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In time-domain astronomy, a substantial number of transients will be discovered by multi-wavelength and multi-messenger observatories, posing a great challenge for follow-up capabilities. We have thus proposed an intelligent X-ray constellation, the Chasing All Transients Constellation Hunters (CATCH) space mission. Consisting of 126 micro-satellites in three types, CATCH will have the capability to perform follow-up observations for a large number of different types of transients simultaneously. Each satellite in the constellation will carry lightweight X-ray optics and use a deployable mast to increase the focal length. The combination of different optics and detector systems enables different types of satellites to have multiform observation capabilities, including timing, spectroscopy, imaging, and polarization. Controlled by the intelligent system, different satellites can cooperate to perform uninterrupted monitoring, all-sky follow-up observations, and scanning observations with a flexible field of view (FOV) and multi-dimensional observations. Therefore, CATCH will be a powerful mission to study the dynamic universe. Here, we present the current design of the spacecraft, optics, detector system, constellation configuration and observing modes, as well as the development plan., Accepted for publication in Experimental Astronomy. 43 pages, 15 figures, 4 tables

Lung adenocarcinoma (LUAD), a prevalent form of non-small cell lung cancer (NSCLC), has a high incidence and mortality rate. However, its molecular regulatory mechanisms have yet to be fully understood. The purpose of this study was to look into how NADPH quinone oxidoreductase-1 (NQO1) and it miR-485-5p and affected LUAD cells. The levels of miR-485-5p and NQO1 expression in LUAD cells and tissues were determined by means of quantitative reverse transcription polymerase chain reaction. The viability, proliferation, migration, and apoptosis of LUAD cells were assessed using cell counting Kit-8, 5-bromo-2'-deoxyuridine, transwell, and caspase-3 assays, respectively. Western blot experiments were used to examine the relative protein expression of matrix metallopeptidase 2 and matrix metallopeptidase 9, as well as the phosphorylation of phosphoinositide 3-kinase (PI3K) and protein kinase B (Akt) in LUAD cells. Luciferase and RNA pull-down experiments were also conducted for the verification of miR-485-5p's underlying relationship with NQO1. In our study, we found that LUAD cells and tissues had miR-485-5p downregulation and NQO1 upregulation. The experimental outcomes indicated that miR-485-5p overexpression in LUAD cells reduced their malignant behaviors, suppressed PI3K and Akt phosphorylation, and facilitated apoptosis. The results also revealed that NQO1 was a direct miR-485-5p target, and that NQO1 could reverse miR-485-5p's inhibitory effect on the malignant phenotype of LUAD cells. Furthermore, it was also observed that through targeting NQO1, miR-485-5p could suppress LUAD cell migration and proliferation, further blocking the phosphorylation of PI3K and Akt and inducing apoptosis among LUAD cells. In conclusion, the miR-485-5/NQO1 axis regulates LUAD progression through the PI3K/Akt pathway.

Backgrounds Freezing of gait (FOG) and cognitive impairment are serious symptoms of Parkinson’s disease (PD). Understanding the association between FOG and cognition may help formulate specific interventions for PD individuals. Objectives We aimed to investigate the associations of cognitive impairment in different domains with FOG status using multiple neuropsychological tests. Methods Two cohorts including 691 and 104 participants were recruited from Parkinson’s progression markers initiative (PPMI) and central China, respectively. All participants underwent FOG assessment and neuropsychological tests, and 595 individuals from PPMI and 51 from central China were enrolled for longitudinal observation. Cross-sectional and longitudinal associations between cognition and FOG status were evaluated using multivariable-adjusted models. Results Worse cognitive performances were observed in patients with FOG compared to those without FOG in both cohorts (β = − 0.020, p p Conclusions FOG is a risk factor for cognitive decline in PD, which emphasizes the need for early detection and monitoring of cognitive changes and interventions on cognitive impairments in PD patients with FOG.

Background Lung cancer is a malignant tumour with the fastest increase in morbidity and mortality around the world. The clinical treatments available have significant side effects, thus it is desirable to identify alternative modalities to treat lung cancer. Shashen Maidong decoction (SMD) is a commonly used traditional Chinese medicine (TCM) formula for treating lung cancer in the clinic. While the key functional components (KFC) and the underlying mechanisms of SMD treating lung cancer are still unclear. Methods We propose a new integrated pharmacology model, which combines a novel node-importance calculation method and the contribution decision rate (CDR) model, to identify the KFC of SMD and to deduce their mechanisms in the treatment of lung cancer. Results The enriched effective Gene Ontology (GO) terms selected from our proposed node importance detection method could cover 97.66% of enriched GO terms of reference targets. After calculating CDR of active components in key functional network, the first 82 components covered 90.25% of the network information, which were defined as KFC. 82 KFC were subjected to functional analysis and experimental validation. 5–40 μM protocatechuic acid, 100–400 μM paeonol or caffeic acid exerted significant inhibitory activity on the proliferation of A549 cells. The results show that KFC play an important therapeutic role in the treatment of lung cancer by targeting Ras, AKT, IKK, Raf1, MEK, and NF-κB in the PI3K-Akt, MAPK, SCLC, and NSCLC signaling pathways active in lung cancer. Conclusions This study provides a methodological reference for the optimization and secondary development of TCM formulas. The strategy proposed in this study can be used to identify key compounds in the complex network and provides an operable test range for subsequent experimental verification, which greatly reduces the experimental workload.

The persistent dysregulation and accumulation of poisonous proteins from destructive neural tissues and cells activate pathological mechanisms after traumatic brain injury (TBI). The lymphatic drainage system of the brain, composed of the glymphatic system and meningeal lymphatic vessels (MLVs), plays an essential role in the clearance of toxic waste after brain injury. The neuroprotective effect of interleukin 33 (IL-33) in TBI mice has been demonstrated; however, its impact on brain lymphatic drainage is unclear. Here, we established a fluid percussion injury model to examine the IL-33 administration effects on neurological function and lymphatic drainage in the acute brain of TBI mice. We verified that exogenous IL-33 could improve the motor and memory skills of TBI mice and demonstrated that in the acute phase, it increased the exchange of cerebrospinal and interstitial fluid, reversed the dysregulation and depolarization of aquaporin-4 in the cortex and hippocampus, improved the drainage of MLVs to deep cervical lymph nodes, and reduced tau accumulation and glial activation. We speculate that the protective effect of exogenous IL-33 on TBI mice’s motor and cognitive functions is related to the enhancement of brain lymphatic drainage and toxic metabolite clearance from the cortex and hippocampus in the acute stage. These data further support the notion that IL-33 therapy may be an effective treatment strategy for alleviating acute brain injury after TBI.

Objective Cancer-induced bone pain (CIBP) is a kind of pain with complex pathophysiology. Proteinase-activated receptor 2 (PAR-2) is involved in CIBP. This study explored the effects of PAR-2 on CIBP rats. Methods CIBP rat model was established by injecting Walker 256 rat breast cancer cells into the left tibia of female Sprague-Dawley rats and verified by tibial morphology observation, HE staining, and mechanical hyperalgesia assay. CIBP rats were injected with PAR-2 inhibitor, ERK activator, and CREB inhibitor through the spinal cord sheath on the 13th day after operation. CIBP behaviors were measured by mechanical hyperalgesia assay. On the 14th day after operation, L4-5 spinal cord tissues were obtained. PAR-2 expression, co-expression of PAR-2 and astrocyte marker GFAP, GFAP mRNA and protein levels and the ERK pathway-related protein levels were detected by Western blot, immunofluorescence double staining, RT-qPCR, and Western blot. Results CIBP rats had obvious mechanical hyperalgesia and thermal hyperalgesia from the 7th day after modeling; mechanical hyperalgesia threshold and thermal threshold were decreased; PAR-2 was increased in spinal cord tissues and was co-expressed with GFAP. PAR-2 silencing alleviated rat CIBP by inhibiting astrocyte activation. p-ERK/t-ERK and p-CREB/t-CREB levels in CIBP spinal cord were elevated, the ERK/CREB pathway was activated, while the ERK/CREB pathway was inhibited by PAR-2 silencing. The alleviating effect of PAR-2 inhibitor on hyperalgesia behaviors in CIBP rats were weakened by ERK activator, while were partially restored by CREB inhibitor. Conclusions PAR-2 knockdown inhibited the ERK/CREB pathway activation and astrocyte activation, thus alleviating CIBP in rats.

This study investigates the search capability, stability, and computational efficiency of four improved penalty-free constraint-handling techniques (CHTs), including the death penalty, the Deb rule, the filter method, and the mapping strategy, in structural optimization using harmony search (HS). The first three general-purpose CHTs have been improved by hybridizing with a structural analysis filter strategy to enhance their computational efficiency based on the characteristics of structural optimization and the solution updating rule of the HS. This study also has modified the mapping operator of the mapping strategy to handle size and shape optimization with Euler buckling constraints. Four numerical examples examine the performances of these CHTs. The comparative results show that the mapping strategy exhibits apparent superiority both in search capability and stability among the four. However, it also demands the most computational cost, and the improved Deb rule becomes the most competitive method while considering computational efficiency. The difference between the death penalty and the Deb rule method is that the feasible solution initialization process required by the death penalty method will deteriorate its computational efficiency in problems with small feasible space. The filter method always reserves some infeasible solutions to guide the search in the iteration process. However, its performances are inferior to the other three approaches in four benchmark problems.

Full design analysis of a bridge usually has lots of load cases and leads to the analysis process, which is extremely time-consuming. To reduce the computational cost, the existing hanger pre-tensioning force optimization approaches for cable-supported bridges always consider only a few critical load cases. This study presents an efficient approach for hanger pre-tensioning force optimization of steel tied-arch bridges, which can include all the operational loads and their combinations based on the code requirements. The proposed method develops a load decoupling approach (LDA) to enhance the computational efficiency of structural analysis in optimization and utilize the particle swarm optimization algorithm to search the global optimal design. The LDA divides the structural analysis procedure into two systems: (1) iteration-varying and (2) iteration-invariant. The former evaluates the structural responses induced by different hanger forces using unit load method (ULM) and the latter solely needs a single finite element (FE) analysis before iteration to calculate the structural responses caused by the other loads. The superposition principle combines the results from the two systems to identify the minimum safety margin of the bridge under the ultimate limit state. A practical bridge is examined to compare the proposed method with three traditional hanger force determination approaches. The obtained results illustrate that the proposed method not only owns high efficiency but also leads to a safer design and has a broader application range.

Articular cartilage injury, as a hallmark of arthritic diseases, is difficult to repair and causes joint pain, stiffness, and loss of mobility. Over the years, the most significant problems for the drug-based treatment of arthritis have been related to drug administration and delivery. In recent years, much research has been devoted to developing new strategies for repairing or regenerating the damaged osteoarticular tissue. The RNA interference (RNAi) has been suggested to have the potential for implementation in targeted therapy in which the faulty gene can be edited by delivering its complementary Short Interfering RNA (siRNA) at the post-transcriptional stage. The successful editing of a specific gene by the delivered siRNA might slow or halt osteoarthritic diseases without side effects caused by chemical inhibitors. However, cartilage siRNA delivery remains a challenging objective because cartilage is an avascular and very dense tissue with very low permeability. Furthermore, RNA is prone to degradation by serum nucleases (such as RNase H and RNase A) due to an extra hydroxyl group in its phosphodiester backbone. Therefore, successful delivery is the first and most crucial requirement for efficient RNAi therapy. Nanomaterials have emerged as highly advantage tools for these studies, as they can be engineered to protect siRNA from degrading, address barriers in siRNA delivery to joints, and target specific cells. This review will discuss recent breakthroughs of different siRNA delivery technologies for cartilage diseases.

For each microarray data set, only a small number of genes are beneficial. Due to the high-dimensional problem, gene selection research work remains a challenge. In order to solve the high-dimensional problem, we propose a dimensionality reduction algorithm named K value maximum relevance minimum redundancy improved grey wolf optimizer (KMR2IGWO). First, in the processing of KMR2, the K genes are selected. Second, the K genes are initialized by two ways according to random selection feature and different proportions of selection feature. Finally, the IGWO algorithm selects the optimal classification accuracy and the optimal combination of gene by adjusting the parameters of fitness function. The algorithm has a significant dimensionality reduction effect and is suitable for high-dimensional data sets. Experimental results show that the proposing KMR2IGWO strategy significantly reduces the dimension of microarray data and removes the redundant features. On the 14 microarray data sets, compared with the four algorithms mRMR + PSO, mRMR + GA, mRMR + BA, mRMR + CS, the proposed algorithm has higher performance in classification accuracy and feature subset length. In five data sets, the proposed algorithm average classification accuracy is 100%. On the 14 data sets, the proposed algorithm has a very significant dimensionality reduction effect, and the dimensionality reduction range is between 0.4% and 0.04%.

Feature selection is an important filtering method for data analysis, pattern classification, data mining, and so on. Feature selection reduces the number of features by removing irrelevant and redundant data. In this paper, we propose a hybrid filter---wrapper feature subset selection algorithm called the maximum Spearman minimum covariance cuckoo search (MSMCCS). First, based on Spearman and covariance, a filter algorithm is proposed called maximum Spearman minimum covariance (MSMC). Second, three parameters are proposed in MSMC to adjust the weights of the correlation and redundancy, improve the relevance of feature subsets, and reduce the redundancy. Third, in the improved cuckoo search algorithm, a weighted combination strategy is used to select candidate feature subsets, a crossover mutation concept is used to adjust the candidate feature subsets, and finally, the filtered features are selected into optimal feature subsets. Therefore, the MSMCCS combines the efficiency of filters with the greater accuracy of wrappers. Experimental results on eight common data sets from the University of California at Irvine Machine Learning Repository showed that the MSMCCS algorithm had better classification accuracy than the seven wrapper methods, the one filter method, and the two hybrid methods. Furthermore, the proposed algorithm achieved preferable performance on the Wilcoxon signed-rank test and the sensitivity---specificity test.

Background Hand, foot, and mouth disease (HFMD) has been recognized as one of the leading infectious diseases among children in China, which causes hundreds of annual deaths since 2008. In China, the reports of monthly HFMD cases usually have a delay of 1–2 months due to the time needed for collecting and processing clinical information. This time lag is far from optimal for policymakers making decisions. To alleviate this information gap, this study uses a meta learning framework and combines publicly Internet-based information (Baidu search queries) for real-time estimation of HFMD cases. Methods We incorporate Baidu index into modeling to nowcast the monthly HFMD incidences in Guangxi, Zhejiang, Henan provinces and the whole China. We develop a meta learning framework to select appropriate predictive model based on the statistical and time series meta features. Our proposed approach is assessed for the HFMD cases within the time period from July 2015 to June 2016 using multiple evaluation metrics including root mean squared error (RMSE) and correlation coefficient (Corr). Results For the four areas: whole China, Guangxi, Zhejiang, and Henan, our approach is superior to the best competing models, reducing the RMSE by 37, 20, 20, and 30% respectively. Compared with all the alternative predictive methods, our estimates show the strongest correlation with the observations. Conclusions In this study, the proposed meta learning method significantly improves the HFMD prediction accuracy, demonstrating that: (1) the Internet-based information offers the possibility for effective HFMD nowcasts; (2) the meta learning approach is capable of adapting to a wide variety of data, and enables selecting appropriate method for improving the nowcasting accuracy. Electronic supplementary material The online version of this article (10.1186/s12879-018-3285-4) contains supplementary material, which is available to authorized users.

In the original publication of the article error has occurred in Fig. 1b.

Glioblastoma is a highly aggressive brain tumor. Aberrant Notch pathway has been implicated in the formation and progression of glioblastoma. The present study attempted to investigate the expression of Notch ligand Jagged1 and its association with patient outcome in primary glioblastoma. Tumor tissues from 82 patients with primary glioblastoma were analyzed using immunohistochemistry for Jagged1 expression. Relationships between Jagged1 expression and clinical features (age, gender, KPS, symptom duration, extent of resection and Ki67 index) were evaluated. The prognostic value of Jagged1 was assessed using the Kaplan–Meier survival estimates and the Cox proportional hazard models. Immunohistochemistry results showed markedly increased Jagged1 expression in glioblastoma tissues compared to adjacent non-neoplastic brain tissues. Univariate analysis documented that high Jagged1 expression in tumor cells (TC) and endothelial cells (EC) were both statistically associated with reduced time to progression (TTP) (P

Rosette nanotubes (RNTs) are novel, biomimetic, synthetic, self-assembled drug delivery agents. Because of base stacking and hydrophobic interactions, the RNT hollow-tube structure can be used for incorporating drugs. Another advantage of using RNTs is their ability to be injected and become solid at body temperatures for orthopedic applications without the use of any external stimuli (such as UV light or crosslinking agents). The nano-features of RNTs create a favorable, biologically-inspired, cellular environment. In this study, methods to incorporate dexamethasone (DEX, a bone growth promoting drug) into RNTs were investigated. The drug-loaded RNTs were characterized using Nuclear Magnetic Resonance (NMR), Diffusion Ordered Spectroscopy (DOSY) and Ultraviolet-visible Spectroscopy (UV-vis). Results showed that small molecular drugs with hydrophobic aromatic rings were incorporated into RNTs. Subsequent drug release experiments demonstrated that DEX was released from the RNTs and had a positive impact on osteoblast functions. Importantly, compared to other drug carriers, RNTs increased the total drug loading and was the highest when DEX was incorporated during the RNT self-assembly process. Thus, this study offered a novel drug delivery device that itself is bioactive and can be used to deliver a variety of drugs for various orthopedic applications.

The current clinical treatment of cartilage defects involves autologous chondrocyte implantation into cartilage defect sites. However, one of the complications associated with this method is the lack of bonding between the implanted materials and natural tissue. Helical rosette nanotubes (HRNs) are novel biomimetic self-assembled supramolecular structures whose basic building blocks are DNA base-pairs. HRNs are similar in size to collagen in cartilage. Moreover, previous studies have shown that HRNs are biocompatible and increase the adhesion of numerous cells compared to other commonly used cartilage implant materials (like hydrogels and Ti). In addition, HRNs can solidify into a viscous gel at body temperatures under short periods of time. Thus, it is hoped that HRNs can serve as a novel in situ tissue implant to improve cartilage cell adhesion and functions. In this study, in order to heal cartilage rupture and regenerate cartilage during possible implantation, the mechanical properties of select hydrogel/HRN composites were tested. In addition, electro-spinning was used to generate three-dimensional, implantable, composite fibers encapsulated with chondrocytes and fibroblast-like type-B synoviocytes (SFB cells, a type of mesenchymal stem cell). Importantly, results showed that drug-delivered HRNs enhanced hydrogel adhesive strength and created a scaffold with nanometer-rough surface structures pertinent for cartilage regeneration. In this manner, this study provided an alternative cartilage regenerative material which relies on nanotechnology that can be injected as a liquid, solidify at body temperatures under short periods of time, have suitable mechanical properties to collagen, and promote cell functions.