A compilation of known QSOs for the Gaia mission

Quasars are essential for astrometric in the sense that they are spatial stationary because of their large distance from the Sun. The European Space Agency (ESA) space astrometric satellite Gaia is scanning the whole sky with unprecedented accuracy up to a few muas level. However, Gaia's two fields of view observations strategy may introduce a parallax bias in the Gaia catalog. Since it presents no significant parallax, quasar is perfect nature object to detect such bias. More importantly, quasars can be used to construct a Celestial Reference Frame in the optical wavelengths in Gaia mission. In this paper, we compile the most reliable quasars existing in literatures. The final compilation (designated as Known Quasars Catalog for Gaia mission, KQCG) contains 1843850 objects, among of them, 797632 objects are found in Gaia DR1 after cross-identifications. This catalog will be very useful in Gaia mission

Protective effect of low-dose risedronate against osteocyte apoptosis and bone loss in ovariectomized rats

Osteocyte apoptosis is the first reaction to estrogen depletion, thereby stimulating osteoclastic bone resorption resulting in bone loss. We investigated the effects of two different risedronate (RIS) doses (high and low) on osteocyte apoptosis, osteoclast activity and bone loss in ovariectomized rats. Forty rats with ovariectomy (OVX) and sham ovariectomy (SHAM) were divided into 4 groups: 1) SHAM rats treated with saline (SHAM); 2) OVX rats treated with saline (OVX); 3) OVX rats treated with low-dose RIS (OVX-LR, 0.08 μg/kg/day); 4) OVX rats treated with high-dose RIS (OVX-HR, 0.8 μg/kg/day). All animals were sacrificed 90 days after surgery for the examinations of osteocyte apoptosis by caspase-3 staining, osteoclast activity by TRAP staining and bone volume by micro-CT scanning in lumbar vertebral cancellous bone. Both low and high dose RIS significantly reduced caspase-3 positive osteocytes, empty lacunae and TRAP positive osteoclasts in OVX rats. Although the difference in caspase-3 positive osteocytes was not significant between the OVX-LR and OVX-HR groups, numerically these cells were significantly more prevalent in OVX-HR (not OVX-LR) group than in SHAM group. TRAP positive osteoclasts were significantly higher in OVX-LR group than in SHAM or OVX-HR group. There was no significant difference in bone volume among the OVX-LR, OVX-HR and SHAM groups, but lower in OVX group alone. However, significant increase in trabecular thickness only occurred in OVX-LR group. We conclude that both low and high dose RIS significantly inhibit osteocyte apoptosis and osteoclast activity in OVX rats, but the low-dose RIS has weaker effect on osteoclast activity. However, low-dose RIS preserves cancellous bone mass and microarchitecture as well as high-dose RIS after estrogen depletion

Distributional Drift Adaptation with Temporal Conditional Variational Autoencoder for Multivariate Time Series Forecasting

Due to the nonstationary nature, the distribution of real-world multivariate time series (MTS) changes over time, which is known as distribution drift. Most existing MTS forecasting models greatly suffer from distribution drift and degrade the forecasting performance over time. Existing methods address distribution drift via adapting to the latest arrived data or self-correcting per the meta knowledge derived from future data. Despite their great success in MTS forecasting, these methods hardly capture the intrinsic distribution changes, especially from a distributional perspective. Accordingly, we propose a novel framework temporal conditional variational autoencoder (TCVAE) to model the dynamic distributional dependencies over time between historical observations and future data in MTSs and infer the dependencies as a temporal conditional distribution to leverage latent variables. Specifically, a novel temporal Hawkes attention mechanism represents temporal factors subsequently fed into feed-forward networks to estimate the prior Gaussian distribution of latent variables. The representation of temporal factors further dynamically adjusts the structures of Transformer-based encoder and decoder to distribution changes by leveraging a gated attention mechanism. Moreover, we introduce conditional continuous normalization flow to transform the prior Gaussian to a complex and form-free distribution to facilitate flexible inference of the temporal conditional distribution. Extensive experiments conducted on six real-world MTS datasets demonstrate the TCVAE's superior robustness and effectiveness over the state-of-the-art MTS forecasting baselines. We further illustrate the TCVAE applicability through multifaceted case studies and visualization in real-world scenarios.Comment: 13 pages, 6 figures, submitted to IEEE Transactions on Neural Networks and Learning Systems (TNNLS

SARS-CoV-2 Lambda Variant: Spatiotemporal Distribution and Potential Public Health Impact

Various SARS-CoV-2 variants have continually emerged since the summer of 2020. Recently, the spread and potential effects of the Lambda variant on public health have caused great scientific and public concern. The Lambda variant (C.37), first identified in Peru in December 2020, contains a novel deletion (Δ246–252) and two novel mutations, L452Q and F490S, not present in the ancestral strain and other variants. The Lambda variant was designated a variant of interest in April of 2021. By the end of July, this variant sequence was detected in more than 30 countries worldwide, mostly in South America. This study analyzed the global spatiotemporal distribution of the Lambda variant from the beginning of January to the end of July from publicly available data. The Lambda variant spread rapidly in Peru and became predominant in March. Circulation of the Lambda variant has also been observed in some neighboring countries, i.e., Argentina, Chile and Ecuador, where it has remained at remarkably low levels. The circulation of the Lambda variant in other countries in South America (e.g., Brazil and Colombia) and other regions of the world has also occurred at very low levels, even though this variant has been known for a long time. Multivariate linear regression analyses of the proportion of case fatalities attributable to the Lambda variant, the new deaths and the new confirmed cases per million (7-day rolling average) in Peru did not show significant associations. A review of the most recent data on the Lambda variant has suggested this variant’s relatively high infectivity in cultured cells and low neutralizing titers of convalescent sera and vaccine-elicited antibodies in vitro. However, the exact effects of this variant on clinical severity and vaccine effectiveness remain poorly documented. The currently authorized COVID-19 vaccines are still believed to provide efficient protection against the Lambda variant

Urania: Visualizing Data Analysis Pipelines for Natural Language-Based Data Exploration

Exploratory Data Analysis (EDA) is an essential yet tedious process for examining a new dataset. To facilitate it, natural language interfaces (NLIs) can help people intuitively explore the dataset via data-oriented questions. However, existing NLIs primarily focus on providing accurate answers to questions, with few offering explanations or presentations of the data analysis pipeline used to uncover the answer. Such presentations are crucial for EDA as they enhance the interpretability and reliability of the answer, while also helping users understand the analysis process and derive insights. To fill this gap, we introduce Urania, a natural language interactive system that is able to visualize the data analysis pipelines used to resolve input questions. It integrates a natural language interface that allows users to explore data via questions, and a novel data-aware question decomposition algorithm that resolves each input question into a data analysis pipeline. This pipeline is visualized in the form of a datamation, with animated presentations of analysis operations and their corresponding data changes. Through two quantitative experiments and expert interviews, we demonstrated that our data-aware question decomposition algorithm outperforms the state-of-the-art technique in terms of execution accuracy, and that Urania can help people explore datasets better. In the end, we discuss the observations from the studies and the potential future works

Reasoning over Hierarchical Question Decomposition Tree for Explainable Question Answering

Explainable question answering (XQA) aims to answer a given question and provide an explanation why the answer is selected. Existing XQA methods focus on reasoning on a single knowledge source, e.g., structured knowledge bases, unstructured corpora, etc. However, integrating information from heterogeneous knowledge sources is essential to answer complex questions. In this paper, we propose to leverage question decomposing for heterogeneous knowledge integration, by breaking down a complex question into simpler ones, and selecting the appropriate knowledge source for each sub-question. To facilitate reasoning, we propose a novel two-stage XQA framework, Reasoning over Hierarchical Question Decomposition Tree (RoHT). First, we build the Hierarchical Question Decomposition Tree (HQDT) to understand the semantics of a complex question; then, we conduct probabilistic reasoning over HQDT from root to leaves recursively, to aggregate heterogeneous knowledge at different tree levels and search for a best solution considering the decomposing and answering probabilities. The experiments on complex QA datasets KQA Pro and Musique show that our framework outperforms SOTA methods significantly, demonstrating the effectiveness of leveraging question decomposing for knowledge integration and our RoHT framework.Comment: has been accepted by ACL202

Mouse-adapted scrapie strains 139A and ME7 overcome species barrier to induce experimental scrapie in hamsters and changed their pathogenic features

<p>Abstract</p> <p>Background</p> <p>Transmissible spongiform encephalopathy (TSE) diseases are known to be zoonotic diseases that can infect different kinds of animals. The transmissibility of TSE, like that of other infectious diseases, shows marked species barrier, either being unable to infect heterologous species or difficult to form transmission experimentally. The similarity of the amino acid sequences of PrP among species is believed to be one of the elements in controlling the transmission TSE interspecies. Other factors, such as prion strains and host's microenvironment, may also participate in the process.</p> <p>Methods</p> <p>Two mouse-adapted strains 139A and ME7 were cerebrally inoculated to Golden hamsters. Presences of scrapie associate fibril (SAF) and PrP^Scin brains of the infected animals were tested by TEM assays and Western blots dynamically during the incubation periods. The pathogenic features of the novel prions in hamsters, including electrophoretic patterns, glycosylating profiles, immunoreactivities, proteinase K-resistances and conformational stabilities were comparatively evaluated. TSE-related neuropathological changes were assayed by histological examinations.</p> <p>Results</p> <p>After long incubation times, mouse-adapted agents 139A and ME7 induced experimental scrapie in hamsters, respectively, showing obvious spongiform degeneration and PrP^Scdeposits in brains, especially in cortex regions. SAF and PrP^Scin brains were observed much earlier than the onset of clinical symptoms. The molecular characteristics of the newly-formed PrP^Scin hamsters, 139A-ha and ME7-ha, were obviously distinct from the original mouse agents, however, greatly similar as that of a hamster-adapted scrapie strain 263 K. Although the incubation times and main disease signs of the hamsters of 139A-ha and ME7-ha were different, the pathogenic characteristics and neuropathological changes were highly similar.</p> <p>Conclusions</p> <p>This finding concludes that mouse-adapted agents 139A and ME7 change their pathogenic characteristics during the transmission to hamsters. The novel prions in hamsters' brains obtain new molecular properties with hamster-specificity.</p

Prediction of Progression to Severe Stroke in Initially Diagnosed Anterior Circulation Ischemic Cerebral Infarction

Purpose: Accurate prediction of the progression to severe stroke in initially diagnosed nonsevere patients with acute–subacute anterior circulation nonlacuna ischemic infarction (ASACNLII) is important in making clinical decision. This study aimed to apply a machine learning method to predict if the initially diagnosed nonsevere patients with ASACNLII would progress to severe stroke by using diffusion-weighted images and clinical information on admission.Methods: This retrospective study enrolled 344 patients with ASACNLII from June 2017 to August 2020 on admission, and 108 cases progressed to severe stroke during hospitalization within 3–21 days. The entire data were randomized into a training set (n = 271) and an independent test set (n = 73). A U-Net neural network was employed for automatic segmentation and volume measurement of the ischemic lesions. Predictive models were developed and used for evaluating the progression to severe stroke using different feature sets (the volume data, the clinical data, and the combination) and machine learning methods (random forest, support vector machine, and logistic regression).Results: The U-Net showed high correlation with manual segmentation in terms of Dice coefficient of 0.806 and R2 value of the volume measurements of 0.960 in the test set. The random forest classifier of the volume + clinical combination achieved the best area under the receiver operating characteristic curve of 0.8358 (95% CI 0.7321–0.9269), and the accuracy, sensitivity, and specificity were 0.7780 (0.7397–0.7945), 0.7695 (0.6102–0.9074), and 0.8686 (0.6923–1.0), respectively. The Shapley additive explanation diagram showed the volume variable as the most important predictor.Conclusion: The U-Net was fully automatic and showed a high correlation with manual segmentation. An integrated approach combining clinical variables and stroke lesion volumes that were derived from the advanced machine learning algorithms had high accuracy in predicting the progression to severe stroke in ASACNLII patients