Miracle-dz/UniFAR-FTUs huggingface dataset:
Human auditory belt areas specialized in sound recognition: a functional magnetic resonance imaging study

The human primary auditory cortex is surrounded by at least six other, anatomically distinct areas that process auditory information. We have investigated their specialization with respect to sound recognition or sound localization with triple epoch functional magnetic resonance imaging paradigm (recognition–localization–rest) in 18 normal individuals. In each study participant, the pattern of selective activation by the recognition or by the localization tasks was superimposed on the map of the nonprimary auditory areas, as identified in previous anatomical studies. Two areas, anterior lateral and anterior areas, were activated bilaterally in significantly more individuals by the recognition than by the localization task. They are proposed to be human homologues of macaque anterolateral auditory belt area.
Can Controlled Vestibular Stimulation Delay Brain Aging

Aging is believed to be a first-order risk factor for most neurodegenerative disorders. Brain changes do not occur to the same extent in all brain regions.7 Men and women may also differ with frontal and temporal lobes most affected in men compared with the hippocampus and parietal lobes in women. The neurotransmitters most often discussed with regard to ageing are dopamine, serotonin and acetyl-choline. Vestibular stimulation modulates the neuro-transmitters which are involved in brain aging and delay aging. Hence we recommend controlled vestibular stimulation to all. This in the need of time to identify the importance of vestibular system and to start translational research in this area.
Analysis of Host Responses to Mycobacterium tuberculosis Antigens in a Multi-Site Study of Subjects with Different TB and HIV Infection States in Sub-Saharan Africa

Background Tuberculosis (TB) remains a global health threat with 9 million new cases and 1.4 million deaths per year. In order to develop a protective vaccine, we need to define the antigens expressed by Mycobacterium tuberculosis (Mtb), which are relevant to protective immunity in high-endemic areas. Methods We analysed responses to 23 Mtb antigens in a total of 1247 subjects with different HIV and TB status across 5 geographically diverse sites in Africa (South Africa, The Gambia, Ethiopia, Malawi and Uganda). We used a 7-day whole blood assay followed by IFN-γ ELISA on the supernatants. Antigens included PPD, ESAT-6 and Ag85B (dominant antigens) together with novel resuscitation-promoting factors (rpf), reactivation proteins, latency (Mtb DosR regulon-encoded) antigens, starvation-induced antigens and secreted antigens. Results There was variation between sites in responses to the antigens, presumably due to underlying genetic and environmental differences. When results from all sites were combined, HIV- subjects with active TB showed significantly lower responses compared to both TST- and TST+ contacts to latency antigens (Rv0569, Rv1733, Rv1735, Rv1737) and the rpf Rv0867; whilst responses to ESAT-6/CFP-10 fusion protein (EC), PPD, Rv2029, TB10.3, and TB10.4 were significantly higher in TST+ contacts (LTBI) compared to TB and TST- contacts fewer differences were seen in subjects with HIV co-infection, with responses to the mitogen PHA significantly lower in subjects with active TB compared to those with LTBI and no difference with any antigen. Conclusions Our multi-site study design for testing novel Mtb antigens revealed promising antigens for future vaccine development. The IFN-γ ELISA is a cheap and useful tool for screening potential antigenicity in subjects with different ethnic backgrounds and across a spectrum of TB and HIV infection states. Analysis of cytokines other than IFN-γ is currently on-going to determine correlates of protection, which may be useful for vaccine efficacy trials.
Context Ontology for Secure Interoperability

During interoperability exchanges, organizations are jointly conducting computation and sharing tasks. However, organizations can have different security policies. To guarantee good interoperability exchanges, organizations need to share with other participants information about the services they provide. In addition, to be compliant with security requirements during interoperability, security policies have to be dynamic. One purpose of this paper is to provide this dynamic behavior by taking care about context of access parameters. The context-aware security requirements may be met by using a contextual access control model to define the security policy of each party involved in the interaction, and OrBAC (Organization based Access Control) is an adequate model for this purpose. Elaborating an ontology based security model provides a mean to ensure sharing of understandable knowledge, in particular knowledge needed to derive the authorized accesses and usages during the interoperability sessions. In this paper, we thus suggest a context ontology to be combined with an ontological representation of the OrBAC model and show how it can be used to ease the security rules definition and derivation during interoperability sessions.
Physical Human – Robot Interaction Mutual Learning and Adaptation •

C lose physical interaction between robots and humans is a particularly challenging aspect of robot development. For successful interaction and cooperation, the robot must have the ability to adapt its behavior to the human counterpart. Based on our earlier work, we present and evaluate a computationally efficient machine learning algorithm that is well suited for such close-contact interaction scenarios. We show that this algorithm helps to improve the quality of the interaction between a robot and a human caregiver. To this end, we present two human-in-the-loop learning scenarios that are inspired by human parenting behavior, namely, an assisted standing-up task and an assisted walking task.
Identification of Transcriptional Subgroups in EGFR-Mutated and EGFR/KRAS Wild-Type Lung Adenocarcinoma Reveals Gene Signatures Associated with Patient Outcome

Purpose: In lung adenocarcinoma, EGFR and KRAS mutations dominate the mutational spectrum and have clear therapeutic implications. We sought to determine whether transcriptional subgroups of clinical relevance exist within EGFR-mutated, KRAS-mutated, or EGFR and KRAS wild-type (EGFRwt/KRASwt) adenocarcinomas. Experimental Design: Gene expression profiles from 1,186 adenocarcinomas, including 215 EGFR-mutated, 84 KRAS-mutated, and 219 EGFRwt/KRASwt tumors, were assembled and divided into four discovery (n = 522) and four validation cohorts (n = 664). Subgroups within the mutation groups were identified by unsupervised consensus clustering, significance analysis of microarrays (SAM) analysis, and centroid classification across discovery cohorts. Genomic alterations in identified mutation subgroups were assessed by integration of genomic profiles for 158 cases with concurrent data. Multicohort expression subgroup predictors were built for each mutation group using the discovery cohorts, and validated in the four validation cohorts. Results: Consensus clustering within the mutation groups identified reproducible transcriptional subgroups in EGFR-mutated and EGFRwt/KRASwt tumors, but not in KRAS-mutated tumors. Subgroups displayed differences in genomic alterations, clinicopathologic characteristics, and overall survival. Multicohort gene signatures derived from the mutation subgroups added independent prognostic information in their respective mutation group, for adenocarcinoma in general and stage I tumors specifically, irrespective of mutation status, when applied to the validation cohorts. Consistent with their worse clinical outcome, high-risk subgroups showed higher expression of proliferation-related genes, higher frequency of copy number alterations/amplifications, and association with a poorly differentiated tumor phenotype. Conclusions: We identified transcriptional subgroups in EGFR-mutated and EGFRwt/KRASwt adenocarcinomas with significant differences in clinicopathologic characteristics and patient outcome, not limited to a mutation-specific setting. Clin Cancer Res; 19(18); 5116–26. ©2013 AACR.
Social Support and Motivation to Transfer as Predictors of Training Transfer: Testing Full and Partial Mediation Using Meta‐Analytic Structural Equation Modelling

Social support and motivation to transfer are important components in conceptual models on transfer of training. Previous research indicates that both support and motivation influence transfer. To date, however, it is not yet clear if social support influences transfer of training directly, or if this influence is mediated by motivation to transfer. Conceptually, some models assume that motivation to transfer fully mediates the influence of social support on transfer (full mediation models), whereas other models also include direct relationships between social support and transfer of training (partial mediation models). In addition, some models specify finer sub‐dimensions of social support, such as supervisor support, peer support, supervisor sanctions and feedback/coaching. What is the relative influence of these dimensions on the transfer of training? To what extent does motivation to transfer mediate the influence of different support dimensions and transfer? Aimed at answering these questions, the present meta‐analysis (k = 32 studies, N = 5487 participants) examined the relationships between social support, motivation to transfer and transfer of training. Social support was conceptualized in four dimensions: supervisor support, peer support, supervisor sanctions and feedback/coaching. Meta‐analytic structural equation modelling was used to test a partial mediation model and a full mediation model. Full mediation resulted in a better model fit. Peer support was the strongest predictor of motivation to transfer, and feedback/coaching was the strongest predictor of transfer of training. Theoretical and practical implications of the findings for developing conceptual models, measurement instruments and training interventions are discussed.
Action research in outdoor learning: promoting social and emotional learning in young people with social emotional and behavioural difficulties

This research applies a non-typical action research approach to design, implementation and monitoring of an outdoor learning intervention situated within a UK special school for learners with social emotional and behavioural difficulties (SEBD). The rationale for the research is based upon practitioner assumptions that an earlier skills orientated outdoor learning curriculum was inappropriate and that change was required to incorporate opportunities for the learners to develop their social and emotional learning (SEL) skills. The thesis describes the historical role of outdoor learning in relation to SEBD intervention and applies structuration theory (Giddens, 1984) to gain an understanding of previous outdoor learning interventions. The theory has also been used to create a narrative from which to describe the augmentation of SEL skills within the participant group. It was found that SEL augmentation in individuals contributed toward the production of improved social structures within the participant group. Participant attendance on the outdoor learning intervention is also reviewed in the context of alternative curriculum discourse. It was found that participants had improved attendance, punctuality and motivation on intervention days.
A fuzzy-MCDM based value investing method for banking stocks evaluation

Stock investing decisions are always made under a dynamic, complex and risky environment. In the long run, investors must rely on the invested companies' financial outcomes to share with their equity shareholders. Thus, in the stock market, Arms' financial statements should be scrutinized and diagnosed before making investment decisions, which is so-called fundamental analysis. Although scholars and investment professionals have proposed some investing rules to outperform market, few robust methods are available to meet this goal. Among previous studies, the accounting-based fundamental analysis (F-Score) [1] has been a widely accepted model for value investing. However, the manner which is based on financial information and traditional regression models would face difficulty of acquiring implicit knowledge and interactive considerations of senior experts. To improve the applicability of F-Score system, this research integrates fuzzy set theory and decision method for multiple criteria to propose an innovative model for distinguishing strong financial prospect stocks among high book-to-market (B/M) banking stocks. Our empirical study shows the practicability of the proposed model.
Social and cognitive influences on prescribing decisions among non-medical prescribers

Non-medical prescribers make an increasing contribution to healthcare across the UK yet little is known about influences on their prescribing decision-making. The aim of this programme of research was to explore and describe prescribing decision-making by non-medical prescribers. A two stage programme of research was carried out. Stage 1 was a systematic review of the social and cognitive influences on prescribing decision-making by non-medical prescribers. Despite a paucity of research, various influences on prescribing decision-making were reported including evidence based guidelines, peer support and patient (or parental) relationships and expectations. While confidence and clinical experience as a practitioner were cited as influences, the lack of prescribing experience and aspects of pharmacological knowledge also impacted on prescribing decision-making, resulting in a cautious approach. Stage 2 of the research employed a phenomenological methodology underpinned by the Theoretical Domains Framework of behavioural determinants (TDF). It comprised three phases. In Phase 1, semi-structured interviews with five nurse prescribers and eight pharmacist prescribers in NHS Grampian explored their experiences and perceptions of influences on their prescribing decision-making, and the impact of these influences. Multiple and sometimes contradictory influences were uncovered. Twelve of the fourteen domains of the TDF were found to be influential along with multi-disciplinary working and experience; optimism and reinforcement did not feature. In Phase 2, these participants recorded reflections on prescribing decisions which they considered noteworthy in relation to their practice, and in Phase 3 participants were interviewed about their reflections. Complexity was a feature of many, in the patients clinical or social circumstances or in relation to wider concerns. The same 12 domains were found to be influential as were multi-disciplinary working, experience and complexity. This programme of research has produced original findings which it is hoped will impact on the education, training and practice of these increasingly important prescribers.
3 D Segmentation of Fluid-Associated Abnormalities in Retinal OCT : Probability Constrained Graph-Search – Graph-Cut A

This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.  Abstract—An automated method is reported for segmenting 3D fluid and fluid-associated abnormalities in the retina, so-called Symptomatic Exudate-Associated Derangements (SEAD), from 3D OCT retinal images of subjects suffering from exudative age-related macular degeneration. In the first stage of a two-stage approach, retinal layers are segmented, candidate SEAD regions identified, and the retinal OCT image is flattened using a candidate-SEAD aware approach. In the second stage, a probability constrained combined graph search – graph cut method refines the candidate SEADs by integrating the candidate volumes into the graph cut cost function as probability constraints. The proposed method was evaluated on 15 spectral domain OCT images from 15 subjects undergoing intravitreal anti-VEGF injection treatment. Leave-one-out evaluation resulted in a true positive volume fraction (TPVF), false positive volume fraction (FPVF) and relative volume difference ratio (RVDR) of 86.5%, 1.7% and 12.8%, respectively. The new graph cut – graph search method significantly outperformed both the traditional graph cut and traditional graph search approaches (p<0.01, p<0.04) and has the potential to improve clinical management of patients with choroidal neovascularization due to exudative age-related macular degeneration.
Contextualizing Knowledge Management Readiness to Support Change Management Strategies

Research on knowledge management (KM) readiness has matured. However, recent organizational structures have emerged which prevent traditional instruments from adequately measuring KM readiness in an organization. For example, service-oriented enterprise (SOE) structure and agile software development techniques are characterized by multiple groups working together with each performing "services" as part of project pattern or process. Each group acts in its own role as a sub-organization with its unique helping patterns and cultures [17]. Traditional instruments which measure aggregate constructs across the entire organization will miss important between-group differences in an SOE that might influence KM readiness. This paper presents a case-based field-study from a large Fortune 500 financial firm transitioning its structure to SOE and considering agile software development methodologies. Survey data was collected along with a series of interviews with key managers and developers. Findings indicate statistically significant differences in KM readiness between groups and the need for alignment.
Trial and Error: How Student Affairs Staff in Community Colleges Learn to Supervise

ABSTRACT Supervisory skills play an important role in individual staff development and the achievement of organizational objectives. These skills are vital to professionals in community college student affairs, who support a diverse student population and a wide array of programming. This study was conducted to describe the kind of supervisory training experience student affairs professionals received. Through interviews with community college student affairs professionals, we found that many experienced a lack of formal training and acquired supervisory skills over time on the job, through trial and error, and from workshops.
The Dynamics of Plutinos

Plutinos are Kuiper belt objects that share the 3:2 Neptune resonance with Pluto. The long-term stability of Plutino orbits depends on their eccentricity. Plutinos with eccentricities close to Pluto's (fractional eccentricity difference Δe/eP = |e - eP|/eP ≲ 0.1) can be stable because the longitude difference librates, in a manner similar to the tadpole and horseshoe libration in co-orbital satellites. Plutinos with Δe/eP ≳ 0.3 can also be stable; the longitude difference circulates and close encounters are possible, but the effects of Pluto are weak because the encounter velocity is high. Orbits with intermediate eccentricity differences are likely to be unstable over the age of the solar system, in the sense that encounters with Pluto drive them out of the 3:2 Neptune resonance and thus into close encounters with Neptune. This mechanism may be a source of Jupiter-family comets.
The Submodular Secretary Problem Goes Linear

During the last decade, the matroid secretary problem (MSP) became one of the most prominent classes of online selection problems. The interest in MSP is twofold: on the one hand, there are many interesting applications of MSP, and on the other hand, there is strong hope that MSP admits O(1)-competitive algorithms, which is the claim of the well-known matroid secretary conjecture. Partially linked to its numerous applications in mechanism design, substantial interest arose also in the study of nonlinear versions of MSP, with a focus on the sub modular matroid secretary problem (SMSP). The fact that sub modularity captures the property of diminishing returns, a very natural property for valuation functions, is a key reason for the interest in SMSP. So far, O(1)-competitive algorithms have been obtained for SMSP over some basic matroid classes. This created some hope that, analogously to the matroid secretary conjecture, one may even obtain O(1)-competitive algorithms for SMSP over any matroid. However, up to now, most questions related to SMSP remained open, including whether SMSP may be substantially more difficult than MSP, and more generally, to what extend MSP and SMSP are related. Our goal is to address these points by presenting general black-box reductions from SMSP to MSP. In particular, we show that any O(1)-competitive algorithm for MSP, even restricted to a particular matroid class, can be transformed in a black-box way to an O(1)-competitive algorithm for SMSP over the same matroid class. This implies that the matroid secretary conjecture is equivalent to the same conjecture for SMSP. Hence, in this sense SMSP is not harder than MSP. Also, to find O(1)-competitive algorithms for SMSP over a particular matroid class, it suffices to consider MSP over the same matroid class. Using our reductions we obtain many first and improved O(1)-competitive algorithms for SMSP over various matroid classes by leveraging known algorithms for MSP. Moreover, our reductions imply an O(log log(rank))-competitive algorithm for SMSP, thus, matching the currently best asymptotic algorithm for MSP, and substantially improving on the previously best O(log(rank))-competitive algorithm for SMSP.
Mental Models , Decision Making and Performance in Complex Tasks

Previous studies have used the mental models construct as an ex-post explanation for poor performance on complex tasks, but this relationship has remained untested. This experimental study measured and tested the role of mental models in a complex decision environment. Participants worked on a product lifecycle management simulation under one of two levels of complexity across three phases of decision trial blocks spanning fifteen weeks. The results indicate that ability and task complexity are significant predictors of mental model accuracy, and that mental model accuracy and complexity are significant predictors of performance. Finding empirical support for the connection between mental model accuracy and performance, and measuring the magnitude of this effect is a key step forward in understanding why decision makers perform so poorly in complex decision environments. The results suggest there is potential to increase performance in such contexts , such as our increasingly large and complex organizations, by up to 100% through improving decision making. Validation of measures of mental model accuracy will enable researchers to incorporate this variable into their study designs in future research, and begin to identify levers for improving causal inferences, mental model accuracy, decision heuristics and performance.