Miracle-dz/UniFAR-FTUs huggingface dataset:
Actin‐associated protein synaptopodin in the rat hippocampal formation: Localization in the spine neck and close association with the spine apparatus of principal neurons

Dendritic spines are sites of synaptic plasticity in the brain and are capable of remodeling their shape and size. However, little is known about the cellular mechanisms that regulate spine morphology and motility. Synaptopodin is a recently described actin‐associated protein found in renal podocytes and dendritic spines (Mundel et al. J Cell Biol. [1997] 139:193–204), which is believed to play a role in spine plasticity. The presentstudy analyzed the distribution of synaptopodin in the hippocampal formation. In situ hybridization histochemistry revealed a high constitutive expression of synaptopodin mRNA in the principal cell layers. Light microscopic immunohistochemistry showed that the protein is distributed throughout the hippocampal formation in a region‐ and lamina‐specific manner. Postembedding immunogold histochemistry demonstrated that synaptopodin is exclusively present in dendrites and spines, specifically in the spine neck in close association with the spine apparatus. Spines lacking a spine apparatus are not immunoreactive for synaptopodin. These data suggest that synaptopodin links the spine apparatus to actin and may thus be involved in the actin‐based plasticity of spines. J. Comp. Neurol. 418:164–181, 2000. © 2000 Wiley‐Liss, Inc.
Reaction Force-Based Position Sensing for Magnetic Levitation Platform with Exceptionally Large Hovering Distance

This work introduces a novel sensing concept based on reaction forces for determining the position of the levitating magnet (mover) for magnetic levitation platforms (MLPs). Besides being effective in conventional magnetic bearings, the applied approach enables operation in systems where the mover is completely isolated from the actuating electromagnets (EMs) of the stator (e.g., located inside a sealed process chamber) while levitating at an extreme levitation height. To achieve active position control of the levitating mover by properly controlling the stator’s EM currents, it is necessary to employ a dynamic model of the complete MLP, including the reaction force sensor, and implement an observer that extracts the position from the force-dependent signals, given that the position is not directly tied to the measured forces. Furthermore, two possible controller implementations are discussed in detail: a basic PID controller and a more sophisticated state-space controller that can be chosen depending on the characteristics of the MLP and the accuracy of the employed sensing method. To show the effectiveness of the proposed position-sensing and control concept, a hardware demonstrator employing a 207 mm outer-diameter (characteristic dimension, CD) stator with permanent magnets, a set of electromagnets, and a commercial multi-axis force sensor is built, where a 0.36 kg mover is stably levitated at an extreme air gap of 104 mm.
eScience for Sea Science: A Semantic Scientific Knowledge Infrastructure for Marine Scientists

The COastal and Marine Perception Application for Scientific Scholarship (COMPASS) is a knowledge infrastructure that supports enhanced discovery of scientific resources, including publications, data sets and web services. It provides users with the ability to discover resources on the basis of domain knowledge using ontologies, and scientific knowledge, including the scientific models, theories and methods that were used to conduct the research described by the resource. The application includes an architecture that adopts standards from the geospatial information community to ensure interoperability between repositories and allow interaction with content from digital libraries. The architecture shows how ontologies can be used as a registry for an interoperable infrastructure. A prototype was successfully implemented and evaluated with users, finding enthusiasm and support for the approach, with some suggestions for improvements of the prototype implementation.
Experimentally studying the sensornet point-to-point routing techniques spectrum

The performance of point-to-point routing protocols is limited by a trade-off between routing state and routing stretch. A wireless sensor network (WSN) designer can choose from a whole spectrum of routing techniques that exploit this trade-off at varying granularity. We aim to make such choices more informed; our contributions are twofold. First, we present the first point-to-point routing framework for WSNs that provides uniform implementations of four routing techniques that, from the state-stretch trade-off perspective, represent the entire spectrum. Second, using the framework we conduct an unprecedented experimental comparison of the techniques in TOSSIM and on two testbeds. In particular, we show that most techniques with sub-linear state offer a small stretch in WSNs. These results can guide the initial choice of potential routing techniques for a given WSN; the framework can facilitate the choice of a particular technique by enabling experiments in one's own settings.
Rhesus Macaques (Macaca mulatta) Are Natural Hosts of Specific Staphylococcus aureus Lineages

Currently, there is no animal model known that mimics natural nasal colonization by Staphylococcus aureus in humans. We investigated whether rhesus macaques are natural nasal carriers of S. aureus. Nasal swabs were taken from 731 macaques. S. aureus isolates were typed by pulsed-field gel electrophoresis (PFGE), spa repeat sequencing and multi-locus sequence typing (MLST), and compared with human strains. Furthermore, the isolates were characterized by several PCRs. Thirty-nine percent of 731 macaques were positive for S. aureus. In general, the macaque S. aureus isolates differed from human strains as they formed separate PFGE clusters, 50% of the isolates were untypeable by agr genotyping, 17 new spa types were identified, which all belonged to new sequence types (STs). Furthermore, 66% of macaque isolates were negative for all superantigen genes. To determine S. aureus nasal colonization, three nasal swabs from 48 duo-housed macaques were taken during a 5 month period. In addition, sera were analyzed for immunoglobulin G and A levels directed against 40 staphylococcal proteins using a bead-based flow cytometry technique. Nineteen percent of the animals were negative for S. aureus, and 17% were three times positive. S. aureus strains were easily exchanged between macaques. The antibody response was less pronounced in macaques compared to humans, and nasal carrier status was not associated with differences in serum anti-staphylococcal antibody levels. In conclusion, rhesus macaques are natural hosts of S. aureus, carrying host-specific lineages. Our data indicate that rhesus macaques are useful as an autologous model for studying S. aureus nasal colonization and infection prevention.
BASYQUE: Aplicación para el estudio de la variación sintáctica

En este articulo presentamos BASYQUE, la aplicacion que hemos desarrollado para el estudio de la variacion sintactica. Aunque este proyecto se centra fundamentalmente en los dialectos del Pais Vasco frances (Iparralde), BASYQUE es una aplicacion multilingue util tambien para el analisis de otras lenguas y/o dialectos. Ademas de presentar las opciones que ofrece BASYQUE, abordaremos los aspectos metodologicos y los criterios que hemos seguido para realizar esta aplicacion. El desarrollo de este proyecto cuenta con la colaboracion del Centro IKER con sede en Bayona (Francia) y el Grupo IXA de la Universidad del Pais Vasco (UPV).
Stimulating Higher Education Students' Entrepreneurial Intention: Observing the Influence of Entrepreneurship Education and Lecturers' Personal Competence

Entrepreneurship is becoming an increasing topic of discussion. It has attracted the attention of academics worldwide due to its beneficial impact on economic and social development worldwide. For this reason, it is essential to stimulate the intention to engage in entrepreneurial endeavors as early as possible, preferably while still a student. Unfortunately, no research has explored the role of higher education, such as universities, as providers and supporters of a supportive environment in fostering entrepreneurial intentions, which can ultimately produce entrepreneurs. Based on these problems, this research examines essential factors such as entrepreneurship education and lecturers' competencies, which influence students' entrepreneurial intentions. Using a quantitative method involving 314 respondents through Partial Least Squares Structural Equation Modeling (PLS-SEM) analysis, this research concludes that entrepreneurship education and lecturers' personal competencies positively and significantly influence students' entrepreneurial intention. This research also offers educational institutions and the government an increase in students' intentions to become entrepreneurs. On the other hand, this study suggests that both the mixed methods approach and longitudinal models should be expanded to provide a more thorough knowledge of the readiness of individuals to engage in entrepreneurial enterprise.
Adaptive fuzzy control for a class of perturbed strict-feedback nonlinear systems with unknown time-varying delays

In this paper, a novel fuzzy adaptive backstepping design procedure is proposed for a class of perturbed strict-feedback nonlinear systems with unknown time-varying delays. The fuzzy logic systems are used to approximate the nonlinear uncertainties, then, the adaptive fuzzy tracking controller is constructed by combining Lyapunov-Krasovskii functionals and the backstepping approach. The proposed controller guarantees that all the signals in the closed-loop system are bounded and the system output converges to a small neighborhood of the desired reference signal. The main advantage of the scheme is that the controller can handle system with time-varying delays. Finally, simulation studies are given to demonstrate the effectiveness of the proposed design scheme.
Labeling glycans on living cells by a chemoenzymatic glycoengineering approach

ABSTRACT Structural glycobiology has traditionally been a challenging field due to a limited set of tools available to investigate the diverse and complex glycan molecules. However, we cannot ignore that glycans play critical roles in health as well as in disease, and are present in more than 50% of all proteins and on over 80% of all surface proteins. Chemoenzymatic glycoengineering (CGE) methods are a powerful set of tools to synthesize complex glycans, but the full potential of these methods have not been explored in cell biology yet. Herein, we report the labeling of live Chinese hamster ovary (CHO) cells by employing three highly specific glycosyltransferases: a sialyltransferase, a galactosyltransferase, and an N-acetyl-glucosaminyl transferase. We verified our results by bio-orthogonal blots and further rationalized them by computational modeling. We expect CGE applications in cell biology to rise and their implementation will assist in structural-functional discoveries in glycobiology. This research will contribute to this effort. Summary: A novel chemoenzymatic glycoengineering method was developed to selectively modify and label surface glycoconjugates in living cells by implementing glycosyltransferases and azido-modified activated sugar analogs.
Summary generation approaches based on semantic analysis for news documents

With the exponential growth of the internet, a lot of online news reports are produced on the web every day. The news stream flows so rapidly that no one has the time to look at each and every item of information. In this situation, a person would naturally prefer to read updated information at certain time intervals. Document updating technique is very helpful for individuals to acquire new information or knowledge by eliminating out-of-date or redundant information. Existing summarization systems involve identifying the most relevant sentences from the text and putting them together to create a concise initial summary. In the process of identifying the important sentences, features influencing the relevance of sentences are determined. Based on these features the salience of the sentence is calculated and an initial summary is generated from highly important sentences at different compression rates. These types of initial summaries work on a batch of documents and do not consider the documents that may arrive at later time, so that corresponding summaries need to get updated. The update summarization system addresses this issue by taking into account the documents read by the user in the past and seeks to present only fresh or different information. The first step is to create an initial summary based on basic and additional features. The next step is to create an update summary based on the basic, additional and update features. In this paper, two approaches are proposed for generating initial and update summary from multiple documents about given news. The first approach performs semantic analysis by modifying the vector space model with dependency parse relations and applying latent semantic analysis on it to create a summary. The second approach applies sentence annotation based on aspects, prepositions and named entities to generate summary. Experimental results show that the proposed approaches generate better initial and update summaries compared with the existing systems.
An Empathic Robotic Tutor in a Map Application

In this demonstration, we describe a scenario developed in the EMOTE project [2]. The overall goal of the EMOTE project is to develop an empathic robot tutor for 11-13 year old school students in an educational setting. The pedagogical domain we demonstrate here is to assist students in learning and testing their map-reading skills typically learned as part of the geography curriculum in schools. We demonstrate this scenario with a NAO robot interacting with the students whilst performing map-reading tasks in the form of a game on a touch-screen device.
SUSCEPTIBILITY OF THE SIBERIAN POLECAT TO SUBCUTANEOUS AND ORAL YERSINIA PESTIS EXPOSURE

To determine if the Siberian polecat (Mustela eversmannii) represents a suitable model for the study of plague pathogenesis and prevention in the black-footed ferret (Mustela nigripes), polecats were exposed to 103, 107, or 1010 Yersinia pestis organisms by subcutaneous injection; an additional group was exposed to Y. pestis via ingestion of a plague-killed mouse. Plague killed 88% of polecats exposed to Y. pestis (71% mortality in the 103 group, 100% mortality in the 107 and 1010 groups, and 83% mortality in the mouse-fed group). Within the challenged group, mean day of death post-challenge ranged from 3.6 to 7.6 days; all polecats died on or before day 12 post-challenge. Animals receiving the lowest parenteral dose survived significantly longer than those receiving higher parenteral doses. Within challenged animals, mean survival time was lower in those presenting with significant weight loss by day 3, lethargy, and low fecal output; time to onset of lethargy and other signs was also related to risk of dying and/or plague dose. Six polecats developed serum antibody titers to the Y. pestis F1 protein. Three seropositive polecats survived the initial challenge and a subsequent exposure to a plague-killed mouse, while two seropositive animals later died. This study confirms that the Siberian polecat is susceptible to plague and suggests that this species will offer an appropriate surrogate for black-footed ferrets in future plague studies and related vaccine trials.
Posterior distributions, sampling distributions and inter-laboratory comparison data

Weighted least-squares estimation methods are often used in the analysis of inter-laboratory comparison data. In classical statistics, least-squares methods can be justified in terms of properties of sampling distributions. However, not all distributions associated with laboratory results can be regarded sampling distributions and, for these cases, least-squares methods are a less straightforward to justify. Furthermore, they can give results that differ significantly from a Bayesian analysis. We illustrate these differences for the case in which the results of the laboratories are derived from repeated measurements.
Memory Models of Adaptive Behavior

Adaptive response to varying environment is a common feature of biological organisms. Reproducing such features in electronic systems and circuits is of great importance for a variety of applications. We consider memory models inspired by an intriguing ability of slime molds to both memorize the period of temperature and humidity variations and anticipate the next variations to come, when appropriately trained. Effective circuit models of such behavior are designed using: 1) a set of LC contours with memristive damping and 2) a single memcapacitive system-based adaptive contour with memristive damping. We consider these two approaches in detail by comparing their results and predictions. Finally, possible biological experiments that would discriminate between the models are discussed. In this paper, we also introduce an effective description of certain memory circuit elements.
Mitochondrial Metabolism in Major Depressive Disorder: From Early Diagnosis to Emerging Treatment Options

Major Depressive Disorder (MDD) is one of the most disabling diseases in the world. MDD is traditionally diagnosed based on a patient’s symptoms, which can lead to misdiagnosis. Although the pathogenic mechanisms of MDD are unknown, several studies have identified mitochondrial dysfunction as a central factor in the onset and progression of MDD. In the context of MDD, alterations in mitochondrial metabolism can lead to imbalances in energy production and oxidative stress, contributing to the disorder´s underlying pathophysiological mechanisms. Consequently, the identification of mitochondrial dysfunction as a key biomarker for early and accurate diagnosis of MDD represents a significant challenge. Faced with the limits of traditional treatments with antidepressants, new pharmacological therapeutic targets are being investigated such as ketamine/esketamine, psychedelics, or anti-inflammatories. All of these drugs show potential antidepressant effects due to their speed of action and ability to modulate neuroplasticity and/or motor processing. In parallel, non-pharmacological therapeutic targets are studied, like Transcranial Magnetic Stimulation (TMS) and Deep Brain Stimulation (DBS), recognized for their ability to modulate neuronal activity and offer treatment alternatives. As cellular activity is directly related to mitochondrial respiration, the aim of this review is examining the link between mitochondrial dysfunction and MDD, assessing how mitochondrial biomarkers could provide a more objective and precise diagnostic tool, and exploring other treatments in addition to traditional antidepressants, with a specific focus on emerging therapeutic targets. Finally, a detailed analysis of the strengths, weaknesses, opportunities, and threats of these approaches was carried out, highlighting the key challenges that must be addressed.
Spacetime stereo: a unifying framework for depth from triangulation

Depth from triangulation has traditionally been investigated in a number of independent threads of research, with methods such as stereo, laser scanning, and coded structured light considered separately. We propose a common framework called spacetime stereo that unifies and generalizes many of these previous methods. To show the practical utility of the framework, we develop two new algorithms for depth estimation: depth from unstructured illumination change and depth estimation in dynamic scenes. Based on our analysis, we show that methods derived from the spacetime stereo framework can be used to recover depth in situations in which existing methods perform poorly.
Comprehensive transcriptomic analysis of molecularly targeted drugs in cancer for target pathway evaluation

Targeted therapy is a rational and promising strategy for the treatment of advanced cancer. For the development of clinical agents targeting oncogenic signaling pathways, it is important to define the specificity of compounds to the target molecular pathway. Genome‐wide transcriptomic analysis is an unbiased approach to evaluate the compound mode of action, but it is still unknown whether the analysis could be widely applicable to classify molecularly targeted anticancer agents. We comprehensively obtained and analyzed 129 transcriptomic datasets of cancer cells treated with 83 anticancer drugs or related agents, covering most clinically used, molecularly targeted drugs alongside promising inhibitors of molecular cancer targets. Hierarchical clustering and principal component analysis revealed that compounds targeting similar target molecules or pathways were clustered together. These results confirmed that the gene signatures of these drugs reflected their modes of action. Of note, inhibitors of oncogenic kinase pathways formed a large unique cluster, showing that these agents affect a shared molecular pathway distinct from classical antitumor agents and other classes of agents. The gene signature analysis further classified kinome‐targeting agents depending on their target signaling pathways, and we identified target pathway‐selective signature gene sets. The gene expression analysis was also valuable in uncovering unexpected target pathways of some anticancer agents. These results indicate that comprehensive transcriptomic analysis with our database (http://scads.jfcr.or.jp/db/cs/) is a powerful strategy to validate and re‐evaluate the target pathways of anticancer compounds.
Aligning traces for performance evaluation

For many performance analysis problems, the ability to reason across traces is invaluable. However, due to non-determinism in the OS and virtual machines, even two identical runs of an application yield slightly different traces. For example, it is unlikely that two identical runs of an application will suffer context switches at exactly the same points. These sorts of variations across traces make it difficult to reason across traces. This paper describes and evaluates an algorithm, dynamic time warping (DTW) that can be used to align traces, thus enabling us to reason across traces. While DTW comes from prior work our use of DTW is novel. Also we describe and evaluate an enhancement to DTW that significantly improves the quality of its alignments. Our results show that for applications whose performance varies significantly over time, DTW does a great job at aligning the traces. For applications whose performance stays largely constant for significant periods of time, the original DTW does not perform well; however, our enhanced DTW performs much better.