Miracle-dz/UniFAR-FTUs huggingface dataset:
Covenantal Pluralism in “Homogenous” Japan: Finding a Space for Religious Pluralism

This article explores the concept of covenantal pluralism in a context of postwar Japan, with a focus on the legal framework separating the state from religion and guaranteeing the rights of religious minorities. The article argues that while there are relatively few instances of sectarian strife, the lack of agreement on how to interpret the category of religion, in particular in relation to Shrine Shinto, continues to lead to struggles between different camps. The article also questions the feasibility of covenantal pluralism as an ideal in a country where national identity is so closely linked to ideas of social homogeneity.
Rapid decline of adaptation of Pseudomonas fluorescens to soil biotic environment

Interactions between microbes can both constrain and enhance their adaptation to the environment. However, most studies to date have employed simplified microbial communities and environmental conditions. We determined how the presence of a commercial potting compost microbial community affected adaptation of the soil bacterium Pseudomonas fluorescens SBW25 in potting compost. Pseudomonas fluorescens clones isolated from populations evolved in both the presence and absence of the community showed similar fitness increases when measured in the absence of the community. This suggests the presence of the community did not constrain adaptation. By contrast, fitness measured in the presence of the community increased for community-evolved populations, but decreased below the ancestral state for populations evolved in the absence of the community. This suggests some, but not all, mutations that were beneficial with respect to the abiotic environment were costly in the presence of the community, with the former selected against in the presence of the community. Whole-genome sequencing supports this interpretation: most mutations underpinning fitness changes were clone-specific, suggesting multiple genetic pathways to adaptation. Such extreme mutational effects have not been observed in comparable in vitro studies, suggesting that caution is needed when extrapolating results from simplified in vitro systems to natural contexts.
New challenges for an expanding generation of older persons with haemophilia

Abstract Background Increasing survival among people with haemophilia means that more individuals are at risk of developing age-related morbidity. Little is known about morbidity and health-related quality of life (HRQoL) in different age groups within a single large population of people with haemophilia. Aim This study aimed to explore the association between increasing age and comorbidity among people with haemophilia and to compare their HRQoL with that of a sample of the general population in England. Methods The prevalence of comorbidity recorded in medical records and HRQoL assessed by EQ-5D were compared by age group in participants in the Cost of Haemophilia in Europe: A Socioeconomic Survey study (CHESS) in Europe. HRQoL was compared with that of a sample of the general population taken from the 2012 Health Survey for England (HSE). Results Younger adults in CHESS were more likely to have received prophylaxis from an early age. The mean number of affected joints in younger adults was 1.0; participants aged 41–50 (1.25) and 51–60 years (1.41) had the highest mean number of affected joints. The prevalence of comorbidity was 36% in patients aged 18–30, 61% in 31–60-year-olds and 68% in those aged 61+. HRQoL impairment in young adults with haemophilia was comparable with that in the HSE population aged over 60. Conclusions Older people with haemophilia have impaired quality of life compared with younger adults and an increasing prevalence of several age-related disorders affecting mental health and cardiovascular and bone health. Young adults with haemophilia report impaired HRQoL comparable with that in a general population aged 61+.
Pandemic rumor identification on social networking sites: A case study of COVID-19

Digital Rumors, because of the ease and innovations in social networking technologies, has become an important issue. These rumors become a critical issue in a disaster, epidemic, or pandemic. Considering classification power of conventional and deep learning techniques, we propose a hybrid learning technique that identifies rumors effectively. For this, TF-IDF description has been used to build a stack of multiple conventional learning techniques; logistic regression, Naïve Bayes, and random forest. Whereas, word-embedding features have been used for purpose of deep learning; LSTM and LSTM-RNN. The combination of LSTM and RNN makes this study unique in the field of rumor detection. With LSTM and RNN gated architectures, huge series rumor tweets may be efficiently managed. To aggregate the decisions, the labels of deep learning and the stack of conventional learning have been combined using majority voting based ensemble classification. To evaluate the performance of the proposed technique, we used publically available standard COVID-19 RUMOR dataset. The proposed technique obtains 99.02% accuracy, which shows its effectiveness. The dataset utilized and the ensemble model created for rumor identification distinguish our work from existing methods.
Learning Knowledge Uncertainty from the Pretrained Language Model

Uncertain knowledge graphs, with each fact assigned a confidence value between 0 and 1, is a kind of graph structured knowledge bases. Knowledge representation is the foundation for most of knowledge-driven applications, in which knowledge are encoded into a continuous vector space for rapidly computing. Unfortunately, it is still a big challenge to encode meaningful knowledge features into the embedding space, such as uncertainty, inferring structures, and commonsense knowledge. An uncertain knowledge graphs embedding model, UKGEbert, is proposed to embed latent commonsense semantics by the pretrained natural language model. In the model, each knowledge fact is treated as a short sentence, which is fed into BERT for training. After that, the model learns uncertainty distribution of knowledge confidence by the recurrent neural network. Experiments on several benchmark datasets show that an effective prediction of confidence can help enhancing ability of knowledge inferring in the embedding space. Furthermore, the model achieves state of the art in several main metrics on the link prediction task of uncertain knowledge graphs.
Integrated Target Tracking and Control for Automated Car-Following of Truck Platforms

This article proposes a perception model for enhancing the accuracy and stability of car-following control of a longitudinally automated truck. We applied a fusion-based tracking algorithm on measurements of a single preceding vehicle needed for car following control. This algorithm fuses two types of data, radar and LiDAR data, to obtain more accurate and robust longitudinal perception of the subject vehicle in various weather conditions. The filter's resulting signals are fed to the gap control algorithm at every tracking loop composed by a high-level gap control and lower acceleration tracking system. Several highway tests have been performed with two trucks. The tests show accurate and fast tracking of the target, which impacts on the gap control loop positively. The experiments also show the fulfilment of control design requirements, such as fast speed variations tracking and robust time gap following.
Internet of Things (IoT) and Indoor Air Quality (IAQ) Monitoring in the Health Domain

Monitoring indoor air quality is becoming crucial as people spend most of their time indoors. In recent years, respiratory problems such as chronic obstructive pulmonary disease and asthma appear as among the common diseases that are the reasons for hospitalization among people. Monitoring the indoor pollutant, using the low-cost sensor-based IoT system, can play an important role in the management of these chronic conditions. In this paper, we set out the design of a trial involving an unobtrusive IoT-based monitoring system to observe indoor environment passively in the house of people suffering from Asthma. The system monitors Particulate Matter (PM2.5 & PM10) and Carbon dioxide (CO2) in the indoor environment together with householder's indoor activities such as type of cooking, smoking, ventilation hour, cleaning, and so on.
NURSE: eNd-UseR IoT malware detection tool for Smart homEs

IoT devices keep entering our homes with the promise of delivering more services and enhancing user experience; however, these new devices also carry along an alarming number of vulnerabilities and security issues. In most cases, the users of these devices are completely unaware of the security risks that connecting these devices entail. Current tools do not provide users with essential security information such as whether a device is infected with malware. Traditional techniques to detect malware infections were not meant to be used by the end-user and current malware removal tools and security software cannot handle the heterogeneity of IoT devices. In this paper, we design, develop, and evaluate a tool, called NURSE, to fill this information gap, i.e., enabling end-users to detect IoT-malware infections in their home networks. NURSE follows a modular approach to analyze IoT traffic as captured by means of an ARP spoofing technique which does not require any network modification or specific hardware. Thus, NURSE provides zero-configuration IoT traffic analysis within everybody’s reach. After testing NURSE in 83 different IoT network scenarios with a wide variety of IoT device types, results show that NURSE identifies malware-infected IoT devices with high-accuracy (86.7%) using device network behaviour and contacted destinations.
A Term Selection Method Combined with Sentiment Score for Twitter Sentiment Analysis

Sentiment analysis of tweets is to automatically judge the emotional tendency of text based on text data, which is usually divided into positive emotion and negative emotion. This is of positive significance to automatically analyze the emotional tendencies of user reviews and make timely product adjustments. Feature selection is a very important link in sentiment analysis of tweets. Based on the traditional method of document frequency feature selection, we designed a method to calculate sentiment score by using tag information, and then proposed a feature selection method combining document frequency and sentiment score. We compared the effect of our proposed method with the traditional document frequency method on the sentiment analysis of tweets using Logistic regression classifier experiments. The experimental results show that the proposed feature selection method can improve the effect of sentiment analysis of tweets to a certain extent.
A-type FHFs mediate resurgent currents through TTX-resistant voltage-gated sodium channels

Resurgent currents (INaR) produced by voltage-gated sodium channels are required for many neurons to maintain high-frequency firing, and contribute to neuronal hyperexcitability and disease pathophysiology. Here we show, for the first time, that INaR can be reconstituted in a heterologous system by co-expression of sodium channel α-subunits and A-type fibroblast growth factor homologous factors (FHFs). Specifically, A-type FHFs induces INaR from Nav1.8, Nav1.9 tetrodotoxin-resistant neuronal channels and, to a lesser extent, neuronal Nav1.7 and cardiac Nav1.5 channels. Moreover, we identified the N-terminus of FHF as the critical molecule responsible for A-type FHFs-mediated INaR. Among the FHFs, FHF4A is the most important isoform for mediating Nav1.8 and Nav1.9 INaR. In nociceptive sensory neurons, FHF4A knockdown significantly reduces INaR amplitude and the percentage of neurons that generate INaR, substantially suppressing excitability. Thus, our work reveals a novel molecular mechanism underlying TTX-resistant INaR generation and provides important potential targets for pain treatment.
Life cycle assessment of an Internet of Things product: Environmental impact of an intelligent smoke detector

Digitization and sustainability are the two big topics of our current time. As the usage of digital products like IoT devices continues to grow, it affects the energy consumption caused by the Internet. At the same time, more and more companies feel the need to become carbon neutral and sustainable. Determining the environmental impact of an IoT device is challenging, as the production of the hardware components should be considered and the electricity consumption of the Internet since this is the primary communication medium of an IoT device. Estimating the electricity consumption of the Internet itself is a complex task. We performed a life cycle assessment (LCA) to determine the environmental impact of an intelligent smoke detector sold in Germany, taking its whole life-cycle from cradle-to-grave into account. We applied the impact assessment method ReCiPe 2016 Midpoint and compared its results with ILCD 2011 Midpoint+ to check the robustness of our results. The LCA results showed that electricity consumption during the use phase is the main contributor to environmental impacts. The mining of coal causes this contribution, which is a part of the German electricity mix. Consequently, the smoke detector mainly contributes to the impact categories of freshwater and marine ecotoxicity, but only marginally to global warming.
CBCP: A Method of Causality Extraction from Unstructured Financial Text

Extracting causality information from unstructured natural language text is a challenging problem in natural language processing. However, there are no mature special causality extraction systems. Most people use basic sequence labeling methods, such as BERT-CRF model, to extract causal elements from unstructured text and the results are usually not well. At the same time, there is a large number of causal event relations in the field of finance. If we can extract enormous financial causality, this information will help us better understand the relationships between financial events and build related event evolutionary graphs in the future. In this paper, we propose a causality extraction method for this question, named CBCP (Center word-based BERT-CRF with Pattern extraction), which can directly extract cause elements and effect elements from unstructured text. Compared to BERT-CRF model, our model incorporates the information of center words as prior conditions and performs better in the performance of entity extraction. Moreover, our method combined with pattern can further improve the effect of extracting causality. Then we evaluate our method and compare it to the basic sequence labeling method. We prove that our method performs better than other basic extraction methods on causality extraction tasks in the finance field. At last, we summarize our work and prospect some future work.
Revealing cell populations catching the early stages of the human embryo development in naïve pluripotent stem cells

Naïve human pluripotent stem cells (hPSCs) are defined as the in vitro counterpart of the human preimplantation embryo’s epiblast and are used as a model system to study developmental processes. In this study, we report the discovery and characterization of distinct cell populations coexisting with epiblast-like cells in 5iLAF naïve human induced PSCs (hiPSCs) cultures. Noteworthily these populations closely resemble different cell types of the human embryo at early developmental stages. While epiblast-like cells represented the main cell population, interestingly we detected a cell population with gene and transposable element expression profile closely resembling the totipotent 8-Cell (8C) stage human embryo, and three cell populations analogous to trophectoderm (TE) cells at different stages of their maturation process: transition, early and mature stage. Thus, 5iLAF naïve hiPSCs cultures provide an excellent opportunity to model the earliest events of human embryogenesis, from the 8C stage to the peri-implantation period. Graphical abstract
EBBE-Text : Visualisation de la frontière de décision des réseaux de neurones en classification automatique de textes

Résumé. En classiﬁcation automatique de textes, de nombreux travaux récents portent sur l’interprétation des réseaux de neurones par la production d’explications des prédictions. L’approche originale présentée dans cet article consiste à visualiser la frontière de décision et le positionnement des données vis-à-vis de celle-ci offrant ainsi une nouvelle approche de l’explication. Notre méthode calcule tout d’abord un espace de représentation des phrases, puis en exploite la structure linéaire aﬁn de visualiser la répartition et le regroupement des données autour de la frontière de décision. Le principal apport de notre méthode est le processus de visualisation de la frontière de décision, permettant d’explorer la distance à la frontière de décision (et donc la certitude d’un réseau en ses prédictions)
Why a Naive Way to Combine Symbolic and Latent Knowledge Base Completion Works Surprisingly Well

We compare a rule-based approach for knowledge graph completion against current state-of-the-art, which is based on embeddings. Instead of focusing on aggregated metrics, we look at several examples that illustrate essential diﬀerences between symbolic and latent approaches. Based on our insights, we construct a simple method to combine the outcome of rule-based and latent approaches in a post-processing step. Our method improves the results constantly for each model and dataset used in our experiments
Measuring Attentional Distraction in Children With ADHD Using Virtual Reality Technology With Eye-Tracking

Objective: Distractions inordinately impair attention in children with Attention-Deficit Hyperactivity Disorder (ADHD) but examining this behavior under real-life conditions poses a challenge for researchers and clinicians. Virtual reality (VR) technologies may mitigate the limitations of traditional laboratory methods by providing a more ecologically relevant experience. The use of eye-tracking measures to assess attentional functioning in a VR context in ADHD is novel. In this proof of principle project, we evaluate the temporal dynamics of distraction via eye-tracking measures in a VR classroom setting with 20 children diagnosed with ADHD between 8 and 12 years of age. Method: We recorded continuous eye movements while participants performed math, Stroop, and continuous performance test (CPT) tasks with a series of “real-world” classroom distractors presented. We analyzed the impact of the distractors on rates of on-task performance and on-task, eye-gaze (i.e., looking at a classroom whiteboard) versus off-task eye-gaze (i.e., looking away from the whiteboard). Results: We found that while children did not always look at distractors themselves for long periods of time, the presence of a distractor disrupted on-task gaze at task-relevant whiteboard stimuli and lowered rates of task performance. This suggests that children with attention deficits may have a hard time returning to tasks once those tasks are interrupted, even if the distractor itself does not hold attention. Eye-tracking measures within the VR context can reveal rich information about attentional disruption. Conclusions: Leveraging virtual reality technology in combination with eye-tracking measures is well-suited to advance the understanding of mechanisms underlying attentional impairment in naturalistic settings. Assessment within these immersive and well-controlled simulated environments provides new options for increasing our understanding of distractibility and its potential impact on the development of interventions for children with ADHD.
Balance Impairment in Fahr’s Disease: Mixed Signs of Parkinsonism and Cerebellar Disorder. A Case Study

Fahr’s disease is a rare idiopathic degenerative disease characterized by calcifications in the brain, and has also been associated with balance impairment. However, a detailed analysis of balance in these patients has not been performed. A 69-year-old woman with Fahr’s disease presented with a long-lasting subjective imbalance. Balance was analyzed using both clinical (EquiScale, Timed Up and Go test, and Dizziness Handicap Inventory-short form) and instrumented tests (the sway of the body center of mass during quiet, perturbed, and self-perturbed stance, and the peak curvature of the center of mass during single stance while walking on a force-treadmill). The patient’s balance was normal during clinical tests and walking. However, during standing, a striking impairment in vestibular control of balance emerged. The balance behavior displayed mixed parkinsonian (e.g., slowness and reduced amplitude of movement) and cerebellar (e.g., increased sway during standing in all conditions and decomposition of movement) features, with a discrepancy between the high severity of the static and the low severity of the dynamic balance impairment. The balance impairment characteristics outlined in this study could help neurologists and physiatrists detect, stage, and treat this rare condition.
Regional Differences in Mucociliary Clearance in the Upper and Lower Airways

As the nasal cavity is the portal of entry for inspired air in mammals, this region is exposed to the highest concentration of inhaled particulate matter and pathogens, which must be removed to keep the lower airways sterile. Thus, one might expect vigorous removal of these substances via mucociliary clearance (MCC) in this region. We have investigated the rate of MCC in the murine nasal cavity compared to the more distal airways (trachea). The rate of MCC in the nasal cavity (posterior nasopharynx, PNP) was ∼3–4× greater than on the tracheal wall. This appeared to be due to a more abundant population of ciliated cells in the nasal cavity (∼80%) compared to the more sparsely ciliated trachea (∼40%). Interestingly, the tracheal ventral wall exhibited a significantly lower rate of MCC than the tracheal posterior membrane. The trachealis muscle underlying the ciliated epithelium on the posterior membrane appeared to control the surface architecture and likely in part the rate of MCC in this tracheal region. In one of our mouse models (Bpifb1 KO) exhibiting a 3-fold increase in MUC5B protein in lavage fluid, MCC particle transport on the tracheal walls was severely compromised, yet normal MCC occurred on the tracheal posterior membrane. While a blanket of mucus covered the surface of both the PNP and trachea, this mucus appeared to be transported as a blanket by MCC only in the PNP. In contrast, particles appeared to be transported as discrete patches or streams of mucus in the trachea. In addition, particle transport in the PNP was fairly linear, in contrast transport of particles in the trachea often followed a more non-linear route. The thick, viscoelastic mucus blanket that covered the PNP, which exhibited ∼10-fold greater mass of mucus than did the blanket covering the surface of the trachea, could be transported over large areas completely devoid of cells (made by a breach in the epithelial layer). In contrast, particles could not be transported over even a small epithelial breach in the trachea. The thick mucus blanket in the PNP likely aids in particle transport over the non-ciliated olfactory cells in the nasal cavity and likely contributes to humidification and more efficient particle trapping in this upper airway region.