Miracle-dz/UniFAR-FTUs huggingface dataset:
Transport ATPase cytochemistry: ultrastructural localization of potassium-dependent and potassium-independent phosphatase activities in rat kidney cortex

A cytochemical method for the light and electron microscope localization of the K- and Mg-dependent phosphatase component of the Na- K-ATPase complex was applied to rat kidney cortex, utilizing p- nitrophenylphosphate (NPP) as substrate. Localization of K-N-ATPase activity in kidneys fixed by perfusion with 1% paraformaldehyde -0.25% glutaraldehyde demonstrated that distal tubules are the major cortical site for this sodium transport enzyme. Cortical collecting tubules were moderately reactive, whereas activity in proximal tubules was resolved only after short fixation times and long incubations. In all cases, K-NPPase activity was restricted to the cytoplasmic side of the basolateral plasma membranes, which are characterized in these neplron segments by elaborate folding of the cell surface. Although the rat K- NPPase appeared almost completely insensitive to ouabain with this cytochemical medium, parallel studies with the more glycoside-sensitive rabbit kidney indicated that K-NPPase activity in these nephron segments is sensitive to this inhibitor. In addition to K-NPPase, nonspecific alkaline phosphatase also hydrolyzed NPP. The latter could be differentiated cytochemically from the specific phosphatase, since alkaline phosphatase was K-independent, insensitive to ouabain, and specifically inhibited by cysteine. Unlike K-NPPPase, alkaline phosphatase was localized primarily to the extracellular side of the microvillar border of proximal tubules. A small amount of cysteine- sensitive activity was resolved along peritubular surfaces of proximal tubules. Distal tubules were unreactive. In comparative studies, Mg- ATPase activity was localized along the extracellular side of the luminal and basolateral surfaces of proximal and distal tubules and the basolateral membranes of collecting tubules.
“A Form of Practical Machinery”: The Origins of Research Ethics Committees in the UK, 1967–1972

Given the centrality of ethics review by independent committees (called Research Ethics Committees, or RECs, in the UK) to modern biomedical research, and the ubiquity of complaints about such review on the part of researchers, it is curious that little attention has been paid to these organizations by medical historians in contrast to the work done on the role of institutions such as the British Medical Association (BMA) and the General Medical Council (GMC) in the development of medical professional ethics, and the general evolution of medical professionals' ethical values. Thus while some work has explored the origins of modern medical ethics teaching in the UK and the parallel development of academic bioethics, there has been very little consideration of how Research Ethics Committees specifically were set up and evolved in the late 1960s and early 1970s. Although some scholars have discussed the development of the British REC system, this work tends to provide little beyond an outline of major events. These might include a report from the Royal College of Physicians (RCP) in 1967, the Department of Health's ‘Red Book’ of 1991 outlining the responsibilities of Local Research Ethics Committees (LRECs) and, more recently, the introduction of multicentre RECs (MRECs) in 1997.
Organizational respect dampens the impact of group-based relative deprivation on willingness to protest pay cuts.

Although group-based relative deprivation predicts people's willingness to protest unfair outcomes, perceiving that one's subgroup is respected increases employees' support for organizations. An integration of these perspectives suggests that subgroup respect will dampen the impact of group-based relative deprivation on workers' responses to unfair organizational outcomes. We examined this hypothesis among university faculty (N = 804) who underwent a system-wide pay cut. As expected, group-based relative deprivation predicted protest intentions. This relationship was, however, muted among those who believed university administrators treated their area of expertise (i.e., their subgroup) with a high (vs. low) level of respect. Moderated mediation analyses confirmed that group-based relative deprivation had a conditional indirect effect on protest intentions via participants' (dis)identification with their university at low to moderate, but not high, levels of subgroup respect. Our finding that satisfying relational needs can attenuate responses to group-based relative deprivation demonstrates the benefits of integrating insights from distinct research traditions.
Reflex Control of Robotic Gait Using Human Walking Data

Control of human walking is not thoroughly understood, which has implications in developing suitable strategies for the retraining of a functional gait following neurological injuries such as spinal cord injury (SCI). Bipedal robots allow us to investigate simple elements of the complex nervous system to quantify their contribution to motor control. RunBot is a bipedal robot which operates through reflexes without using central pattern generators or trajectory planning algorithms. Ground contact information from the feet is used to activate motors in the legs, generating a gait cycle visually similar to that of humans. Rather than developing a more complicated biologically realistic neural system to control the robot's stepping, we have instead further simplified our model by measuring the correlation between heel contact and leg muscle activity (EMG) in human subjects during walking and from this data created filter functions transferring the sensory data into motor actions. Adaptive filtering was used to identify the unknown transfer functions which translate the contact information into muscle activation signals. Our results show a causal relationship between ground contact information from the heel and EMG, which allows us to create a minimal, linear, analogue control system for controlling walking. The derived transfer functions were applied to RunBot II as a proof of concept. The gait cycle produced was stable and controlled, which is a positive indication that the transfer functions have potential for use in the control of assistive devices for the retraining of an efficient and effective gait with potential applications in SCI rehabilitation.
Modeling and characterization of thin film broadband resistors on LCP for RF applications

Resistors have several applications in high frequency circuits including uses in attenuators, terminations, and power dividers among others. To date, there has been very little attempt to characterize embedded resistor performance on organics above 18 GHz. In this paper, RF measurements of embedded thin film resistors up to 40 GHz are presented on a liquid crystal polymer (LCP) substrate using a commercially available laminated foil to form the thin film NiCrAlSi resistors. Measurements have been demonstrated to be accurate to 5% of their simulated values across the frequency band
Faster tissue interface analysis from Raman microscopy images using compressed factorisation

The structure of an artificial ligament was examined using Raman microscopy in combination with novel data analysis. Basis approximation and compressed principal component analysis are shown to provide efficient compression of confocal Raman microscopy images, alongside powerful methods for unsupervised analysis. This scheme allows the acceleration of data mining, such as principal component analysis, as they can be performed on the compressed data representation, providing a decrease in the factorisation time of a single image from five minutes to under a second. Using this workflow the interface region between a chemically engineered ligament construct and a bone-mimic anchor was examined. Natural ligament contains a striated interface between the bone and tissue that provides improved mechanical load tolerance, a similar interface was found in the ligament construct.
An adaptive energy-efficient and low-latency MAC protocol for wireless sensor networks

In this paper, an adaptive MAC protocol (variable load adaptive (VLA)-MAC) is proposed for wireless sensor networks. This protocol can achieve high energy efficiency and provide low latency under variable-traffic-load conditions. In the case of VLA-MAC, traffic load is measured online and used for adaptive adjustment. Sensor nodes transmit packets in bursts under high load conditions to alleviate packet accumulation and reduce latency. This also removes unnecessary listen action and decreases energy consumption in low load conditions. Simulation results show that the energy efficiency, latency, and throughput achieved by VLA-MAC are higher than those achieved by some traditional approaches.
Mirror symmetry by O 3-planes

We construct the three dimensional mirror theory of SO(2k) and SO(2k+1) gauge groups by using O3-planes. An essential ingredient in constructing the mirror is the splitting of a physical brane (NS-brane or D5-brane) on O3-planes. In particular, matching the dimensions of moduli spaces of mirror pair (for example, the SO(2k + 1) and its mirror) there is a D3-brane creation or annihilation accompanying the splitting. This novel dynamical process gives a nontrivial prediction for strongly coupled field theories, which will be very interesting to check by Seiberg-Witten curves. Furthermore, applying the same idea, we revisit the mirror theory of Sp(k) gauge group and find new mirrors which differ from previously known results. Our new result for Sp(k) gives another example to a previously observed fact, which shows that different theories can be mirror to the same theory. We also discussed the phenomena such as “hidden FI-parameters” when the number of flavors and the rank of the gauge group satisfy certain relations, “incomplete Higgsing” for the mirror of SO(2k + 1) and the “hidden global symmetry”. After discussing the mirror for a single Sp or SO gauge group, we extend the study to a product of two gauge groups in two different models, namely the elliptic and the non-elliptic
A variational approach for 3D object classification with retrieval of missing data

In this paper, we propose a classification method for single views of 3D objects with missing data retrieval. A mobile robot equipped with range sensors basically obtains only single view information of a 3D scene. Therefore, large amount of information is missing by self-occlusion, which leads to severe restriction on the object classification exploiting the whole shapes of 3D objects. Humans can precisely identify the objects from single view, since they already have concepts of the entire shape of 3D objects by learning process. Based on these concepts, humans can infer the entire shape and category of the object from a single view. Inspired from this, the proposed algorithm learns concepts in abbreviated form for the shapes of 3D objects, then infers the entire shape and object category from these concepts simultaneously. We apply a generative model based on variational auto-encoder (VAE) to learn the concepts for complex shapes of 3D objects. Our method is evaluated on 3D CAD model dataset, and also compared with other state-of-the-art methods.
Source Domains as Concept Domains in Metaphorical Expressions

The use of lexical resources in linguistic analysis has expanded rapidly in recent years. However, most lexical resources, such as WordNet or online dictionaries, at this point do not usually indicate figurative meanings, such as conceptual metaphors, as part of a lexical entry. Studies that attempt to establish the relationships between literal and figurative language by detecting the connectivity between WordNet relations usually do not deal with linguistic data directly. However, the present study demonstrates that SUMO definitions can be used to identify the source domains used in conceptual metaphors. This is achieved by identifying the relationships between metaphorical expressions and their corresponding ontological nodes. Such links are important because they show which lexical items are mapped under which concepts. This, in turn, helps specify which lexical items in electronic resources involve conceptual mappings. Looking specifically at the concept of PERSON, this work also establishes connectivity between lexical items which are related to “Organism. ” Therefore, the methodology reported herein not only aids the categorizing of lexical items according to their conceptual domains but also can establish links between these items. Such bottom-up and top-down analyses of lexical items may provide a means of representing metaphorical entries in lexical resources.
Free-Form 3D Object Recognition In Range Data Using Weak Correspondence Between Local Features

Model-Based 3D object recognition systems have a variety of potential applications, but widespread use of such systems has not occurred, due to a number of factors including the representational limitations of models. One historical limitation is the discriminatory representation of free-form objects. The system described in this paper recognizes free-form objects in dense range data acquired by a structured light rangefinder. Images and object models are represented as a network of salient segments which are then brought into correspondence until a reliable pose estimate is available. Experiments with a database of images and object models highlight the contributions of this system.
Obfuscating Gender in Social Media Writing

The vast availability of textual data on social media has led to an interest in algorithms to predict user attributes such as gender based on the user’s writing. These methods are valuable for social science research as well as targeted advertising and proﬁling, but also compromise the privacy of users who may not realize that their personal idiolects can give away their demographic identities. Can we automatically modify a text so that the author is clas-siﬁed as a certain target gender, under limited knowledge of the classiﬁer, while preserving the text’s ﬂuency and meaning? We present a basic model to modify a text using lexical substitution, show empirical results with Twitter and Yelp data, and outline ideas for extensions.
Dense Object Reconstruction with Semantic Priors

We present a dense reconstruction approach that overcomes the drawbacks of traditional multiview stereo by incorporating semantic information in the form of learned category-level shape priors and object detection. Given training data comprised of 3D scans and images of objects from various viewpoints, we learn a prior comprised of a mean shape and a set of weighted anchor points. The former captures the commonality of shapes across the category, while the latter encodes similarities between instances in the form of appearance and spatial consistency. We propose robust algorithms to match anchor points across instances that enable learning a mean shape for the category, even with large shape variations across instances. We model the shape of an object instance as a warped version of the category mean, along with instance-specific details. Given multiple images of an unseen instance, we collate information from 2D object detectors to align the structure from motion point cloud with the mean shape, which is subsequently warped and refined to approach the actual shape. Extensive experiments demonstrate that our model is general enough to learn semantic priors for different object categories, yet powerful enough to reconstruct individual shapes with large variations. Qualitative and quantitative evaluations show that our framework can produce more accurate reconstructions than alternative state-of-the-art multiview stereo systems.
Accurate, Validated and Fast Evaluation of Bézier Tensor Product Surfaces

This paper proposes a compensated algorithm to evaluate Bezier tensor product surfaces with floating-point coefficients and coordinates. This algorithm is based on the application of error-free transformations to improve the traditional de Casteljau tensor product algorithm. This compensated algorithm extends the compensated de Casteljau algorithm for the evaluation of a Bezier curve to the case of tensor product surfaces. Forward error analysis and numerical experiments illustrate the accuracy and efficiency of the proposed algorithm.
Least Squares Estimation of Probability Measures in the Prohorov Metric Framework

Abstract : We consider nonparametric estimation of probability measures for parameters in problems where only aggregate (population level) data are available. We summarize an existing computational method for the estimation problem which has been developed over the past several decades. Significant new theoretical results are presented which establish the existence and consistency of very general (ordinary, generalized and other) least squares estimates for the measure estimation problem.
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The assignment of atomic charges to a regular computational grid and the interpolation of forces from the grid back to the original atomic positions are crucial steps in a multigrid approach to the calculation of molecular forces. For purposes of grid assignment, atomic charges are modeled as truncated Gaussian distributions. The charge assignment and back interpolation methods are currently bottlenecks, and take up to one-third the execution time of the multigrid method each. Here, we propose alternative approaches to both charge assignment and back interpolation where convolution is used both to map Gaussian representations of atomic charges onto the grid and to map the forces computed at grid points back to atomic positions. These approaches achieve the same force accuracy with reduced run time. The proposed charge assignment and back interpolation methods scale better than baseline multigrid computations with both problem size and number of processors. © 2005 Wiley Periodicals, Inc. J Comput Chem 26: 000–000, 2005
Automated neural network model selection algorithm for feedback linearization based control

For the best model identification a set of neural networks (NNs) must be trained. First of all it is necessary to obtain the optimal structure of the NN. In addition a good choice of the initial values of the NN parameters can be of tremendous help in a successful control application. Further fit of the model is evaluated using several control criteria, and the optimal among them is selected. This article presents an automated NN model selection method for control based on feedback linearization.
Learning Bayesian Network Models from Incomplete Data using Importance Sampling

We propose a Bayesian approach to learning Bayesian network models from incomplete data. The objective is to obtain the posterior distribution of models, given the observed part of the data. We describe a new algorithm, called eMC, to simulate draws from this posterior distribution. One of the new ideas in our algorithm is to use importance sampling to approximate the posterior distribution of models given the observed data and the current imputation model. The importance sampler is constructed by defining an approximate predictive distribution for the unobserved part of the data. In this way existing (heuristic) imputation methods can be used that don’t require exact inference for generating imputations. We illustrate eMC by its application to modeling the risk factors of coronary heart disease. In the experiments we consider different missing data mechanisms and different fractions of missing data.
A prescription for orthodontic induced White Spot Lesions (WSLs) control: A stepwise approach

White spot lesions (WSLs) are considered one of the most common adverse effects of orthodontic treatment. They present an esthetic problem that could progress into cavitation. The increased incidence of WSLs exposes the orthodontist to the risk of malpractice and litigation. This article represents a chart that gives a stepwise approach for WSLs control before, during and after orthodontic treatment that is based on the most recent scientific data. If the orthodontist can follow the checklist step by step, the high risk patients for WSLs can be identified before treatment and the orthodontists will be able to take the necessary measures to control WSLs. The inclusion of this checklist in the orthodontic practice could add peace of mind to orthodontists. Keywords-Litigation, orthodontic treatment, prescription, prevention,white spot lesions (WSL)
HANDS: heterogeneous architectures and networks-on-chip design and simulation

In current multi-core scenario, Networks-on-Chip (NoC) represent a suitable choice to face the increasing communication and performance requirements, however introducing additional design challenges to already complex architectures. In this perspective, there is a need for flexible and configurable virtual platforms for early-stage design exploration. We present the Heterogeneous Architectures and Networks-on-Chip Design and Simulation framework for large-scale high-performance computer simulation, integrating performance, power, thermal and reliability metrics under a unique methodology. Moreover, NoC exploration is possible from a reliability/performance and thermal/performance trade-offs.
The INSPIRE study: Do personality traits predict general quality of life (Short form-36) in distressed patients with ulcerative colitis and Crohn's disease?

Objective. To assess the role of personality as a predictor of Short form-36 (SF-36) in distressed patients (perceived stress questionnaire, PSQ) with ulcerative colitis (UC) and Crohn's disease (CD). Material and methods. Fifty-four patients with CD and 55 with UC (age 18–60 years) who had relapsed in the previous 18 months, i.e. with an activity index (AI) for UC or CD≥4, PSQ≥60, and without severe mental or other major medical conditions, completed the Buss-Perry Aggression Questionnaire (BPA), the Neuroticism and Lie scales of the Eysenck Personality Questionnaire (EPQ-N and -L), the Multidimensional Health Locus of Control Scale (LOC) (Internal (I), Powerful Other (PO), Chance (C)), the Toronto Alexithymia Scale (TAS) and the SF-36. Results. Multiple linear regression analyses controlling for gender, age and clinical disease activity (AI) in separate analyses for UC and CD showed that the mental and vitality subscales were predicted by neuroticism in both UC and CD. The highest explained variance was 43.8% on the “mental” subscale in UC. The social function subscale was related to alexithymia only in UC, while the role limitation and pain subscales were related to personality in CD only. The physical function subscale related differently to personality in UC and CD. Conclusions. While mental and vitality subscales were predicted by neuroticism in both UC and CD, other subscales had different relationships to personality, suggesting different psychobiological interactions in UC and CD.