Miracle-dz/UniFAR-FTUs huggingface dataset:
Phylogenetic analysis and in situ identification of bacteria in activated sludge

The bacterial community structure of activated sludge of a large municipal wastewater treatment plant was investigated by use of the rRNA approach. Almost-full-length genes coding for the small-subunit rRNA (rDNA) were amplified by PCR and subsequently cloned into the pGEM-T vector. Clones were screened by dot blot hybridization with group-specific oligonucleotide probes. The phylogenetic affiliations of clones were compared with the results obtained with the original sample by in situ hybridization with fluorescently labeled, rRNA-targeted oligonucleotide probes and found to be in general agreement. Twenty-five 16S rDNA clones were fully sequenced, 11 were almost fully (> 80%) sequenced, and 27 were partially sequenced. By comparative sequence analyses, the majority of the examined clones (35 of 67) could be affiliated with the beta subclass of the class Proteobacteria. The gamma and alpha subclasses of Proteobacteria were represented by 13 and 4 clones, respectively. Eight clones grouped with the epsilon group of Proteobacteria, and five clones grouped with gram-positive bacteria with a low DNA G+C content. The 16S rDNA of two clones showed similarity with 16S rDNA genes of members of the phyla Chlamydiae and Planctomyces. 16S rRNA-targeted oligonucleotide probes were designed and used for the enumeration of the respective bacteria. Interestingly, potentially pathogenic representatives of the genus Arcobacter were present in significant numbers (4%) in the activated sludge sample examined. Pairs of probes targeted to the 5' and 3' regions were used for detection of chimeric sequences by in situ hybridization. Two clones could be identified as chimera by applying such a pair of probes.
Emotion regulation choice: the role of environmental affordances

ABSTRACT Which emotion regulation strategy one uses in a given context can have profound affective, cognitive, and social consequences. It is therefore important to understand the determinants of emotion regulation choice. Many prior studies have examined person-specific, internal determinants of emotion regulation choice. Recently, it has become clear that external variables that are properties of the stimulus can also influence emotion regulation choice. In the present research, we consider whether reappraisal affordances, defined as the opportunities for re-interpretation of a stimulus that are inherent in that stimulus, can shape individuals’ emotion regulation choices. We show that reappraisal affordances have stability across people and across time (Study 1), and are confounded with emotional intensity for a standardised set of picture stimuli (Study 2). Since emotional intensity has been shown to drive emotion regulation choice, we construct a context in which emotional intensity is separable from reappraisal affordances (Study 3) and use this context to show that reappraisal affordances powerfully influence emotion regulation choice even when emotional intensity and discrete emotions are taken into account (Study 4).
Pattern and timing of diversification in Yucca (Agavaceae): specialized pollination does not escalate rates of diversification

The yucca–yucca moth interaction is one of the most well-known and remarkable obligate pollination mutualisms, and is an important study system for understanding coevolution. Previous research suggests that specialist pollinators can promote rapid diversification in plants, and theoretical work has predicted that obligate pollination mutualism promotes cospeciation between plants and their pollinators, resulting in contemporaneous, parallel diversification. However, a lack of information about the age of Yucca has impeded efforts to test these hypotheses. We used analyses of 4322 AFLP markers and cpDNA sequence data representing six non-protein-coding regions (trnT–trnL, trnL, trnL intron, trnL–trnF, rps16 and clpP intron 2) from all 34 species to recover a consensus organismal phylogeny, and used penalized likelihood to estimate divergence times and speciation rates in Yucca. The results indicate that the pollination mutualism did not accelerate diversification, as Yucca diversity (34 species) is not significantly greater than that of its non-moth-pollinated sister group, Agave sensu latissimus (240 species). The new phylogenetic estimates also corroborate the suggestion that the plant–moth pollination mutualism has at least two origins within the Agavaceae. Finally, age estimates show significant discord between the age of Yucca (ca 6–10 Myr) and the current best estimates for the age of their pollinators (32–40 Myr).
A Novel Edge Detection Algorithm for Digital Mammogram

Detection of edges in an image is a very important step towards understanding image features. Since edges often occur at image locations representing object boundaries, edge detection is extensively used in image segmentation when images are divided into areas corresponding to different objects. This can be used specifically for enhancing the tumor area in mammographic images. Different methods are available for edge detection like Roberts, Sobel, Prewitt, Kirsch and Laplacian of Gaussian edge operators. In this paper a novel algorithm for edge detection has been proposed for mammographic images. Breast boundary, pectoral region and tumor location can be seen clearly by using this method. For comparison purpose Roberts, Sobel, Prewitt, Kirsch and Laplacian of Gaussian edge operators are used and their results are displayed. KeywordsMammographic Images, Edge Detection, Segmentation, Edge Operator, Filter
Communication-Eﬀicient Proactive MPC for Dynamic Groups with Dishonest Majorities

Secure multiparty computation (MPC) has recently been increasingly adopted to secure cryptographic keys in enterprises, cloud infrastructure, and cryptocurrency and blockchain-related settings such as wallets and exchanges. Using MPC in blockchains and other distributed systems highlights the need to consider dynamic settings. In such dynamic settings, parties, and potentially even parameters of underlying secret sharing and corruption tolerance thresholds of sub-protocols, may change over the lifetime of the protocol. In particular, stronger threat models – in which mobile adversaries control a changing set of parties (up to t out of n involved parties at any instant), and may eventually corrupt all n parties over the course of a protocol’s execution – are becoming increasingly important for such real world deployments; secure protocols designed for such models are known as Proactive MPC (PMPC). In this work, we construct the first efficient PMPC protocol for dynamic groups (where the set of parties changes over time) secure against a dishonest majority of parties. Our PMPC protocol only requires O ( n 2 ) (amortized) communication per secret, compared to existing PMPC protocols that require O ( n 4 ) and only consider static groups with dishonest majorities. At the core of our PMPC protocol is a new efficient technique to perform multiplication of secret shared data (shared using a bivariate scheme) with O ( n √ n ) communication with security against a dishonest majority without requiring pre-computation. We also develop a new efficient bivariate batched proactive secret sharing (PSS) protocol for dishonest majorities, which may be of independent interest. This protocol enables multiple dealers to contribute different secrets that are efficiently shared together in one batch; previous batched PSS schemes required all secrets to come from a single dealer.
Women and work‐home conflict: a dual paradigm approach

Work‐home conflict of women in a multinational computer organisation was examined in two phases using quantitative and qualitative methods. In Phase 1 a questionnaire was sent to all directly employed females (n = 204), who were also invited to participate in Phase 2, a one‐hour on‐site focus group. A total of 31 women contributed to four focus groups. In line with previous research, Phase 1 identified the dominant construct to be work interfering with home. Almost one in ten women scored in the highest quartile of possible scores (24 to 30) for work‐to‐home conflict. In Phase 2, issues such as lack of time and lack of social support, family, careers and guilt were discussed in depth. Central to the development of conflict was commitment to both domains. The different meanings women attached to “work” and “home” added to the complexity of these issues. In contrast with past studies, part‐time working status was found to contribute to conflict and intensified the feelings of “juggling”, as did being single. Poor physical and psychological health was a common outcome. A minority of participants experienced positive work‐home “spill‐over”. Witnessing that other women accepted and acknowledged the negative effects of the stressors was thought to be a first step to resolving them. The dual approach adopted facilitated a greater understanding of the concept of work‐home conflict that health professionals may use both to develop and to deliver effective interventions.
PATTERNS AND DETERMINANTS OF COPING BEHAVIOUR OF WIVES OF ALCOHOLICS

One hundred wives of alcoholics with a confirmed diagnosis of alcohol dependence syndrome according to DCR 10 were studied with a “coping with drinking questionnaire”. “Avoidance” was the most commonly endorsed coping behaviour. There was a significant correlation between all the coping components and alcohol related problems. No correlation was observed between neuroticism scores and coping behaviour. It is evident from the study that both personality and situational variables play a role in determining the coping behaviour of the wives of alcoholics.
What you need to know about the state-of-the-art computational models of object-vision: A tour through the models

Models of object vision have been of great interest in computer vision and visual neuroscience. During the last decades, several models have been developed to extract visual features from images for object recognition tasks. Some of these were inspired by the hierarchical structure of primate visual system, and some others were engineered models. The models are varied in several aspects: models that are trained by supervision, models trained without supervision, and models (e.g. feature extractors) that are fully hard-wired and do not need training. Some of the models come with a deep hierarchical structure consisting of several layers, and some others are shallow and come with only one or two layers of processing. More recently, new models have been developed that are not hand-tuned but trained using millions of images, through which they learn how to extract informative task-related features. Here I will survey all these different models and provide the reader with an intuitive, as well as a more detailed, understanding of the underlying computations in each of the models.
Interest-Group Influence on the Media Agenda: A Case Study

Attempting to add to the growing literature on setting the media agenda (or agenda building), the study uses agenda-setting theory in a case study of testing the correlation between the agendas of an interest group (Christian Coalition) and media (major U.S. newspapers). Highly significant relationships were found in correlations cross-lagged at three months between the agenda of the group's official newspaper and the media agenda, and statistically significant second-level effects were also noted. One-and two-month lags yielded lower correlations, but also in the direction of the Coalition setting the media agenda for coverage of the group.
Social-Psychological Origins of Feelings of Presence: Creating Social Presence With Machine-Generated Voices

We demonstrate, via 2 experiments (N = 72 and N = 80) done in e-commerce contexts, that social responses to technology influence feelings of social presence. Users feel stronger social presence when they hear a computer-synthesized voice that manifests a personality that (a) is similar to the user (especially to the extrovert user) as compared to dissimilar, (b) is consistent with the text's personality, and (c) is extroverted as compared to introverted. We discuss various applications of these findings to the design of human-computer interfaces, as well as to the study of presence.
Parkinson-related LRRK 2 mutation R 1441 C / G / H impairs PKA phosphorylation of LRRK 2 and disrupts its interaction with 14-3-3

Department of Biochemistry, University of Kassel, 34132 Kassel, Germany; Division of Biomedical Engineering, School of Engineering, University of Glasgow, Glasgow G12 8LT, United Kingdom; Medical Proteome Center, Institute for Ophthalmic Research, Eberhard Karls University Tübingen, 72074 Tübingen, Germany; Institute of Structural Biology, Protein Expression and Purification Facility, Helmholtz Zentrum München, 85764 Neuherberg, Germany; Functional Genome Analysis, Deutsches Krebsforschungszentrum, 69120 Heidelberg, Germany; and Research Unit Protein Science, Helmholtz Zentrum München, 85764 Neuherberg, Germany
Effects of Employment Gaps on Satisfactions and Career Prospects of Managerial and Professional Women

Describes a study which examined the relationship of employment gaps to measures of work outcomes and emotion wellbeing. Data were collected from a diverse sample of 792 managerial and professional women using questionnaires completed anonymously. Managerial women with more employment gaps were older, more likely married, had more children if married, earned less income and worked fewer hours and extra‐hours per week. Women with more employment gaps also reported less career satisfaction, less job involvement and less optimistic career prospects. Employment gaps were not, however, related to psychological wellbeing. Women reporting more employment gaps earned less income though having more work experience and similar educational qualifications and credentials.
Relationship between the Tsh Autotransporter and Pathogenicity of Avian Escherichia coli and Localization and Analysis of the tsh Genetic Region

ABSTRACT The temperature-sensitive hemagglutinin Tsh is a member of the autotransporter group of proteins and was first identified in avian-pathogenic Escherichia coli (APEC) strain χ7122. The prevalence of tsh was investigated in 300 E. coli isolates of avian origin and characterized for virulence in a 1-day-old chick lethality test. Results indicate that among thetsh-positive APEC isolates, 90.6% belonged to the highest virulence class. Experimental inoculation of chickens with χ7122 and an isogenic tsh mutant demonstrated that Tsh may contribute to the development of lesions within the air sacs of birds but is not required for subsequent generalized infection manifesting as perihepatitis, pericarditis, and septicemia. Conjugation and hybridization experiments revealed that the tsh gene is located on a ColV-type plasmid in many of the APEC strains studied, including strain χ7122, near the colicin V genes in most of these strains. DNA sequences flanking the tsh gene of strain χ7122 include complete and partial insertion sequences and phage-related DNA sequences, some of which were also found on virulence plasmids and pathogenicity islands present in various E. coli pathotypes and other pathogenic members of theEnterobacteriaceae. These results demonstrate that thetsh gene is frequently located on the ColV virulence plasmid in APEC and suggest a possible role of Tsh in the pathogenicity of E. coli for chickens in the early stages of infection.
Optimizing the Power Delivery Network in a Smartphone Platform

Smartphones consume a significant amount of power. Indeed, they can hardly provide a full day of use between charging operations even with a 2000 mAh battery. While power minimization and dynamic power management techniques have been heavily explored to improve the power efficiency of modules (processors, memory, display, GPS, etc.) inside a smartphone platform, there is one critical factor that is often overlooked: the power conversion efficiency of the power delivery network (PDN). This paper focuses on dc-dc converters, which play a pivotal role in the PDN of the smartphone platform. Starting from detailed models of the dc-dc converter designs, two optimization methods are presented: 1) static switch sizing to maximize the efficiency of a dc-dc converter under statistical loading profiles and 2) dynamic switch modulation to achieve the high efficiency enhancement under dynamically varying load conditions. To verify the efficacy of the optimization methods in actual smartphone platforms, this paper also presents a characterization procedure for the PDN. The procedure is as follows: 1) group the modules in the smartphone platform together and use profiling to estimate their average and peak power consumption levels and 2) build an equivalent dc-dc converter model for the power delivery path from the battery source to each group of modules and use linear regression to estimate the conversion efficiency of the corresponding equivalent converter. Experimental results demonstrate that the static switch sizing can achieve 6% power conversion efficiency enhancement, which translates to 19% reduction in power loss general usage of the smartphone. The dynamic switch modulation accomplishes similar improvement at the same condition, while also achieving high efficiency enhancement in various load conditions.
Probabilistic Inference Using Markov Chain Monte Carlo Methods

Probabilistic inference is an attractive approach to uncertain reasoning and empirical learning in artificial intelligence. Computational difficulties arise, however, because probabilistic models with the necessary realism and flexibility lead to complex distributions over high-dimensional spaces. Related problems in other fields have been tackled using Monte Carlo methods based on sampling using Markov chains, providing a rich array of techniques that can be applied to problems in artificial intelligence. The “Metropolis algorithm” has been used to solve difficult problems in statistical physics for over forty years, and, in the last few years, the related method of “Gibbs sampling” has been applied to problems of statistical inference. Concurrently, an alternative method for solving problems in statistical physics by means of dynamical simulation has been developed as well, and has recently been unified with the Metropolis algorithm to produce the “hybrid Monte Carlo” method. In computer science, Markov chain sampling is the basis of the heuristic optimization technique of “simulated annealing”, and has recently been used in randomized algorithms for approximate counting of large sets. In this review, I outline the role of probabilistic inference in artificial intelligence, present the theory of Markov chains, and describe various Markov chain Monte Carlo algorithms, along with a number of supporting techniques. I try to present a comprehensive picture of the range of methods that have been developed, including techniques from the varied literature that have not yet seen wide application in artificial intelligence, but which appear relevant. As illustrative examples, I use the problems of probabilistic inference in expert systems, discovery of latent classes from data, and Bayesian learning for neural networks.
Criteria for the Selection of Student-Teaching Centers

THE PRACTICE of sending undergraduate students who are majoring in teacher education into the elementary and secondary schools of the state for their initial or pre-employment experiences in teaching is re ceiving increasingly favorable consideration among educators through out the country. Prior to 1940, very little student teaching, other than in the vocational subjects, was done in "off-campus" schools, except in those schools which were in close proximity to the teacher-training institution. However, the recent trend is to provide student-teaching facilities in schools away from the college campus in which a period of time (usually several weeks) is devoted solely to student teaching and related activities. In order that maximum benefit may be secured from the student teach ing period, it is highly imperative that care and good judgment be exer cised in the selection of the schools to be utiliized for this purpose. Al though this study was restricted to the selection of student-teaching cen ters in vocational agriculture, the writer believes that a number of the criteria suggested would be worthy of consideration when developing cri teria for the selection of student-teaching centers for prospective tea chers of academic classes. Since the suggested set of criteria shown below was the result of a study which included several phases, the procedure in obtaining data is shown. This makes possible a more complete interpretation of the au thor's recommendations.
A hybrid language model based on a combination of N-grams and stochastic context-free grammars

In this paper, a hybrid language model is defined as a combination of a word-based <i>n</i>-gram, which is used to capture the local relations between words, and a category-based stochastic context-free grammar (SCFG) with a word distribution into categories, which is defined to represent the long-term relations between these categories. The problem of unsupervised learning of a SCFG in General Format and in Chomsky Normal Form by means of estimation algorithms is studied. Moreover, a bracketed version of the classical estimation algorithm based on the Earley algorithm is proposed. This paper also explores the use of SCFGs obtained from a treebank corpus as initial models for the estimation algorithms. Experiments on the UPenn Treebank corpus are reported. These experiments have been carried out in terms of the test set perplexity and the word error rate in a speech recognition experiment.
iFR: Interactively Pose Corrected Face Recognition

Although face recognition applications are growing, robust face recognition is still a challenging task due e.g. to variations in face poses, facial expressions, or lighting conditions. In this paper, we propose a new method which allows both automatic face detection and recognition and incorporates an interactive selection of facial features in conjunction with our new pose-correction algorithm. Our resulting system we called iFR successfully recognizes faces across pose, while being computationally efficient and outperforming standard approaches, as demonstrated in tests carried out on publicly available standard datasets.
Pr´eparation eﬃcace des donn´ees d’apprentissage Application `a la classiﬁcation d’images pour la d´etection du cancer du sein

dataset dans le cadre d’une tˆache de classiﬁcation est une question diﬃ-cile. Dans cet article, nous essayons de circonscrire cette notion en introduisant de nouvelles mesures et en ´enon¸cant quelques principes sur la pr´eparation des donn´ees. Nous exp´erimentons l’int´erˆet de ces mesures et la validit´e de ces principes en introduisant plusieurs protocoles destin´es `a comparer les diﬀ´erentes mani`eres de pr´eparer les donn´ees. Nous concluons en mettant en relation l’eﬃcacit´e de la pr´eparation des donn´ees et sa diversit´e th´eorique.
Quantitative Morphology : Volumes , Shapes and Voxel-Based Measures

Most of the approaches dedicated to automatic morphometry rely on a pointby-point strategy based on warping each brain towards a reference coordinate system. The coordinate system is three dimensional for the comparison of the local densities of grey and white matter (voxel-based morphometry), or two dimensional (spherical) for the comparison of cortical thickness. A more intuitive alternative approach is based on Regions of Interest defined either maually or authomatically. The volumes and shapes of these ROIs (for insatnce sulci and gyri) can be compared across subjects. This talk will give an overview of these different strategies.