The Fundamental Plane of Open Clusters

We utilize the data from the Apache Point Observatory Galactic Evolution Experiment-2 (APOGEE-2) in the fourteenth data release of the Sloan Digital Sky Survey (SDSS) to calculate the line-of-sight velocity dispersion $\sigma_{1D}$ of a sample of old open clusters (age larger than 100\,Myr) selected from the Milky Way open cluster catalog of Kharchenko et al. (2013). Together with their $K_s$ band luminosity $L_{K_s}$, and the half-light radius $r_{h}$ of the most probable members, we find that these three parameters show significant pairwise correlations among each other. Moreover, a fundamental plane-{\it like} relation among these parameters is found for the oldest open clusters (age older than 1\,Gyr), $L_{K_s}\propto\sigma_{1D}^{0.82\pm0.29}\cdot r_h^{2.19\pm0.52}$ with $rms \sim\, 0.31$\,mag in the $K_s$ band absolute magnitude. The existence of this relation, which deviates significantly from the virial theorem prediction, implies that the dynamical structures of the old open clusters are quite similar, when survived from complex dynamical evolution to age older than 1 Gyr.Comment: accepted publication for ApJ lette

Multi-task feature selection via supervised canonical graph matching for diagnosis of autism spectrum disorder

In this paper, we propose a novel framework for ASD diagnosis using structural magnetic resonance imaging (MRI). Our method deals explicitly with the distributional differences of gray matter (GM) and white matter (WM) features extracted from MR images. We project linearly the GM and WM features onto a canonical space where their correlations are mutually maximized. In this canonical space, features that are highly correlated with the class labels are selected for ASD diagnosis. In addition, graph matching is employed to preserve the geometrical relationships between samples when projected onto the canonical space. Our evaluations based on a public ASD dataset show that the proposed method outperforms all competing methods on all clinically important measures in differentiating ASD patients from healthy individuals

UKnow: A Unified Knowledge Protocol for Common-Sense Reasoning and Vision-Language Pre-training

This work presents a unified knowledge protocol, called UKnow, which facilitates knowledge-based studies from the perspective of data. Particularly focusing on visual and linguistic modalities, we categorize data knowledge into five unit types, namely, in-image, in-text, cross-image, cross-text, and image-text. Following this protocol, we collect, from public international news, a large-scale multimodal knowledge graph dataset that consists of 1,388,568 nodes (with 571,791 vision-related ones) and 3,673,817 triplets. The dataset is also annotated with rich event tags, including 96 coarse labels and 9,185 fine labels, expanding its potential usage. To further verify that UKnow can serve as a standard protocol, we set up an efficient pipeline to help reorganize existing datasets under UKnow format. Finally, we benchmark the performance of some widely-used baselines on the tasks of common-sense reasoning and vision-language pre-training. Results on both our new dataset and the reformatted public datasets demonstrate the effectiveness of UKnow in knowledge organization and method evaluation. Code, dataset, conversion tool, and baseline models will be made public

Superior efficacy of a human immunodeficiency virus vaccine combined with antiretroviral prevention in simian-human immunodeficiency virus-challenged nonhuman primates

International audienc

Comparison of negative and positive ion electrospray tandem mass spectrometry for the liquid chromatography tandem mass spectrometry analysis of oxidized deoxynucleosides

AbstractOxidized deoxynucleosides are widely used as biomarkers for DNA oxidation and oxidative stress assessment. Although gas chromatography mass spectrometry is widely used for the measurement of multiple DNA lesions, this approach requires complex sample preparation contributing to possible artifactual oxidation. To address these issues, a high performance liquid chromatography (HPLC)-tandem mass spectrometric (LC-MS/MS) method was developed to measure 8-hydroxy-2′-deoxyguanosine (8-OH-dG), 8-hydroxy-2′-deoxyadenosine (8-OH-dA), 2-hydroxy-2′-deoxyadenosine (2-OH-dA), thymidine glycol (TG), and 5-hydroxymethyl-2′-deoxyuridine (HMDU) in DNA samples with fast sample preparation. In order to selectively monitor the product ions of these precursors with optimum sensitivity for use during quantitative LC-MS/MS analysis, unique and abundant fragment ions had to be identified during MS/MS with collision-induced dissociation (CID). Positive and negative ion electrospray tandem mass spectra with CID were compared for the analysis of these five oxidized deoxynucleosides. The most abundant fragment ions were usually formed by cleavage of the glycosidic bond in both positive and negative ion modes. However, in the negative ion electrospray tandem mass spectra of 8-OH-dG, 2-OH-dA, and 8-OH-dA, cleavage of two bonds within the sugar ring produced abundant S1 type ions with loss of a neutral molecule weighing 90 u, [M − H − 90]−. The signal-to-noise ratio was similar for negative and positive ion electrospray MS/MS except in the case of thymidine glycol where the signal-to-noise was 100 times greater in negative ionization mode. Therefore, negative ion electrospray tandem mass spectrometry with CID would be preferred to positive ion mode for the analysis of sets of oxidized deoxynucleosides that include thymidine glycol. Investigation of the fragmentation pathways indicated some new general rules for the fragmentation of negatively charged oxidized nucleosides. When purine nucleosides contain a hydroxyl group in the C8 position, an S1 type product ion will dominate the product ions due to a six-membered ring hydrogen transfer process. Finally, a new type of fragment ion formed by elimination of a neutral molecule weighing 48 (CO2H4) from the sugar moiety was observed for all three oxidized purine nucleosides

MRI-Based Intelligence Quotient (IQ) Estimation with Sparse Learning

In this paper, we propose a novel framework for IQ estimation using Magnetic Resonance Imaging (MRI) data. In particular, we devise a new feature selection method based on an extended dirty model for jointly considering both element-wise sparsity and group-wise sparsity. Meanwhile, due to the absence of large dataset with consistent scanning protocols for the IQ estimation, we integrate multiple datasets scanned from different sites with different scanning parameters and protocols. In this way, there is large variability in these different datasets. To address this issue, we design a two-step procedure for 1) first identifying the possible scanning site for each testing subject and 2) then estimating the testing subject’s IQ by using a specific estimator designed for that scanning site. We perform two experiments to test the performance of our method by using the MRI data collected from 164 typically developing children between 6 and 15 years old. In the first experiment, we use a multi-kernel Support Vector Regression (SVR) for estimating IQ values, and obtain an average correlation coefficient of 0.718 and also an average root mean square error of 8.695 between the true IQs and the estimated ones. In the second experiment, we use a single-kernel SVR for IQ estimation, and achieve an average correlation coefficient of 0.684 and an average root mean square error of 9.166. All these results show the effectiveness of using imaging data for IQ prediction, which is rarely done in the field according to our knowledge

Correlation of 3'-phosphoadenosine-5'-phosphosulfate synthase 1 (PAPSS1) expression with clinical parameters and prognosis in esophageal squamous cell carcinoma

Background. In recent years, 3'- phosphoadenosine-5'-phosphosulfate synthase 1 (PAPSS1) has been found to be highly expressed in some cancers and significantly associated with prognosis. Nevertheless, the role of PAPSS1 in esophageal squamous cell carcinoma (ESCC) is poorly understood. Methods. In this study, PAPSS1 expression in ESCC samples was researched through real-time quantitative polymerase chain reaction (qPCR), immunohistochemistry (IHC), and western blot (WB) techniques. siRNA technology was then used to inhibit PAPSS1 expression in ESCC cells, and cytologic tests were conducted to research gene affection on cell apoptosis, proliferation, and migration. Then, the expression of Bcl2, Ki67, and Snail was detected using qPCR and WB tests. These experimental data were analyzed by GraphPad software, where the P-value <0.05 was statistically significant. Results. The results showed that PAPSS1 expression level in ESCC tissues was higher than in the adjacent tissues. The data also showed that PAPSS1 was significantly correlated with N stage, and that the patients with high expressions had longer survival time. After transfection for 48 hours, the cell apoptosis rate of siRNA-PAPSS1 transfected groups decreased significantly, whereas the cell proliferation rate and migration ability increased relative to the control. At the same time, the expression levels of Bcl2, Ki67 and Snail were all upregulated by siRNA-PAPSS1. PAPSS1, however, was suppressed. Conclusions. PAPSS1 may be an ESCC suppressor gene, and its specific molecular mechanism in ESCC needs to be further studied