Miracle-dz/UniFAR-FTUs huggingface dataset:
”What color are the fish’s scales?” Exploring parents’ and children’s natural interactions with a child-friendly virtual agent during storybook reading

With increasing integration of AI-powered agents into educational technologies available to families with young children, the landscape of how caregivers and children interact together or separately with these technologies is underexplored. Understanding the nature of these interactions could critically inform the design of educational technologies to facilitate children’s learning, research methods for use in future studies involving adult-child dyad technology use, and policy decisions regarding the use of educational technology with young children. In this study, we explored the natural interactions among parent, child, and a child-friendly virtual rabbit character named Floppy. Floppy is a virtual agent in a Smart Speaker app that models adult dialogic reading and conversational strategies for use with young children (ages 4-6 years). Over a span of four to six weeks, 18 parent-child dyads read The Rainbow Fish, a classic children’s book by Marcus Pfister, during 24 at-home, remote sessions with Floppy. Of the 189 conversations generated during this time, 125 were initiated by a prompt spoken by Floppy. Though there were some variations among the dyads, across all conversations, parent-driven interactions made up 63% of the conversations, followed by child-driven conversations at 15.3%, Floppy-driven at 14.3%, and Floppy-and-parent-driven at 7.4%. A select few parents were more comfortable having their children interact directly with Floppy, whereas the majority of the parents would direct children’s attention back to themselves or help children understand the questions by repeating or reformulating Floppy’s prompts. More than half of the parents reported that their children formed emotional connections with the virtual character. These findings point to a need to clearly define the role of virtual agents, even ones with limited AI, in this type of triadic interaction.
Targeted Policy Recommendations using Outcome-aware Clustering

Policy recommendations using observational data typically rely on estimating an econometric model on a sample of observations drawn from an entire population. However, different policy actions could potentially be optimal for different subgroups of a population. In this paper, we propose outcome-aware clustering, a new methodology to segment a population into different clusters and derive cluster-level policy recommendations. Outcome-aware clustering differs from conventional clustering algorithms across two basic dimensions. First, given a specific outcome of interest, outcome-aware clustering segments the population based on selecting a small set of features that closely relate with the outcome variable. Second, the clustering algorithm aims to generate near-homogeneous clusters based on a combination of cluster size-balancing constraints, inter and intra-cluster distances in the reduced feature space. We generate targeted policy recommendations for each outcome-aware cluster based on a standard multivariate regression of a condensed set of actionable policy features (which may partially overlap or differ from the features used for segmentation) from the observational data. We implement our outcome-aware clustering method on the Living Standards Measurement Study - Integrated Surveys on Agriculture (LSMS-ISA) dataset to generate targeted policy recommendations for improving farmers outcomes in sub-Saharan Africa. Based on a detailed analysis of the LSMS-ISA, we derive outcome-aware clusters of farmer populations across three sub-Saharan African countries and show that the targeted policy recommendations at the cluster level significantly differ from policies that are generated at the population level.
Children’s Impressions of Early Spelling Assessment through Handwriting on Tablets vs. Paper-based Method

Traditional paper-based children’s spelling assessments were hampered due to Covid-19 because existing technologies did not provide strategic signals to teachers, such as the child’s handwriting direction and how they read what they write. Our project emerged as a novel method to assess children’s spelling by touchscreens in this context. Hence, this paper aims to extend community knowledge concerning children’s experience and perception of handwriting spelling on tablet devices. The experiment consisted in presenting three handwriting methods (paper and pencil, finger and pen writing) and was conducted with eight Brazilian children between 4.5 and 7 years old. In addition to observation, in our experimental protocol we adopted the Fun Sorter, Again-Again Table, and the Smileyometer as evaluation tools. Our results show children were excited about handwriting using a touch pen on the tablet. Most of them even revealed they prefer the pen tablet mode to the traditional paper and pencil mode. However, the majority of children did not feel comfortable writing by finger, and it required more time than other methods. Furthermore, we observed child’s handwriting using finger looks different when compared to paper and pencil, while the tracing using a touch pen is similar to the registration produced on paper.
Design and control of electric vehicle using HOMER

Abstract The rising demand of EVs has been increasing the burden on the power grid in the southern Asian countries because of the high energy consumption of the same. Problems like rapid energy consumption, effect on environmental and energy shortages leads to the investigation of other energy resources that can be easily accessible and are environmentally friendly. The country has great potential when it comes to renewable resources like biomass and biogas. In this paper, the design of a 20kW electric vehicle charging station utilizing a renewable resource (biomass) is proposed. A comprehensive efficiency studies has also been done of HOMER model and MATLAB SIMULINK. Results indicate that the proposed ELECTRIC VEHICLE CHARGING STATIONS saves monthly $ 16.31 and $ 29.46, respectively, on lightweight electric bicycles and auto-rickshaw models in southern Asian countries compared to grid charging.
Characterizing the Social Ties between Black and Tech Communities on Twitter

The slow increasing rate of representation of women and other minorities, particularly in Information Technology (IT) companies, suggests that the current recruiting strategies for attracting underrepresented minorities (URMs) may not be effective. While much can be done to improve hiring strategies, little attention has been paid to how job seekers’ perceptions (positive or otherwise) of a company may affect its attractiveness. For instance, newer generations of professionals are more likely to avoid companies that are not committed to diversity causes. Studies have in fact shown that job seekers increasingly rely on social media to inform themselves about targeted companies. In this paper, we investigate the “social-mediascape” of Black Tech communities and IT companies, namely, the spaces of interactions and communications and their influences on individuals’ perceptions of particular companies. To this end, we look into the Twitter Black and Tech communities in Brazil and in the United States. We rely on “Twitter lists” that are curated by the users of the platform and effective in capturing topical homophily. A research challenge in itself, we provide the first large-scale compositions of Black Tech communities and their connection to IT companies based on followership data on Twitter. After analyzing these compositions, we can then create perceptual maps between communities and IT companies. Our results suggest that there is a stronger correlation between Black activism and technology in the US context. For the Brazilian context, we found a stronger correlation between the Black Tech and general Software Developer users communities suggesting that both racial activism and technology are important topics to attract the Brazilian Black Tech community’s interests.
Children Learning and Parents Earning: Exploring the Average and Heterogeneous Effects of Head Start on Parental Earnings

Abstract Head Start (HS) is our nation’s largest two-generation program that provides early education services to children and a variety of family support services that may promote economic wellbeing. Yet, no prior research has documented or described the effects of HS on parental earnings. We explore whether the program promotes parental earnings on average, investigate for whom these effects are greatest, evaluate the extent to which earnings impacts vary across HS sites, and identify which characteristics of centers associate with cross-site variation. We find that HS does not improve earnings overall. However, the program does increase parental earnings in a younger program cohort two and three years after random assignment. These effects are larger for single parents and those who are initially employed or in school. Earnings effects are typically homogenous across sites, although we do observe increasing variation over time that reaches statistical significance four years after random assignment. We are generally unable to explain this variation using measures of what HS sites do or provide apart from the economic wellbeing of the families they serve.
Numerical Verification Method of Solutions for Elliptic Variational Inequalities

In this chapter, we propose numerical techniques which enable us to verify the existence of solutions for the free boundary problems governed by two kinds of elliptic variational inequalities. Based upon the finite element approximations and explicit a priori error estimates for some elliptic variational inequalities, we present effective verification procedures that, through numerical computation, generat a set which includes exact solutions. We describe a survey of the previous works as well as show newly obtained results up to now.
Bacterial profiles of the human placenta from term and preterm deliveries

Whether the human placenta is a sterile organ is under debate. Yet, infection of the amniotic cavity, including the placenta, is causally linked to preterm birth. This study compares the bacterial profiles of term and preterm placentas through culture and 16S rRNA gene sequencing of the amnion, amnion-chorion interface, subchorion, villous tree, and basal plate, while accounting for patient identity, mode of delivery, presence/absence of labor, and potential background DNA contamination. As no evidence of a placental microbiota in term pregnancy was found, these placentas were considered as controls. Placentas from preterm birth cases were more likely to yield bacterial cultures, and their bacterial DNA profiles were less rich than those of term controls, suggesting the predominance of only a few bacteria. Nevertheless, the bacterial DNA profiles of placentas from preterm cases and term controls were not consistently different. The placentas from preterm cases may often have a microbiota but the bacteria constituting these communities varied among the women. Mode of delivery had a pronounced effect on the bacterial profiles of all sampled levels of the placenta. Specifically, the bacterial DNA profiles of vaginally delivered placentas had higher relative abundances of Finegoldia, Gardnerella, Peptoniphilus, and Prevotella (each a common resident of the vaginal microbiota) than the profiles of cesarean-delivered placentas. Collectively, these data indicate that there is a not a placental microbiota in normal term pregnancy, and that although the placentas of some preterm cases were populated by bacteria, the identities of these bacteria varied among women delivering preterm. IMPORTANCE If a placental microbiota exists, then current understanding of the roles of microorganisms in pregnancy outcomes need to be reconsidered. For instance, we will need to determine if a placental microbiota is beneficial to pregnancy outcome by excluding potential pathogens from colonizing the placenta and/or effectively priming the fetal immune system, and furthermore which characteristics of the placental microbiota preclude versus promote placental infection, which can result in pregnancy complications such as preterm birth. Our findings here are consistent with prior investigations that have reported that there is not a placental microbiota in typical human pregnancies. Yet, bacteria can be detected in placentas from preterm deliveries. The principal source of microorganisms invading the amniotic cavity, including the placenta, is the vaginal microbiota. Focus should be on elucidating the metabolic and/or virulence characteristics of the subset of bacteria within the vaginal microbiota that commonly invade the amniotic cavity, resulting in infection.
Waterlogging Stress Induces Antioxidant Defense Responses, Aerenchyma Formation and Alters Metabolisms of Banana Plants

Flooding caused or exacerbated by climate change has threatened plant growth and food production worldwide. The lack of knowledge on how crops respond and adapt to flooding stress imposes a major barrier to enhancing their productivity. Hence, understanding the flooding-responsive mechanisms of crops is indispensable for developing new flooding-tolerant varieties. Here, we examined the banana (Musa acuminata cv. Berangan) responses to soil waterlogging for 1, 3, 5, 7, 14, and 24 days. After waterlogging stress, banana root samples were analyzed for their molecular and biochemical changes. We found that waterlogging treatment induced the formation of adventitious roots and aerenchyma with conspicuous gas spaces. In addition, the antioxidant activities, hydrogen peroxide and malondialdehyde contents of the waterlogged bananas increased in response to waterlogging stress. To assess the initial response of bananas toward waterlogging stress, we analyzed the transcriptome changes of banana roots. A total of 3,508 unigenes were differentially expressed under 1-day waterlogging conditions. These unigenes comprise abiotic stress-related transcription factors, such as ethylene response factors, basic helix-loop-helix, myeloblastosis, plant signal transduction, and carbohydrate metabolisms. The findings of the study provide insight into the complex molecular events of bananas in response to waterlogging stress, which could later help develop waterlogging resilient crops for the future climate.
Differential roles of delta and theta oscillations in understanding semantic gist during natural audiovisual speech perception: Functional and anatomical evidence

Understanding the main topic of naturalistic speech in a multi-speaker environment is demanding though the availability of visual speech can be beneficial for speech comprehension. Recent studies provided evidence that low-frequency brain rhythms play an important role in the processing of acoustic speech features. However, at present, the neural dynamics of brain rhythms implementing a higher-order semantic system during naturalistic audiovisual speech perception is unknown. Here we investigated information processing carried by low-frequency oscillations in delta and theta bands for audiovisual speech integration for high-level semantic gist processing using a representational interaction approach. By manipulating the degree of high-level semantic content (speech chunks with high versus low topic probability) using Latent Dirichlet Allocation (LDA) topic modelling algorithm and complexity of speaker environment (single versus multi-speaker), we first found that delta and theta phase exert distinctive roles in high-level semantic processing where delta phase represents auditory and visual inputs synergistically whereas theta band does so redundantly. Next, we show both forms of representational interaction are observed to be greater for speech with low semantic gist, supported by speech comprehension and white matter tractography. Furthermore, we show that the delta phase-specific synergistic interaction in the right auditory, temporal, and inferior frontal areas is sensitive to the speaker environment, whereas theta band activity showing redundant representations is sensitive to semantic content. Our results shed new light on dynamic neural mechanisms of implementing higher-order semantic systems through representational interactions between audiovisual speech information and differential roles of delta and theta bands depending on the speaker environment.
Within-host evolutionary dynamics and tissue compartmentalization during acute SARS-CoV-2 infection

ABSTRACT The global evolution of SARS-CoV-2 depends in part upon the evolutionary dynamics within individual hosts with varying immune histories. To characterize the within-host evolution of acute SARS-CoV-2 infection, we sequenced saliva and nasal samples collected daily from vaccinated and unvaccinated individuals early during infection. We show that longitudinal sampling facilitates high-confidence genetic variant detection and reveals evolutionary dynamics missed by less-frequent sampling strategies. Within-host dynamics in both unvaccinated and vaccinated individuals appeared largely stochastic; however, in rare cases, minor genetic variants emerged to frequencies sufficient for forward transmission. Finally, we detected significant genetic compartmentalization of viral variants between saliva and nasal swab sample sites in many individuals. Altogether, these data provide a high-resolution profile of within-host SARS-CoV-2 evolutionary dynamics. IMPORTANCE We detail the within-host evolutionary dynamics of SARS-CoV-2 during acute infection in 31 individuals using daily longitudinal sampling. We characterized patterns of mutational accumulation for unvaccinated and vaccinated individuals, and observed that temporal variant dynamics in both groups were largely stochastic. Comparison of paired nasal and saliva samples also revealed significant genetic compartmentalization between tissue environments in multiple individuals. Our results demonstrate how selection, genetic drift, and spatial compartmentalization all play important roles in shaping the within-host evolution of SARS-CoV-2 populations during acute infection. We detail the within-host evolutionary dynamics of SARS-CoV-2 during acute infection in 31 individuals using daily longitudinal sampling. We characterized patterns of mutational accumulation for unvaccinated and vaccinated individuals, and observed that temporal variant dynamics in both groups were largely stochastic. Comparison of paired nasal and saliva samples also revealed significant genetic compartmentalization between tissue environments in multiple individuals. Our results demonstrate how selection, genetic drift, and spatial compartmentalization all play important roles in shaping the within-host evolution of SARS-CoV-2 populations during acute infection.
Autophagy degrades myelin proteins and is essential for maintaining CNS myelin homeostasis

(Macro)autophagy comprises a major lysosome-dependent degradation mechanism which engulfs, removes and recycles unwanted cytoplasmic material, including damaged organelles and toxic protein aggregates. Although a few studies implicate autophagy in CNS demyelinating pathologies, its role, particularly in mature oligodendrocytes and CNS myelin, remains poorly studied. Here, using both pharmacological and genetic inhibition of the autophagic machinery, we provide evidence that autophagy is an essential mechanism for oligodendrocyte maturation in vitro. Our study reveals that two core myelin proteins, namely proteolipid protein (PLP) and myelin basic protein (MBP) are incorporated into autophagosomes in oligodendrocytes, resulting in their degradation. Furthermore, we ablated atg5, a core gene of the autophagic machinery, specifically in myelinating glial cells in vivo by tamoxifen administration (plp-CreERT2; atg5F/F) and showed that myelin maintenance is perturbed, leading to PLP accumulation. Significant morphological defects in myelin membrane such as decompaction accompanied with increased axonal degeneration are observed. As a result, the mice exhibit behavioral deficits. In summary, our data highlight that the maintenance of adult myelin homeostasis in the CNS requires the involvement of a fully functional autophagic machinery.
MicroProteins miP1b/BBX30 and miP1a/BBX31 form a positive feedback loop with ABI5 to retard seedling establishment

In plants the switch to autotrophic growth involves germination followed by post-germination seedling establishment. When environmental conditions are not favorable, the stress hormone Abscisic Acid (ABA) signals plants to postpone seedling establishment by inducing the expression of the transcription factor ABI5. The levels of ABI5 determine the efficiency of the ABA mediated post-germination developmental growth arrest. The molecular mechanisms regulating the stability and activity of ABI5 during the transition to light are less known. We found that two microProteins miP1a/ BBX31 and miP1b/BBX30 alongwith ABI5 inhibit post-germination seedling establishment in a partially interdependent manner. MicroProteins are single-domain proteins that interact with multi-domain proteins to modulate their post-translational activity. miP1a/ BBX31 and miP1b/BBX30 physically interact with ABI5 to stabilize it and promote its binding to promoters of downstream genes in light. ABI5 reciprocally induces the expression of BBX30 and BBX31 by directly binding to their promoter. ABI5 and the two microProteins thereby form a positive feedback loop to promote ABA-mediated developmental arrest of seedlings. Our study highlights the functional versatility of microProteins which can act as regulators of crucial developmental transitions in plants as well as other eukaryotes.
Psychophysics with R: The R Package MixedPsy

Psychophysical methods are widely used in neuroscience to investigate the quantitative relation between a physical property of the world and its perceptual representation provided by the senses. Recent studies introduced the Generalized Linear Mixed Model (GLMM) to fit the responses of multiple participants in psychophysical experiments. Another approach (two-level approach) requires fitting psychometric functions to each individual participant data using a Generalized Linear Model (GLM), and then testing the hypotheses on the multiple participants by means of a second level analysis. For either options, the implementation of the statistical analysis in R is possible and beneficial. Here, we introduce the package MixedPsy to model and fit psychometric data in R, either with two-level and GLMM approaches. The package, freely available in the CRAN repository, uses different methods for the estimation of Point of Subjective Equivalence (PSE) and Just Noticeable Difference (JND), and provides utilities for immediate visualization and plotting of the fitted results. This manuscript aims to provide researchers with a practical tutorial for implementing a complete analysis pipeline for psychophysical data using MixedPsy and other packages and basic functionalities of the R programming environment.