Miracle-dz/UniFAR-FTUs huggingface dataset:
Stoichiometry of Recombinant N-Methyl-d-Aspartate Receptor Channels Inferred from Single-channel Current Patterns

Single-channel currents were recorded from mouse NR1-NR2B (ζ-ε2) receptors containing mixtures of wild-type and mutant subunits expressed in Xenopus oocytes. Mutant subunits had an asparagine-to-glutamine (N-to-Q) mutation at the N0 site of the M2 segment (NR1:598, NR2B:589). Receptors with pure N or Q NR1 and NR2 subunits generated single-channel currents with distinctive current patterns. Based on main and sublevel amplitudes, occupancy probabilities, and lifetimes, four patterns of current were identified, corresponding to receptors with the following subunit compositions (NR1/NR2): N/N, N/Q, Q/N, and Q/Q. Only one current pattern was apparent for each composition. When a mixture of N and Q NR2 subunits was coexpressed with pure mutant NR1 subunits, three single-channel current patterns were apparent. One pattern was the same as Q/Q receptors and another was the same as Q/N receptors. The third, novel pattern presumably arose from hybrid receptors having both N and Q NR2 subunits. When a mixture of N and Q NR1 subunits was coexpressed with pure mutant NR2 subunits, six single-channel current patterns were apparent. One pattern was the same as Q/Q receptors and another was the same as N/Q receptors. The four novel patterns presumably arose from hybrid receptors having both N and Q NR1 subunits. The relative frequency of NR1 hybrid receptor current patterns depended on the relative amounts of Q and N subunits that were injected into the oocytes. The number of hybrid receptor patterns suggests that there are two NR2 subunits per receptor and is consistent with either three or five NR1 subunits per receptor, depending on whether or not the order of mutant and wild-type subunits influences the current pattern. When considered in relation to other studies, the most straightforward interpretation of the results is that N-methyl-d-aspartate receptors are pentamers composed of three NR1 and two NR2 subunits.
Human Bocavirus in Children with Acute Gastroenteritis, Chile, 1985–2010

We detected human bocavirus in 89 (19.3%) of 462 fecal samples collected during 3 periods from 1985 through 2010 from children <5 years of age in Chile who were hospitalized with acute gastroenteritis. Our findings confirm the long-term circulation of human bocavirus in Chile.
User-Pairing Based Non-Orthogonal Multiple Access (NOMA) System

Non-orthogonal multiple access (NOMA) is a promising multiple access scheme for next generation wireless networks. This paper investigates the sum rate maximization problem for NOMA system over a frequency selective fading channel with its users being paired according to their channel powers. A hierarchical power allocation process is proposed, whereby the users are divided into two groups such that a closed form power allocation solution can be applied. This divide-and allocate approach is repeated until all users are allocated with a transmission power. The proposed architecture facilitates a large number of users for NOMA system. Simulation results show that the proposed scheme achieves comparable performances to the optimal one based on numerical solution, and outperforms other approaches.
The Effects of Alzheimer Disease on Driving‐Related Abilities

Summary Ten Alzheimer disease (AD) patients and 12 healthy elderly controls were evaluated on two tests of driving-related abilities: the Driver Performance Test (DPT) and the Driving Advisement System (DAS). Subjects were administered a battery of neuropsychological tests to determine if severity of dementia in AD correlates with driving performance. On the DPT, the AD patients scored in the average range in two of five skill areas (predicting the effects of a hazard, deciding how to avoid it); below average in two areas (searching for a hazard, executing evasive actions); and poorly in one area (identifying hazards). The elderly controls scored at an average level in all five skill areas. On the DAS, AD patients were significantly slower than the elderly controls on simple, two-choice, and conditional reaction time tests and were much slower than drivers in general. The AD patients' performances on two cognitive tests, the Mini-Mental State Examination (MMSE) and the Category Fluency Test, correlated significantly with aspects of performance on the DPT and the DAS. Although these are preliminary results from a pilot investigation, they suggest that AD patients' driving-related abilities are adversely affected by the disease and that driving-related performance tests and neuropsychological tests may be useful in assessing the impact of AD on driving.
Dielectric properties of rutile structure (Zn1/3B2/3)x(TiySn1ߝy)1ߝxO2 (B=Nb5+,Ta5+) at microwave frequencies

The microwave dielectric properties of rutile (Zn<sub>1/3</sub>B<sub>2/3</sub>)<sub>x</sub>(Ti<sub>y</sub>Sn<sub>1-y</sub>)<sub>1-x</sub>O<sub>2</sub> (B = Nb<sup>5+</sup>, Ta<sup>5+</sup>, 0.05 ≤ × ≤ 0.35, y = 0.2, 0.8) were investigated with structural characteristics. A single phase with tetragonal rutile (P4<sub>2</sub>/mnm, no. 136, Z = 2) structure was obtained through the entire range of compositions. With an increase of (Zn<sub>1/3</sub>B<sub>2/3</sub>)O<sub>2</sub> content, the dielectric constant (K) of sintered specimens with (Ti<sub>0.2</sub>Sn<sub>0.8</sub>)O<sub>2</sub> increased, whereas that of the specimens with (Ti<sub>0.8</sub>Sn<sub>0.2</sub>)O<sub>2</sub> decreased. The temperature coefficient of resonant frequency (TCF) of sintered specimens was dependent upon oxygen octahedral distortion and/or cations bond valence of the rutile structure. The quality factor (Qf) was dependent upon (Zn<sub>1/3</sub>B<sub>2/3</sub>)O<sub>2</sub> content, and the specimens with (Ti<sub>0.2</sub>Sn<sub>0.8</sub>)O<sub>2</sub> showed larger Qf values than those with (Ti<sub>0.8</sub>Sn<sub>0.2</sub>)O<sub>2</sub> for the same content of (Zn<sub>1/3</sub>B<sub>2/3</sub>)O<sub>2</sub>.
A Severe Acute Respiratory Syndrome Coronavirus That Lacks the E Gene Is Attenuated In Vitro and In Vivo

ABSTRACT A deletion mutant of severe acute respiratory syndrome coronavirus (SARS-CoV) has been engineered by deleting the structural E gene in an infectious cDNA clone that was constructed as a bacterial artificial chromosome (BAC). The recombinant virus lacking the E gene (rSARS-CoV-ΔE) was rescued in Vero E6 cells. The recovered deletion mutant grew in Vero E6, Huh-7, and CaCo-2 cells to titers 20-, 200-, and 200-fold lower than the recombinant wild-type virus, respectively, indicating that although the E protein has an effect on growth, it is not essential for virus replication. No differences in virion stability under a wide range of pH and temperature were detected between the deletion mutant and recombinant wild-type viruses. Although both viruses showed the same morphology by electron microscopy, the process of morphogenesis seemed to be less efficient with the defective virus than with the recombinant wild-type one. The rSARS-CoV-ΔE virus replicated to titers 100- to 1,000-fold lower than the recombinant wild-type virus in the upper and lower respiratory tract of hamsters, and the lower viral load was accompanied by less inflammation in the lungs of hamsters infected with rSARS-CoV-ΔE virus than with the recombinant wild-type virus. Therefore, the SARS-CoV that lacks the E gene is attenuated in hamsters, might be a safer research tool, and may be a good candidate for the development of a live attenuated SARS-CoV vaccine.
Dynamic prediction of communication flow using social context

In this paper, we develop a temporally evolving representation framework for context that can efficiently predict communication flow in social networks between a given pair of individuals. The problem is important because it facilitates determining social and market trends as well as efficient information paths among people. We describe communication flow by two parameters: the intent to communicate and communication delay. To estimate these parameters, we design features to characterize communication and social context. Communication context refers to the attributes of current communication. Social context refers to the patterns of participation in communication (information roles) and the degree of overlap of friends between two people (strength of ties). A subset of optimal features of the communication and social context is chosen at a given time instant using five different feature selection strategies. The features are thereafter used in a Support Vector Regression framework to predict the intent to communicate and the delay between a pair of individuals. We have excellent results on a real world dataset from the most popular social networking site, www.myspace. com. We observe interestingly that while context can reasonably predict intent, delay seems to be more dependent on the personal contextual changes and other latent factors characterizing communication, e.g. 'age' of information transmitted and presence of cliques among people.
Towards a Continuous Knowledge Learning Engine for Chatbots

Although chatbots have been very popular in recent years, they still have some serious weaknesses which limit the scope of their applications. One major weakness is that they cannot learn new knowledge during the conversation process, i.e., their knowledge is fixed beforehand and cannot be expanded or updated during conversation. In this paper, we propose to build a general knowledge learning engine for chatbots to enable them to continuously and interactively learn new knowledge during conversations. As time goes by, they become more and more knowledgeable and better and better at learning and conversation. We model the task as an open-world knowledge base completion problem and propose a novel technique called lifelong interactive learning and inference (LiLi) to solve it. LiLi works by imitating how humans acquire knowledge and perform inference during an interactive conversation. Our experimental results show LiLi is highly promising.
Optimization of grounding system of 60/30 kV substation of Ain El-Melh using GAO

This paper presents a safe and economic grounding system design for the substation of Ain El-Melh located in M'Sila city (Algeria) using genetic algorithm optimization (GAO). The main objective is to minimize the cost function of the considered grounding system in accordance with the security restrictions required by the ANSI/IEEE Std. 80-2000. A new mathematical model has been proposed for the cost function. This latter includes number of conductors, conductor dimension, number of rods, length of rods and total area of revetment. The study results have showed the effectiveness of the established GAO algorithm in designing of grounding system in economic view. A good accordance has been obtained when comparing the results found by GAO algorithm and the calculation code CYMGrd ones.
Differentiation and Memory Formation Antigen-Dependent Murine B Cell Transcriptional Profiling of

Humoral immunity is characterized by the generation of Ab-secreting plasma cells and memory B cells that can more rapidly generate speciﬁc Abs upon Ag exposure than their naive counterparts. To determine the intrinsic differences that distinguish naive and memory B cells and to identify pathways that allow germinal center B cells to differentiate into memory B cells, we compared the transcriptional proﬁles of highly puriﬁed populations of these three cell types along with plasma cells isolated from mice immunized with a T-dependent Ag. The transcriptional proﬁle of memory B cells is similar to that of naive B cells, yet displays several important differences, including increased expression of activation-induced deaminase and several antiapoptotic genes, chemotactic receptors, and costimulatory molecules. Retroviral expression of either Klf2 or Ski , two transcriptional regulators speciﬁcally enriched in memory B cells relative to their germinal center precursors, imparted a competitive advantage to Ag receptor and CD40-engaged B cells in vitro. These data suggest that humoral recall responses are more rapid than primary responses due to the expression of a unique transcriptional program by memory B cells that allows them to both be maintained at high frequencies and to detect and rapidly respond to antigenic re-exposure. The Journal of Immunology, 2007, 179: 6808–6819.
A 3-dimensional triangulation scheme to improve the accuracy of indoor localization for IoT services

Beacon allows Bluetooth [1] devices to broadcast and receive tiny information within short distance, which consists of two parts: a broadcaster (beacon device) and a receiver (smartphone app). So, it is being widely used in URL (Uniform Resource Locator) broadcast, distance measurement, localization etc. However, because of its deviation, measured distance value is not always accuracy. In this paper, we propose a new scheme to improve the distance measurement accuracy. The distance measurement function can be used for localization flexibly, but in some special situation such as a faulted or bad beacon is present, or obstacle is present between the beacon and user, the localization will be incorrect. We also call the beacon is “malicious beaon”. These malicious beacons will significantly impact the accuracy of user's localization. In this paper, we provide a new scheme to mitigate the influence of the “malicious beacon” use the improved distance measurement, and identify it. At last, we compare the performance of our scheme with the general scheme. The simulation result shows that the proposed scheme is better than the general scheme.
The Mid‐Domain Effect and Species Richness Patterns:What Have We Learned So Far?

If species' ranges are randomly shuffled within a bounded geographical domain free of environmental gradients, ranges overlap increasingly toward the center of the domain, creating a “mid‐domain” peak of species richness. This “mid‐domain effect” (MDE) has been controversial both in concept and in application. Empirical studies assess the degree to which the evolutionary, ecological, and historical processes that undeniably act on individual species and clades produce geographical patterns that resemble those produced by MDE models. MDE models that resample empirical range size frequency distributions (RSFDs) balance the risk of underestimating and overestimating the role of MDE, whereas theoretical RSFDs are generally biased toward underestimating MDE. We discuss the inclusion of nonendemic species in MDE models, rationales for setting domain limits, and the validity of one‐ and two‐dimensional MDE models. MDE models, though null models, are not null hypotheses to be simplistically rejected or accepted. They are a means of estimating the expected effect of geometric constraints within the context of multiple causality. We call for assessment of MDE on an equal statistical footing with other candidate explanations for richness gradients. Although some critics have categorically dismissed MDE, an overview of the 21 MDE studies published to date reveals a substantial signature of MDE in natural patterns and justifies continued work.
Closed String Tachyons, Non-Supersymmetric Orbifolds and Generalised McKay Correspondence

We study closed string tachyon condensation on general non-supersymmetric orbifolds of C^2. Extending previous analyses on Abelian cases, we present the classification of quotients by discrete finite subgroups of GL(2; C) as well as the generalised Hirzebruch-Jung continued fractions associated with the resolution data. Furthermore, we discuss the intimate connexions with certain generalised versions of the McKay Correspondence.
HotLig: A Molecular Surface-Directed Approach to Scoring Protein-Ligand Interactions

Accurate prediction of ligand-binding poses is crucial for understanding molecular interactions and is very important for drug discovery, structural design, and optimization. In this study, we developed a novel scoring program, HotLig, which applies the Connolly surface of a protein to calculate hydrophobic interaction and paired pharmacophore interactions with ligands. In addition to molecular surface distance, ligand-contacting areas and hydrogen-bond angles were also introduced to the scoring functions in HotLig. Four individual energy scoring functions for H-bonds, ionic pairs, metal coordination, and hydrophobic effects were derived from 600 protein-ligand complexes, and then, their weighting factors were optimized through an interaction-characterized training set. Success rates of ligand-binding-pose predictions (with a root mean squared deviation of ≤2 Å) for the Wang, GOLD, and Cheng data sets were respectively validated to be 91.0%, 87.0%, and 85.6%. HotLig was found to possess equally good predictive powers for the hydrophilic (88.6%) and hydrophobic subsets (87.5%), and the success rate for the mixed subset was as high as 96.9%. The Spearman correlation coefficients were as good as 0.609 to 0.668, which indicates HotLig also has satisfactory predictive power for binding affinities. These results suggested that the HotLig can analyze diverse ligands, including peptides, and is expected to be a powerful tool for drug design and discovery.
Heterogeneous Ensemble Combination Search Using Genetic Algorithm for Class Imbalanced Data Classification

Classification of datasets with imbalanced sample distributions has always been a challenge. In general, a popular approach for enhancing classification performance is the construction of an ensemble of classifiers. However, the performance of an ensemble is dependent on the choice of constituent base classifiers. Therefore, we propose a genetic algorithm-based search method for finding the optimum combination from a pool of base classifiers to form a heterogeneous ensemble. The algorithm, called GA-EoC, utilises 10 fold-cross validation on training data for evaluating the quality of each candidate ensembles. In order to combine the base classifiers decision into ensemble’s output, we used the simple and widely used majority voting approach. The proposed algorithm, along with the random sub-sampling approach to balance the class distribution, has been used for classifying class-imbalanced datasets. Additionally, if a feature set was not available, we used the (α, β) − k Feature Set method to select a better subset of features for classification. We have tested GA-EoC with three benchmarking datasets from the UCI-Machine Learning repository, one Alzheimer’s disease dataset and a subset of the PubFig database of Columbia University. In general, the performance of the proposed method on the chosen datasets is robust and better than that of the constituent base classifiers and many other well-known ensembles. Based on our empirical study we claim that a genetic algorithm is a superior and reliable approach to heterogeneous ensemble construction and we expect that the proposed GA-EoC would perform consistently in other cases.
Priming Stimulation Enhances the Depressant Effect of Low-Frequency Repetitive Transcranial Magnetic Stimulation

Low-frequency (1 Hz) repetitive transcranial magnetic stimulation (rTMS) can depress the excitability of the cortex locally and has been proposed for the treatment of disorders such as schizophrenia and epilepsy. Some have speculated that the depressant effect is related to long-term depression (LTD) of cortical synapses. Because in vitro LTD can be enhanced by pretreatment of synapses with higher-frequency stimulation, we hypothesized that if rTMS depression had mechanisms in common with LTD, higher-frequency priming would increase it also. In 25 healthy volunteers in two experiments, we measured motor-evoked potentials (MEPs) from TMS of the motor cortex to define the baseline response. Subthreshold rTMS (6 Hz, fixed rate or frequency modulated) was used to prime the motor cortex, followed by suprathreshold 1 Hz stimulation for 10 min at just above the MEP threshold. Over the next 60 min, we recorded MEPs every 10 sec and found significant increases in the amount of cortical depression with both types of 6 Hz priming rTMS relative to sham. The MEP depression from 6 Hz-primed 1 Hz rTMS showed no evidence of decay after 60 min. Pretreatment with 6 Hz primes both 1 Hz rTMS depression and LTD. Although not conclusive evidence, this strengthens the case for overlapping mechanisms and suggests a potent new technique for enhancing low-frequency rTMS depression that may have experimental and clinical applications.
Sketch-based generation and editing of quad meshes

Coarse quad meshes are the preferred representation for animating characters in movies and video games. In these scenarios, artists want explicit control over the edge flows and the singularities of the quad mesh. Despite the significant advances in recent years, existing automatic quad remeshing algorithms are not yet able to achieve the quality of manually created remeshings. We present an interactive system for manual quad remeshing that provides the user with a high degree of control while avoiding the tediousness involved in existing manual tools. With our sketch-based interface the user constructs a quad mesh by defining patches consisting of individual quads. The desired edge flow is intuitively specified by the sketched patch boundaries, and the mesh topology can be adjusted by varying the number of edge subdivisions at patch boundaries. Our system automatically inserts singularities inside patches if necessary, while providing the user with direct control of their topological and geometrical locations. We developed a set of novel user interfaces that assist the user in constructing a curve network representing such patch boundaries. The effectiveness of our system is demonstrated through a user evaluation with professional artists. Our system is also useful for editing automatically generated quad meshes.
Inductive Principles for Learning Restricted Boltzmann Machines

We explore the training and usage of the Restricted Boltzmann Machine for un supervised feature extraction. We investigate the many different aspects inv olved in their training, and by applying the concept of iterate averaging we show tha t it is possible to greatly improve on state of the art algorithms. We also derive estimato rs based on the principles of pseudo-likelihood, ratio matching, and score matc hing, and we test them empirically against contrastive divergence, and stocha ic maximum likelihood (also known as persistent contrastive divergence). Our results show that ratio matching and score matching are promising approaches to lear ning Restricted Boltzmann Machines. By applying score matching to the Restricted Boltzmann Machine, we show that training an auto-encoder neural networ k with a particular kind of regularization function is asymptotically consistent. Finally, we discuss the concept of deep learning and its relationship to training Restr icted Boltzmann Machines, and briefly explore the impact of fine-tuning on the par ameters and performance of a deep belief network.
Cost-driven scheduling of grid workflows using Partial Critical Paths

Recently, utility grids have emerged as a new model of service provisioning in heterogeneous distributed systems. In this model, users negotiate with providers on their required Quality of Service and on the corresponding price to reach a Service Level Agreement. One of the most challenging problems in utility grids is workflow scheduling, i.e., the problem of satisfying users' QoS as well as minimizing the cost of workflow execution. In this paper, we propose a new QoS-based workflow scheduling algorithm based on a novel concept called Partial Critical Path. This algorithm recursively schedules the critical path ending at a recently scheduled node. The proposed algorithm tries to minimize the cost of workflow execution while meeting a user-defined deadline. The simulation results show that the performance of our algorithm is very promising.