Miracle-dz/UniFAR-FTUs huggingface dataset:
Production, characterization, and antibody specificity of a mouse monoclonal antibody reactive with Cryptococcus neoformans capsular polysaccharide

Two monoclonal immunoglobulin G1 antibodies reacting with Cryptococcus neoformans capsular polysaccharide (CNPS) were produced in mice by using a carefully defined procedure for immunization with unmodified CNPS purified from C. neoformans serotype A. Since the antibodies were found to have the same pattern of specificity, only one of them (E1) is described. This anti-CNPS monoclonal antibody reacted with the glucuronoxylomannan component of CNPS but not with the constituent monosaccharides or with the mannose alpha(1----3)-linked oligosaccharide structures present on CNPS. E1 appeared to be specific for C. neoformans serotype A by agglutination of whole cells; it was specific for soluble CNPS A by gel immunoprecipitation. However, indirect immunofluorescence and competitive-binding enzyme-linked immunosorbent assay experiments showed low levels of cross-reactivity with serotypes B and D but not with serotype C. Concentrations 10,000 times higher for serotypes B and D cells than for serotype A cells were required for a 50% inhibition of E1 anti-CNPS A activity as measured by enzyme-linked immunosorbent assay. Among the other yeasts tested, a cross-reaction was only detected with Trichosporon beigelii. The four serotypes of C. neoformans could be distinguished based on intensities and patterns of fluorescence in an indirect immunofluorescence assay using the monoclonal anti-CNPS A antibody. Monoclonal anti-CNPS A antibodies could be useful for fundamental studies on the glucuronoxylomannan structure, as well as for clinical applications such as serotyping and possibly the serological diagnosis of cryptococcosis.
StarD13 is a tumor suppressor in breast cancer that regulates cell motility and invasion

Breast cancer is one of the most commonly diagnosed cancers in women around the world. In general, the more aggressive the tumor, the more rapidly it grows and the more likely it metastasizes. Members of the Rho subfamily of small GTP-binding proteins (GTPases) play a central role in breast cancer cell motility and metastasis. The switch between active GTP-bound and inactive GDP-bound state is regulated by guanine nucleotide exchange factors (GEFs), GTPase-activating proteins (GAPs) and guanine-nucleotide dissociation inhibitors (GDIs). We studied the role of StarD13, a recently identified Rho-GAP that specifically inhibits the function of RhoA and Cdc42. We aimed to investigate its role in breast cancer proliferation and metastasis. The levels of expression of this Rho-GAP in tumor tissues of different grades were assayed using immunohistochemistry. We observed that, while the level of StarD13 expression decreases in cancer tissues compared to normal tissues, it increases as the grade of the tumor increased. This was consistent with the fact that although StarD13 was indeed a tumor suppressor in our breast cancer cells, as seen by its effect on cell proliferation, it was needed for cancer cell motility. In fact, StarD13 knockdown resulted in an inhibition of cell motility and cells were not able to detach their tail and move forward. Our study describes, for the first time, a tumor suppressor that plays a positive role in cancer motility.
High-performance inertial measurements using a redundant array of inexpensive gyroscopes (RAIG)

Orientation estimates derived from gyroscopes are limited in quality by the noise and bias characteristics of the sensors. Presently, there is a large gap in price and performance between high-end fiber-optic gyroscopes and inexpensive MEMS gyroscopes. In this paper, we propose using a redundant array of inexpensive gyroscopes (RAIG) in order to obtain significant improvements in performance. A naïve analysis predicts that n gyros should reduce angular rate measurement noise by a factor of √n. In this paper, we describe our custom array of 72 gyros which we use to evaluate the empirical scaling performance. This evaluation includes comparisons between several standard filtering and bias estimation algorithms. In addition, we propose a principled algorithm that exploits non-linear dynamics, significantly improving performance.
Medical and Surgical Management of Acute Disseminated Encephalomyelitis

Two children with a recent history of viral illness developed visual loss secondary to optic neuritis. Clinical findings and neuroimaging were consistent with acute disseminated encephalomyelitis (ADEM). Markedly elevated opening pressures were noted on lumbar puncture. The patients demonstrated an initial favorable response to high-dose corticosteroid administration. Both had recurrence of symptoms after being tapered off oral corticosteroids. High-dose corticosteroids were reinstituted and a bilateral optic nerve sheath decompression was performed on one patient who developed profound visual loss. A second patient underwent a lumboperitoneal shunt. Both children had resolution of their symptoms and had a recovery of normal visual acuity.
Pigment epithelium-derived factor suppresses ischemia-induced retinal neovascularization and VEGF-induced migration and growth.

PURPOSE To determine the effect of pigment epithelium-derived factor (PEDF) in a mouse model of ischemia-induced retinal neovascularization and on vascular endothelial growth factor (VEGF)--induced migration and growth of cultured microvascular endothelial cells. METHODS Human recombinant PEDF was expressed in the human embryonic kidney 293 cell line and purified by ammonium sulfate precipitation and cation exchange chromatography. C57BL/6 mice were exposed to 75% oxygen from postnatal day (P)7 to P12 and then returned to room air. Mice received intravitreal injections of 2 microg PEDF in one eye and vehicle in the contralateral eye on P12 and P14. At P17, mice were killed and eyes enucleated for quantitation of retinal neovascularization. The mitogenic and motogeneic effects of VEGF on cultured bovine retinal and adrenal capillary endothelial cells were examined in the presence or absence of PEDF, using cell counts and migration assays. RESULTS Two species of human recombinant PEDF, denoted A and B, were purified to apparent homogeneity. PEDF B appeared to comigrate on SDS-PAGE with PEDF from human vitreous samples. Changes in electrophoretic mobility after peptide-N-glycosidase F (PNGase F) digestion suggest that both PEDF forms contain N-linked carbohydrate. Analyses of the intact proteins by liquid chromatography-electrospray mass spectrometry (LC-ESMS) revealed the major molecular weight species for PEDF A (47,705 +/- 4) and B (46,757 +/- 5). LC-ESMS analysis of tryptic peptides indicated that PEDF A and B exhibit differences in glycopeptides containing N-acetylneuraminic acid (NeuAc) and N-acetylhexosamine (HexNAc). Intravitreal administration of either species of PEDF significantly inhibited retinal neovascularization (83% for PEDF A and 55% for PEDF B; P = 0.024 and 0.0026, respectively). PEDF A and B (20 nM) suppressed VEGF-induced retinal microvascular endothelial cell proliferation by 48.8% and 41.4%, respectively, after 5 days (P < 0.001) and VEGF-induced migration by 86.5% +/- 16.7% and 78.1% +/- 22.3%, respectively, after 4 hours (P = 0.004 and P = 0.008, respectively). CONCLUSIONS These data indicate that elevated concentrations of PEDF inhibit VEGF-induced retinal endothelial cell growth and migration and retinal neovascularization. These findings suggest that localized administration of PEDF may be an effective approach for the treatment of ischemia-induced retinal neovascular disorders.
Real-time RGB-D Tracking with Depth Scaling Kernelised Correlation Filters and Occlusion Handling

We present a real-time RGB-D object tracker which manages occlusions and scale changes in a wide variety of scenarios. Its accuracy matches, and in many cases outperforms, state-of-the-art algorithms for precision and it far exceeds most in speed. We build our algorithm on the existing colour-only KCF tracker which uses the ‘kernel trick’ to extend correlation filters for fast tracking. We fuse colour and depth cues as the tracker’s features and exploit the depth data to both adjust a given target’s scale and to detect and manage occlusions in such a way as to maintain real-time performance, exceeding on average 35fps when benchmarked on two publicly available datasets. We make our easy-to-extend modularised code available to other researchers.
Mechanism of the Koehler dislocation multiplication process

Abstract In the Koehler dislocation multiplication process, segments of screw dislocations glide off the primary glide plane onto secondary planes, and then back onto a primary plane parallel to the first plane. This generates trailing dipoles for small excursions between the two primary planes, or the pinning points of Frank-Read mills for large excursions. The concern of this paper is the mechanism that causes the lines to cross-glide, and the distribution of dipoles heights that results. It is proposed that self-excited oscillations of moving dislocation lines, induced by thermally induced shear-strain fluctuations, cause the cross-gliding. The properties of this process are described. It is similar to a two-dimensional random walk, and this determines the dipole height distribution. The Koehler process is dominant for dislocation multiplication in structural materials, especially at high strain rates. The resulting dipole height distribution is an important factor in determining various properties of .
Structured dictionary learning with 2-D non-separable oversampled lapped transform

This work proposes a novel design method of a two-dimensional (2-D) Non-Separable Oversampled Lapped Transform (NSOLT) for a given image by introducing a typical two stage procedure of dictionary learning. NSOLT is a lattice-structure-based transform and yields a redundant dictionary of which atoms satisfy the non-separable, symmetric, real-valued, overlapping and compact-support property. In addition, the Parseval tight frame constraint can structurally be imposed, while the redundancy R is flexibly controlled by the ratio of the number of channels P and the downsampling ratio M. Compared with the other dictionary learning approaches, the proposed method is moderately structured so that it is capable of multiscale construction as well as atom termination at image boundary. The significance of the proposed method is verified by showing an example of learned dictionary and sparse approximation results.
Fast Linear-Programming decoding of LDPC codes over GF(2m)

Recently Linear Programming (LP) decoding is attracting much attention as an alternative to Belief Propagation (BP) decoding for LDPC codes. It is well known for the BP decoding that nonbinary LDPC codes can improve the decoding error probability considerably. On the other hand, Flanagan et al. proposed an LP decoding scheme for LDPC codes over finite rings. Although their scheme is applicable to LDPC codes over finite fields, the number of variables in the LP grows rapidly and hence, its implementation becomes harder as the field size increases. To overcome this defect we propose a new LP decoding scheme for GF(2m), in which the number of variables increases linearly in the field size. Although our scheme relaxes a maximum likelihood decoding problem more loosely to an LP problem than their scheme, the deterioration of the decoding error probability is small.
Comparison of the results of an authorship-based expert recommender against data from a directory of experts

Expert recommender systems have been introduced and implemented in various ways. But due to legal issues, the special nature of human-bound knowledge and lack of acceptance by the staff only few case studies and even less evaluations in real world scenarios can be found. This paper presents a real world implementation of a simple expert recommender algorithm based on document authorship as well as an implementation of a directory of experts within one single company. Both have been implemented and therefore it is possible to compare the results of the recommender against data from the expert directory. Thus we wanted to gather first quantitative insights on how many documents a person should be author of before she might feel knowledgeable on a certain topic and even subscribe to it in the directory of experts. These insights will also be presented.
An Artificial Neural Network Based Robot Controller that Uses Rat's Brain Signals

Abstract: Brain machine interface (BMI) has been proposed as a novel technique to control prosthetic devices aimed at restoring motor functions in paralyzed patients. In this paper, we propose a neural network based controller that maps rat’s brain signals and transforms them into robot movement. First, the rat is trained to move the robot by pressing the right and left lever in order to get food. Next, we collect brain signals with four implanted electrodes, two in the motor cortex and two in the somatosensory cortex area. The collected data are used to train and evaluate different artificial neural controllers. Trained neural controllers are employed online to map brain signals and transform them into robot motion. Offline and online classification results of rat’s brain signals show that the Radial Basis Function Neural Networks (RBFNN) outperforms other neural networks. In addition, online robot control results show that even with a limited number of electrodes, the robot motion generated by RBFNN matched the motion generated by the left and right lever position.
Relations of serum MMP-9 and TIMP-1 levels to left ventricular measures and cardiovascular risk factors: a population-based study

Background Extracellular matrix remodeling is a hallmark of pathological left ventricular (LV) hypertrophy and heart failure. This process is tightly controlled by the degrading matrix metalloproteinases (MMPs) and their tissue inhibitors (TIMPs). We hypothesized that circulating MMP-9 and TIMP-1 levels are altered already in persons with the signs of LV remodeling that forego clinical heart failure. Design Cross-sectional study in the Prospective Investigation of the Vasculature in Uppsala Seniors, a community-based cohort of 891 70-year-old men and women free from valvular disease, heart failure, and myocardial infarction. Methods We examined relations of serum MMP-9 and TIMP-1 to echocardiographic LV geometry and function. All models were adjusted for sex, height, intra-arterial systolic and diastolic blood pressures, antihypertensive medication use, and serum freezer time. Results Serum TIMP-1 was positively related to LV mass and wall thickness (r=0.15, P < 0.0001 and r =0.16, P < 0.0001, respectively), with a 32 g higher LV mass and 2.2 mm thicker walls in the fourth compared with the first quartile of serum TIMP-1. Serum TIMP-1 was also inversely related to LV ejection fraction (r = −0.10, P = 0.009), but not to LV dimension or diastolic function indices. Serum MMP-9 was only weakly related to LV wall thickness and isovolumic relaxation time (r =0.08, P =0.04 and r = −0.08, P = 0.04). Conclusion In this large population-based sample, serum TIMP-1 levels were related to LV mass, wall thickness, and inversely to systolic function. This may imply that extracellular matrix remodeling is involved already in the earliest stages of the process leading to heart failure.
Electric vehicle charge planning using Economic Model Predictive Control

Economic Model Predictive Control (MPC) is very well suited for controlling smart energy systems since electricity price and demand forecasts are easily integrated in the controller. Electric vehicles (EVs) are expected to play a large role in the future Smart Grid. They are expected to provide grid services, both for peak reduction and for ancillary services, by absorbing short term variations in the electricity production. In this paper the Economic MPC minimizes the cost of electricity consumption for a single EV. Simulations show savings of 50-60% of the electricity costs compared to uncontrolled charging from load shifting based on driving pattern predictions. The future energy system in Denmark will most likely be based on renewable energy sources e.g. wind and solar power. These green energy sources introduce stochastic fluctuations in the electricity production. Therefore, energy should be consumed as soon as it is produced to avoid the need for energy storage as this is expensive, limited and introduces efficiency losses. The Economic MPC for EVs described in this paper may contribute to facilitating transition to a fossil free energy system.
Investigation on the Weighted RANSAC Approaches for Building Roof Plane Segmentation from LiDAR Point Clouds

RANdom SAmple Consensus (RANSAC) is a widely adopted method for LiDAR point cloud segmentation because of its robustness to noise and outliers. However, RANSAC has a tendency to generate false segments consisting of points from several nearly coplanar surfaces. To address this problem, we formulate the weighted RANSAC approach for the purpose of point cloud segmentation. In our proposed solution, the hard threshold voting function which considers both the point-plane distance and the normal vector consistency is transformed into a soft threshold voting function based on two weight functions. To improve weighted RANSAC’s ability to distinguish planes, we designed the weight functions according to the difference in the error distribution between the proper and improper plane hypotheses, based on which an outlier suppression ratio was also defined. Using the ratio, a thorough comparison was conducted between these different weight functions to determine the best performing function. The selected weight function was then compared to the existing weighted RANSAC methods, the original RANSAC, and a representative region growing (RG) method. Experiments with two airborne LiDAR datasets of varying densities show that the various weighted methods can improve the segmentation quality differently, but the dedicated designed weight functions can significantly improve the segmentation accuracy and the topology correctness. Moreover, its robustness is much better when compared to the RG method.
Experimental Determination of Thermoelectric‐Module Parameters and Modeling for Cooling/Heating Control Design

Over alternative technologies, the use of thermoelectric modules often provides solutions to many difficult thermal management problems where a low to moderate amount of heat is to be handled by simply regulating input voltage. Mathematical modeling of thermal behaviors with working condition parameters is required for practical cooling/heating control design. The article presents a methodology not only to experimentally determine the parameters of the thermoelectric modules in a few steps of measurement, but also to propose input–output descriptive models, which are a readable representation of nonlinear input–output relations for practical cooling/heating control design under working conditions. In this study, three thermoelectric modules with capacities of 45, 60, and 91.2 W are investigated experimentally as concrete examples. Unknown parameters of those thermoelectric modules, including the Seebeck coefficient, the electrical resistance, and the thermal conductivity are determined for the input–output descriptive models based on measurements. To illustrate effectiveness of the proposed methodology, the input–output descriptive model of a 45-W thermoelectric module is used to describe the actual thermal behaviors of cooling/heating control in real-time implementation. The simulated results have very good agreement with the experimental results.
Characterization of the piezoelectric ceramic mechanical nonlinear behavior

A method to measure the mechanical nonlinear coefficients of piezoceramics with high signal excitation is presented. It consists of measuring the maximum admittance and the series resonance frequency for decreasing frequencies. At this jumping point the phase angle remains zero whatever the amplitude of the excitation is. A theoretical model previously described is used to explain the motional impedance variation due to both the nonlinear elasticity and the nonlinear mechanical losses of the piezoelectric materials. An experimental method is proposed to measure the mechanical losses tangent and the compliance variations as a function of the mean square strain in the piezoceramic. Two main coefficients characterizing the material nonlinearity are deduced. Experimental measurements have been carried out to compare the nonlinearity of different ceramic materials in longitudinal and transverse mode.
Compact and low-power ASIP design for lightweight PUF-based authentication protocols

There is a disconnection between the theory and the practice of lightweight physical unclonable function (PUF)-based protocols. At a theoretical level, there exist several PUF-based authentication protocols with unique features and novel efficiency claims, but most of these solutions lack real-world implementations with simple performance figures. On the other hand, practical protocol implementations are ad-hoc designs fixed to a specific functionality and with limited area optimisations. This work aims to bring these approaches on PUF protocols closer. The authors' contribution is twofold. First, they provide a novel ASIP (application-specific instruction set processor) that can efficiently execute PUF-based authentication protocols. The key novelty of the proposed ASIP is optimisation for area without degrading the performance. Second, they demonstrate the capability of their ASIP by mapping three secure PUF-based authentication protocols and benchmark their execution time, memory footprint, communication overhead, and power/energy consumption. Their results demonstrate the advantage of ASIP over dedicated architectures and also as opposed to general-purpose programming on an MSP430. The results further demonstrate various efficiency metrics that can be used to compare PUF-based protocol implementations.
Application of barycenter theory of fuzzy set on soft rock classification

In allusion to the limitation and roughness of soft rock classification by single index or experience. Seven important indices such as integrality coefficient, softening coefficient, cohesion, spacing fracture, saturated uniaxial compressive strength and index of rocky quality were chosen to evaluate the quality of the soft rock synthetically and quantificationally by using the barycenter theory of fuzzy set, whose membership function is Cauchy distribution and weight is fixed by super weighting method. The deficiency of losing information in judgment by normal fuzzy set theory was remedied, and the problem of some indices' being submerged dueing to small weight was solved. Finally, the calculational method of barycenter theory of fuzzy set of the whole factors is clarified combining a material project example. It is proved by comparison and analysis that multi-indices is necessary in soft rock classification, and the classification result by above method is accord to the fact, and superior to normal classification method.