Miracle-dz/UniFAR-FTUs huggingface dataset:
Disease-related amyloidogenic variants of human lysozyme trigger the unfolded protein response and disturb eye development in Drosophila melanogaster

We have created a Drosophila model of lysozyme amyloidosis to investigate the in vivo behavior of disease‐associated variants. To achieve this objective, wild‐type (WT) protein and the amyloidogenic variants F57I and D67H were expressed in Drosophila melanogaster using the UAS‐gal4 system and both the ubiquitous and retinal expression drivers Act5C‐gal4 and gmr‐gal4. The nontransgenic w1118 Drosophila line was used as a control throughout. We utilized ELISA experiments to probe lysozyme protein levels, scanning electron microscopy for eye phenotype classification, and immunohistochemistry to detect the unfolded protein response (UPR) activation. We observed that expressing the destabilized F57I and D67H lysozymes triggers UPR activation, resulting in degradation of these variants, whereas the WT lysozyme is secreted into the fly hemolymph. Indeed, the level of WT was up to 17 times more abundant than the variant proteins. In addition, the F57I variant gave rise to a significant disruption of the eye development, and this correlated to pronounced UPR activation. These results support the concept that the onset of familial amyloid disease is linked to an inability of the UPR to degrade completely the amyloidogenic lysozymes prior to secretion, resulting in secretion of these destabilized variants, thereby leading to deposition and associated organ damage. —Kumita, J. R. , Helmfors, L. , Williams, J., Luheshi, L. M. , Menzer, L. , Dumoulin, M. , Lomas, D. A. , Crowther, D. C. , Dobson, C. M. , Brorsson, A. ‐C. Disease‐related amyloidogenic variants of human lysozyme trigger the unfolded protein response and disturb eye development in Drosophila melanogaster. FASEB J. 26, 192‐202 (2012). www.fasebj. org
An easy non-iterative method for correcting lens radial distortion from point correspondences

This paper presents a new method for calibrating and correcting large radial distortion. It makes use of a number of image point correspondences from two views only. No knowledge of the scene structures, nor camera intrinsic parameters, is required. By using two singularity conditions, the method successfully decouples the estimation of the radial distortion from the estimation of fundamental matrix. The solution technique is basically linear, it thereby does not need any initial guess, nor nonlinear iteration. It also proposes a kernel-density-based roots voting scheme (instead of the conventional RANSAC scheme), which makes the result very reliable and robust to noise. In addition, the method is easy to implement.
Investigating Students' Perceptions of E-Learning within an Occupational Therapy Program

Because of the increasing use of technology to enhance learning in higher education, particularly in the professions, the aim of this study was to explore student perceptions of electronic learning (e-learning) within one module of the occupational therapy curriculum at a university in the United Kingdom. Twenty participants sorted 30 statements taken from the literature by indicating how much each statement reflected their own experience of the e-learning component of their program. Following analysis, two factors were identified indicating two distinct perceptions of e-learning. One factor was interpreted as a preference for traditional lectures along with a perception that technology hindered students' learning. The other factor revealed a view that the use of technology was beneficial and a part of students' learning. Using Q methodology showed the interconnectedness of the issues in e-learning. Ways to improve the instructional design of the module to enable students of both views to engage interactively with the content are discussed.
Hierarchical buffered routing tree generation

This paper presents a solution to the problem of performance-driven buffered routing tree generation for very large scale integrated circuits. Using a novel bottom-up construction algorithm and a local neighborhood search strategy, our polynomial time algorithm finds the optimum solution in an exponential-size solution subspace. The final output is a buffered rectilinear Steiner routing tree that connects the driver of a net to its sink nodes. The two variants of the problem, i.e. maximizing the required time at the driver subject to a maximum total area constraint and minimizing the total area subject to a minimum required time at the driver constraint, are handled by propagating three-dimensional solution curves during the construction phase. Experimental results demonstrate the effectiveness of our algorithm compared to other techniques.
On Reliability-Aware Server Consolidation in Cloud Datacenters

In the past few years, datacenter (DC) energy consumption has become an important issue in technology world. Server consolidation using virtualization and virtual machine (VM) live migration allows cloud DCs to improve resource utilization and hence energy efficiency. In order to save energy, consolidation techniques try to turn off the idle servers, while because of workload fluctuations, these offline servers should be turned on to support the increased resource demands. These repeated on-off cycles could affect the hardware reliability and wear-and-tear of servers and as a result, increase the maintenance and replacement costs. In this paper we propose a holistic mathematical model for reliability-aware server consolidation with the objective of minimizing total DC costs including energyand reliability costs. In fact, we try to minimize the number of active PMs and racks, in a reliability-aware manner. We formulate the problem as a Mixed Integer Linear Programming (MILP) model which is in form of NP-complete. Finally, we evaluate the performance of our approach in different scenarios using extensive numerical MATLAB simulations.
The importance of being first: evidence from Canadian generic pharmaceuticals.

This paper uses pooled cross-section data on Canadian ethical drug sales to examine the effect of entry timing on sales of generic drugs. The data is for all drugs for which the first generic competitor entered during the years 1994-1997. It is found that the first generic entrant has a lasting competitive advantage: being first into the market appears to lead to an increase of around 30% in market share (among generics) over a period of at least 4 years. This finding has considerable implications for the current policy of allowing brandname drug companies to issue pseudo-generic equivalents as a preemptive strike against true generic competitors.
ANALYZING COMPETITION AND COLLUSION STRATEGIES IN ELECTRONIC MARKETPLACES WITH INFORMATION ASYMMETRY

Electronic commerce (EC) researchers are divided between those who show how EC can cause intense Bertrand competition and those who show an absence of this competition. This research takes a more central stance to show when intense competition occurs and when collusion occurs in an EC environment. We present a multi-industry investigation of competition and collusion related to firm pricing behavior using a customized data-collecting Internet agent, the Time Series Agent Retriever (TSAR). Using an analytical model, we show how EC technology increases the ability of firms to tacitly collude to keep prices higher than expected, even amid intense competition. We then develop a probit regression model to test the predictions of our theory. The results show that EC technology allows either intense competition or tacit collusion, and that EC vendors tend to choose one or the other based on industry and firm attributes. ______________________________________________________________________________
Effects of intrinsic noise in temperature-compensated models for circadian rhythms

Circadian rhythms, which are observed in most of organisms, e.g. bacteria, fungi, plants, and animals, are self-sustained oscillations with about 24 hours period and a remarkable temperature compensation property. By using the temperature-compensated and robust temperature-compensated deterministic models for circadian rhythms, we perform the stochastic simulations based on the Gillespie's method by changing temperature over a wide range, and discuss how the deterministic temperature compensation properties fluctuate over the range of temperature in the stochastic setting reflecting the intrinsically noisy environments. In order to strengthen the inference from the stochastic simulations, we also execute a qualitative analysis based on the linear noise approximation.
Exploiting redundancy in Cartesian impedance control of UAVs equipped with a robotic arm

A Cartesian impedance control for UAVs equipped with a robotic arm is presented in this paper. A dynamic relationship between generalized external forces acting on the structure and the system motion, which is specified in terms of Cartesian space coordinates, is provided. Through a suitable choice of such variables and with respect to a given task, thanks to the added degrees of freedom given by the robot arm attached to the UAV, it is possible to exploit the redundancy of the system so as to perform some useful subtasks. The hovering control of a quadrotor, equipped with a 3-DOF robotic arm and subject to contact forces and external disturbances acting on some points of the whole structure, is tested in a simulated case study.
Organizational status of dialysis facilities and patient outcome: does higher injectable medication use mediate increased mortality?

OBJECTIVE Examine the mediating effect of injectable drugs in the relationship between dialysis facility organizational status and patient mortality. STUDY SETTING Medicare dialysis population. STUDY DESIGN Data from the U.S. Renal Data System (USRDS) were used to identify 3,884 freestanding dialysis facilities and 37,942 Medicare patients incident to end-stage renal disease (ESRD) in 2006. The role of injectable medications was evaluated during a 2-year follow-up period by mediational analyses using mixed-effect regression models. DATA COLLECTION USRDS data were matched with Dialysis Facility Report data from Centers for Medicare and Medicaid Services (CMS) and census data. PRINCIPAL FINDINGS There was a strong association found between organizational status and use of injectable drugs. Large for-profit chains used significantly higher injectable medications compared with nonprofit chains and independent facilities. However, the relationship between facility organizational status and patient mortality was not found to be mediated through the higher use of injectable drugs. CONCLUSIONS Large for-profit chain facilities administered higher IV epoetin, iron, and vitamin D dosages, but this did not result in improved survival. Given the associated costs and lack of a survival benefit, the overuse of injectable medications among the U.S. dialysis patients will likely end under the recent bundling of injectable medications without jeopardizing patient outcomes.
On the Classification of Industrial SAT Families

The success of portfolio approaches in SAT solving relies on the observation that different SAT solving techniques perform better on different SAT instances. The Algorithm Selection Problem faces the problem of choosing, using a prediction model, the best algorithm from a predeﬁned set, to solve a particular instance of a problem. Using Machine Learning techniques, this prediction is performed by analyzing some features of the instance and using an empirical hardness model, previously built, to select the expected fastest algorithm to solve such instance. Recently, there have been some attempts to characterize the structure of industrial SAT instances. In this paper, we use some structural features of industrial SAT instances to build some classiﬁers of industrial SAT families of instances. Namely, they are the scale-free structure, the community structure and the self-similar structure. We measure the effectiveness of these classiﬁers by comparing them to other sets of SAT instances features commonly used in portfolio SAT solving approaches. Finally, we evaluate the performance of this set of structural features when used in a real portfolio SAT solver.
Subscribed Content Calcium Carbonate Precipitation by Ureolytic Subsurface Bacteria

Coprecipitation in carbonate minerals offers a means of slowing the transport of divalent radionuclides and contaminant metals (e.g.,90Sr2+, UO2+, Co2+) in the subsurface. It may be possible to accelerate this process by stimulating the native microbial community to generate chemical conditions favoring carbonate precipitation. In a preliminary evaluation of this approach, we investigated the ability of ureolytic subsurface bacteria to produce alkaline conditions conducive to calcium carbonate precipitation. Groundwater samples from the Eastern Snake River Plain (ESRP) aquifer in Idaho were screened for urea-hydrolyzing microorganisms; three isolates were selected for further evaluation. Analysis of 16S rRNA gene sequences indicated that two of the ESRP isolates were of the genus Pseudomonas , and the other was a Variovorax sp. The specific urease activities of the ESRP isolates appeared to be similar to each other but less than that of Bacillus pasteurii , a known urease-positive organism. However, calcium carbonate was rapidly precipitated in all cultures that were supplied with urea and calcium, and X-ray diffraction analyses indicated that calcite was always the predominant carbonate polymorph produced. The correspondence between measured calcium concentrations and equilibrium predictions suggested that the rate of calcite precipitation was directly linked to the rate of urea hydrolysis. These results are promising with respect to the potential utility of this approach for in situ remediation and indicate that further evaluation of this approach under conditions more closely simulating environmental conditions is warranted.
Session-StateReveal is stronger than eCKs EphemeralKeyReveal: using automatic analysis to attack the NAXOS protocol

In the paper, 'stronger security of authenticated key exchange' (LaMacchia et al., 2006, 2007), a new security model for authenticated key exchange protocols (eCK) is proposed. The new model is suggested to be at least as strong as previous models for key exchange protocols, such as the CK model (Canetti and Krawczyk, 2001; Krawczyk, 2005). The model includes a new notion of an EphemeralKeyReveal adversary query, which is claimed in e.g., LaMacchia et al. (2006), Okamoto (2007), and Ustaoglu (2008), to be at least as strong as the Session-StateReveal query. We investigate the relation between the two models by focusing on the difference in adversary queries. We formally model the NAXOS protocol and a variant of the eCK model, called eCK', in which the EphemeralKeyReveal query is replaced by the Session-StateReveal query. Using Scyther, a formal protocol analysis tool, we automatically find attacks on the protocol, showing that the protocol is insecure in the eCK' model. Our attacks prove that the Session-StateReveal query is stronger than the EphemeralKeyReveal query and that the eCK security model is incomparable to the CK model, disproving several claims made in the literature.
A multipath QoS routing protocol in wireless sensor networks

Most of the proposed routing protocols for Wireless Sensor Networks (WSNs) concentrate on efficiently using constrained resources, especially the energy. However, one important factor of the routing protocols, QoS routing, has not been paid enough attention. Transmission collision appears frequently at nodes that receive packets from multiple nodes. Consequently, the network performance is greatly reduced. Furthermore, frequent collisions increase delay, which is important to real-time applications. The services provided by the sensor network are also impacted. In this paper, we propose a mechanism to find multiple paths between one sink and multiple sources with the consideration of reducing collision. We take advantage of the mass deployment of sensor nodes to search for disjoint paths for multiple source nodes in order to avoid collisions. Besides the increase in the network lifetime and throughput, the protocol also decreases the latency. Simulation results are provided to demonstrate the effectiveness of our solution.
Robot Programming by Demonstration with Interactive Action Visualizations

—Existing approaches to Robot Programming by Demonstration (PbD) require multiple demonstrations to capture task information that lets robots generalize to unseen situations. However, providing these demonstrations is cumbersome for end-users. In addition, users who are not familiar with the system often fail to demonstrate sufﬁciently varied demonstrations. We propose an alternative PbD framework that involves demonstrating the task once and then providing additional task information explicitly, through interactions with a visualization of the action. We present a simple action representation that supports this framework and describe a system that implements the framework on a two-armed mobile manipulator. We demonstrate the power of this system by evaluating it on a diverse task benchmark that involves manipulation of everyday objects. We then demonstrate that the system is easy to learn and use for novice users through a user study in which participants program a subset of the benchmark. We characterize the limitations of our system in task generalization and end-user interactions and present extensions that could address some of the limitations.
Adaptation of Relation Extraction Rules to New Domains

This paper presents various strategies for improving the extraction performance of less prominent relations with the help of the rules learned for similar relations, for which large volumes of data are available that exhibit suitable data properties. The rules are learned via a minimally supervised machine learning system for relation extraction called DARE. Starting from semantic seeds, DARE extracts linguistic grammar rules associated with semantic roles from parsed news texts. The performance analysis with respect to different experiment domains shows that the data property plays an important role for DARE. Especially the redundancy of the data and the connectivity of instances and pattern rules have a strong influence on recall. However, most real-world data sets do not possess the desirable small-world property. Therefore, we propose three scenarios to overcome the data property problem of some domains by exploiting a similar domain with better data properties. The first two strategies stay with the same corpus but try to extract new similar relations with learned rules. The third strategy adapts the learned rules to a new corpus. All three strategies show that frequently mentioned relations can help in the detection of less frequent relations.
Data-driven scene parsing method for construction site monitoring

This paper presents the applicability of a nonparametric scene parsing model for recognizing all objects in an image. The data-driven model labels all the pixels of query images as their object classes using the labels in similar images of the existing dataset. The model has a flexible number of parameters depending on the size of the training data, it is possible to add or remove new data without re-training the whole model. The capability of the nonparametric model would improve the monitoring performance on the construction site with updating the size of the image dataset over time.
Document dependent fusion in multimodal music retrieval

In this paper, we propose a novel multimodal fusion framework, document dependent fusion (DDF), which derives the optimal combination strategy for each individual document in the fusion process. For each document, we derive a document weight vector by estimating the descriptive abilities of its different modalities. The document weight vector also enables our framework to be easily integrated with existing multimodal fusion schemes, and achieve a better combination strategy for each document given a query. Experiments are conducted on a 17174-song music database to compare the retrieval accuracy of traditional query independent fusion and query dependent fusion approaches, and that obtained after integrating DDF with them. Experimental results indicate that DDF can significantly improve the retrieval performance of current fusion approaches.
Inflation Targeting and the Anchoring of Inflation Expectations in the Western Hemisphere *

Many central banks have adopted a formal inflationtargeting framework based on the belief and the theoretical predictions that an explicit and clearly communicated numerical objective for the level of inflation over a specified period would, in itself, be a strong communication device that would help anchor long-term inflation expectations.1 Empirically verifying the success of inflationtargeting regimes in this dimension has been difficult, however, as survey data on long-term inflation expectations tend to be of limited availability and low frequency.2 In this paper, we use daily bond yield data for Canada, Chile, and the United States to investigate whether longterm inflation expectations in these countries are anchored, essentially extending the analysis of Gürkaynak, Sack, and Swanson (2005) and Gürkaynak, Levin, and Swanson (2006) to examine comparable data for Canada and Chile. Of these three countries, Canada and Chile have been formal inflation targeters throughout much of the 1990s and 2000s, while the United States has not had an explicit numerical inflation objective. We test the success of inflation targeting in anchoring long-term inflation expectations by Inflation Targeting and the Anchoring of Inflation Expectations in the Western Hemisphere*
Discriminative Paired Dictionary Learning for Visual Recognition

A Paired Discriminative K-SVD (PD-KSVD) dictionary learning method is presented in this paper for visual recognition. To achieve high discrimination and low reconstruction errors simultaneously for sparse coding, we propose to learn class-specific sub-dictionaries from pairs of positive and negative classes to jointly reduce the reconstruction errors of positive classes while keeping the reconstruction errors of negative classes high. Then, multiple sub-dictionaries are concatenated with respect to the same negative class so that the non-zero sparse coefficients can be discriminatively distributed to improve classification accuracy. Compared to the current dictionary learning methods, the proposed PD-KSVD method achieves very competitive performance in a variety of visual recognition tasks on several publicly available datasets.
Cloning of the mouse hepatitis virus (MHV) receptor: expression in human and hamster cell lines confers susceptibility to MHV

The cellular receptor for murine coronavirus mouse hepatitis virus (MHV)-A59 is a member of the carcinoembryonic antigen (CEA) family of glycoproteins in the immunoglobulin superfamily. We isolated a cDNA clone (MHVR1) encoding the MHV receptor. The sequence of this clone predicts a 424-amino-acid glycoprotein with four immunoglobulinlike domains, a transmembrane domain, and a short intracytoplasmic tail, MHVR1 is closely related to the murine CEA-related clone mmCGM1 (Mus musculus carcinoembryonic antigen gene family member). Western blot (immunoblot) analysis performed with antireceptor antibodies detected a glycoprotein of 120 kDa in BHK cells stably transfected with MHVR1. This corresponds to the size of the MHV receptor expressed in mouse intestine and liver. Human and hamster fibroblasts transfected with MHVR1 became susceptible to infection with MHV-A59. Like MHV-susceptible mouse fibroblasts, the MHVR1-transfected human and hamster cells were protected from MHV infection by pretreatment with monoclonal antireceptor antibody CC1. Thus, the 110- to 120-kDa CEA-related glycoprotein encoded by MHVR1 is a functional receptor for murine coronavirus MHV-A59.