Miracle-dz/UniFAR-FTUs huggingface dataset:
Association of maternal prenatal selenium concentration and preterm birth: a multicountry meta-analysis

Background Selenium (Se), an essential trace mineral, has been implicated in preterm birth (PTB). We aimed to determine the association of maternal Se concentrations during pregnancy with PTB risk and gestational duration in a large number of samples collected from diverse populations. Methods Gestational duration data and maternal plasma or serum samples of 9946 singleton live births were obtained from 17 geographically diverse study cohorts. Maternal Se concentrations were determined by inductively coupled plasma mass spectrometry analysis. The associations between maternal Se with PTB and gestational duration were analysed using logistic and linear regressions. The results were then combined using fixed-effect and random-effect meta-analysis. Findings In all study samples, the Se concentrations followed a normal distribution with a mean of 93.8 ng/mL (SD: 28.5 ng/mL) but varied substantially across different sites. The fixed-effect meta-analysis across the 17 cohorts showed that Se was significantly associated with PTB and gestational duration with effect size estimates of an OR=0.95 (95% CI: 0.9 to 1.00) for PTB and 0.66 days (95% CI: 0.38 to 0.94) longer gestation per 15 ng/mL increase in Se concentration. However, there was a substantial heterogeneity among study cohorts and the random-effect meta-analysis did not achieve statistical significance. The largest effect sizes were observed in UK (Liverpool) cohort, and most significant associations were observed in samples from Malawi. Interpretation While our study observed statistically significant associations between maternal Se concentration and PTB at some sites, this did not generalise across the entire cohort. Whether population-specific factors explain the heterogeneity of our findings warrants further investigation. Further evidence is needed to understand the biologic pathways, clinical efficacy and safety, before changes to antenatal nutritional recommendations for Se supplementation are considered.
Human Adipose Mesenchymal Stem Cell-derived Exosomes Protect Mice from DSS-Induced Inflammatory Bowel Disease by Promoting Intestinal-stem-cell and Epithelial Regeneration

Inflammatory bowel disease (IBD) remains a severe disease for most patients, with its incidence and prevalence increasingly globally. Currently, there is no effective treatments for IBD, and traditional treatments have multiple side effects. Therefore, novel therapeutic strategies or alternative drugs are urgently needed. Previous studies have shown that mesenchymal stem cell-derived exosomes have exhibited promising therapeutic effects on inflammatory disease. Here, we performed intravenous injection of human adipose mesenchymal stem cell (hADSC)-derived exosomes (hADSC-Exo) in a DSS-induced IBD mouse model and found that hADSC-Exo promoted functional recovery, downregulated inflammatory responses, reduced intestine cell apoptosis, increased epithelial regeneration and maintained intestinal barrier integrity. Moreover, we established a colon organoid, hADSC-Exo and TNF-α co-cultured system to explore the protective effect of hADSC-Exo on integrity of intestine mucosa and epithelial regeneration. We showed that hADSC-Exo not only can promote the proliferation and regeneration of Lgr5+ ISCs and epithelial cells but also ameliorate the inflammation damage in TNF-α induced inflammatory damaged mice colon organoids. Taken together, our findings indicate that hADSC-Exo protects intestine integrity, activates intestine epithelial cell and ISCs proliferation, suggesting that hADSC-Exo might be a potential effective treatment approach for IBD. We also provide a theoretical basis for new therapeutic strategies for cell-free therapy in inflammatory bowel disease.
GPGM-SLAM: a Robust SLAM System for Unstructured Planetary Environments with Gaussian Process Gradient Maps

Simultaneous Localization and Mapping (SLAM) techniques play a key role towards long-term autonomy of mobile robots due to the ability to correct localization errors and produce consistent maps of an environment over time. Contrarily to urban or man-made environments, where the presence of unique objects and structures offer unique cues for localization, the apperance of unstructured natural environments is often ambiguous and self-similar, hindering the performances of loop closure detection. In this paper, we present an approach to improve the robustness of place recognition in the context of a submap-based stereo SLAM based on Gaussian Process Gradient Maps (GPGMaps). GPGMaps embed a continuous representation of the gradients of the local terrain elevation by means of Gaussian Process regression and Structured Kernel Interpolation, given solely noisy elevation measurements. We leverage the imagelike structure of GPGMaps to detect loop closures using traditional visual features and Bag of Words. GPGMap matching is performed as an SE(2) alignment to establish loop closure constraints within a pose graph. We evaluate the proposed pipeline on a variety of datasets recorded on Mt. Etna, Sicily and in the Morocco desert, respectively Moon- and Mars-like environments, and we compare the localization performances with state-of-the-art approaches for visual SLAM and visual loop closure detection.
The young protostellar disc in IRAS 16293−2422 B is hot and shows signatures of gravitational instability

Deeply embedded protostars are actively fed from their surrounding envelopes through their protostellar disc. The physical structure of such early discs might be different from that of more evolved sources due to the active accretion. We present 1.3 and 3 mm ALMA continuum observations at resolutions of 6.5 and 12 au, respectively, towards the Class 0 source IRAS 16293−2422 B. The resolved brightness temperatures appear remarkably high, with Tb > 100 K within ∼30 au and Tb peak over 400 K at 3 mm. Both wavelengths show a lopsided emission with a spectral index reaching values less than 2 in the central ∼20 au region. We compare these observations with a series of radiative transfer calculations and synthetic observations of magnetohydrodynamic and radiation hydrodynamic protostellar disc models formed after the collapse of a dense core. Based on our results, we argue that the gas kinematics within the disc may play a more significant role in heating the disc than the protostellar radiation. In particular, our radiation hydrodynamic simulation of disc formation, including heating sources associated with gravitational instabilities, is able to generate the temperatures necessary to explain the high fluxes observed in IRAS 16293B. Besides, the low spectral index values are naturally reproduced by the high optical depth and high inner temperatures of the protostellar disc models. The high temperatures in IRAS 16293B imply that volatile species are mostly in the gas phase, suggesting that a self-gravitating disc could be at the origin of a hot corino.
Combined Effects of Low‐Frequency Pulsed Electromagnetic Field and Melatonin on Ovariectomy‐Induced Bone Loss in Mice

Pulsed electromagnetic field (PEMF) therapy and melatonin (MEL) supplementation are expected to be important strategies for the treatment of osteoporosis. The aim of the current study was to investigate the efficacy of PEMF therapy, MEL supplementation, a combination of PEMF therapy, and MEL supplementation (PEMF + MEL) in mice with bilateral ovariectomy (OVX)‐induced osteoporosis. Forty 12‐week‐old female C57/BL mice were randomly assigned to five groups (n = 8/group): OVX, PEMF, MEL, PEMF + MEL, and sham‐operation (sham) groups. All mice in the first four groups were subjected to OVX. The mice in the PEMF and PEMF + MEL groups were exposed to PEMF (75 Hz, 1.6 mT, 1 h/day for 12 weeks), while those in the MEL and PEMF + MEL groups were administered MEL (50 mg/kg, i.p.). Body mass, micro‐computed tomography, histology, immunohistochemistry, and real‐time polymerase chain reaction were performed. PEMF + MEL treatment enhanced bone volume fraction (BV/TV) 2.2‐fold over OVX control (P < 0.001) and increased expression levels of collagen type I (COL1) 1.9‐fold and bone morphogenetic protein 2 (BMP2) 2.5‐fold. PEMF + MEL also reduced the ratio of bone surface/bone volume (BS/BV) by 40% (P < 0.05) and appeared to reduce the number of osteoclasts in the metaphysis area. Preservation of bone value and bone microarchitecture in the combined therapy group were found to be superior to those in the single treatment groups. However, there were no apparent differences between the PEMF and MEL groups. The use of a combination of PEMF therapy and MEL supplementation may be an effective method to treat osteoporosis. © 2021 Bioelectromagnetics Society.
MicroRNA Dysregulation in the Hippocampus of Rats with Noise-Induced Hearing Loss

Although hippocampal changes due to noise-induced hearing loss have been suggested, little is known about the miRNA levels due to these hippocampal changes. Three-week-old Sprague-Dawley rats were divided into noise and control groups (n = 20 per group). The noise group rats were exposed to white Gaussian noise (115 dB SPL, 4 hours per day) for three days. One day after noise exposure, the hippocampi of rats were harvested and miRNA expressions were analyzed using the Affymetrix miRNA 4.0 microarray (n = 6 per group). The predicted target genes of each miRNA were retrieved, and the pathways related to the predicted target genes were analyzed. miR-758-5p, miR-210-5p, miR-370-5p, miR-652-5p, miR-3544, miR-128-1-5p, miR-665, miR-188-5p, and miR-874-5p expression increased in the hippocampal tissue of the noise group compared to that in the control group. The overlapping predicted target genes included Bend4, Creb1, Adcy6, Creb5, Kcnj9, and Pten. The pathways related to these genes were the estrogen signaling pathway, vasopressin-regulated water reabsorption, thyroid hormone synthesis, aldosterone synthesis and secretion, insulin secretion, circadian entrainment, insulin resistance, cholinergic synapse, dopaminergic synapse, cGMP-PKG signaling pathway, cAMP signaling pathway, PI3K-Akt signaling pathway, TNF signaling pathway, and AMPK signaling pathway. miR-448-3p, miR204-5p, and miR-204-3p expression decreased in the hippocampal tissue of the noise group compared to that in the control group. The overlapping predicted target genes of these three miRNAs were Rps6kas, Nfactc3, Rictor, Spred1, Cdh4, Cdh6, Dvl3, and Rcyt1b. Pathway analysis suggested that the Wnt signaling pathway is related to Dvl3 and Nfactc3. Noise-induced hearing loss dysregulates miR-758-5p, miR210-5p, miR370-5p, miR-652-5p, miR-3544, miR-128-1-5p, miR-665, miR-188-5p, miR-874-5p, miR-448-3p, miR-204-5p, miR-204-3p, and miR-140-5p expression in the hippocampus. These miRNAs have been predicted to be associated with hormonal, inflammatory, and synaptic pathways.
Anonymization Framework for IoT Resource Discovery based on Edge Centric Privacy Model

As the efficacy of Internet of Things is expeditiously growing, maintaining privacy with respect users and applications has become a significant aspect. Since the data is getting generated at tremendous rate that includes Sensitive data (any data considered as private by the Data-owner) which has to be hidden, especially the data collected from the Crowd-Source. Due to resource-constrained sensing devices, IoT infrastructures use Edge devices for real-time data processing. Protecting sensitive data from malicious activity becomes a key factor, as all the communication flows through insecure channels. To develop security infrastructures for IoT and distributed Edge networks, this article proposes a user-centric security solution. The proposed security solution shifts from a network-centric approach to a user-centric security approach by authenticating users and devices before communication is established. The method presented herein is applied to an amusement park scenario, which is modeled as a typical smart IoT network. Here, data from sensors and social networks can boost smart lighting to provide citizens with an elegant and safe environment. However, it is challenging and infeasible to transfer and process zillions bytes of data using the current cloud-device architecture due to bandwidth constraints of networks, potentially uncontrollable latency of cloud services, and privacy concerns while collecting data from IoT devices. Firstly, a standalone IoT-edge system is developed, and later, an integrated IoT-based edge-cloud system is designed to compare the systems’ effectiveness. The implementation results show a close correlation between the standalone edge and dual mode edge system. However, the edge-cloud system provides more flexibility and capability to counter the sensitive data streaming and analytics services within the constrained IoT framework. In this paper we have developed a system that uses fog computing approach to perform various tasks and filters the sensitive data, thus helps in preserving privacy.
Policy Optimization Using Semiparametric Models for Dynamic Pricing

In this paper, we study the contextual dynamic pricing problem where the market value of a product is linear in their observed features plus some market noise. Products are sold one at a time, and only a binary response indicating the success or failure of a sale is observed. Our model setting is similar to \cite{JN19} except that we expand the demand curve to a semiparametric model and need to learn dynamically both parametric and nonparametric components. We propose a dynamic statistical learning and decision-making policy that combines semiparametric estimation from a generalized linear model with an unknown link and online decision making to minimize regret (maximize revenue). Under mild conditions, we show that for a market noise c.d. f. $F(\cdot)$ with $m$-th order derivative, our policy achieves a regret upper bound of $\tilde{\cO}_{d}(T^{\frac{2m+1}{4m-1}})$ for $m\geq 2$, where $T$ is time horizon and $\tilde{\cO}_{d}$ is the order that hides logarithmic terms and the dimensionality of feature $d$. The upper bound is further reduced to $\tilde{\cO}_{d}(\sqrt{T})$ if $F$ is super smooth whose Fourier transform decays exponentially. In terms of dependence on the horizon $T$, these upper bounds are close to $\Omega(\sqrt{T})$, the lower bound where the market noise distribution belongs to a parametric class. We further generalize these results to the case when the product features are dynamically dependent, satisfying some strong mixing conditions.
Everything Is All It Takes: A Multipronged Strategy for Zero-Shot Cross-Lingual Information Extraction

Zero-shot cross-lingual information extraction (IE) describes the construction of an IE model for some target language, given existing annotations exclusively in some other language, typically English. While the advance of pretrained multilingual encoders suggests an easy optimism of “train on English, run on any language”, we find through a thorough exploration and extension of techniques that a combination of approaches, both new and old, leads to better performance than any one cross-lingual strategy in particular. We explore techniques including data projection and self-training, and how different pretrained encoders impact them. We use English-to-Arabic IE as our initial example, demonstrating strong performance in this setting for event extraction, named entity recognition, part-of-speech tagging, and dependency parsing. We then apply data projection and self-training to three tasks across eight target languages. Because no single set of techniques performs the best across all tasks, we encourage practitioners to explore various configurations of the techniques described in this work when seeking to improve on zero-shot training.
Enhancing the value to users of machine learning-based clinical decision support tools: A framework for iterative, collaborative development and implementation

Background Health care organizations are integrating a variety of machine learning (ML)-based clinical decision support (CDS) tools into their operations, but practitioners lack clear guidance regarding how to implement these tools so that they assist end users in their work. Purpose We designed this study to identify how health care organizations can facilitate collaborative development of ML-based CDS tools to enhance their value for health care delivery in real-world settings. Methodology/Approach We utilized qualitative methods, including 37 interviews in a large, multispecialty health system that developed and implemented two operational ML-based CDS tools in two of its hospital sites. We performed thematic analyses to inform presentation of an explanatory framework and recommendations. Results We found that ML-based CDS tool development and implementation into clinical workflows proceeded in four phases: iterative solution coidentification, iterative coengagement, iterative coapplication, and iterative corefinement. Each phase is characterized by a collaborative back-and-forth process between the technology’s developers and users, through which both users’ activities and the technology itself are transformed. Conclusion Health care organizations that anticipate iterative collaboration to be an integral aspect of their ML-based CDS tools’ development and implementation process may have more success in deploying ML-based CDS tools that assist end users in their work than organizations that expect a traditional technology innovation process. Practice Implications Managers developing and implementing ML-based CDS tools should frame the work as a collaborative learning opportunity for both users and the technology itself and should solicit constructive feedback from users on potential changes to the technology, in addition to potential changes to user workflows, in an ongoing, iterative manner.
Cognitive Control Promotes Either Honesty or Dishonesty, Depending on One's Moral Default

Cognitive control is crucially involved in making (dis)honest decisions. However, the precise nature of this role has been hotly debated. Is honesty an intuitive response, or is will power needed to override an intuitive inclination to cheat? A reconciliation of these conflicting views proposes that cognitive control enables dishonest participants to be honest, whereas it allows those who are generally honest to cheat. Thus, cognitive control does not promote (dis)honesty per se; it depends on one's moral default. In the present study, we tested this proposal using electroencephalograms in humans (males and females) in combination with an independent localizer (Stroop task) to mitigate the problem of reverse inference. Our analysis revealed that the neural signature evoked by cognitive control demands in the Stroop task can be used to estimate (dis)honest choices in an independent cheating task, providing converging evidence that cognitive control can indeed help honest participants to cheat, whereas it facilitates honesty for cheaters. SIGNIFICANCE STATEMENT Dishonesty causes enormous economic losses. To target dishonesty with interventions, a rigorous understanding of the underlying cognitive mechanisms is required. A recent study found that cognitive control enables honest participants to cheat, whereas it helps cheaters to be honest. However, it is evident that a single study does not suffice as support for a novel hypothesis. Therefore, we tested the replicability of this finding using a different modality (EEG instead of fMRI) together with an independent localizer task to avoid reverse inference. We find that the same neural signature evoked by cognitive control demands in the localizer task can be used to estimate (dis)honesty in an independent cheating task, establishing converging evidence that the effect of cognitive control indeed depends on a person's moral default.
BenchIE: Open Information Extraction Evaluation Based on Facts, Not Tokens

Intrinsic evaluations of OIE systems are carried out either manually—with human evaluators judging the correctness of extractions— or automatically, on standardized benchmarks. The latter, while much more cost-effective, is less reliable, primarily because of the in-completeness of the existing OIE benchmarks: the ground truth extractions do not include all acceptable variants of the same fact, leading to unreliable assessment of models’ performance. Moreover, the existing OIE benchmarks are available for English only. In this work, we introduce BenchIE: a benchmark and evaluation framework for comprehensive evaluation of OIE systems for English, Chinese and German. In contrast to existing OIE benchmarks, BenchIE takes into account informational equivalence of extractions: our gold standard consists of fact synsets , clusters in which we exhaustively list all surface forms of the same fact. We benchmark several state-of-the-art OIE systems using BenchIE and demonstrate that these systems are signiﬁ-cantly less effective than indicated by existing OIE benchmarks. We make BenchIE (data and evaluation code) publicly available 1 .
Symmetry indicators vs. bulk winding numbers of topologically non-trivial bands

The symmetry-indicators provide valuable information about the topological properties of band structures in real materials. For inversion-symmetric, non-magnetic materials, the pattern of parity eigenvalues of various Kramers-degenerate bands at the time-reversal-invariant momentum points are generally analyzed with the combination of strong Z4, and weak Z2 indices. Can the symmetry indicators identify the tunneling configurations of SU(2) Berry’s connections or the threedimensional, winding numbers of topologically non-trivial bands? In this work, we perform detailed analytical and numerical calculations on various effective tight-binding models to answer this question. If the parity eigenvalues are regarded as fictitious Ising spins, located at the vertices of Miller hypercube, the strong Z4 index describes the net ferro-magnetic moment, which is shown to be inadequate for identifying non-trivial bands, supporting even integer winding numbers. We demonstrate that an “anti-ferromagnetic” index, measuring the staggered magnetization can distinguish between bands possessing zero, odd, and even integer winding numbers. The coarse-grained analysis of symmetry-indicators is substantiated by computing the change in rotational-symmetry-protected, quantized Berry’s flux and Wilson loops along various high-symmetry axes. By simultaneously computing ferromagnetic and anti-ferromagnetic indices, we categorize various bands of bismuth, antimony, rhombohedral phosphorus, and Bi2Se3.
Population stratification in type 2 diabetes mellitus: A systematic review

There is increasing interest in using stratification in type 2 diabetes to target resources, individualise care and improve outcomes. We aim to systematically review and collate literature that has utilised population stratification methods in the study of adults with type 2 diabetes; and to describe and compare stratification methodologies, population characteristics, variables used to stratify and outcome variables.
Brief Musculoskeletal Screen and Patient Education for Down Syndrome-Associated Arthritis

Creative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage). Brief Reports