A Frustratingly Easy Plug-and-Play Detection-and-Reasoning Module for Chinese Spelling Check

In recent years, Chinese Spelling Check (CSC) has been greatly improved by designing task-specific pre-training methods or introducing auxiliary tasks, which mostly solve this task in an end-to-end fashion. In this paper, we propose to decompose the CSC workflow into detection, reasoning, and searching subtasks so that the rich external knowledge about the Chinese language can be leveraged more directly and efficiently. Specifically, we design a plug-and-play detection-and-reasoning module that is compatible with existing SOTA non-autoregressive CSC models to further boost their performance. We find that the detection-and-reasoning module trained for one model can also benefit other models. We also study the primary interpretability provided by the task decomposition. Extensive experiments and detailed analyses demonstrate the effectiveness and competitiveness of the proposed module.Comment: Accepted for publication in Findings of EMNLP 202

Is there a common latent cognitive construct for dementia estimation across two Chinese cohorts?

INTRODUCTION: It is valuable to identify common latent cognitive constructs for dementia prevalence estimation across Chinese aging cohorts. METHODS: Based on cognitive measures of 12015 Chinese Longitudinal Healthy Longevity Survey (CLHLS; 13 items) and 6623 China Health and Retirement Longitudinal Study (CHARLS; 9 items) participants aged 65 to 99 in 2018, confirmatory factor analysis was applied to identify latent cognitive constructs, and to estimate dementia prevalence compared to Mini-Mental State Examination (MMSE) and nationwide estimates of the literature. RESULTS: A common three-factor cognitive construct of orientation, memory, and executive function and language was found for both cohorts with adequate model fits. Crude dementia prevalence estimated by factor scores was similar to MMSE in CLHLS, and was more reliable in CHARLS. Age-standardized dementia estimates of CLHLS were lower than CHARLS among those aged 70+, which were close to the nationwide prevalence reported by the COAST study and Global Burden of Disease. DISCUSSION: We verified common three-factor cognitive constructs for both cohorts, providing an approach to estimate dementia prevalence at the national level. HIGHLIGHTS: Common three-factor cognitive constructs were identified in Chinese Longitudinal Healthy Longevity Survey (CLHLS) and China Health and Retirement Longitudinal Study (CHARLS). Crude dementia estimates using factor scores were reliable in both cohorts. Estimates of CHARLS were close to current evidence, but higher than that of CLHLS

Contrasting fate of western Third Pole's water resources under 21st century climate change

Seasonal melting of glaciers and snow from the western Third Pole (TP) plays important role in sustaining water supplies downstream. However, the future water availability of the region, and even today’s runoff regime, are both hotly debated and inadequately quantified. Here, we characterize the contemporary flow regimes and systematically assess the future evolution of total water availability, seasonal shifts, and dry and wet discharge extremes in four most meltwater-dominated basins in the western TP, by using a process-based, well-established glacier-hydrology model, well-constrained historical reference climate data, and the ensemble of 22 global climate models with an advanced statistical downscaling and bias correction technique. We show that these basins face sharply diverging water futures under 21st century climate change. In RCP scenarios 4.5 and 8.5, increased precipitation and glacier runoff in the Upper Indus and Yarkant basins more than compensate for decreased winter snow accumulation, boosting annual and summer water availability through the end of the century. In contrast, the Amu and Syr Darya basins will become more reliant on rainfall runoff as glacier ice and seasonal snow decline. Syr Darya summer river-flows, already low, will fall by 16–30% by end-of-century, and striking increases in peak flood discharge (by >60%), drought duration (by >1 month) and drought intensity (by factor 4.6) will compound the considerable water-sharing challenges on this major transboundary river

Prediction of myopia development among Chinese school-aged children using refraction data from electronic medical records: A retrospective, multicentre machine learning study

Background Electronic medical records provide large-scale real-world clinical data for use in developing clinical decision systems. However, sophisticated methodology and analytical skills are required to handle the large-scale datasets necessary for the optimisation of prediction accuracy. Myopia is a common cause of vision loss. Current approaches to control myopia progression are effective but have significant side effects. Therefore, identifying those at greatest risk who should undergo targeted therapy is of great clinical importance. The objective of this study was to apply big data and machine learning technology to develop an algorithm that can predict the onset of high myopia, at specific future time points, among Chinese school-aged children. Methods and findings Real-world clinical refraction data were derived from electronic medical record systems in 8 ophthalmic centres from January 1, 2005, to December 30, 2015. The variables of age, spherical equivalent (SE), and annual progression rate were used to develop an algorithm to predict SE and onset of high myopia (SE ≤ −6.0 dioptres) up to 10 years in the future. Random forest machine learning was used for algorithm training and validation. Electronic medical records from the Zhongshan Ophthalmic Centre (a major tertiary ophthalmic centre in China) were used as the training set. Ten-fold cross-validation and out-of-bag (OOB) methods were applied for internal validation. The remaining 7 independent datasets were used for external validation. Two population-based datasets, which had no participant overlap with the ophthalmic-centre-based datasets, were used for multi-resource validation testing. The main outcomes and measures were the area under the curve (AUC) values for predicting the onset of high myopia over 10 years and the presence of high myopia at 18 years of age. In total, 687,063 multiple visit records (≥3 records) of 129,242 individuals in the ophthalmic-centre-based electronic medical record databases and 17,113 follow-up records of 3,215 participants in population-based cohorts were included in the analysis. Our algorithm accurately predicted the presence of high myopia in internal validation (the AUC ranged from 0.903 to 0.986 for 3 years, 0.875 to 0.901 for 5 years, and 0.852 to 0.888 for 8 years), external validation (the AUC ranged from 0.874 to 0.976 for 3 years, 0.847 to 0.921 for 5 years, and 0.802 to 0.886 for 8 years), and multi-resource testing (the AUC ranged from 0.752 to 0.869 for 4 years). With respect to the prediction of high myopia development by 18 years of age, as a surrogate of high myopia in adulthood, the algorithm provided clinically acceptable accuracy over 3 years (the AUC ranged from 0.940 to 0.985), 5 years (the AUC ranged from 0.856 to 0.901), and even 8 years (the AUC ranged from 0.801 to 0.837). Meanwhile, our algorithm achieved clinically acceptable prediction of the actual refraction values at future time points, which is supported by the regressive performance and calibration curves. Although the algorithm achieved balanced and robust performance, concerns about the compromised quality of real-world clinical data and over-fitting issues should be cautiously considered. Conclusions To our knowledge, this study, for the first time, used large-scale data collected from electronic health records to demonstrate the contribution of big data and machine learning approaches to improved prediction of myopia prognosis in Chinese school-aged children. This work provides evidence for transforming clinical practice, health policy-making, and precise individualised interventions regarding the practical control of school-aged myopia.This study was funded by the National Key R&D Program of China (2018YFC0116500), the National Natural Science Foundation of China (91546101, 81822010), the Guangdong Science and Technology Innovation Leading Talents (2017TX04R031), and Youth Pearl River Scholar in Guangdong (2016)

Dynamic Subunit Stoichiometry of Kv4.3-KChIP4A Channel Complexes Visualized by Single-Molecule Subunit Counting

SCI(E)MEETING ABSTRACT2279A-279A10

Different KChIPs Compete for Heteromultimeric Assembly with Pore-Forming Kv4 Subunits

Auxiliary Kv channel-interacting proteins 1-4 (KChIPs1-4) coassemble with pore-forming Kv4 alpha-subunits to form channel complexes underlying somatodendritic subthreshold A-type current that regulates neuronal excitability. It has been hypothesized that different KChIPs can competitively bind to Kv4 alpha-subunit to form variable channel complexes that can exhibit distinct biophysical properties for modulation of neural function. In this study, we use single-molecule subunit counting by total internal reflection fluorescence microscopy in combinations with electrophysiology and biochemistry to investigate whether different isoforms of auxiliary KChIPs, KChIP4a, and KChIP4bl, can compete for binding of Kv4.3 to coassemble heteromultimeric channel complexes for modulation of channel function. To count the number of photobleaching steps solely from cell membrane, we take advantage of a membrane tethered k-ras-CAAX peptide that anchors cytosolic KChIP4 proteins to the surface for reduction of background noise. Single-molecule subunit counting reveals that the number of KChIP4 isoforms in Kv4.3-KChIP4 complexes can vary depending on the KChIP4 expression level. Increasing the amount of KChIP4bl gradually reduces bleaching steps of KChIP4a isoform proteins, and vice versa. Further analysis of channel gating kinetics from different Kv4-KChIP4 subunit compositions confirms that both KChIP4a and KChIP4bl can modulate the channel complex function upon coassembly. Taken together, our findings show that auxiliary KChIPs can heteroassemble with Kv4 in a competitive manner to form heteromultimeric Kv4-KChIP4 channel complexes that are biophysically distinct and regulated under physiological or pathological conditions.Ministry of Science and Technology of China [2013CB531302, 2014ZX09507003-006]; National Science Foundation of China [31370741, 81221002]SCI(E)[email protected]

A Novel bHLH Transcription Factor PtrbHLH66 from Trifoliate Orange Positively Regulates Plant Drought Tolerance by Mediating Root Growth and ROS Scavenging

Drought limits citrus yield and fruit quality worldwide. The basic helix-loop-helix (bHLH) transcription factors (TFs) are involved in plant response to drought stress. However, few bHLH TFs related to drought response have been functionally characterized in citrus. In this study, a bHLH family gene, named PtrbHLH66, was cloned from trifoliate orange. PtrbHLH66 contained a highly conserved bHLH domain and was clustered closely with bHLH66 homologs from other plant species. PtrbHLH66 was localized to the nucleus and had transcriptional activation activity. The expression of PtrbHLH66 was significantly induced by polyethylene glycol 6000 (PEG6000) and abscisic acid (ABA) treatments. Ectopic expression of PtrbHLH66 promoted the seed germination and root growth, increased the proline and ABA contents and the activities of antioxidant enzymes, but reduced the accumulation of malondialdehyde (MDA) and reactive oxygen species (ROS) under drought stress, resulting in enhanced drought tolerance of transgenic Arabidopsis. In contrast, silencing the PtrbHLH66 homolog in lemon plants showed the opposite effects. Furthermore, under drought stress, the transcript levels of 15 genes involved in ABA biosynthesis, proline biosynthesis, ROS scavenging and drought response were obviously upregulated in PtrbHLH66 ectopic-expressing Arabidopsis but downregulated in PtrbHLH66 homolog silencing lemon. Thus, our results suggested that PtrbHLH66 acted as a positive regulator of plant drought resistance by regulating root growth and ROS scavenging

Comparative effectiveness research on patients with acute ischemic stroke using Markov decision processes

Abstract Background Several methodological issues with non-randomized comparative clinical studies have been raised, one of which is whether the methods used can adequately identify uncertainties that evolve dynamically with time in real-world systems. The objective of this study is to compare the effectiveness of different combinations of Traditional Chinese Medicine (TCM) treatments and combinations of TCM and Western medicine interventions in patients with acute ischemic stroke (AIS) by using Markov decision process (MDP) theory. MDP theory appears to be a promising new method for use in comparative effectiveness research. Methods The electronic health records (EHR) of patients with AIS hospitalized at the 2nd Affiliated Hospital of Guangzhou University of Chinese Medicine between May 2005 and July 2008 were collected. Each record was portioned into two "state-action-reward" stages divided by three time points: the first, third, and last day of hospital stay. We used the well-developed optimality technique in MDP theory with the finite horizon criterion to make the dynamic comparison of different treatment combinations. Results A total of 1504 records with a primary diagnosis of AIS were identified. Only states with more than 10 (including 10) patients' information were included, which gave 960 records to be enrolled in the MDP model. Optimal combinations were obtained for 30 types of patient condition. Conclusion MDP theory makes it possible to dynamically compare the effectiveness of different combinations of treatments. However, the optimal interventions obtained by the MDP theory here require further validation in clinical practice. Further exploratory studies with MDP theory in other areas in which complex interventions are common would be worthwhile.</p