Miracle-dz/UniFAR-FTUs huggingface dataset:
Forcing of the deep ocean circulation in simulations of the Last Glacial Maximum

[1] From the interpretation of different proxy data it is widely believed that the North Atlantic thermohaline circulation during the maximum of the last ice age ∼21,000 years ago was considerably weaker than today. Recent equilibrium simulations with a coupled ocean-atmosphere-sea ice model successfully simulated a reduction in North Atlantic Deep Water (NADW) formation consistent with reconstructions. Here we examine the influence of different air-sea fluxes on simulated changes in the deep ocean circulation between the Last Glacial Maximum and present day. We find that changes in the oceanic surface freshwater fluxes are the dominant forcing mechanism for the reduced Atlantic overturning. Diminished export of freshwater out of the Atlantic drainage basin through the atmosphere decreases surface salinities in the North Atlantic, leading to less NADW formation in the colder climate. Changes in heat fluxes, which lead to increased sea surface densities in the North Atlantic and therefore to an enhanced overturning, are of secondary importance. Wind stress variations seem to play a negligible role. The degree to which the Atlantic freshwater export and hence the NADW formation are reduced depends on the formulation of the atmospheric hydrological cycle and on the strength of the overturning in the present-day simulation. Simulated changes in sea surface properties for a large variety of overturning strengths are compared with different reconstruction data sets. The results depend strongly on the data set used. Sea surface temperature reconstructions from Climate: Long-Range Investigation, Mapping, and Prediction (CLIMAP) and earlier salinity reconstructions based on planktonic foraminifera are most consistent with a significant reduction of the circulation, while recent reconstructions using dinocyst assemblages allow no unequivocal conclusion.
Spirituality in the salesperson: the impact of the golden rule and personal faith on workplace job attitudes

Spirituality in the Salesperson: The Impact of the Golden Rule and Personal Faith on Workplace Job Attitudes. (May 2007) James Garry Smith, B.B.A., The University of Texas at Tyler; M.B.A., University of North Texas Chair of Advisory Committee: Dr. Charles M. Futrell Do salespeople who follow the Golden Rule or let their faith influence their behavior serve their customers better or like their jobs and employers more than other salespeople? The Golden Rule is a quote from Christ found in Matthew 7:12 NIV and is considered a universal ethical principle taught by all major religions. It is also a behavioral standard for many in business. A review of the sales, marketing, and organizational literatures, however, failed to uncover studies which assess the relationships of following the Golden Rule or a person’s faith or spirituality with key business outcomes. Salespeople impact the performance and perception of their firms, yet are regarded as highly unethical by the public. Therefore, an investigation of how these variables influence their behavior seems justified. A Golden Rule Disposition (GRD) is conceptualized as a higher-order personality disposition which influences the traits of agape love, forgiveness, gratitude, humility, and selflessness. Personal faith is defined as a higher order personality trait blending a desire for a personal relationship with God (the Divine or Supreme Being) with core personality influences on the behaviors of an individual.
Processing of masked and unmasked emotional faces under different attentional conditions: an electrophysiological investigation

In order to investigate the interactions between non-spatial selective attention, awareness and emotion processing, we carried out an ERP study using a backward masking paradigm, in which angry, fearful, happy, and neutral facial expressions were presented, while participants attempted to detect the presence of one or the other category of facial expressions in the different experimental blocks. ERP results showed that negative emotions enhanced an early N170 response over temporal-occipital leads in both masked and unmasked conditions, independently of selective attention. A later effect arising at the P2 was linked to awareness. Finally, selective attention was found to affect the N2 and N3 components over occipito-parietal leads. Our findings reveal that (i) the initial processing of facial expressions arises prior to attention and awareness; (ii) attention and awareness give rise to temporally distinct periods of activation independently of the type of emotion with only a partial degree of overlap; and (iii) selective attention appears to be influenced by the emotional nature of the stimuli, which in turn impinges on unconscious processing at a very early stage. This study confirms previous reports that negative facial expressions can be processed rapidly, in absence of visual awareness and independently of selective attention. On the other hand, attention and awareness may operate in a synergistic way, depending on task demand.
A Perspective on Network Restoration

A heated massage pillow for allowing the massage packs to be removed and for keeping hair looking nice. The heated massage pillow includes a pillow. The pillow is comprised of a first pillow case and a foam rubber cushion. The foam rubber contains a heating means. A power supply means, adapted to fit inside the first pillow case adjacent to the foam rubber, is operationally coupled to the heating means. Vibrating means are adapted to fit inside the first pillow case adjacent to the power supply. The vibrating means are operationally coupled to the power supply means. An actuating means, located outside of said first pillow, is operationally attached to the vibrating means. The actuating means is adapted to activate the vibrating means and the heating means. A second pillow case of satin is adapted to fit over the first pillow case.
A Dynamic Causal Modeling Study on Category Effects: BottomUp or TopDown Mediation?

In this study, we combined functional magnetic resonance imaging (fMRI) and dynamic causal modeling (DCM) to investigate whether object category effects in the occipital and temporal cortex are mediated by inputs from early visual cortex or parietal regions. Resolving this issue may provide anatomical constraints on theories of category specificity which make different assumptions about the underlying neurophysiology. The data were acquired by Ishai, Ungerleider, Martin, Schouten, and Haxby (1999, 2000) and provided by the National fMRI Data Center (http://www.fmridc.org). The original authors used a conventional analysis to estimate differential effects in the occipital and temporal cortex in response to pictures of chairs, faces, and houses. We extended this approach by estimating neuronal interactions that mediate category effects using DCM. DCM uses a Bayesian framework to estimate and make inferences about the influence that one region exerts over another and how this is affected by experimental changes. DCM differs from previous approaches to brain connectivity, such as multivariate autoregressive models and structural equation modeling, as it assumes that the observed hemodynamic responses are driven by experimental changes rather than endogenous noise. DCM therefore brings the analysis of brain connectivity much closer to the analysis of regionally specific effects usually applied to functional imaging data. We used DCM to estimate the influence that V3 and the superior/inferior parietal cortex exerted over category-responsive regions and how this was affected by the presentation of houses, faces, and chairs. We found that category effects in occipital and temporal cortex were mediated by inputs from early visual cortex. In contrast, the connectivity from the superior/inferior parietal area to the category-responsive areas was unaffected by the presentation of chairs, faces, or houses. These findings indicate that category effects in the occipital and temporal cortex can be mediated by bottomup mechanismsa finding that needs to be embraced by models of category specificity.
Quantification of Alloantibody-Mediated Cytotoxicity In Vivo

Background Preexisting, donor-specific antibodies (DSAs) are culprits of hyperacute rejection. Donor-specific antibodies are also formed de novo, and their role in acute and chronic rejection is increasingly appreciated. However, it is difficult to assess damage inflicted exclusively by DSAs when alloreactive T cell and B cell responses coincide. We reasoned that allosensitization with “costimulation-deficient” cells should induce DSA synthesis but not naive cytotoxic T lymphocyte (CTL) precursors' priming via direct allorecognition. Accordingly, we have developed a novel model to quantify DSA-mediated cytotoxicity in vivo. Methods C57BL/6 (H-2b) mice were sensitized with H-2d+ kidney epithelial cells, and a cytofluorimetric killing assay was tailored to the measurement of allocytotoxicity. We took cell/complement depletion, costimulation blockade, and serum transfer approaches to reveal the mediators of cytotoxicity. “Third-party” controls and a skin allotransplantation model were used to confirm DSAs' specificity for allo-major histocompatibility complex. We validated our experimental approach in other mouse strains primed with different allogeneic cell types, including endothelial cells. To demonstrate the usefulness of our model/method for drug efficacy testing, we examined the effect of CTLA4-Ig and rapamycin on DSA-mediated cytolysis. Results Allosensitization of MHC-disparate mouse strains with costimulation-deficient cells led to robust cytotoxicity mediated by complement-fixing DSAs and phagocytic cells. This response was independent of CTLs, natural killer or natural killer T cells. It required CD4+ T cell help, CD40 signaling and CD28-based costimulation during allosensitization and could be reversed by sustained rapamycin treatment. Conclusions The unique model described herein should enable mechanistic studies on sensitization and effector phases of humoral alloreactivity as well as efficacy testing of future immunotherapies to prevent DSA-induced pathology.
An Estimated DSGE Model: Explaining Variation in Nominal Term Premia, Real Term Premia, and Inflation Risk Premia

This paper develops a DSGE model which is shown to explain variation in the nominal and real term structure as well as inflation surveys and four macrovariables for the UK economy. The model is estimated based on a third-order approximation to allow for time-varying term premia. We find a fall in nominal term premia during the 1990s which mainly is caused by lower inflation risk premia. A structural decomposition further shows that this fall is driven by negative preference shocks, lower fixed production costs, positive investment shocks, and a more aggressive response to inflation by the Bank of England.
Dual-Frequency Folded Reflectarray Antenna

Design and experimental results of a dual-frequency folded reflector antenna are presented. With an effective diameter of 180 mm and a depth of 60 mm, gain values of 25 and 30 dB at 20 and 30 GHz, respectively, have been achieved. Beamwidths are 6.2° at 20 GHz and 3.7°/4° at 30 GHz.
Eagerness for Physical Activity Scale: Theoretical background and validation

ABSTRACT The Eagerness for Physical Activity Scale (EPAS) validated in this study is based on the belief that eagerness for physical activity should be seen as a major outcome of interventions and physical activity contexts. The aim of the study was to introduce the concept of eagerness into physical activity research, to validate EPAS, and to explore its relationship with maximal oxygen uptake (VO2max) and motivation. A multi-study validation approach was used. Convergent and discriminant validity was supported and the scale displayed high internal consistency across all studies. When controlling for self-determined motivation, eagerness for physical activity related positively to VO2max, thus supporting incremental validity above and beyond intrinsic motivation, identified regulation, extrinsic regulation, and a-motivation. Based on our results we recommend not only the application of EPAS in the assessment of how intervention programs and regular types of activity contexts affect people of all ages, but also the introduction of the concept of eagerness into physical activity and health research and policy in general.
Mu, Delta, and Kappa opioid receptor agonists induce peripheral antinociception by activation of endogenous noradrenergic system

Opioid receptor agonists induce noradrenaline release in the supraspinal, spinal, and peripheral sites. Endogenous noradrenaline release can induce an antinociceptive effect by activation of the α2 adrenoceptor. This interaction between the opioid and the adrenergic systems could be the alternative mechanism by which opioid receptor agonists mediate peripheral antinociception. Therefore, the aim of the present study was to verify whether peripheral antinociception induced by the μ, δ, and κ opioid receptor agonists DAMGO, SNC80, and bremazocine, respectively, through the endogenous noradrenergic system. All drugs were administered locally into the right hind paw of male Wistar rats. The rat paw pressure test was used, with hyperalgesia induced by intraplantar injection of prostaglandin E2. DAMGO, SNC80, or bremazocine elicited local dose‐dependent peripheral antinociception. This peripheral effect was antagonized by the nonselective α2 adrenoceptor antagonist yohimbine and by the selective α2C adrenoceptor antagonist rauwolscine but not by the selective antagonists for α2A, α2B, and α2D adrenoceptor subtypes (BRL 44 480, imiloxan, and RX 821002, respectively). The opioid‐induced effect was antagonized by the nonselective α1 adrenoceptor antagonist prazosin and by the nonselective β adrenoceptor antagonist propranolol. Guanethidine, a depletor of peripheral sympathomimetic amines, restored approximately 50–60% of the opioid‐induced peripheral antinociception. Furthermore, acute injection of the noradrenaline reuptake inhibitor reboxetine intensified the antinociceptive effects of low‐dose DAMGO, SNC80, or bremazocine. This study provides evidence that DAMGO, SNC80, or bremazocine induces peripheral antinociception by noradrenaline release and interaction with adrenoceptors. © 2012 Wiley Periodicals, Inc.
Hardware architecture for high-accuracy real-time pedestrian detection with CoHOG features

Co-occurrence histograms of oriented gradients (CoHOG) is a powerful feature descriptor for pedestrian detection. However, its calculation cost is large because the feature vector for the CoHOG descriptor is very high-dimensional. In this paper, in order to achieve real-time detection on embedded systems, we propose a novel hardware architecture for the CoHOG feature extraction. Our architecture exploits high degree of fine-grained parallelism and adopts an efficient histogram generator combined with a linear SVM classifier. The proposed architecture is implemented on a Xilinx Virtex-5 FPGA and it achieves real-time pedestrian detection on 38 fps 320×240 video. That is more than 100 times faster than the execution on a state-of-the-art Intel CPU.
Safety and Immunogenicity of Neonatal Pneumococcal Conjugate Vaccination in Papua New Guinean Children: A Randomised Controlled Trial

Background Approximately 826,000 children, mostly young infants, die annually from invasive pneumococcal disease. A 6-10-14-week schedule of pneumococcal conjugate vaccine (PCV) is efficacious but neonatal PCV may provide earlier protection and better coverage. We conducted an open randomized controlled trial in Papua New Guinea to compare safety, immunogenicity and priming for memory of 7-valent PCV (PCV7) given in a 0-1-2-month (neonatal) schedule with that of the routine 1-2-3-month (infant) schedule. Methods We randomized 318 infants at birth to receive PCV7 in the neonatal or infant schedule or no PCV7. All infants received 23-valent pneumococcal polysaccharide vaccine (PPV) at age 9 months. Serotype-specific serum IgG for PCV7 (VT) serotypes and non-VT serotypes 2, 5 and 7F were measured at birth and 2, 3, 4, 9, 10 and 18 months of age. Primary outcomes were geometric mean concentrations (GMCs) and proportions with concentration ≥0.35 µg/ml of VT serotype-specific pneumococcal IgG at age 2 months and one month post-PPV. Results We enrolled 101, 105 and 106 infants, respectively, into neonatal, infant and control groups. Despite high background levels of maternally derived antibody, both PCV7 groups had higher GMCs than controls at age 2 months for serotypes 4 (p<0.001) and 9V (p<0.05) and at age 3 months for all VTs except 6B. GMCs for serotypes 4, 9V, 18C and 19F were significantly higher (p<0.001) at age 2 months in the neonatal (one month post-dose2 PCV7) than in the infant group (one month post-dose1 PCV7). PPV induced significantly higher VT antibody responses in PCV7-primed than unprimed infants, with neonatal and infant groups equivalent. High VT and non-VT antibody concentrations generally persisted to age 18 months. Conclusions PCV7 is well-tolerated and immunogenic in PNG neonates and young infants and induces immunologic memory to PPV booster at age 9 months with antibody levels maintained to age 18 months. Trial Registration ClinicalTrials.gov NCT00219401NCT00219401
The SPARKy (Spring Ankle with Regenerative kinetics) project: Choosing a DC motor based actuation method

The design process of a powered robotic ankle prosthesis presents many obstacles that must be overcome. To be practically implemented, such a mechanism must not only run on batteries, but sustain a long running time between recharging. Using springs to passively and actively store and supply energy to the robotic ankle, small DC motors can be optimized to perform high peak power tasks without sacrificing efficiency and net energy usage. Additional techniques are explored with the potential of substantially reducing the energy requirements as well as the size and weight of the prosthesis. The benefits of adding a unidirectional parallel spring with a Robotic Tendon are weighed and the possibility of actively varying the lever arm at which the spring force is applied is analyzed. The different actuation methods are compared to determine which methods work best in different gait regimes.
Advances in dissimilarity-based data visualisation

The amount of collected digital information grows with each day. This development is facilitated by advanced technology, allowing for more detailed and more complex measurements. As a result, huge data sets gathered in this way are no longer easily comprehensible for a human person. Dimensionality reduction constitutes an important tool to visualise highly complex data in two dimensions, allowing users to gain insight about the structure of the data. In this thesis, we give an overview on dimensionality reduction and discuss challenges arising in this context. Moreover, we elaborate in detail the capabilities of dimensionality reduction on two exemplary techniques: generative topographic mapping, as a parametric technique, generating a model of the data; and t-distributed stochastic neighbour embedding, as a nonparametric projection technique, based on a cost function. As a core contribution of this thesis, we will discuss four major limitations of dimensionality reduction techniques, and present solutions to overcome these problems. (i) One important requirement for a visualisation technique regards the extensibility to new data: after constructing a mapping for given data, new data should be visualised in a consistent way. While this is readily available for parametric techniques, nonparametric methods are lacking this property, and we present a general approach to provide an explicit mapping. (ii) Another limitation regards the treatment of complex data: in many scenarios classical feature vectors are no longer sufficient, instead, the use of pairwise data relations in the form of similarity or dissimilarity measures becomes mandatory. Some algorithms are not capable of dealing with dissimilarity data, therefore we develop an approach to solve this problem via an implicit vectorial embedding. (iii) The techniques working on (dis-)similarity data suffer from high computational and memory complexity, since the matrix with pairwise relations grows quadratically with the number of data points. To drastically reduce the complexity, the Nyström approximation technique can be applied. In this context, we generalize the Nyström method for arbitrary symmetrical (dis-)similarity matrices, and define a universal framework resulting in linear time algorithms. (iv) Finally, we address the problem of data visualisation being ill-posed, since there are multiple ways to reduce the dimensionality and it is in general not clear which one is the best. Therefore, we follow the metric learning paradigm to focus on dimensions which are important for class separation. We investigate this approach for a parametric technique on the one hand and introduce a general framework for nonparametric techniques on the other hand.
Optimal Feedback Control for Predicting Dynamic Stiffness During Arm Movement

Knowledge of the central nervous system (CNS) that constrains dimensions of freedom to control a redundant system of the body would provide inspiration for the robotic engineering. We estimated limb stiffness in Japanese monkeys (Macaca fuscata) during arm reaching movements using a robotic manipulandum and carried out numerical simulations. The estimated joint stiffness showed a diphasic pattern, and the end-point stiffness ellipses were modulated during the movement in a characteristic manner. The pattern of limb stiffness was reproduced by the numerical simulation using a musculoskeletal arm model and an approximate optimal feedback control (OFC). Although the arm model has a redundant system with multiple dimensions of freedom, the OFC minimized the redundancy by enhancing the task-relevant cost function. We suggest that the CNS may control the body according to a similar OFC law, and the knowledge might be useful for developing human-machine systems.
Fault Detection and Diagnosis of Induction Motors Using Motor Current Signature Analysis and a Hybrid FMM–CART Model

In this paper, a novel approach to detect and classify comprehensive fault conditions of induction motors using a hybrid fuzzy min-max (FMM) neural network and classification and regression tree (CART) is proposed. The hybrid model, known as FMM-CART, exploits the advantages of both FMM and CART for undertaking data classification and rule extraction problems. A series of real experiments is conducted, whereby the motor current signature analysis method is applied to form a database comprising stator current signatures under different motor conditions. The signal harmonics from the power spectral density are extracted as discriminative input features for fault detection and classification with FMM-CART. A comprehensive list of induction motor fault conditions, viz., broken rotor bars, unbalanced voltages, stator winding faults, and eccentricity problems, has been successfully classified using FMM-CART with good accuracy rates. The results are comparable, if not better, than those reported in the literature. Useful explanatory rules in the form of a decision tree are also elicited from FMM-CART to analyze and understand different fault conditions of induction motors.