Miracle-dz/UniFAR-FTUs huggingface dataset:
Chronic postoperative pain: recent findings in understanding and management

Chronic postoperative pain is a poorly recognized potential outcome from surgery. It affects millions of patients every year, with pain lasting for months to years, resulting in patient suffering and ensuing economic consequences. The operations with the highest incidence of chronic postoperative pain are amputations, thoracotomies, cardiac surgery, and breast surgery. Other risk factors include preoperative pain, psychological factors, demographics, and the intensity of acute postoperative pain. Attempts to prevent chronic postoperative pain have often led to debatable results. This article presents data from recently published studies examining the incidence, risk factors, mechanisms, treatment options, and preventive strategies for chronic postoperative pain in adults. In summary, many of the previously identified risk factors for chronic postoperative pain have been confirmed and some novel ones discovered, such as the importance of the trajectory of acute pain and the fact that catastrophizing may not always be predictive. The incidence of chronic postoperative pain hasn’t changed over time, and there is limited new information regarding an effective preventive therapy. For example, pregabalin may actually cause more harm in certain surgeries. Further research is needed to demonstrate whether multimodal analgesic techniques have the best chance of significantly reducing the incidence of chronic postoperative pain and to determine which combination of agents is best for given surgical types and different patient populations.
Increased Numbers of IL-7 Receptor Molecules on CD4+CD25−CD107a+ T-Cells in Patients with Autoimmune Diseases Affecting the Central Nervous System

Background High content immune profiling in peripheral blood may reflect immune aberrations associated with inflammation in multiple sclerosis (MS) and other autoimmune diseases affecting the central nervous system. Methods and Findings Peripheral blood mononuclear cells from 46 patients with multiple sclerosis (MS), 9 patients diagnosed with relapsing remitting MS (RRMS), 13 with secondary progressive multiple sclerosis (SPMS), 9 with other neurological diseases (OND) and well as 15 healthy donors (HD) were analyzed by 12 color flow cytometry (TCRαβ, TCRγδ, CD4, CD8α, CD8β, CD45RA, CCR7, CD27, CD28, CD107a, CD127, CD14) in a cross-sectional study to identify variables significantly different between controls (HD) and patients (OND, RRMS, SPMS). We analyzed 187 individual immune cell subsets (percentages) and the density of the IL-7 receptor alpha chain (CD127) on 59 individual immune phenotypes using a monoclonal anti-IL-7R antibody (clone R34.34) coupled to a single APC molecule in combination with an APC-bead array. A non-parametric analysis of variance (Kruskal-Wallis test) was conducted in order to test for differences among the groups in each of the variables. To correct for the multiplicity problem, the FDR correction was applied on the p-values. We identified 19 variables for immune cell subsets (percentages) which allowed to segregate healthy individuals and individuals with CNS disorders. We did not observe differences in the relative percentage of IL-7R-positive immune cells in PBMCs. In contrast, we identified significant differences in IL-7 density, measured on a single cell level, in 2/59 variables: increased numbers of CD127 molecules on TCRαβ+CD4+CD25 (intermed) T-cells and on TCRαβ+CD4+CD25−CD107a+ T-cells (mean: 28376 Il-7R binding sites on cells from HD, 48515 in patients with RRMS, 38195 in patients with SPMS and 33692 IL-7 receptor binding sites on cells from patients with OND). Conclusion These data show that immunophenotyping represents a powerful tool to differentiate healthy individuals from individuals suffering from neurological diseases and that the number of IL-7 receptor molecules on differentiated TCRαβ+CD4+CD25−CD107a+ T-cells, but not the percentage of IL-7R-positive cells, segregates healthy individuals from patients with neurological disorders.
Membrane flow during pinocytosis. A stereologic analysis

HRP has been used as a cytochemical marker for a sterelogic analysis of pinocytic vesicles and secondary lysosomes in cultivated macrophages and L cells. Evidence is presented that the diaminobenzidine technique (a) detects all vaculoes containing encyme and (b) distinguishes between incoming pinocytic vesicles and those which have fused with pre- existing lysosomes to form secondary lososomes. The HRP reactive pinocytic vesicle spaces fills completely within 5 min after exposure to enzyme, while the secondary lysosome compartment is saturated in 45-- 60 min. The size distribution of sectioned (profile) vaculoe diameters was measured at equilibrium and converted to actual (spherical) dimensions using a technique modified from Dr. S. D. Wicksell. The most important findings in this study have to do with the rate at which pinocytosed fluid and surface membrane move into the cell and on their subsequent fate. Each minute macrophages form at least 125 pinocytic vesicles having a fractional vol of 0.43% of the cell's volume and a fractional area of 3.1% of the cell's surface area. The fractional volume and surface area flux rates for L cells were 0.05% and 0.8% per minute respectively. Macrophages and L cells thus interiorize the equivalent of their cell surface area every 33 and 125 min. During a 3- period, the size of the secondary lysosome compartment remains constant and represents 2.5% of the cell volume and 18% of the surface area. Each hour, therefore, the volume and surface area of incoming vesicles is 10 times greater than the dimensions of the secondary lysosomes in both macrophages and L cells. This implies a rapid reduction in vesicle size during the formation of the secondary lysosome and the egress of pinocytosed fluid from the vacuole and the cell. In addition, we postulate that membrane components of the vacuole are subsequently recycled back to the cell surface.
On = 1 exact superpotentials from U(N) matrix models

In this letter we compute the exact effective superpotential of = 1 U(N) supersymmetric gauge theories with Nf fundamental flavors and an arbitrary tree-level polynomial superpotential for the adjoint Higgs field. We use the matrix model approach in the maximally confining phase. When restricted to the case of a tree-level even polynomial superpotential, our computation reproduces the known result of the SU(N) theory.
Modeling plasma virus concentration during primary HIV infection.

During primary HIV infection the viral load in plasma increases, reaches a peak, and then declines. Phillips has suggested that the decline is due to a limitation in the number of cells susceptible to HIV infection, while other authors have suggested that the decline in viremia is due to an immune response. Here we address this issue by developing models of primary HIV-1 infection, and by comparing predictions from these models with data from ten anti-retroviral, drug-naive, infected patients. Applying nonlinear least-squares estimation, we find that relatively small variations in parameters are capable of mimicking the highly diverse patterns found in patient viral load data. This approach yields an estimate of 2.5 days for the average lifespan of productively infected cells during primary infection, a value that is consistent with results obtained by drug perturbation experiments. We find that the data from all ten patients are consistent with a target-cell-limited model from the time of initial infection until shortly after the peak in viremia. However, the kinetics of the subsequent fall and recovery in virus concentration in some patients are not consistent with the predictions of the target-cell-limited model. We illustrate that two possible immune response mechanisms, cytotoxic T lymphocyte destruction of infected target cells and cytokine suppression of viral replication, could account for declines in viral load data not predicted by the original target-cell-limited model. We conclude that some additional process, perhaps mediated by CD8+ T cells, is important in at least some patients.
Synthesis and Biological Evaluation of Novel Eg5 Inhibitors

Human Eg5 is a mitotic kinesin that is essential for bipolar spindle formation and maintenance during mitosis. Recently, the discovery of compounds that inhibit Eg5 and cause mitotic arrest has attracted great interest, due to their potential use as the next generation of antimitotics. Here, we present the synthesis and biological investigation of 3,4‐dihydrophenylquinazoline‐2(1H)‐thiones as selective and potent Eg5 inhibitors.
On the Identification of Peer Effect Models of Cognitive Achievement

The identification problems of general peer effect models have been studied in depth. But cognitive achievement studies suffer two additional sources of bias that arise because the behavioral variable - student effort - is unobserved. This paper studies both sources of bias and analyzes the relative success of the identification strategies most commonly used. In a quadratic model, the equilibrium parameters suffer endogeneity bias except in three particular cases, which are fairly restrictive. Even in these three cases, the structural peer effects are not identified. The most common strategies achieve partial identification at best, even after assuming a unique mechanism for peer effort, and additional assumptions are needed on the production and cost function. This implies that the current empirical studies overestimate the true effect of peers on achievement.
A matroid approach to finding edge connectivity and packing arborescences

We present an algorithm that finds the edge connectivity ? of a graph having n vectices and m edges. The running time is O(? m log(n2/m)) for directed graphs and slightly less for undirected graphs, O(m+? 2n log(n/? )). This improves the previous best time bounds, O(min{mn, ? 2n2}) for directed graphs and O(? n2) for undirected graphs. We present an algorithm that finds k edge-disjoint arborescences on a directed graph in time O((kn)2). This improves the previous best time bound, O(kmn + k3n2). Unlike previous work, our approach is based on two theorems of Edmonds that link these two problems and show how they can be solved.
Working Memory Training Improves Dual-Task Performance on Motor Tasks

ABSTRACT The authors investigated whether working memory training improves motor-motor dual-task performance consisted of upper and lower limb tasks. The upper limb task was a simple reaction task and the lower limb task was an isometric knee extension task. 45 participants (age = 21.8 ± 1.6 years) were classified into a working memory training group (WM-TRG), dual-task training group, or control group. The training duration was 2 weeks (15 min, 4 times/week). Our results indicated that working memory capacity increased significantly only in the WM-TRG. Dual-task performance improved in the WM-TRG and dual-task training group. Our study provides the novel insight that working memory training improves dual-task performance without specific training on the target motor task.
In Vitro, Ex Vivo and In Vivo Techniques to Study Neuronal Migration in the Developing Cerebral Cortex

Neuronal migration is a fundamental biological process that underlies proper brain development and neuronal circuit formation. In the developing cerebral cortex, distinct neuronal populations, producing excitatory, inhibitory and modulatory neurotransmitters, are generated in different germinative areas and migrate along various routes to reach their final positions within the cortex. Different technical approaches and experimental models have been adopted to study the mechanisms regulating neuronal migration in the cortex. In this review, we will discuss the most common in vitro, ex vivo and in vivo techniques to visualize and study cortical neuronal migration.
Adaptive Petri Nets implementation. The execution time controller

The reaction time of a controller is a fundamental matter in discrete event control systems. Petri nets are extensively used in this field. The controller reads the inputs, executes the control Petri net and writes the output in a cyclic manner. The reaction time of this controller depends on the Petri net structure, on the events sequence and on the algorithm that executes the net. With the objective of minimizing the reaction time, we decided to design a supervisor controller, which we have called execution time controller (ETC). The aim of the ETC is to determine in real time which algorithm executes the Petri net fastest and to change the execution algorithm when necessary. In the case of systems control, this minimizes the controller reaction time and also minimizes the power consumed by the controller. One possible application of the technique is the minimization of execution time of the programmable logic controllers programs developed in sequential function chart (SFC).
Sense and collision avoidance of Unmanned Aerial Vehicles using Markov Decision Process and flatness approach

This paper presents a new development of collision avoidance algorithm that ensures an Unmanned Aerial Vehicle (UAV) can avoid multiple intruders autonomously. Firstly, the Markov Decision Process (MDP) based approach generates the multiple threats resolution logic for the collision avoidance system. Secondly, the optimal trajectory is smoothed by the differential flatness technique where the constraints of the UAV dynamics are considered. In such a way, the planned trajectory is feasible for the UAV. The effectiveness of the developed scheme is illustrated by the numerical simulation studies.
L'histoire (faussement) naïve des modèles DSGE

The purpose of the article is to analyze and criticize the way how DSGE macroeconomists working in policy-making institutions think about the history of their own modeling practice. Our contribution is, first of all, historiographical: it investigates an original literature, emphasizing in the history of DSGE as it is told by its own practitioners. The results of this analysis is what we will call a “naive history” of DSGE modeling. Modellers working from this perspective present their models as the achievement of a “scientific progress”, which is linear and cumulative both in macroeconomic theorizing and in the application of formalized methods and econometric techniques to the theory. This article also proposes a critical perspective about the naive history of the DSGE models, which drawns, by contrast, the main lines of an alternative, “non-naive” history. of the DSGE models is incomplete and imprecise. It mainly ignores controversies, failures and blind alleys in previous research; as a consequence, the major theoretical and empirical turning points are made invisible. The naive history also provides an ahistorical account of assessment criteria for modeling (especially for evaluating empirical consistency), which hides the underlying methodological and epistemological debates. Finally, we will claim that the naive history plays an active and rhetoric role in legitimizing the DSGE models as a dominant tool for policy expertise
Structural brain differences and cognitive functioning related to body mass index in older females

Little is known about the effect of obesity on brain structures and cognition in healthy older adults. This study examined the association between body mass index (BMI), regional volume differences in gray and white matter measured by magnetic resonance imaging (MRI), and cognitive functioning in older females. Participants included 95 community‐dwelling older females (ages 52–92 years) who underwent extensive neuropsychological testing and high‐resolution MRI scanning. Optimized voxel‐based morphometry techniques were employed to determine the correlation between BMI and regional gray and white matter volumes. Volumes of significant regions were then correlated with cognitive functioning. Higher BMI was associated with decreased gray matter volumes in the left orbitofrontal, right inferior frontal, and right precentral gyri, a right posterior region including the parahippocampal, fusiform, and lingual gyri, and right cerebellar regions, as well as increased volumes of white matter in the frontal, temporal, and parietal lobes, even when hypertension was considered. Compared to normal weight women, obese women performed poorer on tests of executive functioning. Smaller gray matter volume in the left orbitofrontal region was associated with lower executive functioning. Additionally, despite the lack of significant group differences in memory and visuomotor speed, gray and white matter volumes predicted performance on these measures. The results provide additional evidence for a negative link between increased body fat and brain functioning in older females. Hum Brain Mapp, 2010. © 2009 Wiley‐Liss, Inc.
Generating a Visceral Response

The research applies the theory of visceral influences to investigate the effects of visceral cues in a weight loss advertising context. Three visceral cues are addressed: proximity of reward, visual prime, and vividness of reward. Predictions are derived and three experiments are designed to examine the effects of visceral cues in weight loss advertisements. Results indicate that visceral cues are associated with greater buying impulse, attitudes toward the product, attitudes toward the advertisement, and purchase intentions. A high level of involvement with body weight is shown to increase respondent susceptibility to the visceral cues. Implications for theory and research in advertising are discussed.
Department of Economics Econometrics Working Paper Ewp0906 Finite-sample Properties of the Maximum Likelihood Estimator for the Binary Logit Model with Random Covariates

We examine the finite sample properties of the maximum likelihood estimator for the binary logit model with random covariates. Analytic expressions for the first-order bias and second-order mean squared error function for the maximum likelihood estimator in this model are derived, and we undertake some numerical evaluations to analyze and illustrate these analytic results for the single covariate case. For various data distributions, the bias of the estimator is signed the same as the covariate's coefficient, and both the absolute bias and the mean squared errors increase symmetrically with the absolute value of that parameter. The behaviour of a bias-adjusted maximum likelihood estimator, constructed by subtracting the (maximum likelihood) estimator of the first-order bias from the original estimator, is examined in a Monte Carlo experiment. This bias-correction is effective in all of the cases considered, and is recommended when the logit model is estimated by maximum likelihood with small samples.
Does Culture Matters in Intersection of Individual s Personality and Social Media Engagement?

Personality traits of consumers may be important elements in the increasingly user-generated web for the engagement in this participatory media. Previous studies suggest three personality traitsextraversion, neuroticism and openness to experienceare related to uses of social applications like Facebook. The aim of current research is to evaluate the factors affecting consumer’s social media engagement in terms of liking, commenting and sharing behavior on Facebook brand fan pages, and to evaluate the mediating role of interaction modes and to analyze the moderating role of culture, on relationship between personality traits and engagement behavior of consumers. Data was collected from 748 fans of 15 Facebook brand fan pages of five fast food brands operating in three different countries. Structural equation modelling was used to test the hypothesis. Results revealed that modes of interaction significantly mediate the relationship between personality traits and social media engagement behaviors. While culture moderates this relationship. Results showed the highest impact of personality traits on social media engagement in UK than Australia, while their impact in lowest in USA. It is suggested that consumers of different countries having same personality traits respond differently to Facebook brand page post, specifically when considering the Facebook functions of
Algebraic analysis of high-pass quantization

This article presents an algebraic analysis of a mesh-compression technique called high-pass quantization [Sorkine et al. 2003]. In high-pass quantization, a rectangular matrix based on the mesh topological Laplacian is applied to the vectors of the Cartesian coordinates of a polygonal mesh. The resulting vectors, called Δ-coordinates, are then quantized. The applied matrix is a function of the topology of the mesh and the indices of a small set of mesh vertices (anchors) but not of the location of the vertices. An approximation of the geometry can be reconstructed from the quantized Δ-coordinates and the spatial locations of the anchors. In this article, we show how to algebraically bound the reconstruction error that this method generates. We show that the small singular value of the transformation matrix can be used to bound both the quantization error and the rounding error which is due to the use of floating-point arithmetic. Furthermore, we prove a bound on this singular value. The bound is a function of the topology of the mesh and of the selected anchors. We also propose a new anchor-selection algorithm, inspired by this bound. We show experimentally that the method is effective and that the computed upper bound on the error is not too pessimistic.
Sub-1-us, Sub-20-nJ Pattern Classification in a Mixed-Signal Circuit Based on Embedded 180-nm Floating-Gate Memory Cell Arrays

We have designed, fabricated, and successfully tested a prototype mixed-signal, 28x28-binary-input, 10-output, 3-layer neuromorphic network ("MLP perceptron"). It is based on embedded nonvolatile floating-gate cell arrays redesigned from a commercial 180-nm NOR flash memory. The arrays allow precise (~1%) individual tuning of all memory cells, having long-term analog-level retention and low noise. Each array performs a very fast and energy-efficient analog vector-by-matrix multiplication, which is the bottleneck for signal propagation in most neuromorphic networks. All functional components of the prototype circuit, including 2 synaptic arrays with 101,780 floating-gate synaptic cells, 74 analog neurons, and the peripheral circuitry for weight adjustment and I/O operations, have a total area below 1 mm^2. Its testing on the common MNIST benchmark set (at this stage, with a relatively low weight import precision) has shown a classification fidelity of 94.65%, close to the 96.2% obtained in simulation. The classification of one pattern takes less than 1 us time and ~20 nJ energy - both numbers much better than for digital implementations of the same task. Estimates show that this performance may be further improved using a better neuron design and a more advanced memory technology, leading to a >10^2 advantage in speed and a >10^4 advantage in energy efficiency over the state-of-the-art purely digital (GPU and custom) circuits, at classification of large, complex patterns.