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Gravitational-wave detections provide a novel way to determine the Hubble constant 1 – 3 , which is the current rate of expansion of the Universe. This ‘standard siren’ method, with the absolute distance calibration provided by the general theory of relativity, was used to measure the Hubble constant using the gravitational-wave detection of the binary neutron-star merger, GW170817, by the Laser Interferometer Gravitational-Wave Observatory (LIGO) and Virgo 4 , combined with optical identification of the host galaxy 5 , 6 NGC 4993. This independent measurement is of particular interest given the discrepancy between the value of the Hubble constant determined using type Ia supernovae via the local distance ladder (73.24 ± 1.74 kilometres per second per megaparsec) and the value determined from cosmic microwave background observations (67.4 ± 0.5 kilometres per second per megaparsec): these values differ 7 , 8 by about 3 σ . Local distance ladder observations may achieve a precision of one per cent within five years, but at present there are no indications that further observations will substantially reduce the existing discrepancies 9 . Here we show that additional gravitational-wave detections by LIGO and Virgo can be expected to constrain the Hubble constant to a precision of approximately two per cent within five years and approximately one per cent within a decade. This is because observing gravitational waves from the merger of two neutron stars, together with the identification of a host galaxy, enables a direct measurement of the Hubble constant independent of the systematics associated with other available methods. In addition to clarifying the discrepancy between existing low-redshift (local ladder) and high-redshift (cosmic microwave background) measurements, a precision measurement of the Hubble constant is of crucial value in elucidating the nature of dark energy 10 , 11 . Gravitational-wave observations of binary neutron-star mergers will enable precision measurements of the Hubble constant within five years.

The joint detection of the gravitational wave (GW) GW170817 and its electromagnetic (EM) counterparts GRB170817A and kilonova AT 2017gfo has triggered extensive study of the EM emission of binary neutron star mergers. A parameter which is common to and plays a key role in both the GW and the EM analyses is the viewing angle of the binary’s orbit. If a binary is viewed from different angles, the amount of GW energy changes (implying that orientation and distance are correlated) and the EM signatures can vary, depending on the structure of the emission. Information about the viewing angle of the binary orbital plane is therefore crucial to the interpretation of both the GW and the EM data and can potentially be extracted from either side. In the first part of this study, we present a systematic analysis of how well the viewing angle of binary neutron stars can be measured from the GW data. We show that if the sky position and the redshift of the binary can be identified via the EM counterpart and an associated host galaxy, then for 50% of the systems the viewing angle can be constrained to≤7°uncertainty from the GW data, independent of electromagnetic emission models. On the other hand, if no redshift measurement is available, the measurement of the viewing angle with GWs alone is not informative, unless the true viewing angle is close to 90°. This holds true even if the sky position is measured independently. Then, we consider the case where some constraints on the viewing angle can be placed from the EM data themselves. We show that the EM measurements can then be used in the analysis of GW data to improve the precision of the luminosity distance, and hence of the Hubble constant, by a factor of 2–3.

Purpose The purpose of this paper is to develop an extended Push-Pull-Mooring-Habit (PPMH) framework in order to better understand users’ intention of switching from offline to an online real-person English learning platform service. Design/methodology/approach Based on 301 valid responses collected from an online survey questionnaire, structural equation modeling was employed to examine the research model. Findings The causal model was validated using SmartPLS 3.0, and all study hypotheses were supported. The results show that push effects (learning convenience, service quality and perceived price), pull effects (e-learning motivation, perceived usefulness), mooring effects (learning engagement, switching cost and social presences) and habit effects (relationship inertia) all significantly influence users’ switching intentions from offline to an online real-person English learning platform. Practical implications The findings should help online English learning service providers and marketers to understand the intention of offline English learning users to switch to an online real-person English learning platform, and develop related theories, services and regulations. Originality/value The present study extends the prior research of an online real-person English learning platform by providing PPMH as the general framework and demonstrating its efficacy in explaining user switching intentions.

Background Difficulties in emotion regulation (DER) are widely considered to underlie anxiety and depression. Given the prevalence of anxiety and depression in adolescents and the fact that adolescence is a key period for the development of emotion regulation ability, it is important to examine how DER is related to anxiety and depression in adolescents in clinical settings. Methods In the present study, we assessed 209 adolescents in clinical settings using the Difficulties in Emotion Regulation Scale (DERS) and the Hospital Anxiety and Depression Scale (HADS) and examined the associations between six components of DER and 14 symptoms of anxiety and depression. We used network analysis, constructed circular and multidimensional scaling (MDS) networks, and calculated network centrality, bridge centrality, and stability of centrality indices. Results The results showed that: (1) The global centrality index shows that the Strategy component (i.e., lack of access to strategies) is the center in the whole network, ranking highest in strength, closeness, betweenness, and expected influence. (2) The MDS network showed a closeness of anxiety and depression symptoms, while Awareness component (i.e., lack of emotional awareness) stayed away from other DER components, but Awareness is close to some depression symptoms. (3) The bridge nodes of three groups, Strategy from DERS, Worry and Relax from anxiety symptoms, and Cheerful and Slow from depression symptoms, had the strongest relationships with the other groups. Conclusion Lack of access to strategies remains in the center not only in DER but also in the DER-anxiety-depression network, while lack of awareness is close to depression but not to anxiety. Worrying thoughts and inability to relax are the bridging symptoms for anxiety, while lack of cheerful emotions and slowing down are the bridging symptoms for depression. These findings suggest that making emotion regulation strategies more accessible to patients and reducing these bridging symptoms may yield the greatest rewards for anxiety and depression therapy.

Cryptococcus is the most common fungus causing high-morbidity and -mortality human meningitis. This encapsulated yeast has a unique propensity to travel to the central nervous system to produce disease. Cryptococcus neoformans is a major human central nervous system (CNS) fungal pathogen causing considerable morbidity and mortality. In this study, we provide the widest view to date of the yeast transcriptome directly from the human subarachnoid space and within cerebrospinal fluid (CSF). We captured yeast transcriptomes from C. neoformans of various genotypes in 31 patients with cryptococcal meningoencephalitis as well as several Cryptococcus gattii infections. Using transcriptome sequencing (RNA-seq) analyses, we compared the in vivo yeast transcriptomes to those from other environmental conditions, including in vitro growth on nutritious media or artificial CSF as well as samples collected from rabbit CSF at two time points. We ranked gene expressions and identified genetic patterns and networks across these diverse isolates that reveal an emphasis on carbon metabolism, fatty acid synthesis, transport, cell wall structure, and stress-related gene functions during growth in CSF. The most highly expressed yeast genes in human CSF included those known to be associated with survival or virulence and highlighted several genes encoding hypothetical proteins. From that group, a gene encoding the CMP1 putative glycoprotein (CNAG_06000) was selected for functional studies. This gene was found to impact the virulence of Cryptococcus in both mice and the CNS rabbit model, in agreement with a recent study also showing a role in virulence. This transcriptional analysis strategy provides a view of regulated yeast genes across genetic backgrounds important for human CNS infection and a relevant resource for the study of cryptococcal genes, pathways, and networks linked to human disease. IMPORTANCE Cryptococcus is the most common fungus causing high-morbidity and -mortality human meningitis. This encapsulated yeast has a unique propensity to travel to the central nervous system to produce disease. In this study, we captured transcriptomes of yeasts directly out of the human cerebrospinal fluid, the most concerning site of infection. By comparing the RNA transcript levels with other conditions, we gained insights into how the basic machinery involved in metabolism and environmental responses enable this fungus to cause disease at this body site. This approach was applied to clinical isolates with diverse genotypes to begin to establish a genotype-agnostic understanding of how the yeast responds to stress. Based on these results, future studies can focus on how these genes and their pathways and networks can be targeted with new therapeutics and possibly classify yeasts with bad infection outcomes.

PurposeThis study aimed to develop and validate an online live streaming perceived servicescape (OLSPS) scale that can help platform service providers to develop strategies for new live streaming channel promotions.Design/methodology/approachThis study conceptualized the construct of OLSPS and the four-phase procedure of the 66-item OLSPS scale development, including item generation, item purification, scale validation, measure application and testing of hypotheses. It also provided a research framework to assess audiences' cognition and behavioral intention, and an online survey on 420 live streaming users (social platforms, n = 210; native platforms, n = 210) was conducted.FindingsThis study developed and validated a 35-item OLSPS scale with eight dimensions. The results of the empirical model showed that OLSPS is positively correlated with the audiences' cognition and behavioral intention. Furthermore, parasocial interaction experience showed a positive moderation on channel trust.Originality/valueThis study is a pioneering effort to develop and validate an OLSPS scale. The results could be helpful for researchers in building OLSPS and for managers in assessing and promoting users' acceptance of online live streaming platforms.

Micro-expression has gained a lot of attention because of its potential applications (e.g., transportation security) and theoretical implications (e.g., expression of emotions). However, the duration of micro-expression, which is considered as the most important characteristic, has not been firmly established. The present study provides evidence to define the duration of micro-expression by collecting and analyzing the fast facial expressions which are the leakage of genuine emotions. Participants were asked to neutralize their faces while watching emotional video episodes. Among the more than 1,000 elicited facial expressions, 109 leaked fast expressions (less than 500 ms) were selected and analyzed. The distribution curves of total duration and onset duration for the micro-expressions were presented. Based on the distribution and estimation, it seems suitable to define micro-expression by its total duration less than 500 ms or its onset duration less than 260 ms. These findings may facilitate further studies of micro-expressions in the future.

Background Depression in middle-aged individuals is influenced by multiple factors; however, research focusing on this demographic remains limited. The social-ecological system framework explores how interactions among individual characteristics, social relationships, and environmental contexts contribute to health outcomes. This study aimed to assess the prevalence, core manifestations, and influencing factors of depression through a comprehensive model grounded in the social-ecological system. Methods Using data from the 2018 Chinese Labor Dynamic Survey, a total of 3,799 middle-aged individuals (mean age: 52.35 years; 53.75% female) were included. Depression was assessed using the Center for Epidemiological Studies Depression Scale, with a cut-off score of 36. We used a graphical gaussian model to identify the measurement network and core symptoms of midlife depression, and logistic regression to identify influencing factors. A social-ecological model of middle-aged depression was constructed through a Mixed Graphical Model. Results 23.74% of participants exhibited clinically significant depression. Feelings of disgust (Strength = 2.18, Expected Influence = 1.30) and incapacity (Strength = 1.29, Expected Influence = 1.57) emerged as the most central symptoms. Higher global strength (GS = 8.89, p  < 0.001) of the network suggested stronger associations and mutual exacerbation of symptoms. Logistic regression showed that education, exercise, and subjective feeling were associated with depression risk ( p  < 0.05). The social-ecological system emphasized the importance of education (Strength = 2.50, Expected Influence = 2.50) and complex interaction between subjective and objective influencing factors. Conclusion This study found middle-aged individuals had a high prevalence of depression, characterized by feelings of disgust and incapacity. Influencing factors spanned individual, social and environmental levels. These results emphasize the role of education in depressive individuals, providing guidance for potential future interventions.

The development of civilization and the preservation of environmental ecosystems are strongly dependent on water resources. Typically, an insufficient supply of surface water resources for domestic, industrial, and agricultural needs is supplemented with groundwater resources. However, groundwater is a natural resource that must accumulate over many years and cannot be recovered after a short period of recharge. Therefore, the long-term management of groundwater resources is an important issue for sustainable development. The accurate prediction of groundwater levels is the first step in evaluating total water resources and their allocation. However, in the process of data collection, data may be lost due to various factors. Filling in missing data is a main problem that any research field must address. It is well known that to maintain data integrity, one effective approach is missing value imputation (MVI). In addition, it has been demonstrated that machine learning may be a better tool. Therefore, the main purpose of this study was to utilize a generative adversarial network (GAN) that consists of a generative model and a discriminative model for imputation. Although the GAN could not capture the groundwater level endpoints in every section, the overall simulation performance was still excellent to some extent. Our results show that the GAN can improve the accuracy of water resource evaluations. In the current study, two interdisciplinary deep learning methods, univariate and Seq2val (sequence-to-value), were used for groundwater level estimation. In addition to addressing the significance of the parameter conditions, the advantages and disadvantages of these two models in hydrological simulations were also discussed and compared. Regarding parameter selection, the simulation results for univariate analysis were better than those for Seq2val analysis. Finally, univariate was employed to examine the limits of the models in long-term water level simulations. Our results suggest that the accuracy of CNNs is better, while LSTM is better for the simulation of multistep prediction. Therefore, the interdisciplinary deep learning approach may be beneficial for providing a better evaluation of water resources.

Metabolically quiescent pathogens can persist in a viable non-replicating state for months or even years. For certain infectious diseases, such as tuberculosis, cryptococcosis, histoplasmosis, latent infection is a corollary of this dormant state, which has the risk for reactivation and clinical disease. During murine cryptococcosis and macrophage uptake, stress and host immunity induce Cryptococcus neoformans heterogeneity with the generation of a sub-population of yeasts that manifests a phenotype compatible with dormancy (low stress response, latency of growth). In this subpopulation, mitochondrial transcriptional activity is regulated and this phenotype has been considered as a hallmark of quiescence in stem cells. Based on these findings, we worked to reproduce this phenotype in vitro and then standardize the experimental conditions to consistently generate this dormancy in C. neoformans. We found that incubation of stationary phase yeasts (STAT) in nutriment limited conditions and hypoxia for 8 days (8D-HYPOx) was able to produced cells that mimic the phenotype obtained in vivo. In these conditions, mortality and/or apoptosis occurred in less than 5% of the yeasts compared to 30-40% of apoptotic or dead yeasts upon incubation in normoxia (8D-NORMOx). Yeasts in 8D-HYPOx harbored a lower stress response, delayed growth and less that 1% of culturability on agar plates, suggesting that these yeasts are viable but non culturable cells (VBNC). These VBNC were able to reactivate in the presence of pantothenic acid, a vitamin that is known to be involved in quorum sensing and a precursor of acetyl-CoA. Global metabolism of 8D-HYPOx cells showed some specific requirements and was globally shut down compared to 8D-NORMOx and STAT conditions. Mitochondrial analyses showed that the mitochondrial mass increased with mitochondria mostly depolarized in 8D-HYPOx compared to 8D-NORMox, with increased expression of mitochondrial genes. Proteomic and transcriptomic analyses of 8D-HYPOx revealed that the number of secreted proteins and transcripts detected also decreased compared to 8D-NORMOx and STAT, and the proteome, secretome and transcriptome harbored specific profiles that are engaged as soon as four days of incubation. Importantly, acetyl-CoA and the fatty acid pathway involving mitochondria are required for the generation and viability maintenance of VBNC. Altogether, these data show that we were able to generate for the first time VBNC phenotype in C. neoformans. This VBNC state is associated with a specific metabolism that should be further studied to understand dormancy/quiescence in this yeast.