Miracle-dz/UniFAR-FTUs huggingface dataset:
Baricitinib retention rate: ‘real-life’ data from a mono-centric cohort of patients affected by rheumatoid arthritis

Objectives The aim of this retrospective study was to evaluate baricitinib retention rate in patients affected by rheumatoid arthritis. Secondary aims were to compare the impact on treatment persistence of monotherapy and other variables such as systemic corticosteroid use, line of treatment, disease duration, sex, biomarkers positivity, and Herpes Zoster virus infection. Materials and methods Patients with Rheumatoid Arthritis undergoing baricitinib were consecutively enrolled. Rheumatoid Arthritis diagnosis was performed with 2010 ACR/EULAR classification criteria. The cohort’s demographic, clinical and therapeutical data were retrospectively collected. The whole follow-up duration was 104 weeks. Results Ninety-five patients affected by rheumatoid arthritis and treated with baricitinib were consecutively enrolled. At the end of follow-up, the overall retention rate was 69.3%. No statistically significant difference in retention rate was observed between patients treated with baricitinib in monotherapy or in combination with methotrexate (p = 0.638) while patients undergoing a steroidal treatment showed a significantly reduced treatment retention (p = 0.028). Contrarily, patients treated with baricitinib as a first-line b/tsDMARD showed higher drug retention (p = 0.002) compared to further treatment lines. Steroid employment, steroid dosage and previous treatment with bDMARDs correlated with risk of treatment discontinuation and at univariate analysis (p = 0.028, p < 0.001, and p = 0.002 respectively). Multivariate analysis confirmed significance for higher steroid dosage and previous treatment with bDMARDs (p = 0.002 and p = 0.046). No adverse events such as deep venous thrombosis, pulmonary embolism or tubercular infection/reactivation were reported during the study observation. Conclusion Our data show a good baricitinib retention rate after 12 and 24 months of observation (75.1 and 69.3%, respectively). In our cohort, concomitant treatment with methotrexate did not influence treatment persistence while retention was reduced in patients undergoing a steroidal treatment and/or in multi-failure subjects.
Inference of drowning sites using bacterial composition and random forest algorithm

Diagnosing the drowning site is a major challenge in forensic practice, particularly when corpses are recovered from flowing rivers. Recently, forensic experts have focused on aquatic microorganisms, including bacteria, which can enter the bloodstream during drowning and may proliferate in corpses. The emergence of 16S ribosomal RNA gene (16S rDNA) amplicon sequencing has provided a new method for analyzing bacterial composition and has facilitated the development of forensic microbiology. We propose that 16S rDNA amplicon sequencing could be a useful tool for inferring drowning sites. Our study found significant differences in bacterial composition in different regions of the Guangzhou section of the Pearl River, which led to differences in bacteria of drowned rabbit lungs at different drowning sites. Using the genus level of bacteria in the lung tissue of drowned rabbits, we constructed a random forest model that accurately predicted the drowning site in a test set with 100% accuracy. Furthermore, we discovered that bacterial species endemic to the water were not always present in the corresponding drowned lung tissue. Our findings demonstrate the potential of a random forest model based on bacterial genus and composition in drowned lung tissues for inferring drowning sites.
Mathematical model of an automobile wheel for antilock modes of motion

An automobile is a complex controlled mechanical system with an extremely broad spectrum of motion components-from the motion along a highway during many hours to high-frequency vibrations of its structure. For this reason, when investigating the dynamics of an automobile, engineers utilize various mathematical models, depending on the purpose of the investigation and the range of the spectrum of motions to be considered [1-3]. In the present paper, we suggest a mathematical model describing the motion of an automobile on a time scale of about dozen milliseconds. This time scale is characteristic of the operation of an antilock braking system. This model explains unwanted high-frequency vibrations during braking [3-5] that fail to be described by the traditional quasistatic models by Carter, Rockard, and Fromm.
Thermodynamic Modeling of a Rotating Detonation Engine

In pursuit of greater thermal and propulsive efficiencies in rockets or gas turbines, a onedimensional thermodynamic model of a Rotating Detonation Engine (RDE) is compared to a numerical simulation model with good results. A ZND detonation model is modified to include stagnation properties and account for the velocity vectors that occur upstream of the detonation. Features of the RDE and their impact on the model are discussed. Velocity triangles, commonly used in the gas turbine industry, are shown to be an effective tool for understanding energy transfer in RDE’s.
A weighted generative model of the human connectome

Probabilistic generative network models have offered an exciting window into the constraints governing the human connectome’s organization. In particular, they have highlighted the economic context of network formation and the special roles that physical geometry and self-similarity likely play in determining the connectome’s topology. However, a critical limitation of these models is that they do not consider the strength of anatomical connectivity between regions. This significantly limits their scope to answer neurobiological questions. The current work draws inspiration from the principle of redundancy reduction to develop a novel weighted generative network model. This weighted generative network model is a significant advance because it not only incorporates the theoretical advancements of previous models, but also has the ability to capture the dynamic strengthening or weakening of connections over time. Using a state-of-the-art Convex Optimization Modelling for Microstructure-Informed Tractography (COMMIT) approach, in a sample of children and adolescents (n = 88, aged 8 to 18 years), we show that this model can accurately approximate simultaneously the topology and edge-weights of the connectome (specifically, the MRI signal fraction attributed to axonal projections). We achieve this at both sparse and dense connectome densities. Generative model fits are comparable to, and in many cases better than, published findings simulating topology in the absence of weights. Our findings have implications for future research by providing new avenues for exploring normative developmental trends, models of neural computation and wider conceptual implications of the economics of connectomics supporting human functioning.
Development of SCINFUL-QMD Code to Calculate the Neutron Detection Efficiencies for Liquid Organic Scintillator up to 3 GeV

The Monte Carlo code, designeted SCINFUL-QMD, has been developed to calculate neutron detection efficiency up to 3 GeV for liquid organic scintillators such as NE-213. The existing Monte Carlo code, SCINFUL, is known to reproduce a response function and detection efficiency at incident neutron energies below 80 MeV. We incorporate the quantum molecular dynamics plus statistical decay model (QMD+SDM) into SCINFUL to extend the upper limit of incident neutron energy to 3 GeV. The results by SCINFUL-QMD are compared with experimental data and those of CECIL. The SCINFUL-QMD exhibits an increase in detection efficiency above 300 MeV. This tendency agrees with one of the experimental data. The increase is achived by taking charged pion production into account.
WISE data and sparse photometry used for shape reconstruction of asteroids

Abstract Asteroid disk-integrated sparse-in-time photometry can be used for determination of shapes and spin states of asteroids by the lightcurve inversion method. To clearly distinguish the correct solution of the rotation period from other minima in the parameter space, data with good photometric accuracy are needed. We show that if the low-quality sparse photometry obtained from ground-based astrometric surveys is combined with data from the Wide-field Infrared Survey Explorer (WISE) satellite, the correct rotation period can be successfully derived. Although WISE observed in mid-IR wavelengths, we show that for the period and spin determination, these data can be modelled as reflected light. The absolute fluxes are not required since only relative variation of the flux over the rotation is sufficient to determine the period. We also discuss the potential of combining all WISE data with the Lowell photometric database to create physical models of thousands of asteroids.
The Demand for Long-Term Mortgage Contracts and the Role of Collateral∗

Long-term fixed-rate mortgage contracts protect households against interest rate risk, yet most countries have relatively short interest rate fixation lengths. Using administrative data from the UK, the paper finds that the choice of fixation length tracks the life-cycle decline of credit risk in the mortgage market: the loan-to-value (LTV) ratio decreases and collateral coverage improves over the life of the loan due to principal repayment and house price appreciation. High-LTV borrowers, who pay large initial credit spreads, trade off their insurance motive with reducing credit spreads over time using shorter-term contracts. To quantify demand for long-term contracts, I develop a life-cycle model of optimal mortgage fixation choice. With baseline house price growth and interest rate risk, households prefer shorter-term contracts at high LTV levels, and longer-term contracts once LTV is sufficiently low, in line with the data. The findings help explain reduced and heterogeneous demand for long-term mortgage contracts. JEL classification: D15, E43, G21, G22, G5, G52
The effectiveness of a cognitive behavioral counseling program in improving the level of social skills and reducing isolation behaviors among university students

Introduction. Social skills are considered an influential factor in the adaptation of individuals and their enjoyment of mental health, and the lack of these skills negatively affects the individual’s various performances: professional, academic, family, and societal, which may lead individuals to isolation, introversion and a feeling of inferiority and inferiority. Cognitivebehavioral therapy contributes to developing communication skills, training to solve problems in an effective manner, and reducing behavioral problems in particular, represented by isolation and introversion.
following Cardiac Arrest Correlates with Poor Neurologic Outcome Parieto-Occipital Injury on Diffusion MRI

BACKGROUND AND PURPOSE: MR imaging of the brain provides unbiased neuroanatomic evaluation of brain injury and is useful for neurologic prognostication following cardiac arrest. Regional analysis of diffusion imaging may provide additional prognostic value and help reveal the neuroanatomic underpinnings of coma recovery. The purpose of this study was to evaluate global, regional, and voxelwise differences in diffusion-weighted MR imaging signal in patients in a coma after cardiac arrest. MATERIALS AND METHODS: We retrospectively analyzed diffusion MR imaging data from 81 subjects who were comatose for . 48hours following cardiac arrest. Poor outcome was de ﬁ ned as the inability to follow simple commands at any point during hospitalization. ADC differences between groups were evaluated across the whole brain, locally by using voxelwise analysis and regionally by using ROI-based principal component analysis. RESULTS: Subjects with poor outcome had more severe brain injury as measured by lower average whole-brain ADC (740 [SD, 102] (cid:1) 10 (cid:3) 6 mm 2 /s versus 833 [SD, 23] (cid:1) 10 (cid:3) 6 mm 2 /s, P , . 001) and larger average volumes of tissue with ADC below 650 (cid:1) 10 (cid:3) 6 mm s /s (464 [SD, 469] mL versus 62 [SD, 51] mL, P , .001). Voxelwise analysis showed lower ADC in the bilateral parieto-occipital areas and perirolandic cortices for the poor outcome group. ROI-based principal component analysis showed an association between lower ADC in parieto-occipital regions and poor outcome. CONCLUSIONS: Brain injury affecting the parieto-occipital region measured with quantitative ADC analysis was associated with poor outcomes after cardiac arrest. These results suggest that injury to speci ﬁ c
CAMBRIDGE WORKING PAPERS IN ECONOMICS Vulnerable households and fuel poverty: policy targeting efficiency in Australia’s National Electricity Market

When Australia established its National Electricity Market (NEM) during the 1990s, reforms were focused on maximising economic efficiency. Little thought was given to distributional outcomes. However, by the 2010s sluggish growth in household incomes, sharp rises in electricity prices and material increases in quantities consumed through surging uptake rates of air-conditioning units led to the possibility of (hot climate) fuel poverty. In the NEM’s Queensland region, longstanding customer hardship policy pre-dated the NEM. With the benefit of hindsight, the policy was poorly configured as it focused exclusively on the aged population and was delivered by way of fixed payment. Low income households in the family formation cohort were excluded from the policy despite obvious need. In this article, Queensland’s longstanding customer hardship policy is refined using pre-existing (meanstested) welfare mechanisms in order to target low income households including families, and the payment structure is altered from fixed ($ pa) to variable (% of the bill) while holding the budget constraint constant. Changes to policy targeting produce material improvements in horizontal and vertical efficiency while changes to payment structure further enhance vertical performance, with the incidence and depth of residual fuel poverty reduced. Reference Details CWPE 2129 Published 29 March 2021
The Effects of Topic and Part of Speech on Nonbinary Speakers’ Use of (ING)

This paper investigates the variable usage of (ING) by nonbinary speakers across conversation topics, specifically asking whether nonbinary speakers shift their rates of (ING) variation when discussing the salient topic of gender. 8 nonbinary speakers (4 AFAB and 4 AMAB, ranging from 21 to 27 years old) participated in sociolinguistic interviews conducted by a nonbinary researcher who was familiar with each interview participant. A modular interview guide was developed based on Labov’s Q-GEN-II modules with modifications made to specifically obtain participant narratives on their experiences with gender identity and expression in addition to traditional narratives. The results of the study find that despite a markedly more deliberative style during gender topics, participants do not shift rates of (ING) across topics. The present study further finds that a speaker’s assigned gender at birth plays no predictable role in rates of (ING). Taken together, these results suggest that nonbinary speakers form their own distinct linguistic community which should be analyzed as operating outside of the gender binary. This working paper is available in University of Pennsylvania Working Papers in Linguistics: https://repository.upenn. edu/pwpl/vol29/iss1/19 The Effects of Topic and Part of Speech on Nonbinary Speakers’ Use of (ING) Jack Rechsteiner and Betsy Sneller*
Audio-Text Cross-Modal Retrieval

Cross-modal retrieval is an important area of research. In particular, the current project focuses on audio clip retrieval using text inquiry. The solution investigated here is based on cross modal metric learning, where two separate encoders for audio and text are learned concurrently by presenting positive and negative examples. The project is focused on investigation of the resulting audio and text embedding spaces. Results indicate that dimension of the embedding spaces may have a significant effect on performance and that higher dimension is not necessarily better. Investigation of uniformity of the text and the audio embeddings clearly shows that the distributions are not uniform. A method to incorporate a non-uniformity penalty into the training pipeline has been proposed, but no benefit has been observed to the performance in the cross-modal retrieval task
Statistical Properties of Lower Band Rising Tone Chorus Waves

Lower‐band chorus waves are known to play dual roles in radiation belt dynamics (electron acceleration and precipitation), and understanding their properties and excitation is very important. A systematic study of chorus waves properties in terms of background plasma parameters (electron perpendicular beta β⊥ and the ratio of plasma frequency to electron cyclotron frequency fpe/fce) has not been performed previously. We use burst‐mode data from Van Allen Probe A from 2012 to 2019 and develop an algorithm to extract individual lower band rising tone chorus elements. We statistically analyze four properties of rising tone chorus elements: time duration τ, frequency width Δf, chirping rate Γ, and maximum amplitude Ia. Statistical results show typical properties of chorus waves: Ia ∼ 0.09 nT, Δf ∼ 0.08fce, Γ ∼ 2 × 103 Hz/s, τ ∼ 0.015–0.25 s. On the nightside and dawnside, Γ shows wider spread and is more distributed over larger values, which is associated with the wider distribution of β⊥. Chorus waves at duskside and dayside have larger values of τ, associated with smaller β⊥ and larger fpe/fce. The dependence of chorus wave properties on background plasma parameters is also examined. We show that normalized Δf has positive correlation with β⊥ and fpe/fce. Normalized τ shows negative correlation with β⊥ and positive correlation with fpe/fce. Normalized Γ and Ia show positive correlations with β⊥ and negative correlations with fpe/fce. These results help us better understand chorus waves excitation and their relation to the microburst.
The Case for Learning Machine Language

This paper focuses on enabling modern processors to better predict upcoming instructions that will be executed, in order to improve instruction-related speculations at runtime. Using branch prediction as a case study, we take the first step to motivate the potential of learning semantic correlations in machine language (i.e., CPU instructions), and we demonstrate how to apply language modeling to machine language. Although various approaches have been proposed for instruction-related runtime speculations, they remain general-purpose and rely on language-agnostic features. Furthermore, we present a branch predictor design that takes advantage of our Transformer-based language model. Empirical results from SPEC-CPU-2017 benchmarks (on RISC-V) show that language modeling can improve the branch prediction accuracy by up to 11.03%, and the processor IPC by up to 21.16%.
Uses, Threats and Conservation of Plant Species in Kisere Catchment Area of Kakamega Forest, Kenya

Tropical rainforests are under threat from human encouragement and anthropogenic activities in sub-Saharan Africa. Despite the importance of tropical rainforests, anthropogenic activities are changing vegetation dynamics of tropical rainforests such as Kakamega forest. The aim of the study was to determine the most exploited tree species in Kisere Forest by the local communities and their uses; to assess the impact of anthropogenic activities on plant species in Kisere Forest, to evaluate the contribution of Village Economic Enterprises on conservation of Kisere Forest. To determine the most targeted tree species by the local communities and their uses, semi-structured questionnaires were used and ethno-botanical survey was conducted. The Impact of Village Enterprise funded microenterprises on conservation of Kisere Forest was determined by comparing exploitation of the forest by funded households and unfunded households. This was done by assessing the time spent in the forest and the frequency of visiting forest to collect forest products and the collection of forest products for sale versus subsistence by the funded and unfunded households. Impacts of Village enterprise was also assessed by monitoring trends in forest disturbance. It was found out that most plant species were used for firewood, poles, charcoal burning and source of medicine. Integrated conservation strategies aimed at providing people living around biodiversity hotspots with knowledge, skills and economic opportunities should be encouraged so that local communities can live sustainably with the forest ecosystems and protect local resources against future threats.
Determinant Factors of Corporate Governance on Company Performance: Mediating Role of Capital Structure

The purpose of this study is to investigate the role of capital structure as mediating variable in the relationship between corporate governance and company performance. Data for this study was obtained from financial statements and was done in Indonesia’s non-financial sector. From among the companies listed on the Indonesia Stock Exchange between 2017 and 2021, 15 companies were chosen as a sample. Findings show that corporate governance (board independence, board size, and audit committee) were significantly associated with capital structure and company performance, but gender diversity has an insignificant relationship with capital structure and company performance. Moreover, this research found that capital structure is not able to mediate the effect of corporate governance (board independence, board size, audit committee, and gender diversity) on company performance.
Analysis of the structure and central properties of a sample of massive bulgeless galaxies

The aim of this thesis is to study a sample of red massive bulgeless galaxies, selected from the SDSS DR7 based on an automated algorithm that performed one-dimensional analysis of the galaxy light profiles. From an initial sample of 77 bulgeless candidates we found 38 bulgeless galaxies and 29 galaxies with a pseudo-bulge regarding their large-scale structure using two-dimensional modeling techniques. An additional sample of 20 bulge-dominated candidates was selected to serve as a control sample and all of them were confirmed as bulgy galaxies after the two-dimensional analysis. We found that the disks of pseudo-bulge galaxies have larger effective radius than the bulgeless galaxies. Additionally, using the SDSS optical spectra, we assess some physical properties of the central regions of these galaxies (central 3”). We find that the bulgy galaxies show different properties (stellar mass, metallicity, mean stellar age, dust content) from bulgeless and pseudo-bulge galaxies and that the distinction between the last two classes of galaxies is only possible when considering central stellar mass and central velocity dispersions.