Miracle-dz/UniFAR-FTUs huggingface dataset:
Keeping up with the condensates: The retention, gain, and loss of nuclear membrane-less organelles

In recent decades, a growing number of biomolecular condensates have been identified in eukaryotic cells. These structures form through phase separation and have been linked to a diverse array of cellular processes. While a checklist of established membrane-bound organelles is present across the eukaryotic domain, less is known about the conservation of membrane-less subcellular structures. Many of these structures can be seen throughout eukaryotes, while others are only thought to be present in metazoans or a limited subset of species. In particular, the nucleus is a hub of biomolecular condensates. Some of these subnuclear domains have been found in a broad range of organisms, which is a characteristic often attributed to essential functionality. However, this does not always appear to be the case. For example, the nucleolus is critical for ribosomal biogenesis and is present throughout the eukaryotic domain, while the Cajal bodies are believed to be similarly conserved, yet these structures are dispensable for organismal survival. Likewise, depletion of the Drosophila melanogaster omega speckles reduces viability, despite the apparent absence of this domain in higher eukaryotes. By reviewing primary research that has analyzed the presence of specific condensates (nucleoli, Cajal bodies, amyloid bodies, nucleolar aggresomes, nuclear speckles, nuclear paraspeckles, nuclear stress bodies, PML bodies, omega speckles, NUN bodies, mei2 dots) in a cross-section of organisms (e.g., human, mouse, D. melanogaster, Caenorhabditis elegans, yeast), we adopt a human-centric view to explore the emergence, retention, and absence of a subset of nuclear biomolecular condensates. This overview is particularly important as numerous biomolecular condensates have been linked to human disease, and their presence in additional species could unlock new and well characterized model systems for health research.
Importance of species traits on individual-based seed dispersal networks and dispersal distance for endangered trees in a fragmented forest

Although mutualistic network analyses have sparked a renewed interest in the patterns and drivers of network structures within communities, few studies have explored structural patterns within populations. In an endangered tree species population, plant individuals share their bird seed dispersers; however, the factors affecting individual interaction patterns are poorly understood. In this study, four individual-based networks were built for the endangered Chinese yew, Taxus chinensis, in a fragmented forest based on bird foraging type (swallowing and pecking networks) and habitat type (networks in a bamboo patch and an evergreen broad-leaved forest patch). Species-level network metrics (species degree and specialization, d’) were used to evaluate the effects of species traits (bird and plant traits) on species-level networks and dispersal distance for T. chinensis. It was revealed that the interaction networks between T. chinensis individuals and their bird partners were influenced by foraging type and the habitat of plant distribution. Compared to the other two networks, bird swallowing and bird–fruit networks in the evergreen broad-leaved patch habitat had higher nestedness and connectance but lower modules and specialization. Bird (body weight and wing and bill lengths) and plant traits (height, crop size, and cover) significantly affected species-level network metrics such as degree and specialization. Furthermore, seed dispersal distance was influenced by species traits and the species-level metrics of fruit–bird interaction networks. These results provide new insights into individual-based seed dispersal mutualistic networks of endangered plant species under habitat fragmentation. Moreover, these findings have relevant implications for conserving and managing individual endangered trees in increasingly disturbed ecosystems.
Impact of supervisors' research style on young biomedical scientists' capacity development as measured by REDi, a novel index of crossdisciplinarity

The challenge for medical schools in Japan is to develop research activities for innovation. This study aimed at analyzing the connection between the research output of “promising researchers” (next-generation leaders in terms of research activity) and their supervisors' past research activities to identify those factors that impact researchers' performance. Activity was analyzed from the viewpoints of productivity, coauthorship networks, and research impact using a novel index called the Research Diversity Index (REDi) that quantifies crossdisciplinarity. Research funding, which has not yet been fully utilized in correlation studies of the characteristics of authors, was also considered in this study. For the promising researchers extracted using betweenness centrality scores within coauthorship networks, there were diachronic correlations between the records of the promising researchers and those of their supervisors. Supervisor leadership as measured by the number of last-authored publications and extent of networking had a positive effect on the promising researchers productivity. Supervisors' research style of integrating knowledge from multiple fields, as measured by REDi, was negatively correlated with the publication impact of promising researchers, suggesting that REDi is useful as a novel indicator of research quality not being captured by existing indices. It was also noted that establishing an academic presence through extensive collaborations could be advantageous for obtaining research funding, especially from top-down government programs. The possible implications of this study for promoting research activities are the importance of incorporating new doctorates into research groups at an early stage and that of promoting interinstitutional, crossdisciplinary collaborations. Classification code MSC: 62P10 Applications of statistics to biology and medical sciences; meta-analysis. JEL: Z1Z10 Cultural Economics • Economic Sociology • Economic Anthropology- General.
Cumulative IgE-levels specific for respiratory allergens as biomarker to predict efficacy of anti-IgE-based treatment of severe asthma

Molecular therapies, including anti-IgE, biologicals and small molecules are increasingly used for treatment of asthma. The effectiveness of these therapies may be increased with biomarkers. Aim of this study was to assess the value of measuring cumulative IgE levels specific for respiratory allergens to increase the efficacy of anti-IgE therapy for severe bronchial asthma. One hundred and thirty seven patients with severe asthma were recruited from 2016 to 2022. Standard empirical allergy diagnosis (i.e., anamnesis, skin testing, allergen-specific IgE measurement), blood eosinophil counting, measurement of total IgE and of cumulative IgE-specific for respiratory allergens by Phadiatop™ were performed. Thirty four patients with severe allergic asthma, for whom all three diagnostic methods were performed, were then used to analyze the efficacy of anti-IgE treatment in patients stratified in two groups according to cumulative IgE levels specific for respiratory allergens determined by Phadiatop™. Group #1 patients (n = 8) had cumulative specific IgE values ≥ 0.35 and < 1.53 PAU/l while in group #2 patients (n = 26) they were ≥ 1.53 PAU/l. Treatment with Omalizumab was performed for at least 12 months. The level of asthma control (ACT questionnaire), the number of asthma exacerbations, the quality of life (AQLQ questionnaire), the need for systemic corticosteroids, and the respiratory function (FEV1) was determined by “before-after” analysis for each group, followed by a comparison of the dynamics between groups. In group 2 patients with an initial allergen-specific IgE level ≥ 1.53 kUA/L, the efficacy of Omalizumab treatment was better regarding asthma control, number of exacerbations, and quality of life than in group 1 patients. Our study provides evidence that measuring cumulative levels of IgE specific for respiratory allergens could be a useful screening method for detecting an allergic phenotype of severe asthma and may serve as biomarker to enhance the success of IgE-targeted therapy.
How facial masks alter the interaction of gaze direction, head orientation, and emotion recognition

The COVID-19 pandemic has altered the way we interact with each other: mandatory mask-wearing obscures facial information that is crucial for emotion recognition. Whereas the influence of wearing a mask on emotion recognition has been repeatedly investigated, little is known about the impact on interaction effects among emotional signals and other social signals. Therefore, the current study sought to explore how gaze direction, head orientation, and emotional expression interact with respect to emotion perception, and how these interactions are altered by wearing a face mask. In two online experiments, we presented face stimuli from the Radboud Faces Database displaying different facial expressions (anger, fear, happiness, neutral, and sadness), gaze directions (−13°, 0°, and 13°), and head orientations (−45°, 0°, and 45°) – either without (Experiment 1) or with mask (Experiment 2). Participants categorized the displayed emotional expressions. Not surprisingly, masks impaired emotion recognition. Surprisingly, without the mask, emotion recognition was unaffected by averted head orientations and only slightly affected by gaze direction. The mask strongly interfered with this ability. The mask increased the influence of head orientation and gaze direction, in particular for the emotions that were poorly recognized with mask. The results suggest that in case of uncertainty due to ambiguity or absence of signals, we seem to unconsciously factor in extraneous information.
Actions of Polish Groups and Classification Problems

We will discuss in this paper some aspects of a general program whose goal is the development of the theory of definable actions of Polish groups, the structure and classification of their orbit spaces, and the closely related study of definable equivalence relations. This work is motivated by basic foundational questions, like understanding the nature of complete classification of mathematical objects up to some notion of equivalence by invariants, and creating a mathematical framework for measuring the complexity of such classification problems. This theory, which has been growing rapidly over the last few years, is developed within the context of descriptive set theory, which provides the basic underlying concepts and methods. On the other hand, in view of the broad scope of this theory, there are natural interactions of it with other areas of mathematics, such as the theory of topological groups, topological dynamics, ergodic theory and its relationships with the theory of operator algebras, model theory, and recursion theory.
A Systematic Study Reveals Unexpected Interactions in Pre-Trained Neural Machine Translation

A significant challenge in developing translation systems for the world’s ∼7,000 languages is that very few have sufficient data for state-of-the-art techniques. Transfer learning is a promising direction for low-resource neural machine translation (NMT), but introduces many new variables which are often selected through ablation studies, costly trial-and-error, or niche expertise. When pre-training an NMT system for low-resource translation, the pre-training task is often chosen based on data abundance and similarity to the main task. Factors such as dataset sizes and similarity have typically been analysed independently in previous studies, due to the computational cost associated with systematic studies. However, these factors are not independent. We conducted a three-factor experiment to examine how language similarity, pre-training dataset size and main dataset size interacted in their effect on performance in pre-trained transformer-based low-resource NMT. We replicated the common finding that more data was beneficial in bilingual systems, but also found a statistically significant interaction between the three factors, which reduced the effectiveness of large pre-training datasets for some main task dataset sizes (p-value < 0.0018). The surprising trends identified in these interactions indicate that systematic studies of interactions may be a promising long-term direction for guiding research in low-resource neural methods.
Errors-in-Variables Problems in Financial Models

The errors-in-variables (EIV) problems in finance arise from using incorrectly measured variables or proxy variables in regression models. Errors in measuring the dependent variables are incorporated in the disturbance term and they cause no problems. However, when an independent variable is measured with error, this error appears in both the regressor variable and in the error term of the new regression model. This results in contemporaneous correlation between the regressor and the error term, and leads to a biased OLS (Ordinary Least Squares) estimator (even asymptotically) and inconsistent standard errors. The biases introduced by measurement errors can be significant and can lead to incorrect inferences. Further, when there are more than one regressor variable in the model the direction of the bias is unpredictable. The effect of measurement errors on OLS estimators is discussed extensively in several econometrics texts including Maddala (1992), and Greene (1993). A comprehensive discussion of errors-invariables model is in Fuller (1987) and a discussion in the context of econometric models is in Griliches (1985), and Chamberlain and Goldberger (1990). The errors in the regressor variable could be due to several causes. We can classify them into the following two groups: (1) measurement errors, and (2) use of proxy variables for unobservable theoretical concepts, constructs or latent variables. Measurement errors could be introduced by using estimated values in the regression model. Examples of this are the use of estimated betas as regressors in cross-sectional tests of the CAPM (Capital Asset Pricing Model), and two-pass tests of the APT (Arbitrage Pricing Theory) where estimated rather than actual factor loadings are used in the second pass tests. The second major source of errors arises from the use of proxy variables for unobservable or latent variables. An example of this in finance would be the testing of signaling models where the econometrician observes only a noisy signal of the underlying attribute that is being signaled. In this article we examine several alternative models and techniques employed in financial models to mitigate the errors-in-variables problems. Some areas in finance where errors-in-variables problems are encountered are described below:
Xerostomia – A Comprehensive Review with a Focus on Mid-Life Health

Xerostomia is defined as the subjective complaint of a dry mouth. Xerostomia is common in menopausal women owing to the hormonal changes which take place during midlife. Studies show a higher incidence of oral cancer in the postmenopausal period which substantiates the theory of estrogen deficiency in carcinogenesis. Radiotherapy in the treatment of these cancers can lead to oral dryness. Other etiological factors of xerostomia include systemic diseases commonly occurring in middle-aged individuals and xerogenic drugs. Saliva plays a pivotal role in the maintenance of oropharyngeal health and xerostomia can severely impair the quality of life. The aim of this review was to provide vital information pertaining to the etiology, signs, diagnosis, and treatment of xerostomia with an emphasis on midlife health. The articles for this review were obtained from PubMed Central, Google Scholar, EBSCO, Science Direct, Medknow, Scopus, EMBASE, Web of Science, and authorized textbooks published between 1988 and 2021.
Theatrical Expressivity of Berio’s Sequenza for Viola: Levels of Communication

Background in music psychology. Psychological approach to communicational process during performing and listening is directed towards personal perception and/or experience induced by music, as well as to the understanding roles of expression in compositional and performing processes. Performer intents to convey messages by his/her musical interpretation, but also by the nonverbal channels inducing a kind of ‘motor mimicry’ and emotional apperception. Different levels of experience and comprehension are engaged and intertwined – physical, emotional, cognitive and metaphorical. Background in music history. In requires the ‘virtuosity of knowledge’ from the performer, a virtuosity that often entails a thoroughgoing understanding of the history of instrument. Sequenza VI for viola is a work of considerable difficulty and it draws researcher’s attention to the history of the instrument, as well as to the analytical presentation of the work. In performance actions, the body of the player and the body of the instrument become a single entity. Physical theatricality is also found in aural and visual drama of Sequenza VI (Halfyard). and group of listeners on musical, cognitive, expressional and kinesthetic level. Qualitative content analysis of participants’ reports and narratives has been done. Sample is consisted of 23 participants divided into groups, on the basis of expertise: musical experts and non-musicians. The results suggest that there are patterns of musical, expressional and kinesthetic interactions viewed through the psychological and social context of communication. Music communication model during live performance of Berio’s Sequenza VI has been suggested. Implications. Berio’s music demonstrates that expression does not come necessarily from any sentimentalism: the capacity of expression is written in the human nature. The music invents on this reality which is made of time, of movements, of gestures, of multiform languages (Imberty). Knowledge could be used in music education for defining differential strategies of interaction with audience during performance.
Low-Rank and Temporal Smoothness Regularization on Value-Based Deep Reinforcement Learning

Value-based deep reinforcement learning is an efficient methodology to solve Markov Decision Process (MDP) problems, where a deep neural network work learns a value function, which represents the estimation of future total reward after taking a possible action. Also, it allows reinforcement learning agents to make decisions based on a value function. Existing research has shown that the value function in many reinforcement learning tasks has a low-rank hidden structure. Harnessing the implicit low-rank structure of value functions, we can design and test two different neural network regularization methods to emphasize and train for these properties. First, value functions can be decomposed into a low rank and a sparse component. Second, value functions should have similar outputs for states in adjacent time steps, a property of temporal smoothness. We show that the enforcement of temporal smoothness is implicitly low-rank regularization of the state-action function. As well, we empirically find that models that use these regularization methods outperformed their baseline counterparts with the best performance occurring when both regularization methods are used.
5.5. The detection of a gas-rich bar in the interacting galaxy UGC 2855

Bars fuel the proliic star formation rate in many Starburst galaxies. They provide a mechanism for feeding gas into the nuclei as well as a laboratory for the study of molecular gas that is unbound and diiuse due to tidal strain and cloud collisions. A large percentage of galaxies show a stellar bar which is, however, in most cases almost devoid of (molecular) gas, except in the central region. Thus, long gaseous bars are rare and transient phenomena. Our recent high resolution map of the galaxy UGC 2855 shows that it has a gaseous bar that is second in length only to the bar in NGC 7479 (10 kpc). The observations were done in the 12 CO J=1! 0 transition, at the Caltech six-element Owens Valley Radio Observatory (OVRO) array. UGC 2855 is an IR-bright (L FIR = 4:6 10 10 L), gas rich (Aalto et al. 1995) SBc galaxy, inclined by 60 , at a distance of 20 Mpc. Preliminary Hand K-band spectroscopy, showing Br, vibrationally excited H 2 and Fe II] lines, indicates nuclear or starburst activity. The possible interaction of UGC 2855 with UGC 2866, about 60 kpc distant, may have triggered the development of its pronounced bar. Our map (Fig. 1 a) shows the continuous CO emission throughout the bar, which has a projected length of 8 kpc. If we assume a 'standard' I(CO)-toN (H 2) conversion factor of X = 2:5 10 20 cm ? 2 /K kms ? 1 , the H 2 mass in the bar is 2:3 10 9 M. However, a diiuse gas component which is neither virially bound nor very optically thick in the CO 1 ! 0 transition is likely to exist. Thus, the standard X
Automatic Construction of an Annotated Corpus with Implicit Aspects

Aspect-based sentiment analysis (ABSA) is a task that involves classifying the polarity of aspects of the products or services described in users’ reviews. Most previous work on ABSA has focused on explicit aspects, which appear as explicit words or phrases in the sentences of the review. However, users often express their opinions toward the aspects indirectly or implicitly, in which case the specific name of an aspect does not appear in the review. The current datasets used for ABSA are mainly annotated with explicit aspects. This paper proposes a novel method for constructing a corpus that is automatically annotated with implicit aspects. The main idea is that sentences containing explicit and implicit aspects share a similar context. First, labeled sentences with explicit aspects and unlabeled sentences that include implicit aspects are collected. Next, clustering is performed on these sentences so that similar sentences are merged into the same cluster. Finally, the explicit aspects are propagated to the unlabeled sentences in the same cluster, in order to construct a labeled dataset containing implicit aspects. The results of our experiments on mobile phone reviews show that our method of identifying the labels of implicit aspects achieves a maximum accuracy of 82%.
Neuropathic-like Pain Symptoms and Their Association with Muscle Strength in Patients with Chronic Musculoskeletal Pain

The relationship between sarcopenia and pain remains unclear; thus, this study evaluated whether muscle strength is independently associated with neuropathic-like pain symptoms in patients with chronic musculoskeletal pain. A cut-off score of painDETECT ≥13 was used to indicate a possible neuropathic component. Handgrip strength was measured, and muscle mass was estimated. A total of 2599 patients, including 439 patients who reported neuropathic-like pain symptoms (16.9%), were included for analysis. Handgrip strength was significantly lower in patients experiencing neuropathic-like pain symptoms (23.23 ± 10.57 vs. 24.82 ± 10.43 kg, p < 0.001), and this result was chiefly found in female patients. However, there was no difference in estimated muscle mass. Shorter duration of pain, opioid usage, pain in lower limbs, sleep disturbance, and lower handgrip strength were significantly associated with neuropathic-like pain symptoms. In patients with handgrip strength below the reference values by sex, experiencing radiating pain and at least moderate sensory symptoms by light touch and thermal stimulation were more frequently reported. In conclusion, lower handgrip strength appeared to be an independent factor associated with symptoms suggestive of neuropathic pain in this population. Interventional studies are required to determine whether improvement in muscle strength can reduce the neuropathic pain component in chronic musculoskeletal pain.