Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effect of Temperature on the Vocal Activity of Tropical Anurans: A Comparison of High and Low-elevation Species

The objective of this work was to determine whether behavioral adjustments reduce ex- posure to cold during activity in high-elevation anurans. I studied the daily patterns of vocal activity of two populations (2900 and 3500 m) of high-elevation species in the Colombian Andes (Colostethus sub- punctatus, Eleutherodactylus bogotensis, and Hyla labialis), and compared them with those of some low- elevation Panamanian congeners (C. talamancae, C. flotator, E. diastema, H. ebraccata, and H. microcephala). I did not find interpopulational differences in the patterns of vocal activity that could be interpreted as a response to cold in high-elevation species. In contrast, interspecific differences in the patterns of activity may relate to the cold and variable temperatures experienced by high-elevation frogs. In high-elevation Colostethus vocal activity and environmental temperature are positively correlated, whereas low-elevation congenerics reduce activity when environmental temperatures are highest. Eleutherodactylus bogotensis exhibits maximum vocal activity after dusk, when temperatures are not yet very cold. This pattern contrasts with the more extended nocturnal vocal activity of several lowland congeneric species. The species of Hyla exhibited similar diel activity patterns, which may relate to the moderate temperatures of the high- elevation ponds, calling habitat of Hyla labialis. High-elevation species appear to use temporal or spatial thermal niches that reduce exposure to cold during activity. In spite of these behaviors, substantial mod- ifications of the thermal physiology appear necessary to enhance the capacity for vocal activity at low
Senescent mesenchymal stem/stromal cells and restoring their cellular functions

Mesenchymal stem/stromal cells (MSCs) have various properties that make them promising candidates for stem cell-based therapies in clinical settings. These include self-renewal, multilineage differentiation, and immunoregulation. However, recent studies have confirmed that aging is a vital factor that limits their function and therapeutic properties as standardized clinical products. Understanding the features of senescence and exploration of cell rejuvenation methods are necessary to develop effective strategies that can overcome the shortage and instability of MSCs. This review will summarize the current knowledge on characteristics and functional changes of aged MSCs. Additionally, it will highlight cell rejuvenation strategies such as molecular regulation, non-coding RNA modifications, and microenvironment controls that may enhance the therapeutic potential of MSCs in clinical settings.
Social work practice education and training during the pandemic: Disruptions and discoveries

The COVID-19 pandemic evoked a disruption to social work (SW) practice education and this brief note describes discoveries made in teaching SW practice virtually. One example is Virtual Practice Fridays, adapted to build SW practice competencies online, and another example is a re-designed course on cross-cultural SW practice using simulation-based learning.
The coronavirus disease (COVID‐19) pandemic: simulation‐based assessment of outbreak responses and postpeak strategies

Abstract It is critical to understand the impact of distinct interventions on the ongoing coronavirus disease pandemic. I develop a behavioral dynamic epidemic model for multifaceted policy analysis comprising endogenous virus transmission (from severe or mild/asymptomatic cases), social contacts, and case testing and reporting. Calibration of the system dynamics model to the ongoing outbreak (31 December 2019–15 May 2020) using multiple time series data (reported cases and deaths, performed tests, and social interaction proxies) from six countries (South Korea, Germany, Italy, France, Sweden, and the United States) informs an explanatory analysis of outbreak responses and postpeak strategies. Specifically, I demonstrate, first, how timing and efforts of testing‐capacity expansion and social‐contact reduction interplay to affect outbreak dynamics and can explain a large share of cross‐country variation in outbreak pathways. Second, absent at‐scale availability of pharmaceutical solutions, postpeak social contacts must remain well below prepandemic values. Third, proactive (targeted) interventions, when complementing general deconfinement readiness, can considerably increase admissible postpeak social contacts. © 2020 System Dynamics Society
A preliminary exploration of acute intracranial pressure-cerebrospinal fluid production relationships in experimental hydrocephalus

CONTEXT: By occluding the fourth ventricle simultaneously obtaining telemetric data on intracranial pressure (ICP) and cerebrospinal fluid (CSF) production, the authors of this study investigate a variety of physiologic parameters in cases of experimental hydrocephalus. AIMS: The aim of this study is to provide a new context on the disrupted homeostasis in hydrocephalus and guide toward improved treatment based on multiple physiological parameters. MATERIALS AND METHODS: Hydrocephalus was induced in ten 21-day-old Sprague–Dawley rats by blocking the flow of CSF to the fourth ventricle with kaolin. Ten days post induction, when physical signs of ventriculomegaly reached Evan's ratio (ER) of ≥0.46, CSF flow and ICP were measured while manipulating body position (0°, 45°, 90°) and heart rate. RESULTS: In hydrocephalic animals (ER ≥0.46), we found a near-steady average acute ICP (13.638 ± 2.331) compared to age-matched controls (ER <0.30) (13.068 ± 8.781), whose ICP fluctuated with the position. Hydrocephalic and controls exhibited an insignificant degree of parabolic shifts in CSF production when body position was changed from prone to 90° and again when moved back to the prone position, a trend more noteworthy in controls (P = 0.1322 and 0.2772). A Pearson's Correlation found CSF production and ICP to be correlated at baseline 0° posture (P = 0.05) in the control group, but not the hydrocephalic group. Weight appeared to play a role when animals were held at 90°. No significant changes in ICP or CSF flow patterns were observed when the heart rate was increased within either group. CONCLUSIONS: These preliminary findings suggest that our standard assumptions of posture-dependent changes in ICP created using data from physiologic data may be inaccurate in the hydrocephalic patient, and thus describe a need to further explore these relationships.
Effect of weather parameters on karnal bunt disease in wheat in Karnal region of Haryana.

Data pertaining to average infection (%) of karnal bunt disease of wheat and meteorological parameters of 1st to 12th standard meteorological week (1st January to 25th March) for 25 crop seasons (1981-82 to 2004-05) of Karnal station were correlated to study the effect of weather parameters on Karnal bunt disease in wheat for most sensitive crop growth period corresponding to ear emergence and subsequent growth stages. The frequency of disease intensity and weather parameters indicated that when the maximum temperature exceeded normal accompanied by little or poor rainfall during 6 to 8th SMW, the disease intensity was low. Rainfall during the 3rd week of January showed strong relationship indicating favourable role in the formation and further multiplication of secondary spordia. However, during 9th SMW, maximum temperature, relative humidity, rainfall and sunshine duration showed considerably high correlations, whereas remaining parameters had weak correlation coefficients. Meteorological parameters during 6 to 12 SMW satisfactorily explained the occurrence of Karnal bunt disease with R 2 values of 0.84 that indicated only 16% variation of disease remained unaccounted.
A statistical study of plasmoids associated with a post-CME current sheet

We investigate the properties of plasmoids observed in the current sheet formed after an X-8.3 flare followed by a fast CME eruption on September 10, 2017 using Extreme Ultraviolet (EUV) and white-light coronagraph images. The main aim is to understand the evolution of plasmoids at different spatio-temporal scales using existing ground- and space-based instruments. We identified the plasmoids in current sheet observed in the successive images of {\it Atmospheric Imaging Assembly} (AIA) and white-light coronagraphs, K-Cor and LASCO/C2. We found that the current sheet is accompanied by several plasmoids moving upwards and downwards. Our analysis showed that the downward and upward moving plasmoids have average width of 5.92 Mm and 5.65 Mm, respectively in the AIA field of view (FOV). However, upward moving plasmoids have average width of 64 Mm in the K-Cor which evolves to a mean width of 510 Mm in the LASCO/C2 FOV. Upon tracking the plasmoids in successive images, we observe that downward and upward moving plasmoids have average speeds of $\sim$272 km s$^{-1}$ and $\sim$191 km s$^{-1}$ respectively in the EUV passbands. We note that the plasmoids become super-Alfvenic when they reach at LASCO FOV. Furthermore, we estimate that the null-point of the current sheet at $\approx$ 1.15 R$_\odot$ where bidirectional plasmoid motion is observed. We study the width distribution of plasmoids formed and notice that it is governed by a power law with a power index of -1.12. Unlike previous studies there is no difference in trend for small and large scale plasmoids. The presence of accelerating plasmoids near the neutral point indicates a longer diffusion region as predicted by MHD models.
Predicting the terminal velocity of dipterocarp fruit

We measured the terminal velocity of helicopter‐like fruit from the Dipterocarpaceae family and present a model predicting the terminal velocities for all dipterocarp species in the Malesiana region. A ballistic model of seed dispersal using the observed terminal velocities predicted dispersal distances of 17–77 m under normal atmospheric conditions. These data are of applied use in parametizing models of species coexistence, forest regeneration and habitat connectivity in Southeast Asian tropical forests.
Analogies minus analogy test: measuring regularities in word embeddings

Vector space models of words have long been claimed to capture linguistic regularities as simple vector translations, but problems have been raised with this claim. We decompose and empirically analyze the classic arithmetic word analogy test, to motivate two new metrics that address the issues with the standard test, and which distinguish between class-wise offset concentration (similar directions between pairs of words drawn from different broad classes, such as France-London, China-Ottawa,.) and pairing consistency (the existence of a regular transformation between correctly-matched pairs such as France:Paris::China:Beijing). We show that, while the standard analogy test is flawed, several popular word embeddings do nevertheless encode linguistic regularities.
Is the Best Better? Bayesian Statistical Model Comparison for Natural Language Processing

Recent work raises concerns about the use of standard splits to compare natural language processing models. We propose a Bayesian statistical model comparison technique which uses k-fold cross-validation across multiple data sets to estimate the likelihood that one model will outperform the other, or that the two will produce practically equivalent results. We use this technique to rank six English part-of-speech taggers across two data sets and three evaluation metrics.
Mysterious Globular Cluster System of the Peculiar Massive Galaxy M85

We present a study on the stellar population and kinematics of globular clusters (GCs) in the peculiar galaxy M85. We obtain optical spectra of 89 GCs at 8 kpc < R < 160 kpc using the MMT/Hectospec. We divide them into three groups, blue/green/red GCs (B/G/RGCs), with their (g − i)0 colors. All GC subpopulations have mean ages of about 10 Gyr, but showing differences in metallicities. The BGCs and RGCs are the most metal-poor ([Z/H] ∼ −1.49) and metal-rich ([Z/H] ∼ −0.45), respectively, and the GGCs are in between. We find that the inner GC system exhibits a strong overall rotation that is entirely due to a disklike rotation of the RGC system. The BGC system shows little rotation. The GGCs show kinematic properties clearly distinct among the GC subpopulations, having higher mean velocities than the BGCs and RGCs and being aligned along the major axis of M85. This implies that the GGCs have an origin different from the other GC subpopulations. The rotation-corrected velocity dispersion of the RGC system is much lower than that of the BGC system, indicating the truncation of the red halo of M85. The BGCs have a flat velocity dispersion profile out to R = 67 kpc, reflecting the dark matter extent of M85. Using the velocity dispersion of the BGC system, we estimate the dynamical mass of M85 to be 3.8 × 1012 M⊙. We infer that M85 has undergone merging events lately, resulting in the peculiar kinematics of the GC system.
Covid-19 and Diabetes: A Complex Bidirectional Relationship

Covid-19 is a recently-emerged infectious disease caused by the novel severe acute respiratory syndrome coronavirus SARS-CoV2. SARS-CoV2 differs from previous coronavirus infections (SARS and MERS) due to its high infectivity (reproduction value, R0, typically 2–4) and pre- or asymptomatic transmission, properties that have contributed to the current global Covid-19 pandemic. Identified risk factors for disease severity and death from SARS-Cov2 infection include older age, male sex, diabetes, obesity and hypertension. The reasons for these associations are still largely obscure. Evidence is also emerging that SARS-CoV2 infection exacerbates the underlying pathophysiology of hyperglycemia in people with diabetes. Here, we discuss potential mechanisms through which diabetes may affect the risk of more severe outcomes in Covid-19 and, additionally, how diabetic emergencies and longer term pathology may be aggravated by infection with the virus. We consider roles for the immune system, the observed phenomenon of microangiopathy in severe Covid-19 infection and the potential for direct viral toxicity on metabolically-relevant tissues including pancreatic beta cells and targets of insulin action.
Effects of Rearing Conditions and Sex on Cecal Microbiota in Ducks

The intestinal microbiome influences the health of animals. However, little is known about the impact of indoor conditions and sex on intestinal microbiome diversity and composition in ducks. The present study aimed to investigate differences in the cecal microbiome between male and female ducks reared on the floor (PY group) or in cages (LY group). We also determined the relationships between cecal microbiota composition and slaughter traits, and the expression levels of mucosal and intestinal structural genes in ducks. There was a slight difference in slaughter traits among the groups, with cecum weight being significantly lighter in the LY compared with the PY group, especially in females (p < 0.05). Analysis of the alpha diversity of the cecal microbiota between males and females in the LY and PY groups showed that LY males had significantly lower diversity and richness. Beta diversity analysis demonstrated differences in the microbiota composition in relation to rearing conditions, and a significant difference between the sexes in the PY groups. The dominant bacterial phyla in duck cecum were Bacteroidetes, Firmicutes, Proteobacteria, and Fusobacteria. The relative abundances of the most common bacteria revealed that the intestinal microbiota diversity and composition were affected by both feeding conditions and sex. Several bacterial genera were detected differentially among the groups. These genera were correlated with slaughter traits and expression levels of mucosal and cecal structural genes in ducks. In conclusion, rearing conditions, sex, and associated changes in the cecal microbiota are thus associated with gut barrier functions in ducks.
Development of Chinese Junior High School Students’ Creative Potential: Within-Person and Between-Person Effects of Student–Student Support and Need for Cognition

A longitudinal study was conducted to examine the developmental trend of creative potential in Chinese junior high school students and the within-person and between-person effects of student–student support and need for cognition. Two hundred and fourteen Chinese junior high school students participated in the present study (mean age = 13.29 years, SD = 0.49 years, 116 boys). Student–student support, need for cognition, and creative potential were measured once per year for 3 years. Longitudinal multilevel models indicated that (1) Chinese junior high school students’ creative potential showed a downward trend from grades 7 to 9; (2) at the within-person level, time-varying student–student support positively predicted time-varying creative potential; (3) at the within-person level, time-varying need for cognition moderated the positive link between time-varying student–student support and time-varying creative potential; and (4) at the between-person level, no support was found for the links between student–student support, need for cognition, and creative potential. Specifically, average levels of student–student support neither significantly predicted initial levels and developmental rates of creative potential nor moderated the links between average levels of student–student and initial levels and developmental rates of creative potential. The findings highlight that at the within-person and between-person levels, student–student support and need for cognition have differential influences on Chinese junior school students’ creative potential.
Conversational Intelligence Challenge: Accelerating Research with Crowd Science and Open Source

Development of conversational systems is one of the most challenging tasks in natural language processing, and it is especially hard in the case of open-domain dialogue. The main factors that hinder progress in this area are lack of training data and difficulty of automatic evaluation. Thus, to reliably evaluate the quality of such models, one needs to resort to time-consuming and expensive human evaluation. We tackle these problems by organizing the Conversational Intelligence Challenge (ConvAI) — open competition of dialogue systems. Our goals are threefold: to work out a good design for human evaluation of open-domain dialogue, to grow open-source code base for conversational systems, and to harvest and publish new datasets. Over the course of ConvAI1 and ConvAI2 competitions, we developed a framework for evaluation of chatbots in messaging platforms and used it to evaluate over 30 dialogue systems in two conversational tasks — discussion of short text snippets from Wikipedia and personalized small talk. These large-scale evaluation experiments were performed by recruiting volunteers as well as paid workers. As a result, we succeeded in collecting a dataset of around 5,000 long meaningful human-to-bot dialogues and got many insights into the organization of human evaluation. This dataset can be used to train an automatic evaluation model or to improve the quality of dialogue systems. Our analysis of ConvAI1 and ConvAI2 competitions shows that the future work in this area should be centered around the more active participation of volunteers in the assessment of dialogue systems. To achieve that, we plan to make the evaluation setup more engaging.
Learning to Represent Image and Text with Denotation Graphs

Learning to fuse vision and language information and representing them is an important research problem with many applications. Recent progresses have leveraged the ideas of pre-training (from language modeling) and attention layers in Transformers to learn representation from datasets containing images aligned with linguistic expressions that describe the images. In this paper, we propose learning representations from a set of implied, visually grounded expressions between image and text, automatically mined from those datasets. In particular, we use denotation graphs to represent how specific concepts (such as sentences describing images) can be linked to abstract and generic concepts (such as short phrases) that are also visually grounded. This type of generic-to-specific relations can be discovered using linguistic analysis tools. We propose methods to incorporate such relations into learning representation. We show that state-of-the-art multimodal learning models can be further improved by leveraging automatically harvested structural relations. The representations lead to stronger empirical results on downstream tasks of cross-modal image retrieval, referring expression, and compositional attribute-object recognition. Both our codes and the extracted denotation graphs on the Flickr30K and the COCO datasets are publically available on this https URL.
Scalar dark matter candidates revisited

We revisit the possibility of light scalar dark matter, in the MeV to GeV mass bracket and coupled to electrons through fermion or vector mediators, in light of significant experimental and observational advances that probe new physics below the GeV-scale. We establish new limits from electron colliders and fixed-target beams, and derive the strength of loop-induced processes that are probed by precision physics, among other laboratory probes. In addition, we compute the cooling bound from SN1987A, consider self-scattering, structure formation, and cosmological constraints as well as the limits from dark matter-electron scattering in direct detection experiments. We then show that the combination of constraints largely excludes the possibility that the galactic annihilation of these particles may explain the long-standing INTEGRAL excess of 511 keV photons as observed in the galactic bulge. As caveat to these conclusions we identify the resonant annihilation regime where the vector mediator goes nearly on-shell.
Galileo at SemEval-2020 Task 12: Multi-lingual Learning for Offensive Language Identification Using Pre-trained Language Models

This paper describes Galileo’s performance in SemEval-2020 Task 12 on detecting and categorizing offensive language in social media. For Offensive Language Identification, we proposed a multi-lingual method using Pre-trained Language Models, ERNIE and XLM-R. For offensive language categorization, we proposed a knowledge distillation method trained on soft labels generated by several supervised models. Our team participated in all three sub-tasks. In Sub-task A - Offensive Language Identification, we ranked first in terms of average F1 scores in all languages. We are also the only team which ranked among the top three across all languages. We also took the first place in Sub-task B - Automatic Categorization of Offense Types and Sub-task C - Offence Target Identification.