Miracle-dz/UniFAR-FTUs huggingface dataset:
A Protocol for Cross-Validating Large Crowdsourced Data: The Case of the LIRIS-ACCEDE Affective Video Dataset

Recently, we released a large affective video dataset, namely LIRIS-ACCEDE, which was annotated through crowdsourcing along both induced valence and arousal axes using pairwise comparisons. In this paper, we design an annotation protocol which enables the scoring of induced affective feelings for cross-validating the annotations of the LIRIS-ACCEDE dataset and identifying any potential bias. We have collected in a controlled setup the ratings from 28 users on a subset of video clips carefully selected from the dataset by computing the inter-observer reliabilities on the crowdsourced data. On contrary to crowdsourced rankings gathered in unconstrained environments, users were asked to rate each video through the Self-Assessment Manikin tool. The significant correlation between crowdsourced rankings and controlled ratings validates the reliability of the dataset for future uses in affective video analysis and paves the way for the automatic generation of ratings over the whole dataset.
Local fisher discriminant analysis for spoken language identification

I-vector is a state-of-the-art technique widely used in spoken language identification systems. Since i-vectors include total variability factors, discriminant analysis methods have been introduced to find the most discriminative features while removing the undesired variables for language identification, for example, linear discriminant analysis (LDA) and nonparametric discriminant analysis (NDA). However, these methods either do not consider or use weak local structures of the data. In this study, we introduce a local Fisher discriminant analysis (LFDA) as a post-processing discriminant analysis method to extract the discriminative features from i-vectors. LFDA is a full-rank method which takes the local structure of the data into account for non-Gaussian distribution data, i.e., multimodal. Compared with LDA and NDA, LFDA is a pair-wise local method which enhances the centralization of the distribution of samples in the same class to obtain larger amounts of discriminative features. Experimental results indicate that LFDA is more effective than LDA and NDA for the i-vector-based language identification task.
The M184V Substitution in Human Immunodeficiency Virus Type 1 Reverse Transcriptase Delays the Development of Resistance to Amprenavir and Efavirenz in Subtype B and C Clinical Isolates

ABSTRACT The M184V substitution in human immunodeficiency virus type 1 (HIV-1) reverse transcriptase (RT), encoding high-level resistance to lamivudine (3TC), results in decreased HIV-1 replicative capacity, diminished RT processivity, and increased RT fidelity in biochemical assays. We assessed the effect of M184V on the development of resistance to the nonnucleoside RT inhibitors efavirenz (EFV) and nevirapine, and to the protease inhibitor amprenavir (APV) in tissue culture. Genotypic analysis revealed differences in EFV resistance-conferring mutations in subtype B (K103N) versus subtype C (V106 M), and the appearance of both was significantly delayed in the M184V-containing variants compared with the wild type (WT). Similarly, there was a marked delay in the emergence of mutations associated with APV resistance (I54 M/L/V) in subtype B viruses harboring M184V compared with paired WT viral isolates.
Hybridization and reproductive isolation between diploid Erythronium mesochoreum and its tetraploid congener E. albidum (Liliaceae)

Polyploidy has played an important role in angiosperm diversification, but how polyploidy contributes to reproductive isolation remains poorly understood. Most work has focused on postzygotic reproductive barriers, and the influence of ploidy differences on prezygotic barriers is understudied. To address these gaps, we quantified hybrid occurrence, interspecific self‐compatibility differences, and the contributions of multiple pre‐ and postzygotic barriers to reproductive isolation between diploid Erythronium mesochoreum (Liliaceae) and its tetraploid congener Erythronium albidum. Reproductive isolation between the study species was nearly complete, and naturally occurring hybrids were infrequent and largely sterile. Although postzygotic barriers effected substantial reproductive isolation when considered in isolation, the study species’ spatial distributions and pollinator assemblages overlapped little, such that interspecific pollen transfer is likely uncommon. We did not find evidence that E. albidum and E. mesochoreum differed in mating systems, indicating that self‐incompatibility release may not have fostered speciation in this system. Ultimately, we demonstrate that E. albidum and E. mesochoreum are reproductively isolated by multiple, hierarchically‐operating barriers, and we add to the currently limited number of studies demonstrating that early acting barriers such as pollinator‐mediated isolation can be important for effecting and sustaining reproductive isolation in diploid‐polyploid systems.
Sampling node group structure of social and information networks

Lately, network sampling proved as a promising tool for simplifying large real-world networks and thus providing for their faster and more efficient analysis. Still, understanding the changes of network structure and properties under different sampling methods remains incomplete. In this paper, we analyze the presence of characteristic group of nodes (i.e., communities, modules and mixtures of the two) in social and information networks. Moreover, we observe the changes of node group structure under two sampling methods, random node selection based on degree and breadth-first sampling. We show that the sampled information networks contain larger number of mixtures than original networks, while the structure of sampled social networks exhibits stronger characterization by communities. The results also reveal there exist no significant differences in the behavior of both sampling methods. Accordingly, the selection of sampling method impact on the changes of node group structure to a much smaller extent that the type and the structure of analyzed network.
The Existential Function of Intrinsic Religiousness: Moderation of Effects of Priming Religion on Intercultural Tolerance and Afterlife Anxiety

Managing existential concerns is theorized to be a key function of religion. We posit that priming religion should be related to greater existential security for those high in intrinsic religiosity. In Experiment 1, priming religion increased intercultural tolerance among individuals who were highly intrinsically religious but decreased it for those low in intrinsic religiousness. In Experiment 2, intrinsic religiousness again moderated the effects of the prime, suggesting that priming religion resulted in attenuated afterlife anxiety for intrinsically religious individuals but greater anxiety for individuals low in intrinsic religiousness. Religious reminders appeared to provide existential security—evidenced by tolerance and reduced death anxiety—only to those high in intrinsic religiousness and can be threatening to those low in intrinsic religiousness. Existential outcomes are a specific case in which intrinsic religiousness can moderate the effects of religious primes, suggesting that religion plays a different existential role for different people.
Multi-Relational Learning with Gaussian Processes

Due to their flexible nonparametric nature, Gaussian process models are very effective at solving hard machine learning problems. While existing Gaussian process models focus on modeling one single relation, we present a generalized GP model, named multi-relational Gaussian process model, that is able to deal with an arbitrary number of relations in a domain of interest. The proposed model is analyzed in the context of bipartite, directed, and undirected univariate relations. Experimental results on real-world datasets show that exploiting the correlations among different entity types and relations can indeed improve prediction performance.
Data Analysis on DNA Microarray Expression Values using Self Organizing Map

There is a vast need to develop analytical attitude to analyze and to make use of the information contained in gene expression data. A narrative approach for cancer prediction (similarity gene) from DNA microarray data, First apply feature selection using parametric and nonparametric feature selection methods to extract features and select exact feature by combining both methods then apply principal component analysis in microarray data to reduce dimensionality Then the selected principal components are clustered and classified using self organizing map and compare the results.
Analysis of Cell-Free Human α1 Integrin with a Monoclonal Antibody to the I-Domain: Detection in Ocular Fluid and Function as an Adhesion Substrate

The α1β1 integrin, an inserted (I) domain containing collagen receptor, is expressed in the cell surface membrane of normal and malignant cells, and may play a role in their migration through tissues or in metastatic spread. Here we report that a functional anti-human α1β1 integrin monoclonal antibody (mAb) (1B3.1) directly and specifically binds plastic bound recombinant human α1 I-domain protein containing the collagen binding site. Detection was diminished by acidification of the I-domain protein but was enhanced by increasing concentrations of Mg2+ cation. Furthermore, we detected binding of the mAb to proteins from the ocular fluids of 6 patients, with the highest concentration, corresponding to 22.1 ng/ml of I-domain, found in a sample from the eye of a patient with metastatic lung adenocarcinoma. Interestingly, we found that both SKNSH neuroblastoma cells and virally transformed human T cells adhered specifically to plastic wells coated with either immobilized collagen IV oral I-domain. MAb 1B3.1 inhibited adhesion to collagen IV but not to immobilized I-domain. These results suggest a novel function for cell free α1 I-domain as a substrate for cellular adhesion, which may have relevance in tumor spread in vivo.
Highlights PARP-3 activity is stimulated by DNA double-strand breaks PARP-3 accelerates DSB repair in concert with the ADP-ribose binding protein, APLF PARP-3 and APLF render chromatin ‘permissible’ for DNA ligation by XRCC4/DNA ligase IV

PARP-3 is a member of the ADP-ribosyl transferase super-family of unknown function. We show that PARP-3 is stimulated by DNA double-strand breaks (DSBs) in vitro and functions in the same pathway as the poly (ADP-ribose)-binding protein APLF to accelerate chromosomal DNA doublestrand break repair. We implicate PARP-3 in the accumulation of APLF at DSBs and demonstrate that APLF promotes the retention of XRCC4/DNA ligase IV complex in chromatin, suggesting that PARP-3 and APLF accelerate DNA ligation during non-homologous end-joining (NHEJ). Consistent with this, we show that class-switch recombination in Aplf-/B cells is biased towards microhomology-mediated end-joining, a pathway that operates in the absence of XRCC4/DNA ligase IV, and that the requirement for PARP-3 and APLF for NHEJ is circumvented by overexpression of XRCC4/DNA ligase IV. These data identify molecular roles for PARP-3 and APLF and uncover a novel and unanticipated aspect of chromosomal DNA double-strand break repair reactions. Suggested Reviewers:
Educational attainment, unemployment, and wage inflation

We investigate the impact of rising educational attainment on wage inflation and the equilibrium (non-inflationary) rate of unemployment. Rising educational attainment may reduce wage pressures by shifting the composition of the labor force towards groups with lower equilibrium unemployment rates, or it may increase wage pressures through increased reliance on groups whose wages are relatively responsive to changes in unemployment. A measure of aggregate unemployment adjusted for changes in the age and education structure of the labor force performs well in Phillips curve estimates of the wage inflation process but does not substantially improve the ability to forecast wages or materially alter the estimates of the equilibrium unemployment rate. We also estimate models of wage inflation that are disaggregated by educational attainment and find that college-educated workers face a sharper trade-off between labor market tightness and wage growth than do other groups. We find that forecasts of wage inflation derived from the disaggregated relationships perform better than those from aggregate wage equations.
Taxonomy of Email Reputation Systems

Today a common goal in the area of email security is to provide protection from a wide variety of threats by being more predictive instead of reactive and to identify legitimate messages in addition to illegitimate messages. There has been previous work in the area of email reputation systems that can accomplish these broader goals by collecting, analyzing, and distributing email entities' past behavior characteristics. In this paper, we provide taxonomy that examines the required properties of email reputation systems, identifies the range of approaches, and surveys previous work.
Ariadnima — Android Component Flow Reconstruction and Visualization

Android applications are built with a set of different component types that serve specific purposes. Thanks to Android's system design they can interact with each other in a variety of ways, which makes it possible to complete complex tasks. The downside of the flexible interconnectivity of system components is that the relationship between them can be very complex and hard to understand. To give a better understanding of the interconnectivity of components, this work focuses on the reconstruction of relationships between the components of an app. Our approach focusses on real world applications by minimizing the need for complex calculations (e.g., flow analysis) where possible. On the basis of static code analysis component transitions will be reconstructed, and in a second step these transitions are then visualized to allow an analyst to easily understand the relationships of the app's components.
Speed vs. accuracy in simulation for I/O-intensive applications

This paper presents a family of simulators that have been developed for data-intensive applications, and a methodology to select the most efficient one based on a user-supplied requirement for accuracy. The methodology consists of a series of tests that select an appropriate simulation based on the attributes of the application. In addition, each simulator provides two estimates of application execution time: one for the minimum expected time and the other for the maximum. We present the results of applying the strategy to existing applications and show that we can accurately simulate applications tens to hundreds of times faster than application execution time.
E9-Im9 Colicin DNase−Immunity Protein Biomolecular Association in Water: A Multiple-Copy and Accelerated Molecular Dynamics Simulation Study

Protein−protein transient and dynamic interactions underlie all biological processes. The molecular dynamics (MD) of the E9 colicin DNase protein, its Im9 inhibitor protein, and their E9-Im9 recognition complex are investigated by combining multiple-copy (MC) MD and accelerated MD (aMD) explicit-solvent simulation approaches, after validation with crystalline-phase and solution experiments. Im9 shows higher flexibility than its E9 counterpart. Im9 displays a significant reduction of backbone flexibility and a remarkable increase in motional correlation upon E9 association. Im9 loops 23−31 and 54−64 open with respect to the E9-Im9 X-ray structure and show high conformational diversity. Upon association a large fraction (∼20 nm2) of E9 and Im9 protein surfaces become inaccessible to water. Numerous salt bridges transiently occurring throughout our six 50 ns long MC-MD simulations are not present in the X-ray model. Among these Im9 Glu31−E9 Arg96 and Im9 Glu41−Lys89 involve interface interactions. Through the use of 10 ns of Im9 aMD simulation, we reconcile the largest thermodynamic impact measured for Asp51Ala mutation with Im9 structure and dynamics. Lys57 acts as an essential molecular switch to shift Im9 surface loop towards an ideal configuration for E9 inhibition. This is achieved by switching Asp60−Lys57 and Asp62−Lys57 hydrogen bonds to Asp51−Lys57 salt bridge. E9-Im9 recognition involves shifts of conformational distributions, reorganization of intramolecular hydrogen bond patterns, and formation of new inter- and intramolecular interactions. The description of key transient biological interactions can be significantly enriched by the dynamic and atomic-level information provided by computer simulations.
Median-based robust algorithms for tracing neurons from noisy confocal microscope images

This paper presents a method to exploit rank statistics to improve fully automatic tracing of neurons from noisy digital confocal microscope images. Previously proposed exploratory tracing (vectorization) algorithms work by recursively following the neuronal topology, guided by responses of multiple directional correlation kernels. These algorithms were found to fail when the data was of lower quality (noisier, less contrast, weak signal, or more discontinuous structures). This type of data is commonly encountered in the study of neuronal growth on microfabricated surfaces. We show that by partitioning the correlation kernels in the tracing algorithm into multiple subkernels, and using the median of their responses as the guiding criterion improves the tracing precision from 41% to 89% for low-quality data, with a 5% improvement in recall. Improved handling was observed for artifacts such as discontinuities and/or hollowness of structures. The new algorithms require slightly higher amounts of computation, but are still acceptably fast, typically consuming less than 2 seconds on a personal computer (Pentium III, 500 MHz, 128 MB). They produce labeling for all somas present in the field, and a graph-theoretic representation of all dendritic/axonal structures that can be edited. Topological and size measurements such as area, length, and tortuosity are derived readily. The efficiency, accuracy, and fully-automated nature of the proposed method makes it attractive for large-scale applications such as high-throughput assays in the pharmaceutical industry, and study of neuron growth on nano/micro-fabricated structures. A careful quantitative validation of the proposed algorithms is provided against manually derived tracing, using a performance measure that combines the precision and recall metrics.
Learning from human errors: prediction of phoneme confusions based on modified ASR training

In an attempt to improve models of human perception, the recognition of phonemes in nonsense utterances was predicted with automatic speech recognition (ASR) in order to analyze its applicability for modeling human speech recognition (HSR) in noise. In the first experiments, several feature types are used as input for an ASR system; the resulting phoneme scores are compared to listening experiments using the same speech data. With conventional training, the highest correlation between predicted and measured recognition was observed for perceptual linear prediction features (r = 0:84). Secondly, a new training paradigm for ASR is proposed with the aim of improving the prediction of phoneme intelligibility. For this perceptual training, the original utterance labels are modified based on the confusions measured in HSR tests. The modified ASR training improved the overall prediction, with the best models (r = 0:89) exceeding those obtained with conventional training (r = 0:80). Index Terms: automatic speech recognition, human speech perception, phoneme recognition
Localization of hCAP‐18 on the surface of chemoattractant‐stimulated human granulocytes: analysis using two novel hCAP‐18‐specific monoclonal antibodies

The well‐described antimicrobial and immunoregulatory properties of human cathelicidin antimicrobial protein 18 (hCAP‐18) derive in part from the ability of its proteolytic fragment, LL‐37 (a.k.a. CAP‐37), to associate with activated immune and epithelial cells during inflammation. We now show a stable association between hCAP‐18 and the cell surface of formyl‐Met‐Leu‐Phe (fMLF)‐stimulated neutrophils using two novel hCAP‐18‐specific mAb, H7 and N9, which recognize a single 16‐kDa band, identified by N‐terminal sequencing and mass spectrometry as hCAP‐18. Phage display analysis of epitope‐binding sites showed that both mAb probably recognize a similar five amino acid sequence near the C terminus of the prodomain. Immunoblot analysis of degranulated neutrophil supernatants resulted in mAb recognition of the 14‐kDa prodomain of hCAP‐18. Subcellular fractionation of unstimulated neutrophils on density gradients showed expected cosedimentation of hCAP‐18 with specific granule lactoferrin (LF). fMLF stimulation resulted in an average 25% release of specific granule hCAP‐18, with ∼15% of the total cellular hCAP‐18 recovered from culture media, and ∼10% and ∼75%, respectively, codistributing with plasma membrane alkaline phosphatase and specific granule LF. Surface association of hCAP‐18 on fMLF‐stimulated neutrophils was confirmed by immunofluorescence microscopy and flow cytometry analysis, which also suggested a significant up‐regulation of surface hCAP‐18 on cytochalasin B‐pretreated, fully degranulated neutrophils. hCAP‐18 surface association was labile to 10 mM NaOH treatment but resistant to 1 M NaCl and also partitioned into the detergent phase following Triton X‐114 solubilization, possibly suggesting a stable association with one or more integral membrane proteins. We conclude that fMLF stimulation promotes redistribution of hCAP‐18 to the surface of human neutrophils.
A Decentralized Proximal-Gradient Method With Network Independent Step-Sizes and Separated Convergence Rates

This paper proposes a novel proximal-gradient algorithm for a decentralized optimization problem with a composite objective containing smooth and nonsmooth terms. Specifically, the smooth and nonsmooth terms are dealt with by gradient and proximal updates, respectively. The proposed algorithm is closely related to a previous algorithm, PG-EXTRA (W. Shi, Q. Ling, G. Wu, and W. Yin, “A proximal gradient algorithm for decentralized composite optimization,” IEEE Trans. Signal Process., vol. 63, no. 22, pp. 6013–6023, 2015), but has a few advantages. First of all, agents use uncoordinated step-sizes, and the stable upper bounds on step-sizes are independent of network topologies. The step-sizes depend on local objective functions, and they can be as large as those of the gradient descent. Second, for the special case without nonsmooth terms, linear convergence can be achieved under the strong convexity assumption. The dependence of the convergence rate on the objective functions and the network are separated, and the convergence rate of the new algorithm is as good as one of the two convergence rates that match the typical rates for the general gradient descent and the consensus averaging. We provide numerical experiments to demonstrate the efficacy of the introduced algorithm and validate our theoretical discoveries.