Miracle-dz/UniFAR-FTUs huggingface dataset:
CSF-resident CD4+ T-cells display a distinct gene expression profile with relevance to immune surveillance and multiple sclerosis

Abstract The CNS has traditionally been considered an immune privileged site, but is now understood to have a system of immune surveillance, predominantly involving CD4+ T-cells. Identifying functional differences between CNS and blood CD4+ T-cells, therefore, have relevance to CNS immune surveillance as well as to neurological conditions, such as multiple sclerosis, in which CD4+ T-cells play a central role. Here, CD4+ T-cells were purified from CSF and blood from 21 patients with newly diagnosed treatment-naïve multiple sclerosis and 20 individuals with non-inflammatory disorders using fluorescence-activated cell sorting, and their transcriptomes were profiled by RNA sequencing. Paired comparisons between CD4+ T-cells from CSF and blood identified 5156 differentially expressed genes in controls and 4263 differentially expressed in multiple sclerosis patients at false discovery rate <5%. Differential expression analysis of CD4+ T-cells collected from the CSF highlighted genes involved in migration, activation, cholesterol biosynthesis and signalling, including those with known relevance to multiple sclerosis pathogenesis and treatment. Expression of markers of CD4+ T-cell subtypes suggested an increased proportion of Th1 and Th17 cells in CSF. Gene ontology terms significant only in multiple sclerosis were predominantly those involved in cellular proliferation. A two-way comparison of CSF versus blood CD4+ T-cells in multiple sclerosis compared with non-inflammatory disorder controls identified four significant genes at false discovery rate <5% (CYP51A1, LRRD1, YES1 and PASK), further implicating cholesterol biosynthesis and migration mechanisms. Analysis of CSF CD4+ T-cells in an extended cohort of multiple sclerosis cases (total N = 41) compared with non-inflammatory disorder controls (total N = 38) identified 140 differentially expressed genes at false discovery rate < 5%, many of which have known relevance to multiple sclerosis, including XBP1, BHLHE40, CD40LG, DPP4 and ITGB1. This study provides the largest transcriptomic analysis of purified cell subpopulations in CSF to date and has relevance for the understanding of CNS immune surveillance, as well as multiple sclerosis pathogenesis and treatment discovery.
Using Metacognitive Training with Kinesiology Students

As future healthcare practitioners, kinesiology students must become expert learners who choose strategies resulting in deep and durable learning. Metacognitive instruction goes beyond the use of study skills as it focuses on student reflection and evaluation of their learning success, and ultimately establishes effective learning skills, a requirement for professional practice. To examine if an intervention in a kinesiology course affected metacognitive awareness and use of metacognitive strategies, a quasiexperimental research design utilized a convenience sample of 89 upper division undergraduate occupational therapy students and master’s level athletic training students enrolled in kinesiology courses. Using an online survey including the Metacognitive Awareness Inventory (Schraw & Dennison, 1994) and three Likert scale questions about perception of study skills, pre-test and post-test data were collected over three years, and 6-month follow-up data were collected during the final two years of the study. The intervention included information about metacognition and key study tips, five learning activities, and teaching techniques to promote metacognition. Treating the pre-test group as the reference group, the results showed that the post-test and 6-month follow-up test groups were significant predictors of students’ scores on the Metacognitive Awareness Inventory, indicating an improved and sustained metacognitive awareness after completing the course. The intervention was found to have a positive association with scores of planning, information management, comprehension monitoring, and evaluation. These results indicate the value of metacognition instruction. Considering that not all students come equipped with metacognitive skills, instruction in this area could be beneficial to students.
Unusual Fabrication of Lightweight Injection-Molded Polypropylene Foams by Using Air as the Novel Foaming Agent

We report a novel approach to prepare injection-molded foams using air as the foaming agent, which is realized through our newly developed foam injection molding technology. Microcellular polypropylene (PP) foams exhibiting cell size of ∼7 μm and cell density of 1.63 × 109 cells/cm3 were successfully obtained employing air foaming. Fourier transform infrared spectroscopy and rheological results illustrated that the molecular structures and melt properties of PP were greatly altered in air foaming, resembling a reactive foaming process. Furthermore, compared with nitrogen and carbon dioxide foaming, air foaming gave us a more flexible PP foam with finer cellular structure.
Taking the most out of unemployment insurance under nonstationary job search: unemployment duration, reemployment wages and job tenure∗

This paper puts together the non-distortionary liquidity effect of unemployment insurance and job match quality. It takes advantage of sizeable (6 months) differences on benefits entitlements based only on the age of beneficiaries to set up a sharp regression discontinuity design. We identify a big impact on subsidized unemployment duration and a small impact on wages and tenure on the job that follows the unemployment spell. Wage gains are heterogeneous and concentrated on individuals at the bottom of the pre-unemployment income distribution. The impact on tenure is always rather small. The non-distortionary nature of the liquidity effect reduces the pressure on low income workers to accept lower productivity jobs.
Identiﬁcation of Propaganda Techniques in Internet Memes using Multimodal Fusion Techniques

The exponential rise of social media networks has allowed the production, distribution, and consumption of data at a phenomenal rate. Moreover, the social media revolution has brought a unique phenomenon to social media platforms called Internet memes. Internet memes are one of the most popular contents used on social media, and they can be in the form of images with a witty, catchy, or satirical text description. In this paper, we are dealing with propaganda that is often seen in Internet memes in recent times. Propaganda is communication, which frequently includes psychological and rhetorical techniques to manipulate or inﬂuence an audience to act or respond as the propagandist wants. To detect propaganda in Internet memes, we propose a multimodal deep learning fusion system. Our approach fusing the text and image feature representations and outperforms individual models based solely on either text or images. We conduct extensive experiments on the dataset to assess the efﬁciency of the developed model.
Pseudoplastic uid ows for di erent Prandtl numbers: steady and time-dependent solutions

In this work, a variational multiscale (VMS) nite element formulation is used to approximate numerically the natural convection in square cavity with di erentially heated from sidewalls problem for Newtonian and power-law uids. The problem is characterized for going through a Hopf bifurcation when reaching high enough Reyleigh numbers, which initiates the transition between steady and time dependent behavior, however, results found in the literature are only for air Prandtl number. The presented VMS formulation is validated using existing results, and is used to study highly convective cases, to determine the ow conditions at which it becomes time dependent, and to establish new benchmark solutions for non-Newtonian uid ows for di erent Pr and power-law indexes n. The range of solutions were found in the range 0.6 < n < 1 and 0.01 < Pr < 1, 000, and the critical Rayleigh number (Rac) where Hopf bifurcations appear were identi ed for all cases. Obtained results have good agreement with those previously reported in the speci c literature, and new data related to the heat transfer capabilities of pseudoplastic uids and its oscillatory behavior was identi ed. This non-Newtonian in uence of the uid is later checked in a 3D model of a simpli ed heat exchanger, where the capability of pseudoplastic uids for energy transport proved to be enhanced when compared to the Newtonian case.
Ru(II)-thyminate complexes: new metallodrug candidates against tumor cells

Herein, we used thymine (HThy) as a ligand to form two new ruthenium(II) complexes with formula [Ru(PPh3)2(Thy)(bipy)]PF6 (1) and [Ru(Thy)(bipy)(dppb)]PF6 (2). The complexes were characterized by spectroscopic, spectrometric and X-ray crystallography analyses. Complexes 1 and 2 can interact with ctDNA presenting binding constants, Kb, of 0.4 and 1.2 × 103 M−1, respectively. Their cytotoxic activities towards tumor cell lines (B16-F10, HepG2, K562 and HL-60) and non-tumor cells (PBMCs) were evaluated using the Alamar blue assay. Complex 1 exhibits high cytotoxicity against tumor cells, showing IC50 values of 0.01 and 1.81 μM against the HL-60 and HepG2 cell lines, respectively. Therefore, compound 1 can be considered as a promising antitumor metallodrug.
Pay After I Die: The Estate Tax and the Payout Policy in Family Firms

We examine how incentives to minimize estate taxes affect family firms’ payout policies. Under high estate tax rates, family firms are less likely than nonfamily firms to pay out before owners’ deaths because paid-out profits that are inherited by successors are double-taxed. However, family firms may wait to pay out profits after the deaths of the owners to secure liquidity to make estate tax payments without losing family control. To document the causal impact of estate tax incentives on payouts, we exploit the 2001 U.S. tax reform, which eliminated the 55% federal estate tax rate. We find that after the reform, family firms increased their dividend payouts more than nonfamily firms. The reform’s effect on payouts was greater for family firms without family trusts and with founders who served as senior executives. Using sudden diagnoses of fatal diseases as another shock, we show that firms managed by ill executives reduced payouts after diagnosis but increased dividends after death. Our findings suggest that payout policies can be affected by owners’ incentives to reduce their personal tax burdens.
A Large Bayesian VAR of the United States Economy

We model the United States macroeconomic and financial sectors using a formal and unified econometric model. Through shrinkage, our Bayesian VAR provides a flexible framework for modeling the dynamics of thirty-one variables, many of which are tracked by the Federal Reserve. We show how the model can be used for understanding key features of the data, constructing counterfactual scenarios, and evaluating the macroeconomic environment both retrospectively and prospectively. Considering its breadth and versatility for policy applications, our modeling approach gives a reliable, reduced form alternative to structural models.
Do Divisional Managers Affect Financial Reporting? Evidence from Revenue Management

Using a sample of manually collected data on divisional managers for S&P 500 firms, this study examines the effect of co-opted divisional managers on revenue manipulation. Co-opted divisional managers are those appointed after the incumbent chief executive officer assumes office. I find that a firm with a higher percentage of co-opted divisional managers has more upward revenue management when the firm just meets or beats analyst forecast. Such a finding is more pronounced when the top management has more equity ownership or when co-opted divisional managers face greater career concerns. In contrast, co-opted divisional managers are not significantly associated with revenue management when the firm’s earnings miss or are well above analyst forecast. These results are consistent with my prediction that co-opted divisional managers manipulate revenues to help top management achieve performance targets. Overall, my study suggests that divisional managers have a significant impact on financial reporting through revenue manipulation.
Macroeconomic Predictions using Payments Data and Machine Learning

This paper assesses the usefulness of comprehensive payments data for macroeconomic predictions in Canada. Specifically, we evaluate which type of payments data are useful, when they are useful, why they are useful, and whether machine learning (ML) models enhance their predictive value. We find payments data with a factor model can help improve accuracy up to 25% in predicting GDP, retail, and wholesale sales; and nonlinear ML models can further improve the accuracy up to 20%. Furthermore, we find the retail payments data are more useful than the data from the wholesale system; and they add more value during crisis and at the nowcasting horizon due to the timeliness. The contribution of the payments data and ML models is small and linear during low and normal economic growth periods. However, their contribution is large, asymmetrical, and nonlinear during crises such as COVID-19. Moreover, we propose a cross-validation approach to mitigate overfitting and use tools to overcome interpretability in the ML models to improve their effectiveness for policy use.
Short Sellers in the Realm of Social Media: Arbitrageurs or Manipulators?

We study the interaction of short sellers and social media and the effect on stock prices. We use 75.1 million investment-related social media posts for 3,683 unique Chinese firms. Prior to high short interest, social media tone is abnormally positive. Once highly shorted, the tone flips and is abnormally negative. No such pattern exists with traditional media. Compared to firms that are just highly shorted, highly shorted firms with pump-and-dump patterns in social media tone have abnormal returns that are 2.7x higher before, and 3x lower after, the initiation of high short interest. Evidence from natural experiments involving China’s introduction and subsequent suspension of shorting also suggest social media manipulation. Manipulation is more likely in firms located in provinces with weaker legal environments. Our findings show that in the realm of social media, short sellers may profit more by creating mispricing than by correcting it.
Ratings and Reactivity: How Competing Logics Shape Organizational Responses to Being Rated

We examine why organizations may at times decrease their performance after receiving a positive rating. Incorporating insights from the literature on institutional complexity and competing institutional logics, we argue and demonstrate how the perceived incompatibility between a positive rating and a dominant logic may cause organizations to view the rating as unfavorable and thus strategically reduce their performance on the rated dimension. Utilizing a difference-in-differences design, we examine how companies responded to being rated and recognized as a charitable organization, an evaluation that we maintain was generally perceived as incompatible with the dominant institutional logic of shareholder maximization during the early 1990s. Our results suggest that companies were more likely to decrease philanthropic contributions after being rated as a generous firm. We also found this effect to be amplified and attenuated by factors that increased or decreased the saliency of the perceived incompatibility between the philanthropy rating and the dominant shareholder logic. These findings provide insights for scholarship on how organizations react to external evaluations and raise important questions for scholars and practitioners interested in the effectiveness of evaluation metrics in shaping organizational performance.
Organization Capital and Debt Structure

This paper examines the effect of organization capital on corporate debt structure. We find that firms with higher organization capital rely more on unsecured debt. Using state-level unemployment insurance benefits and industry median organization capital as instrumental variables, we identity the causal effect of organization capital on the corporate use of unsecured debt. Our findings are robust to alternative measures and specifications of organization capital and to controlling for tangible assets. We document further evidence that the effect of organization capital on the security of debt is more pronounced among firms in technology industry.
Jonathan Michael Borwein 1951 - 2016: Life and Legacy

Jonathan M. Borwein (1951−2016) was a prolific mathematician whose career spanned several countries(UK, Canada, USA, Australia) and whose many interests includedanalysis, optimization, number theory, special functions, experimental mathematics, mathematical finance, mathematical education,and visualization. We describe his life and legacy, and give anannotated bibliography of some of his most significant books and papers.
Sudden switch-off of Alfvénic turbulence in the rarefaction region of corotating solar wind high speed streams: solar origin or dynamic evolution?

A canonical description of a corotating solar wind high speed stream, in terms of velocity profile, would indicate three main regions:a stream interface or corotating interaction region characterized by a rapid flow speed increase and by compressive phenomena due to dynamical interaction between the fast wind flow and the slower ambient plasma;a fast wind plateau characterized by weak compressive phenomena and large amplitude fluctuations with a dominant Alfv\'enic character;a rarefaction region characterized by a decreasing trend of the flow speed and wind fluctuations dramatically reduced in amplitude and Alfv\'enic character, followed by the slow ambient wind. Interesting enough, in some cases the region where the severe reduction of these fluctuations takes place is remarkably short in time, of the order of minutes, and located at the flow velocity knee separating the fast wind plateau from the rarefaction region. The aim of this work is to investigate which are the physical mechanisms that might be at the origin of this phenomenon. We firstly looked for the presence of any tangential discontinuity which might inhibit the propagation of Alfv\'enic fluctuations from fast wind region to rarefaction region. The absence of a clear evidence for the presence of this discontinuity between these two regions led us to proceed with ion composition analysis for the corresponding solar wind, looking for any abrupt variation in minor ions parameters (as tracers of the source region) which might be linked to the phenomenon observed in the wind fluctuations. In the lack of a positive feedback from this analysis, we finally propose a mechanism based on interchange reconnection experienced by the field lines at the base of the corona, within the region separating the open field lines of the coronal hole, source of the fast wind, from the surrounding regions mainly characterized by closed field lines.
PR-GCN: A Deep Graph Convolutional Network with Point Refinement for 6D Pose Estimation

RGB-D based 6D pose estimation has recently achieved remarkable progress, but still suffers from two major limitations: (1) ineffective representation of depth data and (2) insufficient integration of different modalities. This paper proposes a novel deep learning approach, namely Graph Convolutional Network with Point Refinement (PR-GCN), to simultaneously address the issues above in a unified way. It first introduces the Point Refinement Network (PRN) to polish 3D point clouds, recovering missing parts with noise removed. Subsequently, the Multi-Modal Fusion Graph Convolutional Network (MMF-GCN) is presented to strengthen RGB-D combination, which captures geometry-aware inter-modality correlation through local information propagation in the graph convolutional network. Extensive experiments are conducted on three widely used benchmarks, and state-of-the-art performance is reached. Besides, it is also shown that the proposed PRN and MMF-GCN modules are well generalized to other frameworks.
Pediatric Automatic Sleep Staging: A Comparative Study of State-of-the-Art Deep Learning Methods

Background: Despite the tremendous prog- ress recently made towards automatic sleep staging in adults, it is currently unknown if the most advanced algorithms generalize to the pediatric population, which displays distinctive characteristics in overnight polysomnography (PSG). Methods: To answer the question, in this work, we conduct a large-scale comparative study on the state-of-the-art deep learning methods for pediatric automatic sleep staging. Six different deep neural networks with diverging features are adopted to evaluate a sample of more than 1,200 children across a wide spectrum of obstructive sleep apnea (OSA) severity. Results: Our experimental results show that the individual performance of automated pediatric sleep stagers when evaluated on new subjects is equivalent to the expert-level one reported on adults. Combining the six stagers into ensemble models further boosts the staging accuracy, reaching an overall accuracy of 88.8%, a Cohen’s kappa of 0.852, and a macro F1-score of 85.8%. At the same time, the ensemble models lead to reduced predictive uncertainty. The results also show that the studied algorithms and their ensembles are robust to concept drift when the training and test data were recorded seven months apart and after clinical intervention. Conclusion: However, we show that the improvements in the staging performance are not necessarily clinically significant although the ensemble models lead to more favorable clinical measures than the six standalone models. Significance: Detailed analyses further demonstrate “almost perfect” agreement between the automatic stagers to one another and their similar patterns on the staging errors, suggesting little room for improvement.
Sparse Regularization with a Non-Convex Penalty for SAR Imaging and Autofocusing

In this paper, SAR image reconstruction with joint phase error estimation (autofocusing) is formulated as an inverse problem. An optimization model utilising a sparsity-enforcing Cauchy regularizer is proposed, and an alternating minimization framework is used to solve it, in which the desired image and the phase errors are estimated alternatively. For the image reconstruction sub-problem (f-sub-problem), two methods are presented that are capable of handling the problem’s complex nature. Firstly, we design a complex version of the forward-backward splitting algorithm to solve the f-sub-problem iteratively, leading to a complex forward-backward autofocusing method (CFBA). For the second variant, techniques of Wirtinger calculus are utilized to minimize the cost function involving complex variables in the f-sub-problem in a direct fashion, leading to Wirtinger alternating minimization autofocusing (WAMA) method. For both methods, the phase error estimation sub-problem is solved by simply expanding and observing its cost function. Moreover, the convergence of both algorithms is discussed in detail. Experiments are conducted on both simulated and real SAR images. In addition to the synthetic scene employed, the other SAR images focus on the sea surface, with two being real images with ship targets, and another two being simulations of the sea surface (one of them containing ship wakes). The proposed method is demonstrated to give impressive autofocusing results on these datasets compared to state-of-the-art methods.