Miracle-dz/UniFAR-FTUs huggingface dataset:
Low Baseline Interleukin-17A Levels Are Associated with Better Treatment Response at 12 Weeks to Tocilizumab Therapy in Rheumatoid Arthritis Patients

T helper 17-related cytokines have been implicated in rheumatoid arthritis (RA) pathogenesis. The study aimed to identify cytokines associated with the treatment response of RA patients to tocilizumab (TCZ), a humanized monoclonal antibody against the interleukin- (IL-) 6 receptor. As an independent substudy of the 24-week, randomized, double-blinded CWP-TCZ301 trial of TCZ in RA patients with an inadequate response to disease-modifying antirheumatic drugs, serum levels of cytokines including tumor necrosis factor-alpha, IL-17A, IL-21, IL-23, IL-6, and soluble IL-6 receptor were measured. Baseline IL-17A levels were significantly lower in RA patients who achieved disease activity score 28 (DAS28) remission at 12 weeks of TCZ treatment, compared to patients not in remission. Patients were stratified into IL-17A low group and IL-17A high group. Significantly more patients in the IL-17A low group achieved remission as compared to the IL-17A high group (47.6 versus 17.4%, P = 0.032). DAS28 improvement was significantly better in the IL-17A low group than in the IL-17A high group at 12 weeks (P = 0.045) and 24 weeks (P = 0.046) after adjustment. Other baseline cytokines were not associated with treatment response to TCZ. The data demonstrate that low baseline IL-17A levels are associated with better clinical response to TCZ treatment in RA patients.
Review of Randomized Control Trials Assessing the Efficacy of Noninvasive Ventilation (NIV) in Acute Hypoxemic Respiratory Failure (AHRF) in Non-COPD and Non-Trauma Cases

The following individuals read and discussed the thesis submitted by student Marja AlYami, and they also evaluated his presentation and response to questions during the final oral examination. They found that the student passed the final oral examination, and that the thesis was satisfactory for a master's degree and ready for any final modifications that they explicitly required. iv ACKNOWLEDGMENTS I would like to thank Dr. Conrad Colby for his wisdom, guidance, and encouragement, and also thank Dr. Uwe Reischl and Dr. Holly Anderson for their dedicated service on my thesis committee. v ABSTRACT Introduction: In the last two decades, there has been strong evidence supporting
Assessment of Intracranial Collaterals on CT Angiography in Anterior Circulation Acute Ischemic Stroke

Different methods of assessing collateral circulation based on CTA were compared in 200 patients with stroke. Only the Miteff scoring system was reliable for predicting favorable outcome in these patients but poor outcomes were predictedby othermethods, too (Maas, Tan, and ASPECTS). BACKGROUND AND PURPOSE: Intracranial collaterals influence the prognosis of patients treated with intravenous tissue plasminogen activator in acute anterior circulation ischemic stroke. We compared the methods of scoring collaterals on pre-tPA brain CT angiography for predicting functional outcomes in acute anterior circulation ischemic stroke. MATERIALS AND METHODS: Two hundred consecutive patients with acute anterior circulation ischemic stroke treated with IV-tPA during 2010–2012 were included. Two independent neuroradiologists evaluated intracranial collaterals by using the Miteff system, Maas system, the modified Tan scale, and the Alberta Stroke Program Early CT Score 20-point methodology. Good and extremely poor outcomes at 3 months were defined by modified Rankin Scale scores of 0–1 and 5–6 points, respectively. RESULTS: Factors associated with good outcome on univariable analysis were younger age, female sex, hypertension, diabetes mellitus, atrial fibrillation, small infarct core (ASPECTS ≥8), vessel recanalization, lower pre-tPA NIHSS scores, and good collaterals according to Tan methodology, ASPECTS methodology, and Miteff methodology. On multivariable logistic regression, only lower NIHSS scores (OR, 1.186 per point; 95% CI, 1.079–1.302; P = .001), recanalization (OR, 5.599; 95% CI, 1.560–20.010; P = .008), and good collaterals by the Miteff method (OR, 3.341; 95% CI, 1.203–5.099; P = .014) were independent predictors of good outcome. Poor collaterals by the Miteff system (OR, 2.592; 95% CI, 1.113–6.038; P = .027), Maas system (OR, 2.580; 95% CI, 1.075–6.187; P = .034), and ASPECTS method ≤5 points (OR, 2.685; 95% CI, 1.156–6.237; P = .022) were independent predictors of extremely poor outcomes. CONCLUSIONS: Only the Miteff scoring system for intracranial collaterals is reliable for predicting favorable outcome in thrombolyzed acute anterior circulation ischemic stroke. However, poor outcomes can be predicted by most of the existing methods of scoring intracranial collaterals.
Graph-Based Learning via Auto-Grouped Sparse Regularization and Kernelized Extension

The key task in developing graph-based learning algorithms is constructing an informative graph to express the contextual information of a data manifold. Since traditional graph construction methods are sensitive to noise and less datum-adaptive to changes in density, a new method called ℓ1-graph was proposed recently. A graph construction needs to have two important properties: sparsity and locality. The ℓ1-graph has a strong sparsity property, but a weak locality property. Thus, we propose a new method of constructing an informative graph using auto-grouped sparse regularization based on the ℓ1-graph, which is called as Group Sparse graph (GSgraph). We also show how to efficiently construct a GS-graph in reproducing kernel Hilbert space with the kernel trick. The new methods, the GS-graph and its kernelized version (KGS-graph), have the same noise-insensitive property as that of ℓ1-graph and also can successively preserve the properties of sparsity and locality simultaneously. Furthermore, we integrate the proposed graph with several graph-based learning algorithms to demonstrate the effectiveness of our method. The empirical studies on benchmarks show that the proposed methods outperform the ℓ1-graph and other traditional graph construction methods in various learning tasks.
The opposite of smoothing: a language model approach to ranking query-specific document clusters

Exploiting information induced from (query-specific) clustering of top-retrieved documents has long been proposed as means for improving precision at the very top ranks of the returned results. We present a novel language model approach to ranking query-specific clusters by the presumed percentage of relevant documents that they contain. While most previous cluster ranking approaches focus on the cluster as a whole, our model also exploits information induced from documents associated with the cluster. Our model substantially outperforms previous approaches for identifying clusters containing a high relevant-document percentage. Furthermore, using the model to produce document ranking yields precision-at-top-ranks performance that is consistently better than that of the initial ranking upon which clustering is performed; the performance also favorably compares with that of a state-of-the-art pseudo-feedback retrieval method.
A Continuous Opinion Dynamic Model in Co-evolving Networks-A Novel Group Decision Approach

Opinion polarization is a ubiquitous phenomenon in opinion dynamics. In contrast to the traditional consensus oriented group decision making (GDM) framework, this paper proposes a framework with the co-evolution of both opinions and relationship networks to improve the potential consensus level of a group and help the group reach a stable state. Taking the bound of confidence and the degree of individual's persistence into consideration, the evolution of the opinion is driven by the relationship among the group. Meanwhile, the antagonism or cooperation of individuals presented by the network topology also evolve according to the dynamic opinion distances. Opinions are convergent and the stable state will be reached in this co-evolution mechanism. We further explored this framework through simulation experiments. The simulation results verify the influence of the level of persistence on the time cost and indicate the influence of group size, the initial topology of networks and the bound of confidence on the number of opinion clusters.
αβ TCR-Mediated Recognition: Relevance to Tumor-Antigen Discovery and Cancer Immunotherapy

αβ T lymphocytes sense perturbations in host cellular body components induced by infectious pathogens, oncogenic transformation, or chemical or physical damage. Millions to billions of these lymphocytes are generated through T-lineage development in the thymus, each endowed with a clonally restricted surface T-cell receptor (TCR). An individual TCR has the capacity to recognize a distinct “foreign” peptide among the myriad of antigens that the mammalian host must be capable of detecting. TCRs explicitly distinguish foreign from self-peptides bound to major histocompatibility complex (MHC) molecules. This is a daunting challenge, given that the MHC-linked peptidome consists of thousands of distinct peptides with a relevant nonself target antigen often embedded at low number, among orders of magnitude higher frequency self-peptides. In this Masters of Immunology article, I review how TCR structure and attendant mechanobiology involving nonlinear responses affect sensitivity as well as specificity to meet this requirement. Assessment of human tumor-cell display using state-of-the-art mass spectrometry physical detection methods that quantify epitope copy number can help to provide information about requisite T-cell functional avidity affording protection and/or therapeutic immunity. Future rational CD8 cytotoxic T-cell–based vaccines may follow, targeting virally induced cancers, other nonviral immunogenic tumors, and potentially even nonimmunogenic tumors whose peptide display can be purposely altered by MHC-binding drugs to stimulate immune attack. Cancer Immunol Res; 3(4); 305–12. ©2015 AACR.
Antioxidant Supplementation in the Treatment of Aging-Associated Diseases

Oxidative stress is generally considered as the consequence of an imbalance between pro- and antioxidants species, which often results into indiscriminate and global damage at the organismal level. Elderly people are more susceptible to oxidative stress and this depends, almost in part, from a decreased performance of their endogenous antioxidant system. As many studies reported an inverse correlation between systemic levels of antioxidants and several diseases, primarily cardiovascular diseases, but also diabetes and neurological disorders, antioxidant supplementation has been foreseen as an effective preventive and therapeutic intervention for aging-associated pathologies. However, the expectations of this therapeutic approach have often been partially disappointed by clinical trials. The interplay of both endogenous and exogenous antioxidants with the systemic redox system is very complex and represents an issue that is still under debate. In this review a selection of recent clinical studies concerning antioxidants supplementation and the evaluation of their influence in aging-related diseases is analyzed. The controversial outcomes of antioxidants supplementation therapies, which might partially depend from an underestimation of the patient specific metabolic demand and genetic background, are presented.
Language‐related Cntnap2 gene is differentially expressed in sexually dimorphic song nuclei essential for vocal learning in songbirds

Multiple studies, involving distinct clinical populations, implicate contactin associated protein‐like 2 (CNTNAP2) in aspects of language development and performance. While CNTNAP2 is broadly distributed in developing rodent brain, it shows a striking gradient of frontal cortical enrichment in developing human brain, consistent with a role in patterning circuits that subserve higher cognition and language. To test the hypothesis that CNTNAP2 may be important for learned vocal communication in additional species, we employed in situ hybridization to characterize transcript distribution in the zebra finch, an experimentally tractable songbird for which the neural substrate of this behavior is well established. Consistent with an important role in learned vocalization, Cntnap2 was enriched or diminished in key song control nuclei relative to adjacent brain tissue. Importantly, this punctuated expression was observed in males, but not females, in accord with the sexual dimorphism of neural circuitry and vocal learning in this species. Ongoing functional work will provide important insights into the relationship between Cntnap2 and vocal communication in songbirds and thereby clarify mechanisms at play in disorders of human cognition and language. J. Comp. Neurol. 518:1995–2018, 2010. © 2010 Wiley‐Liss, Inc.
A New Alternating Minimization Algorithm for Total Variation Image Reconstruction

We propose, analyze, and test an alternating minimization algorithm for recovering images from blurry and noisy observations with total variation (TV) regularization. This algorithm arises from a new half-quadratic model applicable to not only the anisotropic but also the isotropic forms of TV discretizations. The per-iteration computational complexity of the algorithm is three fast Fourier transforms. We establish strong convergence properties for the algorithm including finite convergence for some variables and relatively fast exponential (or $q$-linear in optimization terminology) convergence for the others. Furthermore, we propose a continuation scheme to accelerate the practical convergence of the algorithm. Extensive numerical results show that our algorithm performs favorably in comparison to several state-of-the-art algorithms. In particular, it runs orders of magnitude faster than the lagged diffusivity algorithm for TV-based deblurring. Some extensions of our algorithm are also discussed.
Eliciting the dynamics of leading a sustainable event: Key informant responses

Within the event management literature relating to network development and festival sustainability there is a paucity of research that analyzes the perception of festival sustainability by festival leaders. After an initial review of the context of sustainability, network theory, and an identification of the changing set of competencies for effective leadership, an exploratory and explanatory investigation is made to elicit and identify the critical factors that key informant festival leaders associate with sustainable festivals. The main purpose of this study is to attain a greater depth of understanding of festival leaders’ attitudes towards the dynamics of creating and directing sustainable festivals. Indepth interviews with five elite festival leaders helped to generate the elements of a repertory grid from which a “triading” method was used to elicit constructs. Of the constructs identified, the most significant relate to four areas: the event subject focus; the leadership; the funding; and the organizational culture. The research also revealed that festival leaders conceive sustainability not as an environmental concern, but as a matter of festival survival. Suggestions are then drawn as to the future role of the repertory grid method in identifying and managing stakeholder visions, and future lines of research investigation and application.
A simple method to construct exact density-potential pairs from a homeoidal expansion

We start from a study of the density-potential relation for classical homeoids in terms of an asymptotic expansion for small deviations from spherical symmetry. We then show that such expansion is a useful device that allows us to construct a variety of exact density-potential pairs with spheroidal, toroidal, or triaxial shapes for which the deviation from spherical symmetry is finite. As concrete analytical applications, we describe: (1) The construction of a family of toroidal axisymmetric density-potential pairs one of which is associated with a perfectly flat rotation curve (for a member of this family, the supporting two- integral phase-space distribution function is obtained in closed form); (2) The determination of the aperture velocity dispersion in a wide class of two-integral axisymmetric models not stratified on homeoids with central black hole, which may be useful for the discussion of the dynamical contributions to the character- istics of the Fundamental Plane of early-type galaxies; and (3) For such class of models, the construction of the v/�-ellipticity relation, often considered to assess the role of rotation in the structure of elliptical galaxies.
Nondispersive Decay For The Cubic Wave Equation

We consider the hyperboloidal initial value problem for the cubic focusing wave equation (-partial derivative(2)(t) + Delta(x))nu(t, x) + nu(t, x)(3) = 0, x is an element of R-3 Without symmetry assumptions, we prove the existence of a codimension-4 Lipschitz manifold of initial data that lead to global solutions in forward time which do not scatter to free waves. More precisely, for any delta is an element of (0, 1) we construct solutions with the asymptotic behavior vertical bar vertical bar nu - nu(0) vertical bar vertical bar L-4(t, 2t)L-4(B(1-delta)t) less than or similar to t(-1/2+) as t -> infinity, where nu(0)(t, x) = root 2/t and B(1-delta)t := {x is an element of R-3 : vertical bar x vertical bar < (1-delta)t}.
A Rate-Limiting Role for Dickkopf-1 in Bone Formation and the Remediation of Bone Loss in Mouse and Primate Models of Postmenopausal Osteoporosis by an Experimental Therapeutic Antibody

Genetic studies have linked both osteoporotic and high bone mass phenotypes to low-density lipoprotein receptor-related proteins (LRP4, LRP5, and LRP6). LRPs are receptors for inhibitory Dickkopf-1 (DKK1) protein, and treatment modalities that modulate LRP/DKK1 binding therefore may act as stimulators of bone mass accrual. Here, we report that RH2-18, a fully human monoclonal anti-DKK1 antibody elicits systemic pharmacologic bone efficacy and new bone formation at endosteal bone surfaces in vivo in a mouse model of estrogen-deficiency-induced osteopenia. This was paralleled by partial-to-complete resolution of osteopenia (bone mineral density) at all of the skeletal sites investigated in femur and lumbar-vertebral bodies and the restoration of trabecular bone microarchitecture. More importantly, testing of RH2-18 in adult, osteopenic rhesus macaques demonstrated a rate-limiting role of DKK1 at multiple skeletal sites and responsiveness to treatment. In conclusion, this study provides pharmacologic evidence for the modulation of DKK1 bioactivity in the adult osteopenic skeleton as a viable approach to resolve osteopenia in animal models. Thus, data described here suggest that targeting DKK1 through means such as a fully human anti-DKK1-antibody provides a potential bone-anabolic treatment for postmenopausal osteoporosis.
Existential live sequence charts revisited

Scenario-based specifications are a popular means for describing intended system behaviour. We aim to facilitate early analysis of system behaviour and the development of behaviour models in conjunction with scenarios. In this paper we define a novel scenario-based specification language with an existential semantics and that supports conditional specification of behaviour in the form of prechart and main chart. The language semantics is consistent with existing informal scenario-based and use-case based approaches to requirements engineering. The language provides a good fit with universal live sequence charts as standard existential live sequence charts do not adequately support conditional scenarios. In addition, we define a novel synthesis algorithm that, rather than building arbitrarily one of the many behaviour models that satisfy a scenario, constructs a modal transition system (MTS) which characterizes all behaviour models that conform to the scenario.
Adding Twitter‐specific features to stylistic features for classifying tweets by user type and number of retweets

Recently, Twitter has received much attention, both from the general public and researchers, as a new method of transmitting information. Among others, the number of retweets (RTs) and user types are the two important items of analysis for understanding the transmission of information on Twitter. To analyze this point, we applied text classification and feature extraction experiments using random forests machine learning with conventional stylistic and Twitter‐specific features. We first collected tweets from 40 accounts with a high number of followers and created tweet texts from 28,756 tweets. We then conducted 15 types of classification experiments using a variety of combinations of features such as function words, speech terms, Twitter's descriptive grammar, and information roles. We deliberately observed the effects of features for classification performance. The results indicated that class classification per user indicated the best performance. Furthermore, we observed that certain features had a greater impact on classification. In the case of the experiments that assessed the level of RT quantity, information roles had an impact. In the case of user experiments, important features, such as the honorific postpositional particle and auxiliary verbs, such as “desu” and “masu,” had an impact. This research clarifies the features that are useful for categorizing tweets according to the number of RTs and user types.
A comparison of approaches to automatic language identification using telephone speech

A variety of approaches to language identiication, based on (a) acoustic features, (b) broad-category segmentation, and (c) ne phonetic classiication, are introduced. These approaches are evaluated in terms of their ability to distinguish between English and Japanese utterances spoken over a telephone channel. It is found that the best performance (86.3 % accurate classiication of utterances with a mean length of 13.4 sec) is obtained when ne phonetic features are employed. In addition , the results show the importance of discriminatory training rather than likelihood estimation.
SLACER: a self-organizing protocol for coordination in peer-to-peer networks

Coordination between nodes in open distributed computer systems is a general problem that's becoming increasingly relevant as massive peer-to-peer (P2P) systems are being deployed on the Internet. A major subproblem is establishing and maintaining cooperation between nodes. To solve this problem, we created a simple algorithm, SLACER (a selfish link-based adaptation for cooperation excluding rewiring). When executed in a P2P network's nodes, SLACER self-organizes the network into a robust artificial social network (ASN) with small-world characteristics and high cooperation.