Miracle-dz/UniFAR-FTUs huggingface dataset:
Health Insurance Support on Maternal Health Care: Evidence from Survey Data in India

Maternal healthcare financing is key to the smooth functioning of maternal health systems in a country. In India, maternal healthcare persists as a significant public health issue. Adequate health insurance could transform the utilization of maternal health care services to prevent maternal consequences. This paper aims to examine the health insurance policies that cover maternal health and their performance in India. The unit-level social consumption data on health by the National Sample Survey Organizations (NSSO), conducted in India (2017-18), is used. Bi-variate analysis, logistic regression, and propensity scoring matching (PSM) are used to evaluate the coverage of health insurance coverage on women's maternal health care utilization. Our findings suggest that spending on health insurance can benefit pregnant women, especially among the poor, without financial stress. The study has also minimized the financial burden and prevent high-risk pregnancy-related complications and consequences. Also, there is a need for proactive and inclusive policy development by the Government of India to promote more health insurance schemes in the public and private sectors. This can bring down the risk of maternal mortality and also boost the Indian economy in terms of a better quality of life in the long run, and the way towards more just and more egalitarian societies.
Robust bubble feature extraction in gas-liquid two-phase flow using object detection technique

ABSTRACT Identification of flow regime and feature extraction of bubble characteristics in gas-liquid two-phase flow systems have been significant issues in various engineering disciplines, including nuclear thermal-hydraulics field. In nuclear safety analysis codes, flow regime transition criteria and bubble size-dependent correlations are often utilized to obtain solutions for the governing equations. However, even today, they still rely on flow regime maps and empirical criteria that are based on visual observations and fully-developed flow conditions. In the present study, the AI-based object detection approach was adapted by utilizing convolutional neural networks to extract bubbly flow features. The model network is capable of extracting real-time bubbly flow features in an internal multiphase flow system. From the coordinate information of each individual detected bubbles, features such as void fraction, bubble number density, and average bubble sizes can be acquired instantly. The present approach has the potential to replace the traditional hand-coded image analysis method and open up a path for a fully automated and versatile two-phase flow feature extraction.
Distributed Attack Modeling Approach Based on Process Mining and Graph Segmentation

Attack graph modeling aims to generate attack models by investigating attack behaviors recorded in intrusion alerts raised in network security devices. Attack models can help network security administrators discover an attack strategy that intruders use to compromise the network and implement a timely response to security threats. However, the state-of-the-art algorithms for attack graph modeling are unable to obtain a high-level or global-oriented view of the attack strategy. To address the aforementioned issue, considering the similarity between attack behavior and workflow, we employ a heuristic process mining algorithm to generate the initial attack graph. Although the initial attack graphs generated by the heuristic process mining algorithm are complete, they are extremely complex for manual analysis. To improve their readability, we propose a graph segmentation algorithm to split a complex attack graph into multiple subgraphs while preserving the original structure. Furthermore, to handle massive volume alert data, we propose a distributed attack graph generation algorithm based on Hadoop MapReduce and a distributed attack graph segmentation algorithm based on Spark GraphX. Additionally, we conduct comprehensive experiments to validate the performance of the proposed algorithms. The experimental results demonstrate that the proposed algorithms achieve considerable improvement over comparative algorithms in terms of accuracy and efficiency.
Plant-Based Diets Induce Transcriptomic Changes in Muscle of Zebrafish and Atlantic Salmon

With the expansion of the aquaculture industry in the last two decades, there has been a large increase in the use of plant ingredients in aquafeeds, which has created new challenges in fish growth, health and welfare. Fish muscle growth is an important trait that is strongly affected by diet, but our knowledge on the effect of plant protein-based diets on global gene expression in muscle is still scant. The present study evaluated nutrigenomic effects of the inclusion of proteins from pea, soy and wheat into aquafeeds, compared to a control diet with fishmeal as the main protein source using the zebrafish model by RNA-seq; these results were extended to an important aquaculture species by analyzing selected differentially expressed genes identified in the zebrafish model on on-growing Atlantic salmon fed with equivalent plant protein-based diets. Expression of selected Atlantic salmon paralogues of the zebrafish homologs was analyzed using paralogue-specific qPCR assays. Global gene expression changes in muscle of zebrafish fed with plant-based diets were moderate, with the highest changes observed in the soy diet-fed fish, and no change for the pea diet-fed fish compared to the control diet. Among the differentially expressed genes were mylpfb, hsp90aa1.1, col2a1a, and odc1, which are important in regulating muscle growth, maintaining muscle structure and function, and muscle tissue homeostasis. Furthermore, those genes and their paralogues were differentially expressed in Atlantic salmon fed with the equivalent percentage of soy or wheat protein containing diets. Some of these genes were similarly regulated in both species while others showed species-specific regulation. The present study expands our understanding on the molecular effects of plant ingredients in fish muscle. Ultimately, the knowledge gained would be of importance for the improved formulation of sustainable plant-based diets for the aquaculture industry.
LT3 at SemEval-2020 Task 9: Cross-lingual Embeddings for Sentiment Analysis of Hinglish Social Media Text

This paper describes our contribution to the SemEval-2020 Task 9 on Sentiment Analysis for Code-mixed Social Media Text. We investigated two approaches to solve the task of Hinglish sentiment analysis. The first approach uses cross-lingual embeddings resulting from projecting Hinglish and pre-trained English FastText word embeddings in the same space. The second approach incorporates pre-trained English embeddings that are incrementally retrained with a set of Hinglish tweets. The results show that the second approach performs best, with an F1-score of 70.52% on the held-out test data.
Methyltransferase-like 3 Modulates Severe Acute Respiratory Syndrome Coronavirus-2 RNA N6-Methyladenosine Modification and Replication

Internal chemical modifications of viral RNA play key roles in the regulation of viral replication and gene expression. Although potential internal modifications have been reported in SARS-CoV-2 RNA, the function of the SARS-CoV-2 N6-methyladenosine (m6A) modification in the viral life cycle is unclear. ABSTRACT The coronavirus disease 2019 pandemic caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is an ongoing global public crisis. Although viral RNA modification has been reported based on the transcriptome architecture, the types and functions of RNA modification are still unknown. In this study, we evaluated the roles of RNA N6-methyladenosine (m6A) modification in SARS-CoV-2. Our methylated RNA immunoprecipitation sequencing (MeRIP-Seq) and Nanopore direct RNA sequencing (DRS) analysis showed that SARS-CoV-2 RNA contained m6A modification. Moreover, SARS-CoV-2 infection not only increased the expression of methyltransferase-like 3 (METTL3) but also altered its distribution. Modification of METTL3 expression by short hairpin RNA or plasmid transfection for knockdown or overexpression, respectively, affected viral replication. Furthermore, the viral key protein RdRp interacted with METTL3, and METTL3 was distributed in both the nucleus and cytoplasm in the presence of RdRp. RdRp appeared to modulate the sumoylation and ubiquitination of METTL3 via an unknown mechanism. Taken together, our findings demonstrated that the host m6A modification complex interacted with viral proteins to modulate SARS-CoV-2 replication. IMPORTANCE Internal chemical modifications of viral RNA play key roles in the regulation of viral replication and gene expression. Although potential internal modifications have been reported in SARS-CoV-2 RNA, the function of the SARS-CoV-2 N6-methyladenosine (m6A) modification in the viral life cycle is unclear. In the current study, we demonstrated that SARS-CoV-2 RNA underwent m6A modification by host m6A machinery. SARS-CoV-2 infection altered the expression pattern of methyltransferases and demethylases, while the expression level of methyltransferase-like 3 (METTL3) and fat mass and obesity-associated protein (FTO) was linked to the viral replication. Further study showed that METTL3 interacted with viral RNA polymerase RNA-dependent RNA polymerase (RdRp), which influenced not only the distribution but also the posttranslational modification of METTL3. Our study provided evidence that host m6A components interacted with viral proteins to modulate viral replication.
Polar Deconvolution of Mixed Signals

The signal demixing problem seeks to separate a superposition of multiple signals into its constituent components. This paper studies a two-stage approach that first decompresses and subsequently deconvolves the noisy and undersampled observations of the superposition using two convex programs. Probabilistic error bounds are given on the accuracy with which this process approximates the individual signals. The theory of polar convolution of convex sets and gauge functions plays a central role in the analysis and solution process. If the measurements are random and the noise is bounded, this approach stably recovers low-complexity and mutually incoherent signals, with high probability and with near optimal sample complexity. We develop an efficient algorithm, based on level-set and conditional-gradient methods, that solves the convex optimization problems with sublinear iteration complexity and linear space requirements. Numerical experiments on both real and synthetic data confirm the theory and the efficiency of the approach.
Caractérisation fonctionnelle de la protéine GABARAPL1 par identification de nouveaux partenaires protéiques et étude de l'expression de gabarapll dans les cancers du sein

La proteine GABARAPL1 (GABARAP like 1) ou GEC1 (Glandular Epithelial Cell 1), presente de forts pourcentages d'identite avec les proteines GABARAP (GABAA Receptor-Associated Protein), GATE-16 (Golgi-Associated ATPase Enhancer of 16 kDa) et Atg8 (Autophagy-related 8) ainsi qu'une identite moindre avec les proteines de la sous-famille LC3 (Light Chain 3). GABARAPL1 est exprimee dans tous les tissus, preferentiellement dans le systeme nerveux central, et intervient dans le transport intracellulaire des recepteurs GABAA (Gamma-AminoButyric Acid type A receptor) et KOR (K Opioid Receptor). De plus, une faible expression du gene gabarap1 est observee dans diverses lignees cancereuses, suggerant son implication dans la genese et/ou la progression tumorale. La recherche de partenaires proteiques a abouti a l'identification de differentes proteines. L'interaction entre GABARAPL1 et la proteine HSP90 (Heat Shock Protein 90) nouvellement identifiee a ete etudiee dans le cerveau de rat et dans les cellules MCF-7. Ainsi, nous avons montre que HSP90 protege GABARAPL1 de la degradation par le proteasome. Par ailleurs, il a ete etabli qu'au cours de ! 'autophagie, GABARAPL1 est clivee, maturee puis conjuguee a des phospholipides. Elle co-Iocalise alors partiellement au niveau de lysosomes et d'autophagosomes. Nous avons egalement demontre que gabarap1 est faiblement exprime dans les tissus tumoraux de sein et que la surexpression de la proteine FLAG-GABARAPL1-6HIS dans les cellules MCF-7 diminue considerablement leur croissance. De plus, une forte expression de gabarapl 1 est correlee a une augmentation de la survie de patientes atteintes de cancer du sein avec envahissement ganglionnaire.
On the Presence of a Universal Acceleration Scale in Elliptical Galaxies

Dark matter phenomena in rotationally supported galaxies exhibit a characteristic acceleration scale of g† ≈ 1.2 × 10−10 m s−2. Whether this acceleration is a manifestation of a universal scale, or merely an emergent property with an intrinsic scatter, has been debated in the literature. Here we investigate whether a universal acceleration scale exists in dispersion-supported galaxies using two uniform sets of integral field spectroscopy (IFS) data from SDSS-IV MaNGA and ATLAS3D. We apply the spherical Jeans equation to 15 MaNGA and 4 ATLAS3D slow-rotator E0 (i.e., nearly spherical) galaxies. Velocity dispersion profiles for these galaxies are well determined with observational errors under control. Bayesian inference indicates that all 19 galaxies are consistent with a universal acceleration of m s−2. Moreover, all 387 data points from the radial bins of the velocity dispersion profiles are consistent with a universal relation between the radial acceleration traced by dynamics and that predicted by the observed distribution of baryons. This universality remains if we include 12 additional non-E0 slow-rotator elliptical galaxies from ATLAS . Finally, the universal acceleration from MaNGA and ATLAS3D is consistent with that for rotationally supported galaxies, so our results support the view that dark matter phenomenology in galaxies involves a universal acceleration scale.
Counterfactual Explanations for Machine Learning: A Review

Machine learning plays a role in many deployed decision systems, often in ways that are difficult or impossible to understand by human stakeholders. Explaining, in a human-understandable way, the relationship between the input and output of machine learning models is essential to the development of trustworthy machine-learning-based systems. A burgeoning body of research seeks to define the goals and methods of explainability in machine learning. In this paper, we seek to review and categorize research on counterfactual explanations, a specific class of explanation that provides a link between what could have happened had input to a model been changed in a particular way. Modern approaches to counterfactual explainability in machine learning draw connections to the established legal doctrine in many countries, making them appealing to fielded systems in high-impact areas such as finance and healthcare. Thus, we design a rubric with desirable properties of counterfactual explanation algorithms and comprehensively evaluate all currently-proposed algorithms against that rubric. Our rubric provides easy comparison and comprehension of the advantages and disadvantages of different approaches and serves as an introduction to major research themes in this field. We also identify gaps and discuss promising research directions in the space of counterfactual explainability.
The function of the prereplicative complex and SCF complex components during Drosophila wing development

Chromosomal DNA replication machinery functions in the growing cells and organs in multicellular organisms. We previously demonstrated that its knockdown in several tissues of Drosophila led to a rough eye phenotype, the loss of bristles in the eye and female sterile. In this paper, we investigated in detail the wing phenotype using RNAi flies, and observed that the knockdown not only of Mcm10 but also of some other prereplicative complex components demonstrated wing phenotypes, using Gal4-driver flies. Surprisingly, some SCF complex components, which control cell cycle progression via protein degradation, also showed the wing phenotype. These results showed that the DNA replication machinery contributes to wing development independent of growth, probably through defects in DNA replication and protein degradation at specific places and times. Summary statement We recently outlined the prereplicative complex components, including Mcm10 and SCF complex functions, during Drosophila wing development. In this paper, we detail these findings.
SmellControl: The Study of Sense of Agency in Smell

The Sense of Agency (SoA) is crucial in interaction with technology, it refers to the feeling of 'I did that' as opposed to 'the system did that' supporting a feeling of being in control. Research in human-computer interaction has recently studied agency in visual, auditory and haptic interfaces, however the role of smell on agency remains unknown. Our sense of smell is quite powerful to elicit emotions, memories and awareness of the environment, which has been exploited to enhance user experiences (e.g., in VR and driving scenarios). In light of increased interest in designing multimodal interfaces including smell and its close link with emotions, we investigated, for the first time, the effect of smell-induced emotions on the SoA. We conducted a study using the Intentional Binding (IB) paradigm used to measure SoA while participants were exposed to three scents with different valence (pleasant, unpleasant, neutral). Our results show that participants? SoA increased with a pleasant scent compared to neutral and unpleasant scents. We discuss how our results can inform the design of multimodal and future olfactory interfaces.
Measured asymptotic expanders and rigidity for Roe algebras.

Our main result about rigidity of Roe algebras is the following: if $X$ and $Y$ are metric spaces with bounded geometry such that their Roe algebras are $*$-isomorphic, then $X$ and $Y$ are coarsely equivalent provided that either $X$ or $Y$ contains no sparse subspaces consisting of ghostly measured asymptotic expanders. Note that this geometric condition generalises the existing technical assumptions used for rigidity of Roe algebras. Consequently, we show that the rigidity holds for all bounded geometry spaces which coarsely embed into some $L^p$-space for $p\in [1,\infty)$. Moreover, we also verify the rigidity for the box spaces constructed by Arzhantseva-Tessera and Delabie-Khukhro even though they do \emph{not} coarsely embed into any $L^p$-space. The key step towards our proof for the rigidity is to show that a block-rank-one (ghost) projection on a sparse space $X$ belongs to the Roe algebra $C^*(X)$ if and only if $X$ consists of (ghostly) measured asymptotic expanders. As a by-product, we also deduce that ghostly measured asymptotic expanders are new sources of counterexamples to the coarse Baum-Connes conjecture.
Hypothalamic Gene Expression and Postpartum Behavior in a Genetic Rat Model of Depression

Postpartum depression is a complex illness that often occurs in genetically predisposed individuals. Closely related inbred rat strains are a great resource to identify novel causative genes and mechanisms underlying complex traits such as postpartum behavior. We report differences in these behaviors between the inbred depression model, Wistar Kyoto (WKY) More Immobile (WMI), and the isogenic control Wistar Kyoto Less Immobile (WLI) dams. WMI dams showed significantly lower litter survival rate and frequency of arched back and blanket nursing, but increased pup-directed licking, grooming, and retrieval during postpartum days (PPD) 1–10, compared to control WLIs. This increased pup-directed behavior and the frequency of self-directed behaviors segregated during selective breeding of the progenitor strain of WKY, which is also a depression model. These behaviors are manifested in the WMIs in contrast to those of WLIs. Furthermore, habitual differences in the self-directed behavior between light and dark cycles present in WLIs were missing in WMI dams. Hypothalamic transcript levels of the circadian rhythm-related gene Lysine Demethylase 5A (Kdm5a), period 2 (Per2), and the maternal behavior-related oxytocin receptor (Oxtr), vasopressin (Avp), and vasopressin receptor 1a (Avpr1a) were significantly greater in the post-weaning WMI dams at PPD 24 compared to those of WLIs, and also to those of WMI dams whose litter died before PPD 5. Expression correlation amongst genes differed in WLI and WMI dams and between the two time-points postpartum, suggesting genetic and litter-survival differences between these strains affect transcript levels. These data demonstrate that the genetically close, but behaviorally disparate WMI and WLI strains would be suitable for investigating the underlying genetic basis of postpartum behavior.
Generalized Conditioned Dialogue Generation Based on Pre-trained Language Model

We investigate the general problem of conditioned dialogue, in which a condition label is used as input to designate the type of the target response such as a persona. A major challenge for conditioned dialogue generation is the lack of substantial dialogue data labeled with conditions. Thus, we propose to complement the labeled dialogue data with labeled non-dialogue text data, and fine-tune BERT based on them. Our fine-tuning approach utilizes BERT for both encoder and decoder via different input representations and self-attention masks in order to distinguish the source and target side. On the target (generation) side, we use a new attention routing mechanism to choose between generating a generic word or condition-related word at each position. Our model is instantiated to persona- and topic-related dialogue. Experimental results in both cases show that our approach can produce significantly better responses than the state-of-the-art baselines.
Further evidence against a force-velocity trade-off in muscle driven dynamic lever systems

Abstract Levers impose a force-velocity trade-off. In static conditions, a larger moment arm increases a muscle’s force capacity, and a smaller moment arm amplifies output velocity. However, muscle force is influenced by contractile velocity and fiber length, while contractile velocity is influenced by the inertial properties of the lever system. We hypothesize that these dynamic effects constrain the functional output of a muscle-lever system. We predict that there is an optimal moment arm to maximize output velocity for any given muscle-lever configuration. Here we test this hypothesis by computationally building and systematically modifying a simple lever system. We generated 3600 modifications of this model with muscles with varying optimal fiber lengths, moment arms and starting normalized muscle lengths. For each model we simulated the motion that results from 100% activation and extracted the maximum output lever velocity. In contrast to a tradeoff between force and velocity in a lever system, we found that there was, instead, an optimal moment arm which maximized both velocity and total impulse. Increasing output velocity always required increasing output force. From this we conclude that in a dynamic lever system where muscle activation is held constant, there is no tradeoff between force and velocity.