Deep Reinforcement Learning of Graph Matching

Graph matching (GM) under node and pairwise constraints has been a building block in areas from combinatorial optimization, data mining to computer vision, for effective structural representation and association. We present a reinforcement learning solver for GM i.e. RGM that seeks the node correspondence between pairwise graphs, whereby the node embedding model on the association graph is learned to sequentially find the node-to-node matching. Our method differs from the previous deep graph matching model in the sense that they are focused on the front-end feature extraction and affinity function learning, while our method aims to learn the back-end decision making given the affinity objective function whether obtained by learning or not. Such an objective function maximization setting naturally fits with the reinforcement learning mechanism, of which the learning procedure is label-free. These features make it more suitable for practical usage. Extensive experimental results on both synthetic datasets, Willow Object dataset, Pascal VOC dataset, and QAPLIB showcase superior performance regarding both matching accuracy and efficiency. To our best knowledge, this is the first deep reinforcement learning solver for graph matching

Transcriptional regulation of PEN-2, a key component of the γ-secretase complex, by CREB

Gamma-secretase, which is responsible for the intramembranous cleavage of Alzheimer's P-amyloid precursor protein (APP), the signaling receptor Notch, and many other substrates, is a multiprotein complex consisting of at least four components: presenilin (PS), nicastrin, APH-1, and PEN-2. Despite the fact that PEN-2 is known to mediate endoproteolytic cleavage of full-length PS and APH-1 and nicastrin are required for maintaining the stability of the complex, the detailed physiological function of each component remain elusive. Unlike that of PS, the transcriptional regulation of PEN-2, APH-1, and nicastrin has not been investigated. Here, we characterized the upstream regions of the human PEN-2 gene and identified a 238-bp fragment located 353 bp upstream of the translational start codon as the key region necessary for the promoter activity. Further analysis revealed a CREB binding site located in the 238-bp region that is essential for the transcriptional activity of the PEN-2 promoter. Mutation of the CREB site abolished the transcriptional activity of the PEN-2 promoter. Electrophoretic mobility shift assays and chromatin immunoprecipitation analysis showed the binding of CREB to the PEN-2 promoter region both in vitro and in vivo. Activation of the CREB transcriptional factor by forskolin dramatically promoted the expression of PEN-2 mRNA and protein, whereas the other components of the gamma-secretase complex remained unaffected. Forskolin treatment slightly increases the secretion of soluble APP alpha and A beta without affecting Notch cleavage. These results demonstrate that expression of PEN-2 is regulated by CREB and suggest that the specific control of PEN-2 expression may imply additional physiological functions uniquely assigned to PEN-2

Signal-induced Brd4 release from chromatin is essential for its role transition from chromatin targeting to transcriptional regulation

Bromodomain-containing protein Brd4 is shown to persistently associate with chromosomes during mitosis for transmitting epigenetic memory across cell divisions. During interphase, Brd4 also plays a key role in regulating the transcription of signal-inducible genes by recruiting positive transcription elongation factor b (P-TEFb) to promoters. How the chromatin-bound Brd4 transits into a transcriptional regulation mode in response to stimulation, however, is largely unknown. Here, by analyzing the dynamics of Brd4 during ultraviolet or hexamethylene bisacetamide treatment, we show that the signal-induced release of chromatin-bound Brd4 is essential for its functional transition. In untreated cells, almost all Brd4 is observed in association with interphase chromatin. Upon treatment, Brd4 is released from chromatin, mostly due to signal-triggered deacetylation of nucleosomal histone H4 at acetylated-lysine 5/8 (H4K5ac/K8ac). Through selective association with the transcriptional active form of P-TEFb that has been liberated from the inactive multi-subunit complex in response to treatment, the released Brd4 mediates the recruitment of this active P-TEFb to promoter, which enhances transcription at the stage of elongation. Thus, through signal-induced release from chromatin and selective association with the active form of P-TEFb, the chromatin-bound Brd4 switches its role to mediate the recruitment of P-TEFb for regulating the transcriptional elongation of signal-inducible genes.National Natural Science Foundation of China[30930046, 30670408, 81070307]; Natural Science Foundation of Fujian[C0210005, 2010J01231]; Science Planning Program of Fujian Province[2009J1010, 2010J1008]; National Foundation for fostering talents of basic science[J1030626

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)

A multicentre study on the clinical characteristics of newborns infected with coronavirus disease 2019 during the omicron wave

ObjectiveTo investigate the clinical characteristics and outcomes of newborns infected with coronavirus disease 2019 (COVID-19) during the Omicron wave.MethodsFrom December 1, 2022, to January 4, 2023, clinical data were collected from neonates with COVID-19 who were admitted to 10 hospitals in Foshan City, China. Their epidemiological histories, clinical manifestations and outcomes were analysed. The neonates were divided into symptomatic and asymptomatic groups. The t test or χ2 test was used for comparisons between groups.ResultsA total of 286 children were diagnosed, including 166 males, 120 females, 273 full-term infants and 13 premature infants. They were 5.5 (0–30) days old on average when they were admitted to the hospital. These children had contact with patients who tested positive for COVID-19 and were infected through horizontal transmission. This study included 33 asymptomatic and 253 symptomatic patients, among whom 143 were diagnosed with upper respiratory tract infections and 110 were diagnosed with pneumonia. There were no severe or critical patients. Fever (220 patients) was the most common clinical manifestation, with a duration of 1.1 (1–6) days. The next most common clinical manifestations were cough with nasal congestion or runny nose (4 patients), cough (34 patients), poor appetite (7 patients), shortness of breath (15 patients), and poor general status (1 patient). There were no significant abnormalities in routine blood tests among the neonates infected with COVID-19 except for mononucleosis. However, compared with the asymptomatic group, in the symptomatic group, the leukocyte and neutrophil granulocyte counts were significantly decreased, and the monocyte count was significantly increased. C-reactive protein (CRP) levels were significantly increased (≥10 mg/L) in 9 patients. Myocardial enzyme, liver function, kidney function and other tests showed no obvious abnormalities.ConclusionsIn this study, neonates infected with the Omicron variant were asymptomatic or had mild disease. Symptomatic patients had lower leucocyte and neutrophil levels than asymptomatic patients

Meta-analysis of dose selection for budesonide in the treatment of Chinese patients with AECOPD

Published in August  202

Divalent cation tolerance protein binds to beta-secretase and inhibits the processing of amyloid precursor protein

National Natural Science Foundation of China [81171192]; XMU Basic Training Program of Undergraduate [CXB2011019]; Key Laboratory of Ministry of Education for Cell Biology and Tumor Cell Engineering of Xiamen University [201101]The deposition of amyloid-beta is a pathological hallmark of Alzheimer's disease. Amyloid-beta is derived from amyloid precursor protein through sequential proteolytic cleavages by beta-secretase (beta-site amyloid precursor protein-cleaving enzyme 1) and gamma-secretase. To further elucidate the roles of beta-site amyloid precursor protein-cleaving enzyme 1 in the development of Alzheimer's disease, a yeast two-hybrid system was used to screen a human embryonic brain cDNA library for proteins directly interacting with the intracellular domain of beta-site amyloid precursor protein-cleaving enzyme 1. A potential beta-site amyloid precursor protein-cleaving enzyme 1-interacting protein identified from the positive clones was divalent cation tolerance protein. Immunoprecipitation studies in the neuroblastoma cell line N2a showed that exogenous divalent cation tolerance protein interacts with endogenous beta-site amyloid precursor protein-cleaving enzyme 1. The overexpression of divalent cation tolerance protein did not affect beta-site amyloid precursor protein-cleaving enzyme 1 protein levels, but led to increased amyloid precursor protein levels in N2a/APP695 cells, with a concomitant reduction in the processing product amyloid precursor protein C-terminal fragment, indicating that divalent cation tolerance protein inhibits the processing of amyloid precursor protein. Our experimental findings suggest that divalent cation tolerance protein negatively regulates the function of beta-site amyloid precursor protein-cleaving enzyme 1. Thus, divalent cation tolerance protein could play a protective role in Alzheimer's disease

Effects of Fe-Ions Irradiation on the Microstructure and Mechanical Properties of FeCrAl-1.5wt.% ZrC Alloys

Fe-13Cr-3.5Al-2.0Mo-1.5wt.% ZrC alloy was irradiated by 400 keV Fe+ at 400 °C at different doses ranging from 6.35 × 1014 to 1.27 × 1016 ions/cm2 with a corresponding damage of 1.0–20.0 dpa, respectively, to investigate the effects of different radiation doses on the hardness and microstructure of the reinforced FeCrAl alloys in detail by nanoindentation, transmission electron microscopy (TEM), and atom probe tomography (APT). The results show that the hardness at 1.0 dpa increases from 5.68 to 6.81 GPa, which is 19.9% higher than a non-irradiated specimen. With an increase in dose from 1.0 to 20.0 dpa, the hardness increases from 6.81 to 8.01 GPa, which is an increase of only 17.6%, indicating that the hardness has reached saturation. TEM and APT results show that high-density nano-precipitates and low-density dislocation loops forme in the 1.0 dpa region, compared to the non-irradiated region. Compared with 1.0 dpa region, the density and size of nano-precipitates in the 20.0 dpa region have no significant change, while the density of dislocation loops increases. Irradiation results in a decrease of molybdenum and carbon in the strengthening precipitates (Zr, Mo) (C, N), and the proportionate decrease of molybdenum and carbon is more obvious with the increase in damage

Presenilins regulate the cellular level of the tumor suppressor PTEN

Alzheimer's Disease (AD) is characterized by amyloid plaques consisting of beta-amyloid (A beta) peptides and neurofibrillary tangles consisting of hyperphosphorylated tau protein. A beta is proteolytically derived from its precursor protein through cleavages by beta-secretase and beta-secretase complex comprising presenilins (PS, PS1/PS2), nicastrin, APH-1 and PEN-2. PS1 is also known to activate the PI3K/Akt cell survival pathway in ay-secretase-independent manner. The tumor suppressor PTEN, which antagonizes the PI3K/Akt pathway, has increasingly been recognized to play a key role in neural functions and its level found reduced in AD brains. Here, we demonstrate that the protein level of PTEN is dramatically reduced in cultured cells and embryonic tissues deficient in PS, and in the cortical neurons of PS1/PS2 conditional double knockout mice. Restoration of PS in PS-deficient cells reverses the reduction of PTEN. Regulation of PTEN by PS is independent of the PS/gamma-secretase activity since impaired gamma-secretase by the gamma-secretase inhibitor treatment or due to nicastrin deficiency has little effect on the protein level of PTEN. Our data suggest an important role for PS in signaling pathways involving PI3K/Akt and PTEN that are crucial for physiological functions and the pathogenesis of multiple diseases. (c) 2006 Elsevier Inc. All rights reserved