Miracle-dz/UniFAR-FTUs huggingface dataset:
Mapping of rRNA genes with integrable plasmids in Bacillus subtilis

Integrable plasmids pGR102 and pWR103 containing ribosomal sequences from within the transcriptional units for 16S and 23S were used to transform Bacillus subtilis. To date, these plasmids integrated into 7 of 10 known rrn operons. Two such events occurred at unassigned operons, revealing a close linkage of the CAT gene of the plasmid to pha-1 situated between dal-1 and purB33 for rrnE and to thiA78 situated between glyB133 and re-12 for rrnD. All seven integration events that led to the loss of unique ribosomal BclI fragments can now be assigned to known rrn operons.
Assessing gene-level translational control from ribosome profiling

MOTIVATION The translational landscape of diverse cellular systems remains largely uncharacterized. A detailed understanding of the control of gene expression at the level of messenger RNA translation is vital to elucidating a systems-level view of complex molecular programs in the cell. Establishing the degree to which such post-transcriptional regulation can mediate specific phenotypes is similarly critical to elucidating the molecular pathogenesis of diseases such as cancer. Recently, methods for massively parallel sequencing of ribosome-bound fragments of messenger RNA have begun to uncover genome-wide translational control at codon resolution. Despite its promise for deeply characterizing mammalian proteomes, few analytical methods exist for the comprehensive analysis of this paired RNA and ribosome data. RESULTS We describe the Babel framework, an analytical methodology for assessing the significance of changes in translational regulation within cells and between conditions. This approach facilitates the analysis of translation genome-wide while allowing statistically principled gene-level inference. Babel is based on an errors-in-variables regression model that uses the negative binomial distribution and draws inference using a parametric bootstrap approach. We demonstrate the operating characteristics of Babel on simulated data and use its gene-level inference to extend prior analyses significantly, discovering new translationally regulated modules under mammalian target of rapamycin (mTOR) pathway signaling control.
Synaptic Correlates of Increased Cognitive Vulnerability with Aging: Peripheral Immune Challenge and Aging Interact to Disrupt Theta-Burst Late-Phase Long-Term Potentiation in Hippocampal Area CA1

Variability in cognitive functioning increases markedly with age, as does cognitive vulnerability to physiological and psychological challenges. Exploring the basis of this vulnerability may provide important insights into the mechanisms underlying aging-associated cognitive decline. As we have previously reported, the cognitive abilities of aging (24-month-old) F344 × BN rats are generally good, but are more vulnerable to the consequences of a peripheral immune challenge (an intraperitoneal injection of live Escherichia coli) than those of their younger (3-month-old) counterparts. Four days after the injection, the aging, but not the young rats show profound memory deficits, specific to the consolidation of hippocampus-dependent memory processes. Here, we have extended these observations, using hippocampal slices to examine for the first time the combined effects of aging and a recent infection on several forms of synaptic plasticity. We have found that the specific deficit in long-lasting memory observed in the aged animals after infection is mirrored by a specific deficit in a form of long-lasting synaptic plasticity. The late-phase long-term potentiation induced in area CA1 using theta-burst stimulation is particularly compromised by the combined effects of aging and infection—a deficit that can be ameliorated by intra-cisterna magna administration of the naturally occurring antiinflammatory cytokine IL-1Ra (interleukin-1 receptor antagonist). These data support the idea that the combination of aging and a negative life event such as an infection might produce selective, early-stage failures of synaptic plasticity in the hippocampus, with corresponding selective deficits in memory.
Application of Genetic Data Fusion in Tunnel Monitoring

Improving the accuracy of sensors detection in tunnel is very significant to strengthen the tunnel's management, and protect the tunnel's safe and efficient operation. Wavelet-based neural network genetic algorithm is applied to processing the data fusion collected by the tunnel sensor. And the algorithm is validated by the actual data in the tunnel to achieve more satisfactory results. Genetic wavelet neural network is proved to be effective.
Exploring the impacts of electronic imaging on organizational process, structure, and strategy

Although the benefits of electronic imaging (EI) are relatively easy to evaluate at the process level, there is a growing number of case studies suggesting EI also positively impacts many aspects of the organization's structure and strategy. However, these studies fail to identify the procedural, structural, and strategic constructs that are affected by EI. Based on a field study, the paper identifies organization factors that benefit/suffer from the introduction of electronic imaging. It was found that EI, in addition to its primary function of enhancing corporate document management, does have a critical value in supporting the organization structure and strategy to sustain business competitiveness. EI was also found to be a technology enabler that strengthens linkages between business strategies, on the one hand, and structures and processes that implement them, on the other.
Improved Recovery for Proactive, Distributed Routing

The Optimised Link State Routing (OLSR) protocol is a proactive, ad-hoc distributed routing protocol for Wireless Mesh Networks (WMNs). In this paper we demonstrate that by introducing a new type of message for this protocol (namely an ERROR message), the recovery time for routing in the presence of random link failures decreases. We illustrate our findings via formal model checking experiments in Uppaal. Decreased recovery time is essential for, e.g., emergency response networks and other critical WMNs.
Anticipation in cybernetic systems: a case against mindless anti-representationalism

The developments in behavior-based robotics and in ecological psychology have had a strong effect on theoretical development in some research communities. A new belief has emerged under the name of anti-representationalism, which is strongly opposed to the notion of representations in cognition. This notion is spurred by the inarguably fruitful insight that behavioral complexity can be brought about by simple mechanisms at a low systemic level. Although there are many problems with constructed formal representations in the toy models of traditional artificial intelligence, there is a fundamental problem with extreme anti-representationalism, as well. Representations actually do exist in the biological neural-information processing system. In this paper, a review of neural representation mechanisms are given, looking at perception and motor control in biological systems. Subsequently, it is illustrated that already in simple animal behaviors, simple 'representation-less' reactivity does not suffice. Anticipation exists even in jumping spiders, requiring the existence of a representation as the computational basis for the prediction of future system states.
MICROTUBULES: EVIDENCE FOR 13 PROTOFILAMENTS

When microtubules are fixed in glutaraldehyde in the presence of tannic acid and thin sections cut, the subunit structure of the microtubule is readily observed without the need of image reinforcement. Seven types of microtubules were analyzed: those in the heliozoan axoneme, the mitotic apparatus, the contractile axostyle, repolymerized microtubules derived from the chick brain, the central pair in flagella, and the A tubules of flagella and the basal body. In all cases microtubules were composed of 13 equally spaced protofilaments. The B tubules in flagella and the basal body appear to be composed of 11 subunits. The connections of the B to the A and the C to the B are described. A model of a microtubule is presented.
TEACHERS AS EARLY SOCIAL-EMOTIONAL SKILLS APPRENTICES: THE AFFECTIVE SELF- UNDERSTANDING CHALLENGE BEHIND THE CHAIR

This paper will focus on the Italian reality of the teaching world, and on the new theme for our State in particular: the social-emotional learning (SEL) linked to the role of the teacher. Examining the present literature that highlights some scholastic relational difficulties, we come to recognize the importance of some dimensions of working experience that can condition the well-being can bring discomfort to the teachers, and consequently to the quality of the scholastic reality. It thus emerges that taking care of relationships, is basic also in the world of teachers. Across a research that will start, we come investigate and to describe the emotional experiences lived in the classroom, using the narrative and reflective diary, thus improving a sense of affective self-awareness into the teachers willing to participate in the research, particularly in Verona.
Learning models of speaker head nods with affective information

During face-to-face conversation, the speaker's head is continually in motion. These movements serve a variety of important communicative functions, and may also be influenced by our emotions. The goal for this work is to build a domain-independent model of speaker's head movements and investigate the effect of using affective information during the learning process. Once the model is learned, it can later be used to generate head movements for virtual agents. In this paper, we describe our machine-learning approach to predict speaker's head nods using an annotated corpora of face-to-face human interaction and emotion labels generated by an affect recognition model. We describe the feature selection process, training process, and the comparison of results of the learned models under varying conditions. The results show that using affective information can help predict head nods better than when no affective information is used.
The Effects of Teacher Training on Teachers’ Family-Engagement Practices, Attitudes, and Knowledge: A Meta-analysis

ABSTRACT When families are engaged in their education, children’s academic, behavioral, and social-emotional development is indirectly supported. Many teacher-training programs (TTPs) focused on preparing teachers to communicate and work with families have been developed, although inconsistencies regarding their effectiveness exist. The current meta-analysis of 39 studies systematically analyzed the effects of TTPs on teachers’ family-engagement practices, attitudes, and knowledge. TTPs included preservice university courses, teacher in-service, and professional development programs. Studies were coded for key sample, setting, and quality characteristics. Analyses revealed TTPs had a significant positive effect on all teacher family-engagement outcomes. Key intervention components were also determined (e.g., communication strategies, collaborative planning, and problem solving) and can be used to inform the development of future TTPs. Although results are promising, future TTPs should aim to improve methodological rigor and study quality.
A New Approach for Selecting Informative Features For Text Classification

Selecting useful and informative features to classify text is not only important to decrease the size of the feature space, but as well for the overall performance and precision of machine learning. In this study we propose a new feature selection method called Informative Fea- ture Selector (IFS). Dierent machine learning algorithms and datasets have been utilised to examine the eectiveness of IFS, and it is compared to well-established methods, namely Information Gain, Odd Ratio, Chi Square, Mutual Information and Class Discriminative Measure. Our ex- periments show that IFS is able to outperform aforementioned methods and to produce eective and ecient results.
Final Report for EPSRC Grant GR / S 46710 / 01 Topological Models for Computational Metalanguages

Denotational semantics seeks to provide compositional models of computation, programs and data at the right level of abstraction to be independent of language-specific and implementation details, and domain theory provides the mathematical objects needed to construct such models. Although domain theory is a rich and highly developed mathematical theory, in its traditional form it fails to provide models for a number of significant computational phenomena, especially in combination. For example, although domain theory is able to model any two of: function types (as used in functional languages), probabilistic choice (as used in randomised algorithms) and computability (i.e., the requirement that programs are actually given by algorithms), it is not known how to model all three in combination. This research project undertook the study of a generalised domain theory based on widening the mathematical interpretation of the notion of “domain” to include a broader collection of topological spaces than usually considered. (Traditional “domains” can be viewed as topological spaces under their associated “Scott topology”.) The principal goal was to establish the main mathematical properties of the associated “topological domain theory”, and to demonstrate its ability to overcome limitations of traditional domain theory. That such a programme should be possible was suggested by the PI’s observation that “topological domains” arise naturally as a model for an abstract logic-based approach to domain theory known as “synthetic domain theory”, where such limitations were already known (by experts) not to occur. Due to its highly abstract nature, synthetic domain theory has only ever proved accessible to a handful of researchers with strong mathematical backgrounds. By transferring the rich structure of synthetic domain theory to a particular concrete model, closely related to classical domain theory, the project aimed to make this structure available to and comprehensible by the far wider community of people who understand classical domain theory. A further, more fundamental, motivation was that we saw topological domains as providing a highly natural computational model whose structure was crying out to be investigated. A second goal of the research proposal was to develop “metalanguages” reflecting the rich structure of topological domain theory. Such metalanguages were to take the form of type theories whose types and terms define topological domains and their elements respectively. The reason for constructing such languages is that they can be used as target languages into which programming languages can be translated, thereby giving a convenient means of assigning denotational semantics to programming languages. The envisaged benefit of basing such metalanguages on topological domains was that this would justify the metalanguages containing combinations of features not supported by other traditional domain-theoretic models.
Flux vacua as supersymmetric attractors

We derive algebraic attractor equations describing supersymmetric flux vacua of type IIB string theory in terms of the doublet of the 3-form fluxes, F and H. These equations are similar to the attractor equations for moduli fixed by the charges near the horizon of the supersymmetric black holes.
Point-Counterpoint: Organizational Change in Family Businesses

Although family firms are the most common business structure in the world and organizational change is ubiquitous in modern organizational life (Anderson & Reeb, 2004; By, 2005), research at the intersection of family business and organizational change remains underdeveloped. Studies show that family firms differ from their nonfamily counterparts on issues ranging from human resource management to research and development expenditures (e.g. Chrisman & Patel, 2012; Verbeke & Kano, 2012), but our understanding of how these differences are manifest in organizational change is very limited. Given the pervasiveness of organizational change across the globe, understanding when, why, and how it happens in the family business setting is of vital importance. Thus, change management in family firms is the focus of the inaugural entry in the Journal of Change Management Point-Counterpoint series. In the Point article, Kotlar and Chrisman (2018) draw upon prior family business research to put forth a set of ideas describing the ways family firms differ from their nonfamily counterparts with regard to when, why, and how they change. They first bring the idea of socioemotional wealth (SEW) to the fore by highlighting the notion that family firms have broader goal sets than their nonfamily counterparts because they pursue a unique combination of financial and nonfinancial goals. Kotlar and Chrisman suggest that the pursuit of socioemotional goals may impact both the incidence and processes through which family firms change. Kotlar and Chrisman specifically point to innovation and management succession as family business topics that have broader change implications. They note that family businesses may choose not to innovate because doing so could threaten SEW, and suggest that successors may be constrained in ways that leaders of nonfamily organizations are not. Although successors in nonfamily organizations often have the opportunity to enact their vision for the organization during their transition period, successors in family businesses may be more likely to pursue the status quo rather than seek change due to SEW considerations. Kotlar and Chrisman conclude by pointing out that more investigation is necessary to understand how family firms differ from nonfamily firms in when, why, and how they change. On the other hand, the Counterpoint article by DeMassis, Wang, and Chua (2018) emphasizes that family firms are heterogeneous, and that there may be substantial
Cardiac arrhythmia discrimination using evolutionary computation

The use of Implantable Cardioverter Defibrillators (ICD) for cardiac arrhythmia treatment implies a search for efficiency in terms of discrimination quality and computational complexity, given that improved efficiency will automatically turn into more effective therapy and longer battery lifetime. In this work, we applied evolutionary computation to create classifiers capable of discriminating between ventricular and supraventricular tachycardia (VT/SVT) in episodes registered by ICDs. Evolutionary computation comprises several paradigms emulating natural mechanisms for solving a problem, all of them characterized by a population of individuals (possible solutions) which evolve generation after generation to provide fitter solutions. Genetic programming was the paradigm chosen here because its solutions, coded as decision trees, can be both computationally simple and clinically interpretable. For the experiments, we considered electrograms (EGM) from episodes registered by ICDs in spontaneous/induced tachycardia, previously classified as VT/SVT by clinical experts from several Spanish healthcare centers. Training data were 38 real-valued samples, arranged as the concatenation of two beat segments: a sinus rhythm template immediately previous to the arrhythmic episode (basal reference), and the arrhythmic episode template. Several low complexity trees provided low error rates and allowed physiological interpretation. The best tree yielded an error rate of 1.8%, with both sensitivity and specificity above 98%. This solution compares two samples from the end of the arrhythmic pulse with another two samples from the sinus rhythm, pointing out to a relevant discrimination role of the lasting EGM.
Interorganizational Cooperation: The Role of Information Technology an Empirical Comparison of U.S. and Japanese Supplier Relations

This paper explores the comparative and cumulative influence of a number of factors on the perceived level of cooperation in a dyadic relationship. Drawing upon the transaction costs economics, organization theory and information systems literatures, we hypothese three sets of key influences: (1) factors exogenous to the relationship, i.e., the characteristics of the environment within which the relationship operates, and factors endogeneous to the relationship including (2) economic and behavioral characteristics of the relationship, and (3) interorganizational information technology applications. These factors have been independently examined in separate research streams. A key contribution of this study is therefore to conceptually and empirically capture their collective influence on cooperation. We empirically test the five hypotheses we develop within the context of buyer-supplier relationships in the U.S. and Japanese automobile industries. Multiple regressions conducted on a data set of 447 distin.
Morphosyntactic Resources for Automatic Speech Recognition

Texts generated by automatic speech recognition (ASR) systems have some specificities, related to the idiosyncrasies of oral productions or the principles of ASR systems, that make them more difficult to exploit than more conventional natural language written texts. This paper aims at studying the interest of morphosyntactic information as a useful resource for ASR. We show the ability of automatic methods to tag outputs of ASR systems, by obtaining a tag accuracy similar for automatic transcriptions to the 95-98 % usually reported for written texts, such as newspapers. We also demonstrate experimentally that tagging is useful to improve the quality of transcriptions by using morphosyntactic information in a post-processing stage of speech decoding. Indeed, we obtain a significant decrease of the word error rate with experiments done on French broadcast news from the ESTER corpus; we also notice an improvement of the sentence error rate and observe that a significant number of agreement errors are corrected.
Exotic atom pairs: Repulsively bound states in an optical lattice

We review a recent experiment with ultracold atoms in 3D optical lattices where we have observed a novel kind of bound state of two atoms which is based on repulsive interactions between the particles. These repulsively bound pairs exhibit long lifetimes, even under conditions when they collide with one another. Stable repulsively bound objects should be viewed as a general phenomenon and their existence will be ubiquitous in cold atoms lattice physics. Although the experiment described here is based on bosonic Rb atoms, other composites with fermions or Bose-Fermi mixtures should exist in an analogous manner. Furthermore, repulsively bound objects could also be formed with more than two particles. In the following we will first explain the theoretical background of repulsively atom pairs. Afterwards we will present the experiments which demonstrate several key properties of the pairs. Finally we give a short discussion of how these repulsively bound pairs relate to bound states in some other physical systems.
Symbolic modeling of human anatomy for visualization and simulation

Visualization of human anatomy in a 3D atlas requires both spatial and more abstract symbolic knowledge. Within our 'intelligent volume' model which integrates these two levels, we developed and implemented a semantic network model for describing human anatomy. Concepts for structuring (abstraction levels, domains, views, generic and case-specific modeling, inheritance) are introduced. Model, tools for generation and exploration and applications in our 3D anatomical atlas are presented and discussed.