Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficacy and safety of the oral Janus kinase inhibitor peficitinib (ASP015K) monotherapy in patients with moderate to severe rheumatoid arthritis in Japan: a 12-week, randomised, double-blind, placebo-controlled phase IIb study

Objective To evaluate the efficacy, safety and dose response of a novel oral Janus kinase inhibitor, peficitinib (ASP015K), as monotherapy in Japanese patients with moderate to severe rheumatoid arthritis (RA). Methods In a 12-week, double-blind study, 281 adult patients with RA with active disease not on concomitant disease-modifying antirheumatic drug therapy were randomised equally to once-daily placebo or peficitinib 25, 50, 100 and 150 mg. The primary endpoint was American College of Rheumatology (ACR) 20 response in the peficitinib treatment groups versus placebo at week 12. Results Mean age was 53.0 years, 81.1% were female and 25.3% had previously used antitumour necrosis factor therapy. Peficitinib 50, 100 and 150 mg each showed statistically significantly higher ACR20 response rates compared with placebo, and response rates increased up to 150 mg with a statistically significant dose response. The total incidence of treatment-emergent adverse events (TEAEs) was similar between the placebo (64.3%) and peficitinib 25, 50, 100 and 150 mg groups (70.9%, 64.9%, 52.7% and 67.2%, respectively). TEAEs occurring more frequently in the peficitinib group compared with the placebo group included nasopharyngitis, increased blood creatine phosphokinase and diarrhoea. No cases of serious infections were reported. Herpes zoster occurred in four patients (two each in peficitinib 25 and 100 mg). Conclusions Treatment with peficitinib as monotherapy for 12 weeks in Japanese patients with moderate to severe RA is efficacious and showed acceptable safety profile. These findings support further developments of peficitinib for RA treatment. Trial registration number NCT01649999; Results.
Visual object tracking via random ferns based classification

Designing a robust algorithm for visual object tracking has been a challenging task since many years. There are trackers in the literature that are reasonably accurate for many tracking scenarios but most of them are computationally expensive. This narrows down their applicability as many tracking applications demand real time response. In this paper, we present a tracker based on random ferns. Tracking is posed as a classification problem and classification is done using ferns. We used ferns as they rely on binary features and are extremely fast at both training and classification as compared to other classification algorithms. Our experiments show that the proposed tracker performs well on some of the most challenging tracking datasets and executes much faster than one of the state-of-the-art trackers, without much difference in tracking accuracy.
Processing of the Coronavirus MHV-JHM Polymerase Polyprotein: Identification of Precursors and Proteolytic Products Spanning 400 Kilodaltons of ORF1a

Abstract The replicase of mouse hepatitis virus strain JHM (MHV-JHM) is encoded by two overlapping open reading frames, ORF1a and ORF1b, which are translated to produce a 750-kDa precursor polyprotein. The polyprotein is proposed to be processed by viral proteinases to generate the functional replicase complex. To date, only the MHV-JHM amino-terminal proteins p28 and p72, which is processed to p65, have been identified. To further elucidate the biogenesis of the MHV-JHM replicase, we cloned and expressed five regions of ORF1a in bacteria and prepared rabbit antisera to each region. Using the immune sera to immunoprecipitate radiolabeled proteins from MHV-JHM infected cells, we determined that the MHV-JHM ORF1a is initially processed to generate p28, p72, p250, and p150. Pulse–chase analysis revealed that these intermediates are further processed to generate p65, p210, p40, p27, the MHV 3C-like proteinase, and p15. A putative replicase complex consisting of p250, p210, p40, p150, and a large protein (>300 kDa) coprecipitate from infected cells disrupted with NP-40, indicating that these proteins are closely associated even after initial proteolytic processing. Immunofluorescence studies revealed punctate labeling of ORF1a proteins in the perinuclear region of infected cells, consistent with a membrane-association of the replicase complex. Furthermore,in vitrotranscription/translation studies of the MHV-JHM 3Cpro and flanking hydrophobic domains confirm that 3C protease activity is significantly enhanced in the presence of canine microsomal membranes. Overall, our results demonstrate that the MHV-JHM ORF1a polyprotein: (1) is processed into more than 10 protein intermediates and products, (2) requires membranes for efficient biogenesis, and (3) is detected in discrete membranous regions in the cytoplasm of infected cells.
The Research and Design of 5.5 GHz Bandpass Filter for WLAN

Based on analyzing the characteristics of coupled microstrip lines, a design of RF microstrip bandpass filter for WLAN is studied. This paper presents on bandpass filter operating at center frequency of 5.5GHz with 1GHz bandwidth. The results of full-wave electromagnetic simulation indicate that the filter shows a very good performance with insertion loss below 1.458dB and return loss better than -16.294dB in its passband and stopband attenuation of less than -42.459dB. The group delay in the midband frequency drops to 1.168ns, which further improves the quality of transmission signals.
Micro endeffector with micro pyramids and integrated piezoresistive force sensor

Micro manipulation is required for assembling and maintenance of micro machines and their parts. As the handling objects are miniaturized, attractive forces such van der Waals force, surface tension force and electrostatic force between micro objects and gripper surface become dominant in the air, and they work as adhesive forces. We cannot neglect such adhesive forces in micro manipulation. Considering the physical phenomena in the micro world, we propose reduction methods of adhesive forces. Surface roughness of the endeffector surface is effective to reduce the van der Waals force. We propose to make micro pyramids on the end-effector surface by micromachining techniques. We designed and made a prototype of the micro gripper with a micro end-effector whose gripping surface is covered with micro pyramids. Experimental results show effectiveness of micro pyramids for reduction of the adhesive force. We also made a semiconductor strain gauge at the end of the micro end-effector surface for force measurement. Both micro pyramids and the integrated piezoresistive force sensor are fabricated on the micro end-effector by the micromachining techniques. Performance of this force sensor is shown.
The emergence of grid cells: Intelligent design or just adaptation?

Individual medial entorhinal cortex (mEC) ‘grid’ cells provide a representation of space that appears to be essentially invariant across environments, modulo simple transformations, in contrast to multiple, rapidly acquired hippocampal maps; it may therefore be established gradually during rodent development. We explore with a simplified mathematical model the possibility that the self‐organization of multiple grid fields into a triangular grid pattern may be a single‐cell process, driven by firing rate adaptation and slowly varying spatial inputs. A simple analytical derivation indicates that triangular grids are favored asymptotic states of the self‐organizing system, and computer simulations confirm that such states are indeed reached during a model learning process, provided it is sufficiently slow to effectively average out fluctuations. The interactions among local ensembles of grid units serve solely to stabilize a common grid orientation. Spatial information, in the real mEC network, may be provided by any combination of feedforward cortical afferents and feedback hippocampal projections from place cells, since either input alone is likely sufficient to yield grid fields. © 2008 Wiley‐Liss, Inc.
Quantifying Sub-Pixel Surface Water Coverage in Urban Environments Using Low-Albedo Fraction from Landsat Imagery

The problem of mixed pixels negatively affects the delineation of accurate surface water in Landsat Imagery. Linear spectral unmixing has been demonstrated to be a powerful technique for extracting surface materials at a sub-pixel scale. Therefore, in this paper, we propose an innovative low albedo fraction (LAF) method based on the idea of unconstrained linear spectral unmixing. The LAF stands on the “High Albedo-Low Albedo-Vegetation” model of spectral unmixing analysis in urban environments, and investigates the urban surface water extraction problem with the low albedo fraction map. Three experiments are carefully designed using Landsat TM/ETM+ images on the three metropolises of Wuhan, Shanghai, and Guangzhou in China, and per-pixel and sub-pixel accuracies are estimated. The results are compared against extraction accuracies from three popular water extraction methods including the normalized difference water index (NDWI), modified normalized difference water index (MNDWI), and automated water extraction index (AWEI). Experimental results show that LAF achieves a better accuracy when extracting urban surface water than both MNDWI and AWEI do, especially in boundary mixed pixels. Moreover, the LAF has the smallest threshold variations among the three methods, and the fraction threshold of 1 is a proper choice for LAF to obtain good extraction results. Therefore, the LAF is a promising approach for extracting urban surface water coverage.
Prototyping an Online Elective Surgery Waitlist Management System

Excessive waiting time for elective surgery and a lack of information regarding patient's status in the waiting list have left the patients upset and unsatisfied while waiting for their turn to receive the treatment. The situation was made worse by inefficient waitlist management that possibly results in unexpected delay and negative impacts on the health condition of the patients. In this paper, we investigate current waitlist management systems implemented in Australia's five states and suggest a design prototype that could better address patient needs and empower them by providing personalised information about their waitlist status and decision support on implications of changing their preferences.
Distributed Link Scheduling Under SINR Model in Multihop Wireless Networks

Link adaptation technologies, such as Adaptive Modulation and Coding (AMC) and Multiple-Input-Multiple-Output (MIMO), are used in advanced wireless communication systems to achieve high spectrum efficiency. Communication performance can be improved significantly by adaptive transmissions based on the quality of received signals, i.e., the signal-to-interference-plus-noise ratio (SINR). However, for multihop wireless communications, most link scheduling schemes have been developed under simplified interference models that do not account for accumulative interference and cannot fully exploit the recent advances in PHY-layer communication theory. This paper focuses on developing link scheduling schemes that can achieve optimal performance under the SINR model. One key idea is to treat an adaptive wireless link as multiple parallel virtual links with different signal quality, building on which we develop throughput-optimal scheduling schemes using a two-stage queueing structure in conjunction with recently developed carrier-sensing techniques. Furthermore, we introduce a novel three-way handshake to ensure, in a distributed manner, that all transmitting links satisfy their SINR requirements. We evaluate the proposed schemes through rigorous analysis and simulations.
Bizarre Delusions Follow Dopaminergic Therapy in Parkinson Disease: Challenge in Diagnosis and Treatment

The incidence of psychiatric drug side effects is nearly 20% and hallucinations and delusions happen 4% more with dementia comorbidity and cognitive impairment, increasing age and use of higher dose of levodopa. It is assumed that the main precipitant of psychotic phenomena in Parkinson’s disease is dopaminergic excess secondary to the treatment. Also, cognitive impairment with Parkinson’s disease, associated with senility and onset in older age, may prone to psychotic like states [7].
A strategy learning model for autonomous agents based on classification

Abstract In this paper we propose a strategy learning model for autonomous agents based on classification. In the literature, the most commonly used learning method in agent-based systems is reinforcement learning. In our opinion, classification can be considered a good alternative. This type of supervised learning can be used to generate a classifier that allows the agent to choose an appropriate action for execution. Experimental results show that this model can be successfully applied for strategy generation even if rewards are delayed. We compare the efficiency of the proposed model and reinforcement learning using the farmer-pest domain and configurations of various complexity. In complex environments, supervised learning can improve the performance of agents much faster that reinforcement learning. If an appropriate knowledge representation is used, the learned knowledge may be analyzed by humans, which allows tracking the learning process
Binaural speech enhancement system combining dereverberation and spatial masking-based noise removal for robust speech recognition

In this paper, we present a binaural speech enhancement system as a preprocessing step for the robust speech recognition. This system employs an existing dereverberation technique followed by a spatial masking-based noise removal algorithm where only signals coming from the desired directions are retained by using a threshold angle. While state-of-the art approaches fix the threshold angle heuristically over all time frames, we propose to consider an adaptive computation where this threshold angle is first learned in several noise-only frames and then updated frame by frame. Speech recognition results in real environment show the effectiveness of the proposed speech enhancement approach.
Online Detection Technique of 3D Defects for Steel Strips Based on Photometric Stereo

2D Detection with gray level images is popularly employed in current surface inspection systems of steel strips, which lead to high false detection rate because of interference of pseudo-defects, such as iron oxide, water stains, and grease. Therefore, an online detection technique of 3D defects based on photometric stereo was developed in this paper. The acquired 3D information is utilized to distinguish real 3D defects from pseudo-defects, which can significant improve the performance of surface inspection system. The designed inspection system was equipped with lasers and linear CCD camera to scan the moving surface of steel strips, referred as the photometric stereo laser scanning system. Samples of hot and cold-rolled steels, with pseudo and real defects such as roll imprints, cracks and scratches on surface, were scanned and showed obvious distinguishment.
Content Recommendation for Attention Management in Unified Social Messaging

With the growing popularity of social networks and collaboration systems, people are increasingly working with or socially connected with each other. Unified messaging system provides a single interface for users to receive and process information from multiple sources. It is highly desirable to design attention management solution that can help users easily navigate and process dozens of unread messages from a unified message system. Moreover, with the proliferation of mobile devices people are now selectively consuming the most important messages on the go between different activities in their daily life. The information overload problem is especially acute for mobile users with small screen to display. In this paper, we present \PAM, an intelligent end-to-end Personalized Attention Management solution that employs analytical techniques that can learn user interests and organize and prioritize incoming messages based on user interests. For a list of unread messages, \PAM generates a concise attention report that allows users to quickly scan the important new messages from his important social connections as well as messages about his most important tasks that the user is involved with. Our solution can also be applied in other applications such as news filtering and alerts on mobile devices. Our evaluation results demonstrate the effectiveness of \PAM.
GLOBAL EXISTENCE FOR 3D INCOMPRESSIBLE ISOTROPIC ELASTODYNAMICS VIA THE INCOMPRESSIBLE LIMIT

The motion of isotropic elastic materials occupying all of R features the nonlinear interaction of fast pressure waves and slow shear waves, concentrated along their respective characteristic cones. Under appropriate constitutive assumptions, sending the speed of the pressure waves to infinity penalizes volume changes and drives the motion toward incompressibility. This article presents two results in support of this picture. First, for sufficiently small initial displacements, classical local solutions of the equations of motion are shown to exist with uniform stability estimates yielding a lifespan proportional to the speed of the pressure waves, substantially improving upon [13]. This result depends on the careful assessment of nonlinear wave interactions through the inherent null structure of shear waves in isotropic materials, the small amplitude of pressure waves caused by their rapid dispersion in the incompressible limit, and the separation of the individual wave families. Second, the uniform stability of the local existence family allows for convergence to a global solution of the limiting incompressible equations by means of compactness arguments. The strength of this convergence improves with the degree of incompressibility satisfied by the initial conditions. Instead of the classical second order Lagrangian formulation, the problem will be analyzed within the framework of first order symmetric hyperbolic systems because, with Eulerian coordinates and the proper choice of dependent variables, the singular terms are linear. The transformed system can be viewed as a natural extension of the compressible Euler equations of fluid dynamics in which the inverse deformation gradient is now coupled with the density and velocity. It also shares common features with various relativistic theories of elasticity [3], [12], [19]. With the addition of new variables, certain natural constraints
Synthesis and enzymatic ketonization of the 5-(halo)-2-hydroxymuconates and 5-(halo)-2-hydroxy-2,4-pentadienoates

5-Halo-2-hydroxymuconates and 5-halo-2-hydroxy-2,4-pentadienoates are stable dienols that are proposed intermediates in bacterial meta-fission pathways for the degradation of halogenated aromatic compounds. The presence of the halogen raises questions about how the bulk and/or electronegativity of these substrates would affect enzyme catalysis or whether some pathway enzymes have evolved to accommodate it. To address these questions, 5-halo-2-hydroxymuconates and 5-halo-2-hydroxy-2,4-pentadienoates (5-halo = Cl, Br, F) were synthesized and a preliminary analysis of their enzymatic properties carried out. In aqueous buffer, 5-halo-2-hydroxy-2,4-pentadienoates rapidly equilibrate with the β,γ-unsaturated ketones. For the 5-chloro and 5-bromo derivatives, a slower conversion to the α,β-isomers follows. There is no detectable formation of the α,β-isomer for the 5-fluoro derivative. Kinetic parameters were also obtained for both sets of compounds in the presence of 4-oxalocrotonate tautomerase (4-OT) from Pseudomonas putida mt-2 and Leptothrix cholodnii SP-6. For 5-halo-2-hydroxymuconates, there are no major differences in the kinetic parameters for the two enzymes (following the formation of the β,γ-unsaturated ketones). In contrast, the L. cholodnii SP-6 4-OT is ≈10-fold less efficient than the P. putida mt-2 4-OT in the formation of the β,γ-unsaturated ketones and the α,β-isomers from the 5-halo-2-hydroxy-2,4-pentadienoates. The implications of these findings are discussed. The availability of these compounds will facilitate future studies of the haloaromatic catabolic pathways.
EFFECTS OF INVESTIGATIVE LABORATORY INSTRUCTION ON CONTENT KNOWLEDGE AND SCIENCE PROCESS SKILL ACHIEVEMENT ACROSS LEARNING STYLES

The purpose of this study was to determine the effect of investigative laboratory integration on student content knowledge and science process skill achievement across learning styles. Treatment groups utilized one of three levels of treatment: subject matter approach without laboratory experimentation, subject matter approach with prescriptive laboratory experimentation, and subject matter approach with investigative laboratory experimentation. A nonequivalent control group quasi-experimental design was used. A purposively selected sample based upon the ability of the teacher to effectively deliver the treatments was selected from the population of students enrolled in an introductory agriscience course. Using regression analyses it was determined that learning style, teaching method, ethnicity, content knowledge pretest scores, and science process skill pretest scores accounted for 33% of the variance in content knowledge gain score. Learning style, gender, teaching method, science process skill pretest scores, and content knowledge pretest scores accounted for 36% of the variance in science process skill gain score. Students taught using the subject matter approach or the investigative laboratory approach were reported as having higher content knowledge and science process skill gain scores than students taught using the prescriptive laboratory approach.
Analysis of distributed algorithms for density estimation in VANETs (Poster)

Vehicle density is an important system metric used in monitoring road traffic conditions. Most of the existing methods for vehicular density estimation require either building an infrastructure, such as pressure pads, inductive loop detector, roadside radar, cameras and wireless sensors, or using a centralized approach based on counting the number of vehicles in a particular geographical location via clustering or grouping mechanisms. These techniques however suffer from low reliability and limited coverage as well as high deployment and maintenance cost. In this paper, we propose fully distributed and infrastructure-free mechanisms for the density estimation in vehicular ad hoc networks. Unlike previous distributed approaches, that either rely on group formation, or on vehicle flow and speed information to calculate density, our study is inspired by the mechanisms proposed for system size estimation in peer-to-peer networks. We adapted and implemented three fully distributed algorithms, namely Sample & Collide, Hop Sampling and Gossip-based Aggregation. The extensive simulations of these algorithms at different vehicle traffic densities and area sizes for both highways and urban areas reveal that Hop Sampling provides the highest accuracy in least convergence time and introduces least overhead on the network, but at the cost of higher load on the initiator node.