Miracle-dz/UniFAR-FTUs huggingface dataset:
Sialylated multivalent antigens engage CD22 in trans and inhibit B cell activation

CD22 is an inhibitory coreceptor on the surface of B cells that attenuates B cell antigen receptor (BCR) signaling and, therefore, B cell activation. Elucidating the molecular mechanisms underlying the inhibitory activity of CD22 is complicated by the ubiquity of CD22 ligands. Although antigens can display CD22 ligands, the receptor is known to bind to sialylated glycoproteins on the cell surface. The propinquity of CD22 and cell-surface glycoprotein ligands has led to the conclusion that the inhibitory properties of the receptor are due to cis interactions. Here, we examine the functional consequences of trans interactions by employing sialylated multivalent antigens that can engage both CD22 and the BCR. Exposure of B cells to sialylated antigens results in the inhibition of key steps in BCR signaling. These results reveal that antigens bearing CD22 ligands are powerful suppressors of B cell activation. The ability of sialylated antigens to inhibit BCR signaling through trans CD22 interactions reveals a previously unrecognized role for the Siglec-family of receptors as modulators of immune signaling.
Holocene slip rate of the Wasatch fault zone, Utah, from geodetic data: Earthquake cycle effects

GPS data define a broad zone of present day deformation in the eastern Basin and Range province, western US. Using finite element models with elastic upper crust over viscoelastic lower crust/upper mantle and incorporating earthquake cycle effects, we show that these data are consistent with a model whereby most contemporary fault slip is focused on the Wasatch fault zone. Modeled rates of horizontal extension are 3.0–4.5 mm/yr, in agreement with Holocene geologic data. The models are non‐unique, in part because much of the Wasatch fault is in the late stages of the earthquake cycle, when surface velocity gradients across the fault are low.
Adding Dimensions and Facts to ADF Representation of Legal Cases

Previous work has shown that theories representing a body of case law can be represented as Abstract Dialectical Frameworks (ADF) but that it is desirable to represent degrees of presence of factors, rather than as simply considering factors as present or absent, as is currently standard. This requires consideration not only of factors, but also of the facts on which their attribution to cases is based. In this paper we revive the use of dimensions, from which factors developed, to mediate between facts and factors so as to achieve a structured and transparent representation of degrees of presence of factors. We will illustrate our exploration using the much discussed wild animal cases.
Extraction of Buried Signals in Noise: Correlated Processes

In this paper, we propose extraction of signals correlated with noise in which they are buried. Proposed extraction method uses no a-priori information on the buried signal and works independently of the nature of noise, correlated or not with the signal, colored or white, Gaussian or not, and locations of its spectral extent. Extraction of buried correlated signals is achieved without averaging in the time or frequency domain.
Orientations Optimization for two Runway Configurations

The runway orientation is the result of compromises between the airport usability and additional factors, such as available land, existing obstructions, topographic difficulties, flight path interference, noise pollution and other environmental impacts. Therefore, the solution of a combination of acceptable orientations, which avoids excessive crosswinds at least 95% of the time, as well as the optimal orientation, is essential to conduct those compromises in the runway orientation analysis. The objective of this paper is to develop a computer model which is capable of providing a combination of acceptable runway orientations, changing the allowable crosswind limit flexibly, and determining the optimal orientations of two runway configurations. Instead of visual estimation or geometric computation, this paper presents an analytical method for wind coverage analysis, which is running in spreadsheet and VBA. The numerical example and comparison show that the model is competitively accurate and improved convenient in comparison with previous ones.
IBM Research Report Imperfect Learning for Autonomous Concept Modeling

Most existing supervised machine learning frameworks assume there is no mistake or false interpretation on the training samples. However, this assumption may not be true in practical applications. In some cases, if human being is involved in providing training samples, there may be errors in the training set. In this paper, we study the effect of imperfect training samples on the supervised machine learning framework. We focus on the mathematical framework that describes the learnability of noisy training data. We study theorems to estimate the error bounds of generated models and the required amount of training samples. These errors are dependent on the amount of data trained and the probability of the accuracy of training data. Based on the effectiveness of learnability on imperfect annotation, we describe an autonomous learning framework, which uses cross-modality information to learn concept models. For instance, visual concept models can be trained based on the detection result of Automatic Speech Recognition, Closed Captions, or prior detection results of the same modality. Those detection results on an unsupervised training set serve as imperfect labeling for the models-to-build. A prototype system based on this learning technique has been built. Promising results have been shown on these experiments.
Production of H5N1 Influenza Virus Matrix Protein 2 Ectodomain Protein Bodies in Tobacco Plants and in Insect Cells as a Candidate Universal Influenza Vaccine

The spread of influenza A viruses is partially controlled and prevented by vaccination. The matrix protein 2 ectodomain (M2e) is the most conserved sequence in influenza A viruses, and is therefore a good potential target for a vaccine to protect against multiple virus subtypes. We explored the feasibility of an M2e-based universal influenza A vaccine candidate based on the highly pathogenic avian influenza A virus, H5N1. A synthetic M2e gene was human- and plant-codon optimized and fused in-frame with a sequence encoding the N-terminal proline-rich domain (Zera®) of the γ-zein protein of maize. Zera®M2e was expressed transiently in Nicotiana benthamiana and Sf21 baculovirus/insect cell expression systems, and Zera®M2e protein bodies (PBs) were successfully produced in both expression systems. The plant-produced Zera®M2e PBs were purified and injected into Balb/c mice. Western blot analysis using insect cell-produced Zera®M2e PBs and multiple tandem M2e sequences (5xM2e) fused with the avian influenza H5N1 transmembrane and cytosolic tail (5xM2e_tHA) confirmed the presence of M2e-specific antibodies in immunized mice sera. The immunogenicity of the Zera®M2e indicates that our plant-produced protein has potential as an inexpensive universal influenza A vaccine.
Emotion Recognition and Synthesis Based on MPEG‐4 FAPs

In the framework of MPEG-4 hybrid coding of natural and synthetic data streams, one can include teleconferencing and telepresence applications, in which a synthetic proxy or a virtual agent is capable of substituting the actual user. Such agents can interact with each other, analyzing input textual data entered by the user and multisensory data, including human emotions, facial expressions and nonverbal speech. This not only enhances interactivity, by replacing single media representations with dynamic multimedia renderings, but also assists human–computer interaction issues, letting the system become accustomed to the current needs and feelings of the user. Actual application of this technology [1] is expected in educational environments, 3-D videoconferencing and collaborative workplaces, online shopping and gaming, virtual communities and interactive entertainment. Facial expression synthesis and animation has gained much interest within the MPEG-4 framework; explicit facial animation parameters (FAPs) have been dedicated to this purpose. However, FAP implementation is an open research area [2]. In this chapter we describe a method for generating emotionally enriched human–computer interaction, focusing on analysis and synthesis of primary [3] and intermediate facial expressions [4]. To achieve this goal we utilize both MPEG-4 facial definition parameters (FDPs) and FAPs. The contribution of the work is twofold: it proposes a way of modeling primary expressions using FAPs and it describes a rule-based technique for analyzing both archetypal and intermediate expressions; for the latter we propose an innovative model generation framework. In particular, a relation between FAPs and the activation parameter proposed in classical psychological studies is established, extending the archetypal expression studies that the computer society has concentrated on. The overall scheme leads to a parameterized 144 EMOTION RECOGNITION AND SYNTHESIS BASED ON MPEG-4 FAPs approach to facial expression synthesis that is compatible with the MPEG-4 standard and can be used for emotion understanding.
Agrin Binds BMP2, BMP4 and TGFβ1

The C-terminal 95 kDa fragment of some isoforms of vertebrate agrins is sufficient to induce clustering of acetylcholine receptors but despite two decades of intense agrin research very little is known about the function of the other isoforms and the function of the larger, N-terminal part of agrins that is common to all isoforms. Since the N-terminal part of agrins contains several follistatin-domains, a domain type that is frequently implicated in binding TGFβs, we have explored the interaction of the N-terminal part of rat agrin (Agrin-Nterm) with members of the TGFβ family using surface plasmon resonance spectroscopy and reporter assays. Here we show that agrin binds BMP2, BMP4 and TGFβ1 with relatively high affinity, the KD values of the interactions calculated from SPR experiments fall in the 10−8 M–10−7 M range. In reporter assays Agrin-Nterm inhibited the activities of BMP2 and BMP4, half maximal inhibition being achieved at ∼5×10−7 M. Paradoxically, in the case of TGFβ1 Agrin N-term caused a slight increase in activity in reporter assays. Our finding that agrin binds members of the TGFβ family may have important implications for the role of these growth factors in the regulation of synaptogenesis as well as for the role of agrin isoforms that are unable to induce clustering of acetylcholine receptors. We suggest that binding of these TGFβ family members to agrin may have a dual function: agrin may serve as a reservoir for these growth factors and may also inhibit their growth promoting activity. Based on analysis of the evolutionary history of agrin we suggest that agrin's growth factor binding function is more ancient than its involvement in acetylcholine receptor clustering.
Matrix of Z-module1

In this article, we formalize a matrix of Z-module and its properties. Specially, we formalize a matrix of a linear transformation of Z-module, a bilinear form and a matrix of the bilinear form (Gramian matrix). We formally prove that for a finite-rank free Z-module V , determinant of its Gramian matrix is constant regardless of selection of its basis. Z-module is necessary for lattice problems, LLL (Lenstra, Lenstra and Lovász) base reduction algorithm and cryptographic systems with lattices [? ]. Some theorems in this article are described by translating theorems in [22], [24] and [18] into theorems of Z-module.
The Ebb and Flow of Deep Learning: a Theory of Local Learning

In a physical neural system, where storage and processing are intimately intertwined, the rules for adjusting the synaptic weights can only depend on variables that are available locally, such as the activity of the pre- and post-synaptic neurons, resulting in local learning rules. A systematic framework for studying the space of local learning rules must first define the nature of the local variables, and then the functional form that ties them together into each learning rule. We consider polynomial local learning rules and analyze their behavior and capabilities in both linear and non-linear networks. As a byproduct, this framework enables also the discovery of new learning rules as well as important relationships between learning rules and group symmetries. Stacking local learning rules in deep feedforward networks leads to deep local learning. While deep local learning can learn interesting representations, it cannot learn complex input-output functions, even when targets are available for the top layer. Learning complex input-output functions requires local deep learning where target information is propagated to the deep layers through a backward channel. The nature of the propagated information about the targets, and the backward channel through which this information is propagated, partition the space of learning algorithms. For any learning algorithm, the capacity of the backward channel can be defined as the number of bits provided about the gradient per weight, divided by the number of required operations per weight. We estimate the capacity associated with several learning algorithms and show that backpropagation outperforms them and achieves the maximum possible capacity. The theory clarifies the concept of Hebbian learning, what is learnable by Hebbian learning, and explains the sparsity of the space of learning rules discovered so far.
An interactive speech interface for virtual characters in dynamic environments

In this paper, we propose a new improvement to our 3D Virtual Story Environment System (3DVSE) by adding a real-time animation with voice synthesis. The new system offers a flexible and easy way to generate an interactive 3D virtual Environment (3DVE) as compared to traditional 3D packages. It enables the user to control and interact with the virtual characters via speech instructions so that the characters can respond to the commands in real time. This system has the potential, if combined with artificial intelligence, to act as a dialogue interface for believable agents that have many applications such as computer games, and intelligent multimedia applications. In this system, the agent can talk and listen to fellow agents and human users.
Effect of an Introduced Phytoene Synthase Gene Expression on Carotenoid Biosynthesis in the Marine Diatom Phaeodactylum tricornutum

Carotenoids exert beneficial effects on human health through their excellent antioxidant activity. To increase carotenoid productivity in the marine Pennales Phaeodactylum tricornutum, we genetically engineered the phytoene synthase gene (psy) to improve expression because RNA-sequencing analysis has suggested that the expression level of psy is lower than other enzyme-encoding genes that are involved in the carotenoid biosynthetic pathway. We isolated psy from P. tricornutum, and this gene was fused with the enhanced green fluorescent protein gene to detect psy expression. After transformation using the microparticle bombardment technique, we obtained several P. tricornutum transformants and confirmed psy expression in their plastids. We investigated the amounts of PSY mRNA and carotenoids, such as fucoxanthin and β-carotene, at different growth phases. The introduction of psy increased the fucoxanthin content of a transformants by approximately 1.45-fold relative to the levels in the wild-type diatom. However, some transformants failed to show a significant increase in the carotenoid content relative to that of the wild-type diatom. We also found that the amount of PSY mRNA at log phase might contribute to the increase in carotenoids in the transformants at stationary phase.
CONSEQUENCES OF GOVERNMENT DEFICITS AND DEBT

Over many years, Ben Friedman’s economic research and writings in political economy frame economic analysis of— and moral consideration of—large government budget deficits and the need for fiscal consolidation in the United States. In his book The Moral Consequences of Economic Growth, Friedman emphasizes the salutary effects economic growth for openness and social cohesion. This essay emphasizes economic analysis of government budget deficits. The U.S. economy did not, in many respects, flounder after the budget deficits of the 1980s. Indeed, by the middle of the 1990s, the U.S. economy began a long-lasting expansion in productivity growth. While direct crowding out of private investment through higher real interest rates has, at least in the view of the empirical evidence reviewed in this essay, been modest, three concerns remain. The first is that cumulative increases in debt are now so large that even the small estimated effects identified here can lead to large increases in real interest rates. The second is that one attenuation of effects of higher government debt levels on interest rates may trace to greater reliance on foreign saving, with an accompanying problem of imbalances. The third is that the present trajectory of government spending in the United States presents the very real possibility of higher tax burdens, reducing capital formation, economic growth, and living standards.
High-Resolution Infrared Spectra of Bulge Globular Clusters: The Extreme Chemical Abundances of Terzan 4 and Terzan 5

Using the NIRSPEC spectrograph at Keck II, we have obtained infrared echelle spectra covering the range 1.5–1.8 μm for the highly reddened bulge globular clusters Terzan 4 and Terzan 5. The clusters are of interest because a blue horizontal branch in Terzan 4 is consistent with very low metallicity, while Terzan 5 has been proposed as possibly the most metal-rich known Galactic globular cluster. We report the first detailed abundance analysis for stars in these clusters, and we find [Fe/H] = -1.60 and -0.21 dex for Terzan 4 and Terzan 5, respectively, confirming the presence of a large metallicity spread in the bulge, as well as in the halo of the Galaxy. We find [α/Fe] ≈ +0.5 dex in Terzan 4 and [α/Fe] ≈ +0.3 dex in Terzan 5, consistent with what has been found for field stars in the bulge. The enhanced α-element abundances are also consistent with rapid chemical enrichment, most likely by Type II supernovae. Finally, we measure very low 12C/13C ≤ 10 ratios in all the giant stars, confirming that extra-mixing mechanisms due to cool bottom processing occur during evolution along the RGB.
Chip Level Decision Feedback Equalizer for CDMA Downlink Channel

In most commercial wideband code division multiple access (W-CDMA) systems, the transmitted signal in the downlink channel is spread by orthogonal codes to accommodate different users. However, frequency selective fading destroys the orthogonality and causes multiple access interference (MAI). The rake receiver has been proposed for a receiver in the downlink channel. Although providing reasonable performance due to path diversity, the rake receiver does not restore the orthogonality. As a result, the MAI is still present at the output of the rake receiver and CDMA system becomes an interference limited system. Thus, a better approach to provide MAI suppression shall be considered. The chip level linear equalizer followed by a despreader is an attractive alternative receiver to restore the orthogonality and to suppress the MAI. However, the performance of the chip level linear equalizer depends on the spectral characteristic of the channel and may not be satisfactory for some channels. To overcome this difficulty, the chip level decision feedback equalizer can be used. To improve the performance further, multiple-input multiple-output (MIMO) channels by using multiple antennas can also be employed. Recent research in information theory shows that multiple antennas can mitigate multipath fading in a wireless channel, increase the information capacity of wireless communication systems dramatically, and improve the reliability of the communications over wireless channel.
Creating Objects Using Genetic Programming Techniques

Grammar-Based Object Oriented Genetic Programming leverages the Object Oriented programming paradigm in evolutionary computation. The difference between our approach and others is that the resulting solution is a syntactically correct and executable Java program. Candidate solutions are created and tested against a JUnit test suite. Evolved solutions incorporate useful Object Oriented patterns. To create and compile the Java classes, a new framework for Grammar-Based Genetic Programming was created. The framework is entirely written in Java. By externally compiling and then loading the classes into the framework for testing, true object-oriented encapsulation is achieved. For the first time state and behavior are passed around as an object.
Rapid EPR Oximetry using Sparse Spin Distribution

A method is presented to use continuous wave electron paramagnetic resonance (EPR) imaging for rapid measurement of oxygen partial pressure in three spatial dimensions. A particulate paramagnetic probe is employed to create a sparse distribution of spins in a volume of interest. Data is acquired by varying the orientation and strength of the magnetic field gradient. Reduction of unknown parameters is achieved by using a parametric forward model and exploiting the sparsity of spin distribution. This leads to an order of magnitude reduction in data acquisition time as compared to tradition spectral-spatial imaging. The proposed oximetry method is experimentally demonstrated for a lithium octa-n-butoxy naphthalocyanine (LiNc–BuO) probe using an L-band EPR spectrometer.
Identifying More Equivalent Mutants via Code Similarity

Equivalent mutants are one of the major costs of mutation testing. The undecidable nature of this problem makes a fully automated solution unattainable and necessitates the manual analysis of live mutants. This paper introduces the concept of mirrored mutants, ones that affect similar code fragments. It is argued that mirrored mutants exhibit analogous behavior with respect to their equivalence. Thus, if one of them is equivalent, then the other mirrored mutants should be too. An empirical study, conducted on real world programs, investigates this argument, focusing on both intra-method and inter-method mirrored mutants. The obtained results suggest that mirrored mutants indeed exhibit this kind of behavior and thus can be utilized to ameliorate the adverse effects of the equivalent mutant problem.
The Falls Efficacy Scale International (FES-I). A comprehensive longitudinal validation study.

OBJECTIVE this study aimed to perform a comprehensive validation of the 16-item and 7-item Falls Efficacy Scale International (FES-I) by investigating the overall structure and measurement properties, convergent and predictive validity and responsiveness to change. METHOD five hundred community-dwelling older people (70-90 years) were assessed on the FES-I in conjunction with demographic, physiological and neuropsychological measures at baseline and at 12 months. Falls were monitored monthly and fear of falling every 3 months. RESULTS the overall structure and measurement properties of both FES-I scales, as evaluated with item response theory, were good. Discriminative ability on physiological and neuropsychological measures indicated excellent validity, both at baseline (n = 500, convergent validity) and at 1-year follow-up (n = 463, predictive validity). The longitudinal follow-up suggested that FES-I scores increased over time regardless of any fall event, with a trend for a stronger increase in FES-I scores when a person suffered multiple falls in a 3-month period. Additionally, using receiver-operating characteristic (ROC) curves, cut-points were defined to differentiate between lower and higher levels of concern. CONCLUSIONS the current study builds on the previously established psychometric properties of the FES-I. Both scales have acceptable structures, good validity and reliability and can be recommended for research and clinical purposes. Future studies should explore the FES-I's responsiveness to change during intervention studies and confirm suggested cut-points in other settings, larger samples and across different cultures.