Miracle-dz/UniFAR-FTUs huggingface dataset:
The Inflationary Consequences of Fiscal Policy In Brazil: An Empirical Investigation with Regime Switches and Time-Varying Probabilities

Before the Real plan was launched, five major stabilization plans failed to control Brazil's chronic inflationary process. A loose-stance fiscal policy, associated with widespread price indexation and a passive-stance monetary policy, was thought to drive the ever-growing rate of prices as well as the acceleration of inflation. This direct connection between fiscal imbalances and inflation appeared to cease, however, in the post-Real period. The end of the inflation tax and the lack of new fiscal measures to restore fiscal sustainability caused the government's fiscal imbalance to deteriorate even further after 1994, but intriguingly enough, inflation remained at bay. Trying to shed some light on this matter, I estimate a set of regime-switching models with time-varying transition probability matrices, using Brazilian inflation data from the sample period comprising the six major plans (1981 through 1998). My objective is to investigate the existence of a nonlinear connection between fiscal and monetary policy variables and inflation. The empirical findings presented here indicate that after 1994, the connection between inflation and fiscal/monetary policy is best explained along the lines of Drazen and Helpman's (1990) and Sargent and Wallace's (1981) "unpleasant monetarist arithmetic. " A faster growth rate of real debt to GDP is unambiguously bad news for inflation stabilization because it increases both the chances of remaining in the high-inflation state as well as that of switching from the low- to the high-inflation regime. Following the same argument, high real interest rates have a perverse and unorthodox effect on inflation expectations by boosting the likelihood of a switch away from the low-inflation regime.
Translation, Validity, and Reliability of the Chinese Version of the Breast Cancer Survivor Self-efficacy Scale in China.

BACKGROUND Self-efficacy is a crucial variable that is related to quality of life. Patients who have high self-efficacy will exert sufficient effort and have better health outcomes and improved quality of life. Appropriate and precise measurement of self-efficacy can help promote better care. The Breast Cancer Survivor Self-efficacy Scale (BCSES) is designed to measure the perceived confidence of breast cancer patients in managing the tasks for self-management. Originally developed in America, it has not been used in China. OBJECTIVES The aims of this study were to translate BCSES into Chinese and assess its psychometric properties among Chinese patients. METHODS In phase 1, the translation of BCSES closely followed the Principles of Good Practices. In phase 2, data on reliability and validity were evaluated in terms of internal consistency, item-total correlations, test-retest reliability, criterion validity, and construct validity. A total sample of 630 native Chinese-speaking patients from 5 hospitals in China participated, including a pilot sample of 182 and a validation sample of 448. RESULTS Minor modifications in 5 items were recommended after translation. Both exploratory and confirmatory factor analyses suggested a 2-factor structure was more ideal than the original 1-factor model. Cronbach's α coefficient for the Chinese version of BCSES was . 82, intraclass correlation coefficient was 0.97, and item-total correlations were from 0.61 to 0.76. CONCLUSIONS The Chinese version of BCSES appears to be culturally appropriate, reliable, and valid for assessing self-efficacy among patients with breast cancer in China. IMPLICATION FOR PRACTICE The Chinese version of BCSES could help measure the breast cancer patients' self-efficacy and provide evidence to develop culturally sensitive interventions for Chinese patients.
Autocrine activin A signalling in ovarian cancer cells regulates secretion of interleukin 6, autophagy, and cachexia

The majority of patients with advanced cancer develop cachexia, a weight loss syndrome that severely reduces quality of life and limits survival. Our understanding of the underlying mechanisms that cause the condition is limited, and there are currently no treatment options that can completely reverse cachexia. Several tumour‐derived factors and inflammatory mediators have been suggested to contribute to weight loss in cachectic patients. However, inconsistencies between studies are recurrent. Activin A and interleukin 6 (IL‐6) are among the best studied factors that seem to be important, and several studies support their individual role in cachexia development.
Concurrent and predictive validity of the Mini Nutritional Assessment Short-Form and the Geriatric Nutritional Risk Index in older stroke rehabilitation patients.

BACKGROUND Malnutrition may worsen clinical outcomes in stroke patients. Few malnutrition screening tools have been validated in the rehabilitation setting. The present study aimed to assess the concurrent and predictive validity of two malnutrition screening tools. METHODS We retrospectively collected scores for the Mini Nutritional Assessment Short-Form (MNA-SF) and the Geriatric Nutritional Risk Index (GNRI) in consecutive stroke patients aged ≥65 years in a rehabilitation hospital. Concurrent validity was confirmed against the European Society for Clinical Nutrition and Metabolism diagnostic criteria for malnutrition (ESPEN-DCM). Malnutrition risk within the ESPEN-DCM process was assessed using the Malnutrition Universal Screening Tool. Cut-off values with maximum Youden index, and with sensitivity (Se) >90% and specificity (Sp) >50%, were defined as appropriate for identification and screening of malnutrition, respectively. The Functional Independence Measure and discharge destination were used to explore predictive validity. RESULTS Overall, 420 patients were analysed. Of these, we included 125 patients in the malnutrition group and 295 in the non-malnutrition group based on the ESPEN-DCM. Cut-off values for the identification and screening of malnutrition were 5 (Se: 0.78; Sp: 0.85) and 7 (Se: 0.96; Sp: 0.57) for the MNA-SF; 92 (Se: 0.74; Sp: 0.84) and 98 (Se: 0.93; Sp: 0.50) for the GNRI, respectively. The GNRI predicted discharge to acute care hospital, whereas the MNA-SF did not predict all outcome measures. CONCLUSIONS The MNA-SF and the GNRI have a fair concurrent validity in stroke patients, although lower cut-off values than currently used were required for the MNA-SF. The GNRI exhibits good predictive validity for discharge destination.
Lithium Diffusion in Copper.

Copper is the conventional, broadly applied anode current collector in lithium-ion batteries, since Li does not form intermetallic alloys with Cu at room temperature. Fast diffusion and trapping of lithium in copper were however suggested in the past and the involved diffusion mechanisms are still not clarified. By using three complementary methods, we determine grain boundary and lattice diffusion of lithium in copper. We show that indiffusion into copper is not only possible from metallic lithium deposits at the surface, but also from a Li+-containing electrolyte. Lattice diffusion (D0 = 3.9 x 10-9 cm2/s; Ea = 0.68 eV) and grain boundary diffusion (D0 = 1.5 x 10-11 cm2/s; Ea = 0.36 eV) are found to be 13 orders of magnitude lower than previously published. For practical Li-ion batteries considerations, lithium trapping in copper current collectors, which relies heavily on operating temperature and morphology, is furthermore discussed.
The beginning of a new era in the field of percutaneous valvular interventions (PARTNER 3 and Evolut Low Risk trials).

In 2002, Cribier performed the first transcatheter aortic valve implantation (TAVI) in an inoperable patient using a transseptal antegrade ap-proach and a balloon-expandable aortic valve prosthesis, demonstrating the feasibility of percutaneous valve implantation. From then on, the favourable effects of TAVI vs. optimal medical treatment and similar outcomes compared to surgical aortic valve replacement (SAVR) in inter-mediate- to high-risk patients with severe aortic stenosis have been shown by several studies. 1–3 Thanks to the results of these studies, now-adays, this technique is a part of everyday clinical practice in European countries. 4,5 However, it is still reserved for patients who are considered high surgical risk patients by a multidisciplinary heart team. On the other hand, technological refinements and procedural simplification have contributed to a growing interest in the use of TAVI in lower risk patients. Thus, recently reported results of PARTNER 3 and Evolut Low Risk trials seems to be particularly important for the understanding of the role of TAVI in those patients. 6,7 Both studies were presented during Joint American College of Cardiology/New England Journal of Medicine Late-Breaking Clinical Trials Session on 17 March 2019 and both compared in a randomized fashion TAVI vs. SAVR in patients who had severe aortic stenosis and were
Graph Neural Networks with Adaptive Readouts

An effective aggregation of node features into a graph-level representation via readout functions is an essential step in numerous learning tasks involving graph neural networks. Typically, readouts are simple and non-adaptive functions designed such that the resulting hypothesis space is permutation invariant. Prior work on deep sets indicates that such readouts might require complex node embeddings that can be difficult to learn via standard neighborhood aggregation schemes. Motivated by this, we investigate the potential of adaptive readouts given by neural networks that do not necessarily give rise to permutation invariant hypothesis spaces. We argue that in some problems such as binding affinity prediction where molecules are typically presented in a canonical form it might be possible to relax the constraints on permutation invariance of the hypothesis space and learn a more effective model of the affinity by employing an adaptive readout function. Our empirical results demonstrate the effectiveness of neural readouts on more than 40 datasets spanning different domains and graph characteristics. Moreover, we observe a consistent improvement over standard readouts (i.e., sum, max, and mean) relative to the number of neighborhood aggregation iterations and different convolutional operators.
Experiences and Needs of Perimenopausal Women With Climacteric Symptoms in Singapore: A Qualitative Study

Introduction: Women in the perimenopause stage may face climacteric symptoms where physical and mental challenges are experienced. The purpose of this study was to increase the understanding of the experiences and needs of perimenopausal women with climacteric symptoms in Singapore. Method: This is a descriptive qualitative study. Purposive sampling was used to recruit 20 perimenopausal women with climacteric symptoms from a tertiary public hospital in Singapore. Semistructured face-to-face interviews and thematic analysis were used for data collection and analysis, respectively. Results: Participants lacked knowledge resulting in misconceptions of the condition. Experiencing climacteric symptoms led to mixed feelings. The availability of support varied in different sources and forms. Participants seek for more information, understanding, compassion, and empathy from family members and health care professionals. Discussion: Health care professionals should provide adequate support to cater to the diverse experiences and needs of multiracial perimenopausal women with climacteric symptoms. Future research should include the perspectives of health care professionals and family members.
Ultrasensitive Detection of Clostridioides difficile Toxins in Stool by Use of Single-Molecule Counting Technology: Comparison with Detection of Free Toxin by Cell Culture Cytotoxicity Neutralization Assay

Laboratory tests for Clostridioides difficile infection (CDI) rely on the detection of free toxin or molecular detection of toxin genes. The Singulex Clarity C. diff toxins A/B assay is a rapid, automated, and ultrasensitive assay that detects C. difficile toxins A and B in stool. We compared CDI assays across two prospective multicenter studies to set a cutoff for the Clarity assay and to independently validate the performance compared with that of a cell culture cytotoxicity neutralization assay (CCCNA). ABSTRACT Laboratory tests for Clostridioides difficile infection (CDI) rely on the detection of free toxin or molecular detection of toxin genes. The Singulex Clarity C. diff toxins A/B assay is a rapid, automated, and ultrasensitive assay that detects C. difficile toxins A and B in stool. We compared CDI assays across two prospective multicenter studies to set a cutoff for the Clarity assay and to independently validate the performance compared with that of a cell culture cytotoxicity neutralization assay (CCCNA). The cutoff was set by two sites testing fresh samples from 897 subjects with suspected CDI and then validated at four sites testing fresh samples from 1,005 subjects with suspected CDI. CCCNA testing was performed at a centralized laboratory. Samples with discrepant results between the Clarity assay and CCCNA were retested with CCCNA when the Clarity result agreed with that of at least one comparator method; toxin enzyme immunoassays (EIA), glutamate dehydrogenase (GDH) detection, and PCR were performed on all samples. The cutoff for the Clarity assay was set at 12.0 pg/ml. Compared to results with CCCNA, the Clarity assay initially had 85.2% positive agreement and 92.4% negative agreement. However, when samples with discrepant results between the Clarity assay and CCCNA in the validation study were retested by CCCNA, 13/17 (76.5%) Clarity-negative but CCCNA-positive samples (Clarity+/CCCNA−) became CCCNA−, and 5/26 (19.2%) Clarity+/CCCNA− samples became CCCNA+, resulting in a 96.3% positive agreement and 93.0% negative agreement between Clarity and CCCNA results. The toxin EIA had 59.8% positive agreement with CCCNA. The Clarity assay was the most sensitive free-toxin immunoassay, capable of providing CDI diagnosis in a single-step solution. A different CCCNA result was reported for 42% of retested samples, increasing the positive agreement between Clarity and CCCNA from 85.2% to 96.3% and indicating the challenges of comparing free-toxin results to CCCNA results as a reference standard.
Simulation and Analysis of AODV Routing Protocol in VANETs

-Vehicular Ad Hoc Network (VANET) is a form of Mobile Ad Hoc Networks (MANET). The field of VANETs started gaining attention in 1980s and has now been an active field of research and development. VANETs provide us with the infrastructure for developing new systems to enhance drivers’ and passengers’ safety and comfort. There are many routing protocols that have been proposed and assessed to improve the efficiency of VANET. Simulator tool has been preferred over outdoor experiment because it is simple, easy and cheap. In this paper, simulation of one of the routing protocols i.e. AODV is done on simulators which allow users to generate real world mobility models for VANET simulations. The tools used for this purpose are SUMO, MOVE and NS2. MOVE tool is built on top of SUMO which is an open source micro-traffic simulator. Output of MOVE is a real world mobility model and can be used by network simulator NS-2. Then graphs were plotted using Tracegraph for evaluation. Based on the simulation results obtained, the performance of AODV is analyzed and compared in three different node density i.e. 4, 10 and 25 nodes with respect to various parameters like Throughput, Packet size, Packet drops, End to End delay etc.
Identification of distinctive interferon gene signatures in different types of myositis

Objective Activation of the type 1 interferon (IFN1) pathway is a prominent feature of dermatomyositis (DM) muscle and may play a role in the pathogenesis of this disease. However, the relevance of the IFN1 pathway in patients with other types of myositis such as the antisynthetase syndrome (AS), immune-mediated necrotizing myopathy (IMNM), and inclusion body myositis (IBM) is largely unknown. Moreover, the activation of the type 2 interferon (IFN2) pathway has not been comprehensively explored in myositis. In this cross-sectional study, our objective was to determine whether IFN1 and IFN2 pathways are differentially activated in different types of myositis by performing RNA sequencing on muscle biopsy samples from 119 patients with DM, IMNM, AS, or IBM and on 20 normal muscle biopsies. Methods The expression of IFN1- and IFN2-inducible genes was compared between the different groups. Results The expression of IFN1-inducible genes was high in DM, moderate in AS, and low in IMNM and IBM. In contrast, the expression of IFN2-inducible genes was high in DM, IBM, and AS but low in IMNM. The expression of IFN-inducible genes correlated with the expression of genes associated with inflammation and muscle regeneration. Of note, ISG15 expression levels alone performed as well as composite scores relying on multiple genes to monitor activation of the IFN1 pathway in myositis muscle biopsies. Conclusions IFN1 and IFN2 pathways are differentially activated in different forms of myositis. This observation may have therapeutic implications because immunosuppressive medications may preferentially target each of these pathways.
Active queue management: First steps toward a new control-theoretic viewpoint

Active Queue Management (AQM) for mitigating Internet congestion has been addressed via various feedback control syntheses, among which P, PI, and PID regulators are quite popular and often associated to a Smith predictor. Here, to better account for the many uncertainties, like the round trip time or the number of TCP sessions, the above controllers are replaced by an intelligent proportional controller associated to Model-Free Control (MFC) and by forecasting techniques derived from a new approach to time series. Several computer simulations via a well accepted linear modeling, where the delay is assumed to be constant, are presented and discussed.
A Nonlinear Approach to Forecasting with Leading Economic Indicators

The classical NBER leading indicators model was built solely within a linear framework. With recent developments in nonlinear time-series analysis, several authors have begun to examine the forecasting properties of nonlinear models in the field of forecasting business cycles. The research presented in this paper focuses on the development of a new approach to forecasting with leading indicators based on neural networks. Empirical results are presented for forecasting the Index of Industrial Production. The results demonstrate that a superior performance can be obtained relative to the classical model.
Back and Forth: Reverse Phase Transitions in Numerical Relativity Simulations

Multi-messenger observations of binary neutron star mergers provide a unique opportunity to constrain the dense-matter equation of state. Although it is known from quantum chromodynamics that hadronic matter will undergo a phase transition to exotic forms of matter, e.g., quark matter, the onset density of such a phase transition cannot be computed from ﬁrst principles. Hence, it remains an open question if such phase transitions occur inside isolated neutron stars or during binary neutron star mergers, or if they appear at even higher densities that are not realized in the Cosmos. In this article, we perform numerical-relativity simulations of neutron-star mergers and investigate scenarios in which the onset density of such a phase transition is exceeded in at least one inspiralling binary component. Our simulations reveal that shortly before the merger it is possible that such stars undergo a “ reverse phase transition ”, i.e., densities decrease and the quark core inside the star disappears leaving a purely hadronic star at merger. After the merger, when densities increase once more, the phase transition occurs again and leads, in the cases considered in this work, to a rapid formation of a black hole. We compute the gravitational-wave signal and the mass ejection for our simulations of such scenarios and ﬁnd clear signatures that are related to the postmerger phase transition, e.g., smaller ejecta masses due to the softening of the equation of state through the quark core formation. Unfortunately, we do not ﬁnd measurable imprints of the reverse phase transition.
Repairing Multiple Failures for Scalar MDS Codes

In distributed storage, erasure codes (like Reed–Solomon Codes) are often employed to provide reliability. In this setting, it is desirable to be able to repair one or more failed nodes while minimizing the repair bandwidth. In this paper, motivated by Reed-Solomon codes, we study the problem of repairing multiple failed nodes in a scalar MDS code. We extend the framework of (Guruswami and Wootters, 2017) to give a framework for constructing repair schemes for multiple failures in general scalar MDS codes in the centralized repair model. We then specialize our framework to Reed–Solomon codes, and also extend and improve upon recent results of (Dau et al., 2017).
A Graphical Investigation of the Size and Power of the Granger-Causality Tests in Integrated-Cointegrated VAR Systems

The size and power of the Wald, Edgeworth expansion corrected likelihood-ratio statistic (LRE), and bootstrap tests for Granger causality in integrated-cointegrated VAR systems are considered. By using Monte Carlo methods and simple graphical techniques, the p-value plots, and power-size plots, properties of the tests in two different forms, the standard and the Dolado and L ¨ utkepohl (1996) modified form, are studied. Regarding the size of the tests, we found that the Wald performs badly in small- and medium-size samples, but the corrected tests, and especially the LRE for large samples, exhibit good performances. The bootstrap test showed the best performance in almost all situations. When considering the power results, using the size-power curves on a correct-size-adjusted basis, we found that the bootstrap tests have adequate power.
Nonlinearity in High-Frequency Financial Data and Hierarchical Models

This paper studies nonlinear behavior of high-frequency financial data and employs nonlinear hierarchical models for analyzing such data. We illustrate the analysis by modeling the transaction-bytransaction data of IBM stock on the New York Stock Exchange for a period of 3 months. The variables considered include time durations between trades and price changes. For a short time span of 5 trading days, a simple threshold model is found adequate for modeling time durations between trades after adjusting for the diurnal pattern of the data. When price change and time duration between price changes are considered jointly, we use a hierarchical model that consists of 6 simple conditional models to handle the dynamic structure within a trading day and the variation between trading days for the whole sample. The model shows that dynamic structure exists in the high-frequency data, but there are some special days on which the behavior of the stock seems different from the others. We use Markov chain Monte Carlo methods to estimate the hierarchical model.