Miracle-dz/UniFAR-FTUs huggingface dataset:
Value of Mendelian Laws of Segregation in Families: Data Quality Control, Imputation, and Beyond

When analyzing family data, we dream of perfectly informative data, even whole‐genome sequences (WGSs) for all family members. Reality intervenes, and we find that next‐generation sequencing (NGS) data have errors and are often too expensive or impossible to collect on everyone. The Genetic Analysis Workshop 18 working groups on quality control and dropping WGSs through families using a genome‐wide association framework focused on finding, correcting, and using errors within the available sequence and family data, developing methods to infer and analyze missing sequence data among relatives, and testing for linkage and association with simulated blood pressure. We found that single‐nucleotide polymorphisms, NGS data, and imputed data are generally concordant but that errors are particularly likely at rare variants, for homozygous genotypes, within regions with repeated sequences or structural variants, and within sequence data imputed from unrelated individuals. Admixture complicated identification of cryptic relatedness, but information from Mendelian transmission improved error detection and provided an estimate of the de novo mutation rate. Computationally, fast rule‐based imputation was accurate but could not cover as many loci or subjects as more computationally demanding probability‐based methods. Incorporating population‐level data into pedigree‐based imputation methods improved results. Observed data outperformed imputed data in association testing, but imputed data were also useful. We discuss the strengths and weaknesses of existing methods and suggest possible future directions, such as improving communication between data collectors and data analysts, establishing thresholds for and improving imputation quality, and incorporating error into imputation and analytical models.
Rewriting Identities: Creating Spaces for Students and Teachers to Challenge the Norms of What It Means to Be a Reader in School

In this commentary, I discuss what reading identities are, and how they are defined, created, and enacted in schools by teachers and students. I then consider the positive and negative consequences associated with asking students to take up institutionalized norms around reading. Finally, I share how we can create spaces for students to talk back and rewrite the identities that are often forced on them by schools. I conclude by discussing the importance of creating reading partnerships between teachers and students.
Sequential Models for Sentiment Prediction

We examine the problem of predicting local sentiment flow in documents, and its application to several areas of text analysis. Formally, the problem is stated as predicting an ordinal sequence based on a sequence of word sets. In the spirit of isotonic regression, we develop a variant of conditional random fields that is better suited to handle this problem. Experiments are reported for both sentiment prediction and text summarization, showing the possibility of incorporating sentiment concept into a range of new applications.
New possibilities for the professionalization of UK VET professionals

Analyses the present condition of, and future possibilities for, the professionalization of those who work in vocational education and training in the UK. Argues that for the UK, as for other European countries, a high quality system of vocational education and training (VET) is a precondition for both a competitive economy and democratic society. Begins with a description of the current state and status of VET in the UK, and charts the legacy of 18 years of neo‐liberal conservative government and its implications for the position of VET professionals. Identifies the new demands being made on VET professionals by changes in the global economy and develops a model of the VET professional as a connective specialist for responding to these demands. Explores two aspects of the model ‐ pedagogy and the role of new technologies (telematics in particular) ‐ in some detail. Finally, considers some of the political and economic policies needed for such a model to be implemented.
Channel estimation for millimeter-wave Very-Large MIMO systems

We present an efficient pilot-assisted technique for downlink channel estimation in Very Large MIMO (VL-MIMO) systems operating in a 60 GHz indoor channel. Our estimator exploits the inherent sparsity of the channel and requires quite low pilot overhead. It is based on a coarse estimation stage that capitalizes on compressed sensing, followed by a refinement stage to find the transmit/receive spatial frequencies. Considering a ray-tracing channel model, the system throughput is evaluated from computer simulations by considering different beamforming schemes designed from the estimated channel. Our results show that the proposed channel estimator performs quite well with very low pilot overhead.
Using Specularities to Recover Multiple Light Sources in the Presence of Texture

Recovering multiple point light sources from a sparse set of photographs in which objects of unknown texture can move is challenging. This is because both diffuse and specular reflections appear to slide across surfaces. What is seldom demonstrated, however, is that it can be taken advantage of to address the light-source recovery problem. In this paper, we therefore show that, if 3D models of the moving objects are available or can be computed from the images, we can solve the problem without any a priori constraints on the number of sources, on their color, or on the surface albedos. Our approach involves finding local maxima in individual images, checking them for consistency across images, retaining the apparently specular ones, and having them vote in a Hough-like scheme for potential light source directions. The precise directions of the sources and their relative power are then obtained by optimizing a standard lighting model. As a byproduct we also obtain an estimate of various material parameters such as the unlighted texture and specular properties. We show that the resulting algorithm can operate in presence of arbitrary textures and an unknown number of light sources of possibly different unknown colors. We also estimate its accuracy using ground-truth data
ATP binding cassette transporter gene expression in rat liver progenitor cells

Background and aim: Liver regeneration after severe liver damage depends in part on proliferation and differentiation of hepatic progenitor cells (HPCs). Under these conditions they must be able to withstand the toxic milieu of the damaged liver. ATP binding cassette (ABC) transporters are cytoprotective efflux pumps that may contribute to the preservation of these cells. The aim of this study was to determine the ABC transporter phenotype of HPCs. Methods: HPC activation was studied in rats treated with 2- acetylaminofluorene (2-AAF) followed by partial hepatectomy (PHx). ABC transporter gene expression was determined by real time detection reverse transcription-polymerase chain reaction in isolated HPCs, hepatocytes, cholangiocytes, and cultured progenitor cell-like RLF ϕ 13 cells and by immunohistochemistry of total liver samples. ABC transporter efflux activity was studied in RLF ϕ 13 cells by flow cytometry. Results: 2-AAF/PHx treated animals showed increased hepatic mRNA levels of the genes encoding multidrug resistance proteins Mdr1b, Mrp1, and Mrp3. Immunohistochemistry demonstrated expression of Mrp1 and Mrp3 proteins in periportal progenitor cells and of the Mdr1b protein in periportal hepatocytes. Freshly isolated Thy-1 positive cells and cultured RLF ϕ 13 progenitor cells highly expressed Mrp1 and Mrp3 mRNA while the hepatocyte specific transporters Mdr2, Bsep, Mrp2, and Mrp6 were only minimally expressed. Blocking Mrp activity by MK-571 resulted in accumulation of the Mrp specific substrate carboxyfluorescein in RLF ϕ 13 cells. Conclusion: HPCs express high levels of active Mrp1 and Mrp3. These may have a cytoprotective role in conditions of severe hepatotoxicity.
Generating Entailment Rules from FrameNet

Many NLP tasks need accurate knowledge for semantic inference. To this end, mostly WordNet is utilized. Yet WordNet is limited, especially for inference between predicates. To help filling this gap, we present an algorithm that generates inference rules between predicates from FrameNet. Our experiment shows that the novel resource is effective and complements WordNet in terms of rule coverage.
Multi-speaker DOA estimation in reverberation conditions using expectation-maximization

A novel direction of arrival (DOA) estimator for concurrent speakers in reverberant environment is presented. Reverberation, if not properly addressed, is known to degrade the performance of DOA estimators. In our contribution, the DOA estimation task is formulated as a maximum likelihood (ML) problem, which is solved using the expectation-maximization (EM) procedure. The received microphone signals are modelled as a sum of anechoic and reverberant components. The reverberant components are modelled by a timeinvariant coherence matrix multiplied by time-varying reverberation power spectral density (PSD). The PSDs of the anechoic speech and reverberant components are estimated as part of the EM procedure. It is shown that the DOA estimates, obtained by the proposed algorithm, are less affected by reverberation than competing algorithms that ignore the reverberation. Experimental study demonstrates the benefit of the presented algorithm in reverberant environment using measured room impulse responses (RIRs).
Automatic Real-Time Zooming and Panning on Salient Objects from a Panoramic Video

The proposed demo shows how our system automatically zooms and pans into tracked objects in panorama videos. At the conference site, we will set up a two-camera version of the system, generating live panorama videos, where the system zooms and pans tracking people using colored hats. Additionally, using a stored soccer game video from a five 2K camera setup at Alfheim stadium in Tromsø from the European league game between Tromsø IL and Tottenham Hotspurs, the system automatically follows the ball.
Parallel Long Short-Term Memory for multi-stream classification

Recently, machine learning methods have provided a broad spectrum of original and efficient algorithms based on Deep Neural Networks (DNN) to automatically predict an outcome with respect to a sequence of inputs. Recurrent hidden cells allow these DNN-based models to manage long-term dependencies such as Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM). Nevertheless, these RNNs process a single input stream in one (LSTM) or two (Bidirectional LSTM) directions. But most of the information available nowadays is from multistreams or multimedia documents, and require RNNs to process these information synchronously during the training. This paper presents an original LSTM-based architecture, named Parallel LSTM (PLSTM), that carries out multiple parallel synchronized input sequences in order to predict a common output. The proposed PLSTM method could be used for parallel sequence classification purposes. The PLSTM approach is evaluated on an automatic telecast genre sequences classification task and compared with different state-of-the-art architectures. Results show that the proposed PLSTM method outperforms the baseline n-gram models as well as the state-of-the-art LSTM approach.
Determinants of Learning Outcome for Students at High School in Norway: A Constructivist Approach

A vital part of student learning is the construction of mental structures encompassing categories believed to affect learning outcome. In this study we investigate this research question through the lenses of a constructivist approach. As the first study on our research question at high school in Norway, our empirical findings make up the main contribution of this study. The data were analyzed by a grounded theory methodology. The results identify 6 dimensions of determinants of learning outcome. The dimensions: student activity, work processes and motivation to learn are manifest of the latent dimension process of learning, while the second latent dimension learning content is manifested in the 3 dimensions: correction, information from teacher and putting into context.
The quorum sensing diversity within and between ecotypes of Bacillus subtilis.

Ecological sociobiology is an emerging field that aims to frame social evolution in terms of ecological adaptation. Here we explore the ecological context for evolution of quorum sensing diversity in bacteria, where social communication is limited to members of the same quorum sensing type (pherotype). We sampled isolates of Bacillus subtilis from soil on a microgeographical scale and identified three ecologically distinct phylogenetic groups (ecotypes) and three pherotypes. Each pherotype was strongly associated with a different ecotype, suggesting that it is usually not adaptive for one ecotype to 'listen' to the signalling of another. Each ecotype, however, contained one or more minority pherotypes shared with the other B. subtilis ecotypes and with more distantly related species taxa. The pherotype diversity within ecotypes is consistent with two models: first, a pherotype cycling model, whereby minority pherotypes enter a population through horizontal genetic transfer and increase in frequency through cheating the social interaction; and second, an occasional advantage model, such that when two ecotypes are each below their quorum densities, they may benefit from listening to one another. This is the first survey of pherotype diversity in relation to ecotypes and it will be interesting to further test the hypotheses raised and supported here, and to explore other bacterial systems for the role of ecological divergence in fostering pherotype diversity.
REFLECTIVE BORDERLINE GROUP THERAPY: THE PATIENTS' EXPERIENCE OF BEING BORDERLINE

This paper describes a novel 20-week psycho-educational group therapy for borderline patients. The therapy aims to stabilize the disorganized and chaotic presenting behaviours of the patients when they enter the borderline treatment service by providing a group psycho-educational programme based upon a psychodynamic appreciation of symptoms, causes, defences and affects. We think that borderline patients will only take in such understandings if they feel listened to and understood in a way that may not have been available to them as children. To do this, the therapists need to create a reflective space underpinned by psychodynamic thought and respecting the patient's experience of being borderline. The paper illustrates the difficulties encountered and discusses the importance of containing or modifying the counter-transference stresses felt by the therapists. The authors also report on some recent outcome data collected from the running of the group programme over the last 4 years. Twenty borderline patients were able to complete the pre- and post-treatment self-report questionnaires. The results indicate some positive signs in extra-punitive symptom control and levels of impulsivity. Significant levels of improvement were noted in the BSI total scores (p=0.048) with particular reduction in interpersonal sensitivity (p=0.007) and the hostility (p=0.008) sub scores. Patients also reported a reduction in ‘feeling like’ scores on the Multi Impulsivity Scale – reflecting an improvement in internal affect triggers that might result in impulsive behaviours. The authors discuss the potential value of this pragmatic model of care that is underpinned by psychoanalytic thinking, and its application to managing long waiting lists in psychotherapy services for borderline patients.
Trajectories and Landmark Events: How People Narrate their Lives Analysis of Life Course Facts and Perceptions

Dans l’enquete Biographies et entourage realisee par l’Ined en 2001, la reconstitution des evenements factuels qui jalonnent les trajectoires familiale, residentielle et professionnelle des personnes a ete assortie d’elements subjectifs. Il s’agissait de prendre en compte le regard porte a posteriori sur leur vie par les enquetes qui etaient ainsi amenes a proposer un decoupage explicite de leur trajectoire, a identifier et a interpreter les periodes signifiantes de leur vie, les tournants et la tonalite generale de leur biographie ainsi que les evenements consideres comme marquants. Les premieres analyses de ce materiau foisonnant montrent la facon dont la trajectoire des individus se structure autour de certains jalons factuels, en particulier ceux qui marquent la vie conjugale. Le vecu et la caracterisation des evenements ne sont cependant pas donnes a priori et l’on constate l’interet de ces evaluations retrospectives, qui s’averent nuancees et non conventionnelles. La possibilite de confronter le sens que les individus conferent au deroulement de leur vie et les elements factuels de celle-ci constitue une veritable richesse d’information pour l’interpretation des tournants et ruptures biographiques.
Mood lighting system reflecting music mood

The emotional impact of music or color can be maximized if they are used together. This article presents a mood-lighting system that automatically detects the mood of a piece of music and expresses the mood via synchronized lighting. To do this, the relationship between mood words and colors was analyzed via a web questionnaire (n = 202) on moods associated with music and colors. Data analysis generated lighting scenarios reflecting changes in the mood of music. Each piece of music was divided into several segments using structural analysis, with the mood of each segment detected by the mood classification module using the neural network. A matching color was then assigned. The best performance of our mood classification module was <70%, which is not sufficient for commercial use; however, this figure is high enough to show the potential of this approach. © 2013 Wiley Periodicals, Inc. Col Res Appl, 40, 201–212, 2015
Sequence of Reston Virus Isolate AZ-1435, an Ebolavirus Isolate Obtained during the 1989–1990 Reston Virus Epizootic in the United States

ABSTRACT Reston virus (RESTV) was discovered in 1989–1990 during three connected epizootics of highly lethal viral hemorrhagic fever among captive macaques in primate housing facilities in the United States and Philippines. Currently, only one RESTV isolate from that outbreak (named Pennsylvania) has been sequenced. Here, we report the sequence of a second isolate, Reston virus/M.fascicularis-tc/USA/1990/Philippines89-AZ1435.
SimConcept: a hybrid approach for simplifying composite named entities in biomedicine

Many text-mining studies have focused on the issue of named entity recognition and normalization, especially in the field of biomedical natural language processing. However, entity recognition is a complicated and difficult task in biomedical text. One particular challenge is to identify and resolve composite named entities, where a single span refers to more than one concept (e.g., BRCA1/2). Most bioconcept recognition and normalization studies have either ignored this issue, used simple ad-hoc rules, or only handled coordination ellipsis, which is only one of the many types of composite mentions studied in this work. No systematic methods for simplifying composite mentions have been previously reported, making a robust approach greatly needed. To this end, we propose a hybrid approach by integrating a machine learning model with a pattern identification strategy to identify the antecedent and conjuncts regions of a concept mention, and then reassemble the composite mention using those identified regions. Our method, which we have named SimConcept, is the first method to systematically handle most types of composite mentions. Our method achieves high performance in identifying and resolving composite mentions for three fundamental biological entities: genes (89.29% in F-measure), diseases (85.52% in F-measure) and chemicals (84.04% in F-measure). Furthermore, our results show that, using our SimConcept method can subsequently help improve the performance of gene and disease concept recognition and normalization.
The Impact of Resonance Frequency Breathing on Measures of Heart Rate Variability, Blood Pressure, and Mood

Heart rate variability biofeedback (HRVB) significantly improves heart rate variability (HRV). Breathing at resonance frequency (RF, approximately 6 breaths/min) constitutes a key part of HRVB training and is hypothesized to be a pathway through which biofeedback improves HRV. No studies to date, however, have experimentally examined whether RF breathing impacts measures of HRV. The present study addressed this question by comparing three groups: the RF group breathed at their determined RF for 15 min; the RF + 1 group breathed at 1 breath/min higher than their determined RF for 15 min; and the third group sat quietly for 15 min. After this 15-min period, all groups participated in the Paced Auditory Serial Addition Task (PASAT) for 8 min, and then sat quietly during a 10-min recovery period. HRV, blood pressure, and mood were measured throughout the experiment. Groups were not significantly different on any of the measures at baseline. After the breathing exercise, the RF group reported higher positive mood than the other two groups and a significantly higher LF/HF HRV ratio relative to the control group, a key goal in HRVB training (p < 0.05). Additionally, the RF group showed lower systolic blood pressure during the PASAT and during the recovery period relative to the control group, with the RF + 1 group not being significantly different from either group (p < 0.05). Overall, RF breathing appears to play an important role in the positive effect HRVB has on measures of HRV.
Context-relevant criteria for starting knowledge management initiatives

In qualitative research, context is essential in order to observe and understand the phenomena of interest in a more holistic way. Emphasizing context in knowledge management (KM) initiatives helps managers choose the most suitable way to implement KM in accordance with their business strategies. This paper integrates the concepts of context, Earl's (2001) seven schools of knowledge management (KM), a reverse datainformation-knowledge hierarchy, and knowledge reusability. By considering two important properties of context, namely codifiability and complexity, we propose a practical framework to help KM managers to choose from among different KM strategies. First, we introduce important works on KM and the context issue in the IS field. After explicating the important elements of context, we extract the two important properties. Based on these two dimensions, we analyze the feasibility of implementing and applying different KM initiatives, and then consider how the practical framework can help KM managers choose from among different KM strategies. By a case study in a small business, we show especially the usefulness of the framework for small and medium corporations to initiate or conduct their knowledge management.