and propose a unifying theory to analyze the symptoms and the potential causes of this decline. We first highlight 10 pronounced stylized facts related to declining business dynamism documented in the literature and discuss some of the existing attempts to explain them. We then describe a theoretical framework of endogenous markups, innovation, and competition that can potentially speak to all of these facts jointly. We next explore some theoretical predictions of this framework, which are shaped by two interacting forces: a composition effect that determines the market concentration and an incentive effect that determines how firms respond to a given concentration in the economy. The results highlight that a decline in knowledge diffusion between frontier and laggard firms could be a significant driver of empirical trends observed in the data. This study emphasizes the potential of growth theory for the analysis of factors behind declining business dynamism and the need for further investigation in this direction. (JEL D33, E25, J24, L13, O33, O34)
Digital Technologies in the Activities of Physical Culture and Sports Specialists: Features of Professional Training

The article is devoted to the problem of training specialists in physical culture and sports (PhC&S) for the use of digital technologies in professional activities. Digital technologies characteristics by the professional activity of PhC&S specialists are given. Attention is focused on modern digital health tools. The authors characterize digital technologies in the professional activity of PhC&S specialists and describe the features of training PhC&S specialists for the use of digital technologies in professional activities. The authors argue that the training of future PhC&S specialists for the use of digital technologies in professional activities should be carried out purposefully according to a methodology that integrates specific forms, methods, and means of training. The content of the methodology is the disciplines “Information and communication technologies in the field of PhC&S” (the goal is to form ideas about digital technologies in the field of PhC&S in general) and “Digital health technologies in the field of PhC&S” (the goal is to form: knowledge about health and a healthy lifestyle, digital technologies for maintaining and controlling their health and the health of others; ability to use digital health tools in professional activities; reflection skills about the use of digital health tools). The results of the statistical analysis of the pedagogical experiment are presented, confirming the developed methodology’s effectiveness.
Learning Multi-Scale Features Using Dilated Convolution for Contour Detection

For the contour detection task, we use the EfficientNet model as the backbone network and propose a network model that uses dilated convolution for multi-scale optimization. The network is accumulated top-down layer by layer, combining multiple optimization modules concat together to achieve a richer feature representation. To fuse feature information at different scales, we introduce a new Multi-scale optimization module to replace the use of deeper network structures or more complex decoding methods, which uses channel attention module to learn the correlation between channels and then uses dilated convolution of different scales to enhance contextual information. High generalization performance and accuracy are obtained in comparison with recent deep learning-based contour detection models. We evaluate our approach on two datasets, i.e., BSDS500 and NYUD-v2, achieving an ODS F-measure value of 0.828 on BSDS500. In particular, the results of BSDS500 exceed the human-level performance under more stringent criteria.