Miracle-dz/UniFAR-FTUs huggingface dataset:
The self in conflict: actors and agency in the mediated sequential Simon task

Executive control refers to the ability to withstand interference in order to achieve task goals. The effect of conflict adaptation describes that after experiencing interference, subsequent conflict effects are weaker. However, changes in the source of conflict have been found to disrupt conflict adaptation. Previous studies indicated that this specificity is determined by the degree to which one source causes episodic retrieval of a previous source. A virtual reality version of the Simon task was employed to investigate whether changes in a visual representation of the self would similarly affect conflict adaptation. Participants engaged in a mediated Simon task via 3D “avatar” models that either mirrored the participants’ movements, or were presented statically. A retrieval cue was implemented as the identity of the avatar: switching it from a male to a female avatar was expected to disrupt the conflict adaptation effect (CAE). The results show that only in static conditions did the CAE depend on the avatar identity, while in dynamic conditions, changes did not cause disruption. We also explored the effect of conflict and adaptation on the degree of movement made with the task-irrelevant hand and replicated the reaction time pattern. The findings add to earlier studies of source-specific conflict adaptation by showing that a visual representation of the self in action can provide a cue that determines episodic retrieval. Furthermore, the novel paradigm is made openly available to the scientific community and is described in its significance for studies of social cognition, cognitive psychology, and human–computer interaction.
Comparison of I-Gel and Lma Fastrach as a Conduit for Blind Tracheal Intubation

AIM : To compare success rate of blind tracheal intubation through two different supraglottic airway devices 1) I-GEL, 2) ILMA using silicon reinforced flexometallic endotracheal tube/ PVC endotracheal tube. OBJECTIVES : 1. Evaluation of success rate of blind tracheal intubation through I-GEL and ILMA. 2. To study the complications in both the techniques. METHOD : Prospective randomised study. • 80 patients. • 2 groups- each group 40 patients. • Group A - I-GEL. • Group B- ILMA. PARAMETERS : Primary : Ease of insertion, • Duration of successful attempts, • No. of failed attempts, • Total intubation time, • First attempt success rate. Secondary : Heart rate, • Blood pressure, • Pulse oximetry, • EtCO2, • Complications. RESULTS : Supraglottic device insertion time- least time in I-GEL. • No. of attempts for successful tracheal intubation- I-GEL took least attempts. • Supraglottic device removal time- least time in I-GEL group. • First attempt success rate for tracheal intubation- high in ILMA group • Success rate for intubation- high in ILMA group. • Total intubation time- no statistical difference. • Complications- no statistical difference. • Hemodynamic response post intubation- no statistical difference. • No. of attempts for supraglottic device insertion – no statistical difference. • Time for first attempt tracheal intubation- no statistical difference. CONCLUSION : We conclude that, based on the results of our study, I-GEL aids easy and rapid insertion as a supraglottic airway device, but when it is used as a conduit for blind endotracheal intubation, the failure rate is high as there is more incidence of oesophageal intubation. In contrary, ILMA being a gold standard device meant for intubation guide, has a high first attempt success rate for blind endotracheal intubation.
Mitotic nuclear pore complex segregation involves Nup2 in Aspergillus nidulans

released nucleolar proteins are re-imported into transport-competent G1 nuclei, and new nucleoli Transport through nuclear pore complexes (NPCs) during interphase is facilitated by the nucleoporin Nup2 via its importin α – and Ran-binding domains. However, Aspergillus nidulans and vertebrate Nup2 also locate to chromatin during mitosis, suggestive of mitotic functions. In this study, we report that Nup2 is required for mitotic NPC inheritance in A. nidulans . Interestingly, the role of Nup2 during mitotic NPC segregation is independent of its importin α – and Ran-binding domains but relies on a central targeting domain that is necessary for localization and viability. To test whether mitotic chromatin-associated Nup2 might function to bridge NPCs with chromatin during segregation, we provided an artificial link between NPCs and chromatin via Nup133 and histone H1. Using this approach, we bypassed the requirement of Nup2 for NPC segregation. This indicates that A. nidulans cells ensure accurate mitotic NPC segregation to daughter nuclei by linking mitotic DNA and NPC segregation via the mitotic specific chromatin association of Nup2.
Cell–cell adhesion accounts for the different orientation of columnar and hepatocytic cell divisions

in which one daughter loses contact with the basal lamina. Here we describe a rescue mechanism that drives substrate-parallel spindle alignment of quasi-diagonal metaphase spindles in anaphase. It requires a Rho- and E-cadherin adhesion–dependent, substrate-parallel contractile actin belt at the apex that governs anaphase cell flattening. In contrast to monolayered Madin–Darby canine kidney cells, hepatocytic epithelial cells, which typically feature tilted metaphase spindles, lack this anaphase flattening mechanism and as a consequence maintain their spindle tilt through cytokinesis. This results in out-of-monolayer divisions, which we propose contribute to the stratified organization of hepatocyte cords in vivo.
Omega-3 PUFA intake and the risk of digestive system cancers

Abstract Background: A growing number of epidemiological studies have suggested a possible association between long-chain omega-3 polyunsaturated fatty acid (PUFA) intake and the risk of cancers, but the results have been inconsistent. We aimed to conduct a meta-analysis to assess the association of omega-3 PUFA consumption with digestive system cancers. Methods: Relevant observational studies were identified through a comprehensive search of PubMed, Embase, and the Web of Science through December 2019 and by reviewing the references of the retrieved articles. The relative risks (RRs) of digestive system cancers associated with omega-3 PUFA intake were estimated using a random-effect model and were stratified by region, sex, study design, type of omega-3 PUFAs, smoking status, alcohol consumption, BMI, and physical activity. Results: Twenty-five studies (8 case–control studies and 17 cohort studies) involving 1,247,271 participants and 23,173 patients with digestive system cancers were included in this analysis. The risk of digestive system cancers decreased by 17% in individuals who consumed omega-3 PUFAs (RR = 0.83, 95% confidence interval (CI), 0.76–0.91). The risk estimates of digestive system cancers varied by cancer sites, study location, study design, type of omega-3 PUFAs, and other confounders (smoking, alcohol consumption, body mass index, and physical activity). Visual inspection of funnel plots and the Begg's and Egger's tests revealed no evidence of publication bias. Conclusion: The findings show that omega-3 PUFAs should be as a healthy dietary component for the prevention of digestive system cancers. Cancer incidence decreases with increasing omega-3 PUFAs intake for most digestive system cancer sites. The relation between omega-3 PUFAs and digestive system cancers RR is similar among different populations.
A Small-scale Filament Eruption Inducing a Moreton Wave, an EUV Wave, and a Coronal Mass Ejection

With the launch of the Solar Dynamic Observatory, many extreme ultraviolet (EUV) waves have been observed during solar eruptions. However, joint observations of Moreton and EUV waves are still relatively rare. We present an event in active region NOAA 12740 wherein a small-scale filament eruption simultaneously results in a Moreton wave, an EUV wave, and a coronal mass ejection. First, we find that some dark elongated lanes or filamentary structures in the photosphere that exist under the small-scale filament drift downward; this manifests as the small-scale filament emerging and lifting up from the subsurface. Second, combining simultaneous observations in different EUV and Hα passbands, we study the kinematic characteristics of Moreton and EUV waves. Comparable propagation velocities and similar morphologies of the Moreton and different-passband EUV wave fronts were obtained. We deduce that Moreton and different-passband EUV waves are the perturbations in different temperature-associated layers induced by a coronal magnetohydrodynamic shock wave. We also find refracted, reflected, and diffracted phenomena during the propagation of the EUV wave. By using power-law fittings, the kinematic characteristics of unaffected, refracted, and diffracted waves were obtained. The extrapolation field derived by the potential field source surface model manifests as an interface between different magnetic systems (magnetic separatrix), resulting in the refraction, reflection, and deviation of the EUV wave.
Affordances in Robotic Tasks - A Survey

Affordances are key attributes of what must be perceived by an autonomous robotic agent in order to effectively interact with novel objects. Historically, the concept derives from the literature in psychology and cognitive science, where affordances are discussed in a way that makes it hard for the definition to be directly transferred to computational specifications useful for robots. This review article is focused specifically on robotics, so we discuss the related literature from this perspective. In this survey, we classify the literature and try to find common ground amongst different approaches with a view to application in robotics. We propose a categorisation based on the level of prior knowledge that is assumed to build the relationship among different affordance components that matter for a particular robotic task. We also identify areas for future improvement and discuss possible directions that are likely to be fruitful in terms of impact on robotics practice.
Wasserstein Adversarial Autoencoders for Knowledge Graph Embedding based Drug-Drug Interaction Prediction

Interaction between pharmacological agents can trigger unexpected adverse events. Capturing richer and more comprehensive information about drug-drug interactions (DDI) is one of the key tasks in public health and drug development. Recently, several knowledge graph embedding approaches have received increasing attention in the DDI domain due to their capability of projecting drugs and interactions into a low-dimensional feature space for predicting links and classifying triplets. However, existing methods only apply a uniformly random mode to construct negative samples. As a consequence, these samples are often too simplistic to train an effective model. In this paper, we propose a new knowledge graph embedding framework by introducing adversarial autoencoders (AAE) based on Wasserstein distances and Gumbel-Softmax relaxation for drug-drug interactions tasks. In our framework, the autoencoder is employed to generate high-quality negative samples and the hidden vector of the autoencoder is regarded as a plausible drug candidate. Afterwards, the discriminator learns the embeddings of drugs and interactions based on both positive and negative triplets. Meanwhile, in order to solve vanishing gradient problems on the discrete representation--an inherent flaw in traditional generative models--we utilize the Gumbel-Softmax relaxation and the Wasserstein distance to train the embedding model steadily. We empirically evaluate our method on two tasks, link prediction and DDI classification. The experimental results show that our framework can attain significant improvements and noticeably outperform competitive baselines.
Kvistur 2.0: a BiLSTM Compound Splitter for Icelandic

In this paper, we present a character-based BiLSTM model for splitting Icelandic compound words, and show how varying amounts of training data affects the performance of the model. Compounding is highly productive in Icelandic, and new compounds are constantly being created. This results in a large number of out-of-vocabulary (OOV) words, negatively impacting the performance of many NLP tools. Our model is trained on a dataset of 2.9 million unique word forms and their constituent structures from the Database of Icelandic Morphology. The model learns how to split compound words into two parts and can be used to derive the constituent structure of any word form. Knowing the constituent structure of a word form makes it possible to generate the optimal split for a given task, e.g., a full split for subword tokenization, or, in the case of part-of-speech tagging, splitting an OOV word until the largest known morphological head is found. The model outperforms other previously published methods when evaluated on a corpus of manually split word forms. This method has been integrated into Kvistur, an Icelandic compound word analyzer.
TriggerNER: Learning with Entity Triggers as Explanations for Named Entity Recognition

Training neural models for named entity recognition (NER) in a new domain often requires additional human annotations (e.g., tens of thousands of labeled instances) that are usually expensive and time-consuming to collect. Thus, a crucial research question is how to obtain supervision in a cost-effective way. In this paper, we introduce “entity triggers,” an effective proxy of human explanations for facilitating label-efficient learning of NER models. An entity trigger is defined as a group of words in a sentence that helps to explain why humans would recognize an entity in the sentence. We crowd-sourced 14k entity triggers for two well-studied NER datasets. Our proposed model, Trigger Matching Network, jointly learns trigger representations and soft matching module with self-attention such that can generalize to unseen sentences easily for tagging. Our framework is significantly more cost-effective than the traditional neural NER frameworks. Experiments show that using only 20% of the trigger-annotated sentences results in a comparable performance as using 70% of conventional annotated sentences.
Supplementation with fish oil reduces morphological aspects of muscle damage induced by intense exercise in rats

AIMSː To investigate the effects of resistance exercise and fish oil intake on muscle morphology in Wistar rats. METHODSː Forty-eight animals that performed resistance exercise were initially divided into two groups. One group did not take fish oil and the other group took fish oil. The animals of the second group underwent training and took fish oil for eight weeks. At the end of the last resistance exercise session, the 48 rats were organized into six subgroups of eight each, according to the time gap (12, 24 or 48 hours) elapsed until the gastrocnemius muscle withdrawal procedure. At each established time after the last resistance exercise session, the gastrocnemius muscle was removed for morphological analysis. RESULTSː Skeletal muscle cells of the animals that did not receive fish oil presented higher scores of edema, especially those from the groups that had their muscles withdrawn at 24 and 48 hours of time gap. As for the group that took fish oil, we observed a smaller amount of inflammatory infiltrate and reduced areas of necrosis compared to animals that exercised without the use of fish oil, at all post-exercise time gaps. CONCLUSIONSː Fish oil intake attenuated morphological changes in muscle tissue after high-intensity exercises.
Classification Benchmarks for Under-resourced Bengali Language based on Multichannel Convolutional-LSTM Network

Exponential growths of social media and micro-blogging sites not only provide platforms for empowering freedom of expressions and individual voices, but also enables people to express anti-social behavior like online harassment, cyberbul-lying, and hate speech. Numerous works have been proposed to utilize these data for social and anti-social behavior analysis, document characterization, and sentiment analysis by predicting the contexts mostly for highly resourced languages like English. However, some languages are under-resources, e.g., South Asian languages like Bengali, Tamil, Assamese, Malayalam, that lack of computational resources for natural language processing. In this paper1, we provide several classification benchmarks for Bengali, an under-resourced language. We prepared three datasets of expressing hate, commonly used topics, and opinions for hate speech detection, document classification, and sentiment analysis. We built the largest Bengali word embedding models to date based on 250 million articles, which we call BengFastText. We perform three experiments, covering document classification, sentiment analysis, and hate speech detection. We incorporate word embeddings into a Multichannel Convolutional-LSTM (MC-LSTM) network for predicting different types of hate speech, document classification, and sentiment analysis. Experiments demonstrate that BengFastText can capture the semantics of words from respective contexts correctly. Evaluations against several baseline embedding models, e.g., Word2Vec and GloVe yield up to 92.30%, 82.25%, and 90.45% F1-scores in case of document classification, sentiment analysis, and hate speech detection, respectively during 5-fold cross-validation tests.
Resolving the Optimal Metric Distortion Conjecture

We study the following metric distortion problem: there are two finite sets of points, V and C, that lie in the same metric space, and our goal is to choose a point in C whose total distance from the points in V is as small as possible. However, rather than having access to the underlying distance metric, we only know, for each point in V, a ranking of its distances to the points in C. We propose algorithms that choose a point in C using only these rankings as input and we provide bounds on their distortion (worst-case approximation ratio). A prominent motivation for this problem comes from voting theory, where V represents a set of voters, C represents a set of candidates, and the rankings correspond to ordinal preferences of the voters. A major conjecture in this framework is that the optimal deterministic algorithm has distortion 3. We resolve this conjecture by providing a polynomial-time algorithm that achieves distortion 3, matching a known lower bound. We do so by proving a novel lemma about matching voters to candidates, which we refer to as the ranking-matching lemma. This lemma induces a family of novel algorithms, which may be of independent interest, and we show that a special algorithm in this family achieves distortion 3. We also provide more refined, parameterized, bounds using the notion of decisiveness, which quantifies the extent to which a voter may prefer her top choice relative to all others. Finally, we introduce a new randomized algorithm with improved distortion compared to known results, and also provide improved lower bounds on the distortion of all deterministic and randomized algorithms.
Cellular velocity, electrical persistence and sensing in developed and vegetative cells during electrotaxis

Cells have the ability to detect electric fields and respond to them with directed migratory movement. Investigations identified genes and proteins that play important roles in defining the migration efficiency. Nevertheless, the sensing and transduction mechanisms underlying directed cell migration are still under discussion. We use Dictyostelium discoideum cells as model system for studying eukaryotic cell migration in DC electric fields. We have defined the temporal electric persistence to characterize the memory that cells have in a varying electric field. In addition to imposing a directional bias, we observed that the electric field influences the cellular kinematics by accelerating the movement of cells along their paths. Moreover, the study of vegetative and briefly starved cells provided insight into the electrical sensing of cells. We found evidence that conditioned medium of starved cells was able to trigger the electrical sensing of vegetative cells that would otherwise not orient themselves in the electric field. This observation may be explained by the presence of the conditioned medium factor (CMF), a protein secreted by the cells, when they begin to starve. The results of this study give new insights into understanding the mechanism that triggers the electrical sensing and transduces the external stimulus into directed cell migration. Finally, the observed increased mobility of cells over time in an electric field could offer a novel perspective towards wound healing assays.
A Note on Decoding Order in User Grouping and Power Optimization for Multi-Cell NOMA With Load Coupling

In this technical note, we present a new theoretical result for multi-cell non-orthogonal multiple access (NOMA). For multi-cell scenarios, a so-called load-coupling model has been proposed earlier to characterize the presence of mutual interference for NOMA, and the optimization process relies on the use of fixed-point iterations across cells. One difficulty here is that the order of decoding for successive interference cancellation (SIC) in NOMA is generally not known a priori. This is because the decoding order in one cell depends on interference, which, in turn, is governed by resource usage in other cells, and vice versa. To achieve convergence, previous works have used workarounds that pose restrictions to NOMA, such that the SIC decoding order remains throughout the fixed-point iterations. As a comment to the previous works, we derive and prove the following result: The convergence is guaranteed, even if the order changes over the iterations. The result not only waives the need of previous workarounds, but also implies that a wide class of optimization problems for multi-cell NOMA is tractable, as long as that for single cell is.
Extracting the effective contact rate of COVID-19 pandemic

In the absence of any available vaccines or drugs, prevention of the spread of Coronavirus Disease 2019 (COVID-19) pandemic is being achieved by putting many mitigation measures in place. It is indispensable to have robust and reliable ways of evaluating the effectiveness of these measures. In this work, we assume that, at a very coarse-grained level of description, the overall effect of all the mitigation measures is that we can still describe the spread of the pandemic using the most basic Susceptible-Exposed-Infectious-Removed ($SEIR$) model but with an "effective contact rate" ($\beta$) which is time-dependent. We then use the time series data of the number of infected individuals in the population to extract the instantaneous effective contact rate which is the result of various social interventions put in place. This approach has the potential to be significantly useful while evaluating the impact of mitigation measures on the spread of COVID-19 in near future.
Open and Cultural Data Games for Learning

Educators often seek ways to introduce gaming in the classroom in order to break the usual teaching routine, expand the usual course curriculum with additional knowledge, but mostly as a means to motivate students and increase their engagement with the course content. Even though the vast majority of students find gaming to be appealing and a welcome change to the usual teaching practice, many educators and parents doubt their educational value; in this paper, we discuss a card game designed to teach environmental matters to early elementary school students, using open data. We present a comparative study of how the game increased the students’ interest for the subject, as well as their performance and engagement to the course, compared with conventional teaching and a Prezi presentation used to teach the same content to other student groups.