Miracle-dz/UniFAR-FTUs huggingface dataset:
Advances in cationic lipid-mediated gene delivery Review Article

Benjamin Martin, Abderrahim Aissaoui, Matthieu Sainlos, Noufissa Oudrhiri, Michelle Hauchecorne, Jean-Pierre Vigneron, Jean-Marie Lehn and Pierre Lehn 1 Laboratoire de Chimie des Interactions Moléculaires, CNRS UPR 285, Collège-de-France, 11 Place Marcelin Berthelot, 75005 Paris, France. 2 INSERM U458, Hôpital Robert Debré, 48 Boulevard Sérurier, 75019 Paris, France __________________________________________________________________________________
Examining the relationship between self-control and adolescent TC treatment completion

A type of treatment intervention that is widely regarded in terms of treating adolescents with issues of substance use is the therapeutic community (TC). Despite their effectiveness, empirical studies of drug treatment are mostly atheoretical, even though treatment programming is based on theory. In this study, an attempt was made to apply concepts related to a Gottfredson and Hirschi’s General Theory of Crime to predict treatment completion. Using data collected as part of the Drug Abuse Treatment Outcomes Studies Adolescents (DATOS-A), a multisite prospective study of adolescent drug abuse treatment effectiveness, this study examined whether characteristics associated with low self-control predicted treatment completion. The primary finding was that motivation for treatment was significantly related to treatment completion. Although the characteristics associated with low self-control did not predict treatment completion in the hierarchical linear modeling (HLM) models that included the demographics and risk and protective factors, there were several significant bivariate relationships between pretreatment behaviors and the characteristics associated with low self-control. Because of these relationships, it is important that TC practitioners are aware of how these characteristics might interplay with treatment completion and how TC strategies may help the adolescent to overcome them.
Efficient Mobile Robot Navigation using 3D Surfel Grid Maps

We present robust and efficient means for mobile robot navigation using a 3D representation of the environment. We build 3D surfel grid maps and propose Monte Carlo localization with probabilistic observation models for 2D and 3D sensors in such maps. In contrast to localization methods that utilize a 2D laser scanner in a static horizontal mounting, our method takes advantage of the 3D structure in the environment. This is useful, for instance, to localize in crowds of people: The robot can focus on the static parts of the environment above the person? s height. Finally, we extract navigation maps for 2D path planning from the 3D maps. Our approach avoids discretization effects and considers the complete height range of the robot to estimate traversability. In experiments, we demonstrate the accuracy and robustness of our approach for pose tracking and global localization ? even in a crowded environment.
Balance and gait in older electroconvulsive therapy recipients: a pilot study

Background Electroconvulsive therapy (ECT) is commonly used to treat depression in older adults. Despite its efficacy in this regard, an associated increase in the risk of falls in this population is a downside of treatment. ECT research has focused on the incidence of falls, but its effect on balance and gait – intrinsic factors in instability and falls – has not been studied. Our aim was to examine changes in balance and gait among older adults before and after a single ECT session and explore the effect of patient-related and treatment factors on any changes found. Methods Participants were 21 older adults requiring ECT for depression in public psychiatric services. Patients with clinically overt mobility problems (impairing test participation or increasing the risk of falls) were excluded. Balance and gait testing 1 hour pre-ECT and 1, 2 and 3 hours post-ECT included: (1) steady standing test; (2) perturbation of standing balance by self-initiated movements; (3) perturbation of standing balance by an external perturbation; and (4) timed up and go test. Results No deterioration in test performance was found, using one-way repeated measures analysis of variance. Conclusion Balance and gait did not deteriorate immediately after ECT. Exclusion of participants with clinically overt mobility problems and falls being better attributable to factors unrelated to balance and gait (such as post-ECT confusion) may account for our findings. This research does not repudiate the occurrence of ECT-related falls but calls into question the utility of introducing routine balance and gait assessment among older ECT recipients without pre-existing mobility problems as a means of preventing them.
Mannose Binding Lectin Genotypes Influence Recovery from Hepatitis B Virus Infection

ABSTRACT Mannose binding lectin (MBL) is a central component of the innate immune response and thus may be important for determining hepatitis B virus (HBV) persistence. Since single-nucleotide polymorphisms (SNPs) in the gene encoding MBL (mbl2) alter the level of functional MBL, we hypothesized that mbl2 genotypes are a determinant of HBV persistence or recovery from viral infection. We tested this hypothesis by using a nested case control design with 189 persons with HBV persistence matched to 338 individuals who had naturally recovered from HBV infection. We determined genotypes of two promoter and three exon 1 SNPs in mbl2 and grouped these genotypes according to the amount of functional MBL production. We found that the promoter SNP −221C, which leads to deficient MBL production, was more common in those subjects with viral persistence (odds ratio [OR], 1.38; 95% confidence interval [CI], 1.01 to 1.89; P = 0.04). Those subjects homozygous for the combination of promoter and exon 1 genotypes associated with the highest amount of functional MBL had significantly increased odds of recovery from infection (OR, 0.55; 95% CI, 0.37 to 0.84; P = 0.005). Conversely, those homozygous for the combination of promoter and exon 1 genotypes which produce the lowest amount of functional MBL were more likely to have viral persistence (OR, 1.76; 95% CI, 1.02 to 3.01; P = 0.04). These data are consistent with the hypothesis that functional MBL plays a central role in the pathogenesis of acute hepatitis B.
Psychopathic traits in youth and associations with temperamental features: Results from a performance-based measure

The present study aims to disentangle motivational and self-regulatory pathways to psychopathic traits in youth with severe antisocial behavior. The associations between self-reported psychopathic traits and indices derived from a laboratory measure assessing fear sensitivity and self-regulation were evaluated. Low scores on fear sensitivity and self-regulation were related to high scores on the self-reported Callous/Unemotional factor of psychopathic traits and the Callousness dimension in particular. The present study provides at least partial evidence for both motivational (low-fear hypothesis; Lykken, 1995) and self-regulatory (response modulation hypothesis; Patterson & Newman, 1993) accounts of psychopathic traits in youth.
A model of extended paragraph vector for document categorization and trend analysis

The increasing number of academic papers published each year has led to a growing demand of organizing the papers into different categories according to their topics and analyzing topic trends over time. Domain knowledge such as journal categories and conference sessions are potentially useful for categorizing papers and obtaining trends easily interpretable to users. In this paper, we aim to organize a collection of papers into journal categories which describe research areas of a field, and then analyze the trend of each research area. Conference sessions are used to link with journal categories assuming that papers from the same session are put into the same category. Sessions are also adopted to reflect the trend of a category over years as they are derived by domain experts to describe each year's topics. First, we present a model of extended paragraph vector to model the hierarchical structure of sessions, papers and words, and capture their semantics with distributed vector representations in the same space. Then, we propose a two-stage approach for document categorization, which first chooses a subset of journal categories covering the major research areas in the corpus and then associates each session with its most similar category based on session vectors. Finally, we present the research trend of a category through its matching sessions ordered in time and showing the most similar words of each session.
Support Vector Machine-based Method for Subcellular Localization of Human Proteins Using Amino Acid Compositions, Their Order, and Similarity Search*

Here we report a systematic approach for predicting subcellular localization (cytoplasm, mitochondrial, nuclear, and plasma membrane) of human proteins. First, support vector machine (SVM)-based modules for predicting subcellular localization using traditional amino acid and dipeptide (i + 1) composition achieved overall accuracy of 76.6 and 77.8%, respectively. PSI-BLAST, when carried out using a similarity-based search against a nonredundant data base of experimentally annotated proteins, yielded 73.3% accuracy. To gain further insight, a hybrid module (hybrid1) was developed based on amino acid composition, dipeptide composition, and similarity information and attained better accuracy of 84.9%. In addition, SVM modules based on a different higher order dipeptide i.e. i + 2, i + 3, and i + 4 were also constructed for the prediction of subcellular localization of human proteins, and overall accuracy of 79.7, 77.5, and 77.1% was accomplished, respectively. Furthermore, another SVM module hybrid2 was developed using traditional dipeptide (i + 1) and higher order dipeptide (i + 2, i + 3, and i + 4) compositions, which gave an overall accuracy of 81.3%. We also developed SVM module hybrid3 based on amino acid composition, traditional and higher order dipeptide compositions, and PSI-BLAST output and achieved an overall accuracy of 84.4%. A Web server HSLPred (www.imtech.res.in/raghava/hslpred/ or bioinformatics.uams.edu/raghava/hslpred/) has been designed to predict subcellular localization of human proteins using the above approaches.
Addressing Learning Disabilities With UDL and Technology

CAST created Strategic Reader, a technology-based system blending Universal Design for Learning (UDL) and Curriculum-Based Measurement (CBM) in a digital learning environment to improve reading comprehension instruction. This experimental study evaluates the effectiveness of Strategic Reader using two treatment conditions for measuring progress (online vs. offline). Using both quantitative and qualitative data analysis, strong evidence is found that students using the online tool experience significant growth in comprehension scores. The difference in score growth in the online versus offline conditions is especially large for students with learning disabilities (LDs): Only in the online progress monitoring condition do LD students experience a statistically significant score increase. In addition, students with LDs report being substantially more engaged by (and with) Strategic Reader, finding many aspects of the tool differentially more helpful than their general education peers. In sum, the results provide promising paths for both curricular design and further research in the design of digital environments.
The occupational status of immigrants in Western and non-Western societies

This study examines existing hypotheses on cross-national differences in immigrants’ labor market integration. Unlike previous research, which focused on Western countries, we study the occupational status of immigrants in both Western and non-Western countries. We use census data for 45 Western and non-Western destination countries and test hypotheses derived from human capital and discrimination theory applying multilevel modeling. The analysis shows that differences in immigrants’ occupational status attainment can partly be explained by pre-migration language exposure, economic advancement of the origin country, geographical distance, group size, and the religious as well as socioeconomic distance of immigrant groups and the majority population. Despite differences in the magnitude of effects, patterns of immigrants’ occupational attainment appear comparable between Western and non-Western societies. We do not find compelling evidence that human capital factors are consistently more important in Western societies.
Review and reappraisal of modelling and predicting mental workload in single- and multi-task environments

The concept of mental workload has long been recognized as an important factor in individual performance within complex systems. It is documented that either overload or underload may degrade performance, and further affect the efficiency of the whole system. Therefore, systems designers need some explicit models to predict the mental workload imposed on individuals by the system at an early design phase so that alternative system designs can be evaluated. In examining mental-workload literature, it is found that few predictive mental-workload models have considered factors specific to individuals. This research aims to develop a practical framework for predicting mental workload in both single- and multi-task environments considering such individual factors. In order to describe mental workload more precisely and more completely, a framework for mentalworkload definitions, which contains instantaneous workload, average workload, accumulated workload, peak workload and overall workload, is proposed. In order to model individual factors, two new variables, i.e. effective workload and ineffective workload, are introduced to model the taskgenerated workload and individual-generated workload. The extension of the model to multi-task environments is also discussed. The proposed conceptual models are domain-independent and could be used to guide the development of operational models for different specific tasks.
Convergent and predictive validity of two computerized attention tests in brain-damaged patients

Although computerized attention tests are used frequently, our knowledge about the convergent and predictive validity of these tests is limited. In the present study, 47 stroke and traumatic brain injury patients performed the Attention Network Test (ANT) and corresponding subtests of the Test for Attentional Performance (TAP). In addition, patients and staff completed the Attention Deficits Questionnaire (ADQ). Considering corresponding ANT and TAP subtests, significant correlations (r = .55 – .71) were found for alerting and orienting subtests and overall reaction time. Comparison of subtests of the two attention tests with staff reported deficits in the ADQ revealed moderate to high correlations for ANT (r = .62 – .70) and TAP (r = .36 – .86). The predictive validity of different subtests varies only sightly. The overall reaction time seems to be the best predictor of rated attention deficits. Findings support the convergent and predictive validity of both computerized attention tests.
Comparison of deep neural network architectures for fault detection in Tennessee Eastman process

Process monitoring and fault diagnosis methods are used to detect abnormal events in industrial processes. Process breakdowns hinder the overall productivity of the system which makes the early detection of faults very critical. Due to the highly non-linear nature of modern industrial processes, deep neural networks with several layers of non-linear complex representations fit aptly for contemporary fault diagnosis. Although deep neural networks have found wide array of application areas such as image recognition and speech recognition, their effectiveness in fault detection has not been tested substantially. In this study, a comparison between two deep neural network architectures, namely Deep Stacking Networks and Sparse Stacked Autoencoders for fault detection from process data is presented. The Tennessee Eastman benchmark process is considered to test the effectiveness of these deep architectures. A detailed comparison between the two architectures is illustrated with different hyperparameters. The experiment results show that the Sparse Stacked Autoencoders model has superior average fault detection capability and is also more stable as it has less variation in fault detection rate.
Detecting cyclicity in social interaction.

This article reviews spectral and cross-spectral analytic methods for detecting cyclicity, cross-cyclicity, and lead-lag relationships in continuous data derived from the observation of dyadic interaction. It is found that lead-lag relationships can be assessed using the phase spectrum. Spectral analytic methods are then generalized to categorical observational data, and it is shown that by these methods one can derive the classical information theory definition of social communication and its distribution statistics. Researchers who study social behavior are discovering that there are occasions when cyclical patterns characterize dyadic interaction, and thus they are searching for statistical techniques that can detect these cycles. The spectral analysis of time-series records was briefly suggested by Luce (1970) as a useful technique for the study of biological rhythms such as heart rate, respiration, REM sleep, and other cyclic biochemical and physiological processes. However, spectral analysis is not widely known to behavioral scientists, and it has yet to be used in the study of social interaction. A recent exception is the work of Hayes and Cobb (Note 1), who observed couples living in a laboratory setting, analyzed cycles of talk and silence using spectral analysis of time-series records, and related an observed cycle to human circadian rhythms. Researchers who study dyadic social interaction are also interested in the bivariate case in which two time-series records are obtained, one from each of the two interacting organisms; the research question often involves
Mining Web Access Logs Using Relational Competitive Fuzzy Clustering

The proliferation of information on the world wide web has made the personalization of this information space a necessity. An important component of web personalization is to mine typical user profiles from the vast amount of historical data stored in access logs. In this paper, we define the notion of a “user session” as being a temporally compact sequence of web accesses by a user. We also define a new distance measure between two web sessions that captures the organization of a web site. The Competitive Agglomeration clustering algorithm which can automatically cluster data into the optimal number of components is extended so that it can work on relational data. The resulting Competitive Agglomeration for Relational Data (CARD) algorithm can deal with complex, non-Euclidean, distance/similarity measures. This algorithm was used to successfully analyze server access logs and obtain typical session profiles of users.
Complexity of multiverse networks and their multilayer generalization

Multiverse networks were recently proposed as a method for promoting more effective transfer learning. While an extensive analysis was proposed, this analysis failed to capture two main aspects of these networks: (i) the rank of the representation is much lower than the rank predicted by the analysis; and (ii) the contribution of increased multiplicity in such networks diminishes quickly. In this work, we propose additional analysis of multiverse networks which addresses both deficits. A major contribution of our work is quantifying the Rademacher complexity of the multiverse network. It is shown that the complexity upper bound of multiverse networks is significantly lower than that of conventional networks, and diminishes by a factor of √k, k being the multiplicity. In addition, we generalize the notion of multiverse networks to multilayer multiverse networks. We derive the Rademacher complexity formula to such networks and present experimental results.
Potential roles for BMP and Pax genes in the development of iris smooth muscle

The embryonic optic cup generates four types of tissue: neural retina, pigmented epithelium, ciliary epithelium, and iris smooth muscle. Remarkably little attention has focused on the development of the iris smooth muscle since Lewis ([ 1903 ] J. Am. Anat. 2:405–416) described its origins from the anterior rim of the optic cup neuroepithelium. As an initial step toward understanding iris smooth muscle development, I first determined the spatial and temporal pattern of the development of the iris smooth muscle in the chick by using the HNK1 antibody, which labels developing iris smooth muscle. HNK1 labeling shows that iris smooth muscle development is correlated in time and space with the development of the ciliary epithelial folds. Second, because neural crest is the only other neural tissue that has been shown to generate smooth muscle (Le Lievre and Le Douarin [ 1975 ] J. Embryo. Exp. Morphol. 34:125–154), I sought to determine whether iris smooth muscle development shares similarities with neural crest development. Two members of the BMP superfamily, BMP4 and BMP7, which may regulate neural crest development, are highly expressed by cells at the site of iris smooth muscle generation. Third, because humans and mice that are heterozygous for Pax6 mutations have no irides (Hill et al. [ 1991 ] Nature 354:522–525; Hanson et al. [ 1994 ] Nat. Genet. 6:168–173), I determined the expression of Pax6. I also examined the expression of Pax3 in the developing anterior optic cup. The developing iris smooth muscle coexpresses Pax6 and Pax3. I suggest that some of the eye defects caused by mutations in Pax6, BMP4, and BMP7 may be due to abnormal iris smooth muscle. Developmental Dynamics 232:385–392, 2005. © 2004 Wiley‐Liss, Inc.
Mechanistic implications of enhanced editing by a HyperTRIBE RNA-binding protein

We previously developed TRIBE, a method for the identification of cell-specific RNA binding protein targets. TRIBE expresses an RBP of interest fused to the catalytic domain (cd) of the RNA editing enzyme ADAR and performs Adenosine-to-Inosine editing on RNA targets of the RBP. However, target identification is limited by the low editing efficiency of the ADARcd. Here we describe HyperTRIBE, which carries a previously characterized hyperactive mutation (E488Q) of the ADARcd. HyperTRIBE identifies dramatically more editing sites, many of which are also edited by TRIBE but at a much lower editing frequency. HyperTRIBE therefore more faithfully recapitulates the known binding specificity of its RBP than TRIBE. In addition, separating RNA binding from the enhanced editing activity of the HyperTRIBE ADAR catalytic domain sheds light on the mechanism of ADARcd editing as well as the enhanced activity of the HyperADARcd.
Fuzzy Ontology and Fuzzy-OWL in the KAON Project

In many application domains is necessary to reason with vague information, but, the conceptual formalism supported by a classical ontology is not sufficient for handling it. Thus, we have developed a suited plug-in of the KAON Project in order to introduce fuzziness in an ontology. This extension is based on corresponding fuzzy ontology and Fuzzy-OWL notions.