Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Freshwater Cyanosiphovirus Harboring Integrase

Pelagic cyanobacteria are key players in the functioning of aquatic ecosystems, and their viruses (cyanophages) potentially affect the abundance and composition of cyanobacterial communities. Yet, there are few well-described freshwater cyanophages relative to their marine counterparts, and in general, few cyanosiphoviruses (family Siphoviridae) have been characterized, limiting our understanding of the biology and the ecology of this prominent group of viruses. Here, we characterize S-LBS1, a freshwater siphovirus lytic to a phycoerythrin-rich Synechococcus isolate (Strain TCC793). S-LBS1 has a narrow host range, a burst size of ∼400 and a relatively long infecting step before cell lysis occurs. It has a dsDNA 34,641 bp genome with putative genes for structure, DNA packing, lysis, replication, host interactions, DNA repair and metabolism. S-LBS1 is similar in genome size, genome architecture, and gene content, to previously described marine siphoviruses also infecting PE-rich Synechococcus, e.g., S-CBS1 and S-CBS3. However, unlike other Synechococcus phages, S-LBS1 encodes an integrase, suggesting its ability to establish lysogenic relationships with its host. Sequence recruitment from viral metagenomic data showed that S-LBS1-like viruses are diversely present in a wide range of aquatic environments, emphasizing their potential importance in controlling and structuring Synechococcus populations. A comparative analysis with 16 available sequenced cyanosiphoviruses reveals the absence of core genes within the genomes, suggesting high degree of genetic variability in siphoviruses infecting cyanobacteria. It is likely that cyanosiphoviruses have evolved as distinct evolutionary lineages and that adaptive co-evolution occurred between these viruses and their hosts (i.e., Synechococcus, Prochlorococcus, Nodularia, and Acaryochloris), constituting an important driving force for such phage diversification.
SK2 Channels Associate With mGlu1α Receptors and CaV2.1 Channels in Purkinje Cells

The small-conductance, Ca2+-activated K+ (SK) channel subtype SK2 regulates the spike rate and firing frequency, as well as Ca2+ transients in Purkinje cells (PCs). To understand the molecular basis by which SK2 channels mediate these functions, we analyzed the exact location and densities of SK2 channels along the neuronal surface of the mouse cerebellar PCs using SDS-digested freeze-fracture replica labeling (SDS-FRL) of high sensitivity combined with quantitative analyses. Immunogold particles for SK2 were observed on post- and pre-synaptic compartments showing both scattered and clustered distribution patterns. We found an axo-somato-dendritic gradient of the SK2 particle density increasing 12-fold from soma to dendritic spines. Using two different immunogold approaches, we also found that SK2 immunoparticles were frequently adjacent to, but never overlap with, the postsynaptic density of excitatory synapses in PC spines. Co-immunoprecipitation analysis demonstrated that SK2 channels form macromolecular complexes with two types of proteins that mobilize Ca2+: CaV2.1 channels and mGlu1α receptors in the cerebellum. Freeze-fracture replica double-labeling showed significant co-clustering of particles for SK2 with those for CaV2.1 channels and mGlu1α receptors. SK2 channels were also detected at presynaptic sites, mostly at the presynaptic active zone (AZ), where they are close to CaV2.1 channels, though they are not significantly co-clustered. These data demonstrate that SK2 channels located in different neuronal compartments can associate with distinct proteins mobilizing Ca2+, and suggest that the ultrastructural association of SK2 with CaV2.1 and mGlu1α provides the mechanism that ensures voltage (excitability) regulation by distinct intracellular Ca2+ transients in PCs.
Derealization and self-harm strategies are used to regulate disgust, fear, and sadness in adult survivors of childhood sexual abuse.

Emotion regulation (ER) difficulties have been identified as an important target for clinical intervention in the treatment of post-traumatic stress disorder (PTSD) symptoms in survivors of childhood sexual abuse (CSA). However, there is limited research regarding the use of specific strategies to regulate specific emotions following exposure to traumatic events. The aim of the current study was to investigate the indirect effects of four trauma-related emotions (anger, sadness, disgust, and fear) on PTSD severity via two mediators: derealization and self-harm. In particular, we tested that if the two hypothetical mediators operate sequentially, derealization precedes self-harm and/or self-harm precedes derealization. A predominate female clinical sample (N = 109) of CSA survivors completed measures of experience of emotions, ER, and post-traumatic stress. Bivariate and serial mediation analyses were conducted to test the direct and indirect effects of trauma-related emotions on PTSD severity. Serial mediation analyses indicated that there were significant total effects of all trauma-related emotions on PTSD severity. Three trauma-related emotions (sadness, disgust, and fear) were indirectly associated to PTSD severity via derealization and self-harm and via self-harm and derealization. Results indicate that difficulties in regulating the emotions of sadness, disgust, and fear may result in more severe derealization and self-harm as coping strategies, which in turn lead to greater PTSD severity. The sequence of mediators does not hold great importance in these pathways. Overall, our findings suggest that therapeutically targeting derealization and self-harm might enable the reduction of PTSD among CSA survivors.
Wheat grain protein accumulation and polymerization mechanisms driven by nitrogen fertilization.

In wheat (Triticum aestivum) grain yield and grain protein content are negatively correlated, making the simultaneous increase of the two traits challenging. Apart from genetic approaches, modification of nitrogen fertilization offers a feasible option to achieve this aim. In this study, a range of traits related to nitrogen-use efficiency in six Australian bread wheat varieties were investigated under different nitrogen treatments using 3-year multisite field trials. Changes in the individual storage protein composition were detected by high-performance liquid chromatography. Our results indicated that wheat grain yield and grain protein content reacted similarly to nitrogen availability, with grain yield being slightly more sensitive than grain protein content, and that genotype is a vital determinant of grain protein yield. Measurement of the glutamine synthetase activity of flag leaves and developing grains revealed that high nitrogen availability prompted the participation of glutamine in biological processes. In addition, a more significant accumulation of gluten macropolymer was observed under the high-nitrogen treatment from 21 days post-anthesis, and the underlying mechanism was elucidated by a comparative proteomics study. A yeast two-hybrid experiment confirmed this mechanism. The results of this study revealed that peptidyl-prolyl cis-trans isomerase (PPIase) was SUMOylated with the assistance of small ubiquitin-related modifier 1 and that high nitrogen availability facilitated this connection for the subsequent protein polymerization. Additionally, luminal-binding protein 2 in the endoplasmic reticulum played a similar role to PPIase in the aggregation of protein under high-nitrogen conditions.
Real-time prediction of flight arrival times using surveillance information

Accurate estimations of the time of arrival of a flight prior to its landing are beneficial for most of the stakeholders in the air traffic management and control industry, as they could lead to reductions in potential safety risks and improvements in resources allocation. In this paper, and within the European Commission funded Transforming Transport project, we propose a methodology for real-time prediction of flight arrival times based on the application of machine learning techniques. For this purpose, we employ state-of-the-art data warehousing and broadcasting processes, that allow both the training of a regression machine learning model and the integration of its predictions of current flights on a real-time visualization tool set up for customer usage. The model only makes use of the information included in aircraft surveillance messages. Predictions obtained with such model are compared to those provided by other current services to observe the added value of the application of the proposed system on real-time operations.
Tie Strength in GitHub Heterogeneous Networks

In social networks, the relationship between individuals is defined by many forms of interaction. Here, our goal is to measure the strength of the relationship between GitHub users by considering social and technical features. Thus, we model GitHub's heterogeneous collaboration network with different types of interaction and propose new metrics to the strength of relationships. The results show the new metrics are not correlated, bringing new information to the table. Finally, these metrics may become important tools to determine users' influence and popularity.
A Cluster-Based Boosting Algorithm for Bankruptcy Prediction in a Highly Imbalanced Dataset

Bankruptcy prediction has been a popular and challenging research topic in both computer science and economics due to its importance to financial institutions, fund managers, lenders, governments, as well as economic stakeholders in recent years. In a bankruptcy dataset, the problem of class imbalance, in which the number of bankruptcy companies is smaller than the number of normal companies, leads to a standard classification algorithm that does not work well. Therefore, this study proposes a cluster-based boosting algorithm as well as a robust framework using the CBoost algorithm and Instance Hardness Threshold (RFCI) for effective bankruptcy prediction of a financial dataset. This framework first resamples the imbalance dataset by the undersampling method using Instance Hardness Threshold (IHT), which is used to remove the noise instances having large IHT value in the majority class. Then, this study proposes a Cluster-based Boosting algorithm, namely CBoost, for dealing with the class imbalance. In this algorithm, the majority class will be clustered into a number of clusters. The distance from each sample to its closest centroid will be used to initialize its weight. This algorithm will perform several iterations for finding weak classifiers and combining them to create a strong classifier. The resample set resulting from the previous module, will be used to train CBoost, which will be used to predict bankruptcy for the validation set. The proposed framework is verified by the Korean bankruptcy dataset (KBD), which has a very small balancing ratio in both the training and the testing phases. The experimental results of this research show that the proposed framework achieves 86.8% in AUC (area under the ROC curve) and outperforms several methods for dealing with the imbalanced data problem for bankruptcy prediction such as GMBoost algorithm, the oversampling-based method using SMOTEENN, and the clustering-based undersampling method for bankruptcy prediction in the experimental dataset.
An Answer Ranking Method in Medical Social Networks

Patient-oriented medical social networks aim to help their users, with different levels of knowledge, by providing information and support on specific medical and health issues. As one of such pioneering networks, the MedHelp includes forums which allow interaction between users seeking health and medical information with other users who know or live in the context of the information, so they can share their experiences. However, there is no explicit control over what is being answered by users. Therefore, filtering and sorting the answers are fundamental tasks. This work presents PERank as a method and a tool which aim to order answers to questions on forums of medical social networks. By manipulating documents about Type 1 Diabetes disease, experimentation results demonstrate the feasibility of PERank for a low number of best-ranked answers being analyzed.
Nurses’ knowledge and deliberations crucial to Barcoded Medication Administration technology in a Dutch hospital: Discovering nurses’ agency inside ruling

This article shows how Barcoded Medication Administration technology institutionally organizes and rules the daily actions of nurses. Although it is widely assumed that Barcoded Medication Administration technology improves quality and safety by reducing the risk of human error, little research has been done on how this technology alters the work of nurses. Drawing on empirical and conceptual strategies of analysis, this qualitative study used certain tools of institutional ethnography to provide a view of how nurses negotiate Barcoded Medication Administration technology. The approach also uses elements from practice theory in order to discern how technology operates as a player on the field instead of being viewed as a ‘mere’ tool. A literature review preceded participant observation, whereby 17 nurses were followed and data on an orthopaedic ward were collected over a period of 9 months in 2011 and 2012. Barcoded Medication Administration technology relies on nurses’ knowledge to mediate between the embedded logics of its design and the unpredictable needs of patients. Nurses negotiate their own professional logic of care in the form of moment-to-moment deliberations which subvert the ruling frame of the barcoded system and its objectified model of patient safety. The logic of Barcoded Medication Administration technology differs from the logic of nursing care, as this technology presumes medication distribution to be linear, even though nurses follow another line of actor-bound safety practices that we characterize as ‘deliberations’.
Examining interprofessional learning perceptions among students in a simulation-based operating room team training experience

ABSTRACT High-fidelity simulation (HFS) operating room (OR) inter-professional team training improves healthcare students’ team-based attitudes and behaviours. Such improvements can diminish tribalism among the professions in the OR by overcoming entrenched perceptions of team members. We investigated whether simulation-based interprofessional student OR team training impacts students differently based on their professional background. From 2010 to 2013, HFS OR interprofessional student team training sessions were conducted involving senior medical students, senior undergraduate nursing students, and nurse anaesthesia students. The training involved a two-scenario format, each followed by a structured debriefing focusing on team-based competencies. Before and after each session, students completed a 15-item teamwork competencies self-efficacy survey as well as, from 2012–2013, the Readiness for Interprofessional Learning Scale (RIPLS). At the end of each session, they also completed a 6-item rating scale assessing overall team function during the session. Mean scores were calculated for each student professional group, post/pre mean differences were determined, and student t-test and ANOVA analyses were employed to compare within and between-group differences, respectively. Response rates were over 80% for each scale used. Medical students and undergraduate nursing students had significant improvements in team-based attitudes post- to pre-session. Medical students and nurse anaesthesia students had significant improvements in RIPLS scores. Statistically significant improvements from post- to pre-session were seen overall for both team-based attitudes (effect size = 0.83) and RIPLS (effect size = 0.37). The difference between the team-based scores between professions was significant; RIPLS differences were not. No significant difference existed between professions related to overall teamwork scores. HFS OR team training of healthcare students has beneficial but variable benefits for each professional group.
Online Deep Ensemble Learning for Predicting Citywide Human Mobility

Predicting citywide human mobility is critical to an effective management and regulation of city governance, especially during a rare event (e.g. large event such as New Year's celebration or Comiket). Classical models can effectively predict routine human mobility, but irregular mobility during a rare event (precedented or unprecedented), which is much more difficult to model, has not drawn sufficient attention. Moreover, the complexity and non-linearity of human mobility hinders a simple model from making an accurate prediction. Bearing these facts in mind, we propose a novel online gating neural network framework with two phases. In the offline training phase, we train a gated recurrent unit-based human mobility predictor for each day in our training set, while in the online predicting phase, we construct an online adaptive human mobility predictor as well as a gating neural network that switches among the pre-trained predictors and the online adaptive human predictor. Our approach was evaluated using a real-world GPS-log dataset from Tokyo and Osaka and achieved a higher prediction accuracy than baseline models.
Suplementação de aminoácidos essenciais em dietas de baixo nível proteico para leitões: Revisão

In swine, the development of animal nutrition and a better understanding of protein metabolism makes it possible to optimize diets that meet nutritional requirements in protein and amino acids with a lower environmental impact. Proteins are essential nutrients that exhibit a variety of biological functions, participating primarily in muscle deposition, aiding in hormonal regulation and obtaining energy, and overeating or deficiencies of these can be reflected in plasma concentrations of variables such as urea, total proteins And creatinine which are considered good indicators to analyze the process of degradation of amino acids in the body. At present, the formulation of rations with an ideal profile of amino acids in addition to decreasing the nitrogen excretion allows to reduce the percentage of crude protein without affecting the performance variables of zootechnical importance. The objective of this review is to contribute to a better knowledge and understanding of the importance of essential amino acids in pig feed.
PFS: Particle-Filter-Based Superpixel Segmentation

In this paper, we propose a particle-filter-based superpixel (PFS) segmentation method that extends the original tracking problem as a region clustering problem. The basic idea is to approximate superpixel centers by multiple particles to obtain high intra-region similarity. Specifically, we firstly use a density cluster to initialize single-group particles and introduce the association rule for mining other initial candidate particles. In propagation, particles are transferred to neighboring local regions by a moving step aiming to update local candidate particles with a lower energy cost. We evaluate all particles on the basis of their cluster similarity and estimate the largest particles as the final superpixel centers. The proposed method can locate cluster centers in diverse feature space, which alleviates the risk of a local optimum and produces better segmentation performance. Experimental results on the Berkeley segmentation 500 dataset (BSD500) demonstrate that our method outperforms seven state-of-the-art methods.
Interplay between urban communities and human‐crowd mobility: A study using contributed geospatial data sources

An intense process of urbanization, witnessed particularly in the last decade, has stressed the need to comprehend human mobility behavior in urban settings. Although the emergence of contributed geospatial data (i.e., pervasive activity‐based data) has contributed to substantial progress toward understanding human activity, the relationship between human‐crowd mobility and the functional structure of a city is not yet well understood. In this context, the present research focuses on the intra‐urban origin–destination matrix modeling founded on a combination of two major crowdsourced datasets as well as the inclusion of urban communities’ structure. Specifically, the well‐known “radiation” and “PWO” models were modified through first, identifying the communities embedded in the cyberspace network then employing the identified hierarchical structure of the spatial‐interaction network for the formulation of the users’ movement network and second, imposing proper input variables including the telecommunication activity volume and check‐in frequency. The results obtained by various empirical analyses suggest that the modified community‐constrained origin–destination flow estimation models exhibit better performance levels than those of alternative conventional mobility models.
Reciprocal Regulation of Metabolic Reprogramming and Epigenetic Modifications in Cancer

Cancer cells reprogram their metabolism to meet their demands for survival and proliferation. The metabolic plasticity of tumor cells help them adjust to changes in the availability and utilization of nutrients in the microenvironment. Recent studies revealed that many metabolites and metabolic enzymes have non-metabolic functions contributing to tumorigenesis. One major function is regulating epigenetic modifications to facilitate appropriate responses to environmental cues. Accumulating evidence showed that epigenetic modifications could in turn alter metabolism in tumors. Although a comprehensive understanding of the reciprocal connection between metabolic and epigenetic rewiring in cancer is lacking, some conceptual advances have been made. Understanding the link between metabolism and epigenetic modifications in cancer cells will shed lights on the development of more effective cancer therapies.
A Comparison of Emotion Elicitation Methods for Affective Driving Studies

Advances in sensing technology enable the emotional state of car drivers to be captured and interfaces to be built that respond to these emotions. To evaluate such emotion-aware interfaces, researchers need to evoke certain emotional states within participants. Emotion elicitation in driving studies poses a challenge as the driving task can interfere with the elicitation task. Induced emotions also lose intensity with time and through secondary tasks. This is why we have analyzed different emotion elicitation techniques for their suitability in automotive research and compared the most promising approaches in a user study. We recommend using autobiographical recollection to induce emotions in driving studies, and suggest a way to prolong emotional states with music playback. We discuss experiences from a a driving simulator study, including solutions for addressing potential privacy issues.
Natural Pursuits for Eye Tracker Calibration

Although, gaze-based interaction has been investigated since the 1980s and provides promising concepts to realize cognitive systems and support universal interaction within distributed environments, the main challenges, such as the Midas touch problem [16] or calibration are still frequent topics of research. In this work, Natural Pursuit Calibration is presented, which is a comfortable, unobtrusive technique enabling ongoing attention detection and eye tracker calibration within an off-screen context. The user is able to perform calibration, without a digital user interface, artificial annotation of the environment nor further assistance, by simply following any arbitrary moving target. Due to the characteristics of the calibration process, it can be executed simultaneously to any primary task, without active user participation. A two-stage evaluation process is conducted to (i) optimize parameter settings in a first setup and (ii) compare the accuracy as well as the user acceptance of the proposed procedure to prevailing calibration techniques.