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Abstract Background Glioblastoma (GBM) is an immunosuppressive, universally lethal cancer driven by glioblastoma stem cells (GSCs). The interplay between GSCs and immunosuppressive microglia plays crucial roles in promoting the malignant growth of GBM; however, the molecular mechanisms underlying this crosstalk are unclear. This study aimed to investigate the role of POSTN in maintaining GSCs and the immunosuppressive phenotype of microglia. Methods The expression of POSTN in GBM was identified via immunohistochemistry, quantitative real-time PCR, and immunoblotting. Tumorsphere formation assay, Cell Counting Kit-8 assay and immunofluorescence were used to determine the key role of POSTN in GSC maintenance. ChIP-seq and ChIP-PCR were conducted to confirm the binding sequences of β-catenin in the promoter region of FOSL1. Transwell migration assays, developmental and functional analyses of CD4+ T cells, CFSE staining and analysis, enzyme-linked immunosorbent assays and apoptosis detection tests were used to determine the key role of POSTN in maintaining the immunosuppressive phenotype of microglia and thereby promoting the immunosuppressive tumor microenvironment. Furthermore, the effects of POSTN on GSC maintenance and the immunosuppressive phenotype of microglia were investigated in a patient-derived xenograft model and orthotopic glioma mouse model, respectively. Results Our findings revealed that POSTN secreted from GSCs promotes GSC self-renewal and tumor growth via activation of the αVβ3/PI3K/AKT/β-catenin/FOSL1 pathway. In addition to its intrinsic effects on GSCs, POSTN can recruit microglia and upregulate CD70 expression in microglia through the αVβ3/PI3K/AKT/NFκB pathway, which in turn promotes Treg development and functionality and supports the formation of an immunosuppressive tumor microenvironment. In both in vitro models and orthotopic mouse models of GBM, POSTN depletion disrupted GSC maintenance, decreased the recruitment of immunosuppressive microglia and suppressed GBM growth. Conclusion Our findings reveal that POSTN plays critical roles in maintaining GSCs and the immunosuppressive phenotype of microglia and provide a new therapeutic target for treating GBM.

PurposeThe impact of genetic polymorphisms in the ABCB1 and CES1 genes on dabigatran plasma concentrations remains a subject of debate, and the purpose of this study was to quantitatively assess the effects of genetic polymorphisms on dabigatran esters in healthy Chinese subjects employed a population pharmacokinetic (PopPK) approach.MethodsIn total, 1,926 pharmacokinetic (PK) samples from 123 healthy individuals who were given 150 mg of dabigatran orally during a fasting state or postprandially were analyzed using the PopPK model. A two-compartment model with first-order absorption was found to adequately describe the PK data.ResultsThe results showed that covariates food intake and ABCB1 SNP rs4148738 were shown to have statistically significant impacts. Specifically, in postprandial administration increased lag time (ALAG) and clearance (CL) by 2.65% and 0.51%, respectively, and decreased absorption rate constant (KA) by 0.24%. Additionally, in subjects with CT genotype ABCB1 (rs4148738), the central ventricular volume of distribution (V2) was increased by 0.38%.ConclusionIn summary, the PopPK model developed in this study was robust and effectively characterized the pharmacokinetics of dabigatran in healthy Chinese adults, demonstrating that both food and ABCB1 genetic variation significantly influence the absorption and plasma concentration levels of dabigatran.

Abstract Integer Ambiguity Resolution (IAR) can significantly improve the accuracy of GNSS Precise Orbit Determination (POD). Traditionally, the IAR in POD is achieved at the Double Differenced (DD) level. In this contribution, we develop an Un-Differenced (UD) IAR method for Global Positioning System (GPS)+ BeiDou Navigation Satellite System (BDS) + Galileo navigation satellite system (Galileo)+ Global'naya Navigatsionnaya Sputnikovaya Sistema (GLONASS) quad-system POD by calibrating UD ambiguities in the raw carrier phase and generating the so-called carrier range. Based on this method, we generate the UD ambiguity-fixed orbit and clock products for the Wuhan Innovation Application Center (IAC) of the International GNSS Monitoring and Assessment System (iGMAS). One-year observations in 2020 from 150 stations are employed to investigate performance of orbit and clock products. Notably, the UD Ambiguity Resolution (AR) yields more resolved integer ambiguities than the traditional DD AR, scaling up to 9%, attributable to its avoidance of station baseline formation. Benefiting from the removal of ambiguity parameters, the computational efficiency of parameter estimation undergoes a substantial 70% improvement. Compared with the float solution, the orbit consistencies of UD AR solution achieve the accuracy of 1.9, 5.2, 2.8, 2.1, and 2.7 cm for GPS, BeiDou-2 Navigation Satellite System (BDS-2), BeiDou-3 Navigation Satellite System (BDS-3), Galileo, and GLONASS satellites respectively, reflecting enhancements of 40%, 24%, 54%, 34%, and 42%. Moreover, the standard deviations of Satellite Laser Ranging (SLR) residuals are spanning 2.5–3.5 cm, underscoring a comparable accuracy to the DD AR solution, with discrepancies below 5%. A notable advantage of UD AR lies in its capability to produce the Integer Recovered Clock (IRC), facilitating Precise Point Positioning (PPP) AR without requiring additional Uncalibrated Phase Delay (UPD) products. To assess the performance of quad-system kinematic PPP based on IRC, a network comprising 120 stations is utilized. In comparison to the float solution, the IRC-based PPP AR accelerates convergence time by 31% and enhance positioning accuracy in the east component by 54%.

Abstract The torrent frogs of the genus Amolops represent a great anuran diversification in southern China and Southeast Asia. Previous studies have shown that, the diversity of this genus still remains underestimated. During herpetological surveys from 2021 to 2022, several Amolops specimens were collected from the international border regions of southwestern Yunnan Province, China. Herein, we utilized molecular phylogenetic and morphological data to identify these specimens. Our findings indicate the presence of a separate and previously unknown lineage in the A. viridimaculatus group, which we formally describe as a new species. Furthermore, the specimen from Xishuangbanna National Nature Reserve clustered with A. vitreus from the paratype, supporting the morphological diagnosis. Therefore, we describe a new species and a new species record for China. Our study contributes to the species richness of the genus Amolops as well as the diversity of amphibians in China. Notably, our discovery brings the total number of Amolops species to 85 and the total number of torrent frog species known to occur in China to 53. In addition, our study further confirmed that Yunnan and Indochina Peninsula have similar faunal composition, implying that more studies are needed to achieve a complete understanding of the species diversity and distribution pattern.

Industrial digitalization is an important way for industrial transformation and upgrading of China's industry, and digital transformation has become an important trend in the development of China's industry.The reliability and stability of industrial systems play an important role in the high quality and sustainable development of industrial production.Failures affect the operation of industrial systems and even cause major safety accidents and economic losses.To deal with this problem, fault diagnosis technology has emerged and gradually developed.Efficient and high-quality fault diagnosis digital technology has become a key technology for industrial digital transformation.The research progress of digital methods of fault diagnosis in industry were analyzed.According to the development characteristics, three stages were divided, including domain experience-led modeling methods, data-driven digital methods combining with domain experience, and data-driven digital methods combining with interpretability.Focus on the basic ideas and characteristics of the methods in each stage, the future research directions were discussed, and the references were provided for promoting industrial digital transformation.

This study investigated the enhancement in bioethanol recovery from mixed matrix membranes (MMMs) by functionalizing zeolite framework-8 (ZIF-8) with imidazolate. This study focused on the separation of ethanol from low-concentration ethanol/water mixtures (typical post-fermentation concentrations of 5–10 wt%). Specifically, ZIF-8 was modified by the shell–ligand exchange reaction (SLER) with 5,6-dimethylbenzimidazole (DMBIM), resulting in ZIF-8-DMBIM particles with improved hydrophobicity, organophilicity, larger size, and adjustable pore size. These particles were incorporated into a PEBAX 2533 matrix to produce ZIF-8-DMBIM/PEBAX MMMs using a dilution blending method. The resulting membranes showed significant performance enhancement: 8 wt% ZIF-8-DMBIM loading achieved a total flux of 308 g/m2·h and a separation factor of 16.03, which was a 36.8% increase in flux and 176.4% increase in separation factor compared with the original PEBAX membrane. In addition, performance remained stable during a 130 h cycling test. These improvements are attributed to the enhanced compatibility and dispersion of ZIF-8-DMBIM in the PEBAX matrix. In conclusion, the evaluation of nanofiller content, feed concentration, operating temperature, and membrane stability confirmed that ZIF-8-DMBIM/PEBAX MMM is ideal for ethanol recovery in primary bioethanol concentration processes.

Abstract Background The over-activation of adenosine A2A receptors (A2AR) is closely implicated in cognitive impairments of Alzheimer's disease (AD). Growing evidence shows that A2AR blockade possesses neuroprotective effects on AD. Spatial navigation impairment is an early manifestation of cognitive deficits in AD. However, whether A2AR blockade can prevent early impairments in spatial cognitive function and the underlying mechanism is still unclear. Methods A transgenic APP/PS1 mouse model of AD amyloidosis was used in this study. Behavioral tests were conducted to observe the protective effects of A2AR blockade on early spatial memory deficits in 4-month old APP/PS1 mice. To investigate the underlying synaptic mechanism of the protective effects of A2AR blockade, we further examined long-term potentiation (LTP) and network excitation/inhibition balance of dentate gyrus (DG) region, which is relevant to unique synaptic functions of immature adult-born granule cells (abGCs). Subsequently, the protective effects of A2AR blockade on dendritic morphology and synaptic plasticity of 6-week-old abGCs was investigated using retrovirus infection and electrophysiological recordings. The molecular mechanisms underlying neuroprotective properties of A2AR blockade on the synaptic plasticity of abGCs were further explored using molecular biology methods. Results APP/PS1 mice displayed DG-dependent spatial memory deficits at an early stage. Additionally, impaired LTP and an imbalance in network excitation/inhibition were observed in the DG region of APP/PS1 mice, indicating synaptic structural and functional abnormalities of abGCs. A2AR was found to be upregulated in the hippocampus of the APP/PS1 mouse model of AD. Treatment with the selective A2AR antagonist SCH58261 for three weeks significantly ameliorated spatial memory deficits in APP/PS1 mice and markedly restored LTP and network excitation/inhibition balance in the DG region. Moreover, SCH58261 treatment restored dendritic morphology complexity and enhanced synaptic plasticity of abGCs in APP/PS1 mice. Furthermore, SCH58261 treatment alleviated the impairment of synaptic plasticity in abGCs. It achieved this by remodeling the subunit composition of NMDA receptors and increasing the proportion of NR2B receptors in abGCs of APP/PS1 mice. Conclusions Blockade of A2AR improves early spatial memory deficits in APP/PS1 mice, possibly by reversing synaptic defects of abGCs. This finding suggests that A2AR blockade could be a potential therapy for AD.

Promoting digital transformation becomes the key to achieving high-quality development of education.Based on the survey of the current situation of education digitalization, the essence, development, and related techniques of education digitalization transformation were analyzed.The challenges faced by the digital transformation of higher education in China were analyzed and suggestions on education and management from the perspectives of data, technology, and talent were presented.The results to explore solution on the digital transformation of higher education can provide reference to promote the digitalization of education.

Halogen-free organophosphorus flame retardants have promising application prospects due to their excellent safety and environmental protection properties. A cobalt-coordinated cyclic phosphonitrile flame retardant (Co@CPA) was synthesized via a hydrothermal method using hexachlorocyclotriphosphonitrile (HCCP), 5-amino-tetrazolium (5-AT), and cobalt nitrate hexahydrate (Co(NO3)2∙6H2O) as starting materials. The structure was characterized using Fourier transform infrared (FTIR), nuclear magnetic resonance spectroscopy (1H-NMR), scanning electron microscopy (SEM), and thermogravimetric analysis (TGA). Thermoplastic polyurethane (TPU) composites were prepared by incorporating 10-(2,5-dihydroxyphenyl)-9,10-dihydro-9-oxa-10-phosphame-10-oxide (ODOPB), Co@CPA, and silicon dioxide (SiO2) via melt blending. The flame-retardant performance and thermal stability of the TPU composites were evaluated through limiting oxygen index (LOI), vertical combustion (UL-94), TG, and cone calorimetric (CCT) tests. SEM and Raman spectroscopy were used to analyze the surface morphology and structure of the residual carbon. A synergistic flame-retardant effect of ODOPB and Co@CPA was observed, with the most effective flame retardancy achieved at a TPU:ODOPB:Co@CPA:SiO2 ratio of 75:16:8:1. This composition exhibited an LOI value of 26.5% and achieved a V-0 rating in the UL-94 test. Furthermore, compared to pure TPU, the composite showed reductions in total heat release, CO production, and CO2 production by 6.6%, 39.4%, and 48.9%, respectively. Our research findings suggest that Co@CPA demonstrates outstanding performance, with potential for further expansion in application areas. Different metal-based cyclic phosphonitrile compounds are significant in enriching phosphorus-based fine chemicals.

Abstract Over the past years the International Global Navigation Satellite System (GNSS) Monitoring and Assessment System (iGMAS) Wuhan Innovation Application Center (IAC) dedicated to exploring the potential of multi-GNSS signals and providing a set of products and services. This contribution summarizes the strategies, achievements, and innovations of multi-GNSS orbit/clock/bias determination in iGMAS Wuhan IAC. Both the precise products and Real-Time Services (RTS) are evaluated and discussed. The precise orbit and clock products have comparable accuracy with the precise products of the International GNSS Service (IGS) and iGMAS. The multi-frequency code and phase bias products for Global Positioning System (GPS), BeiDou Navigation Satellite System (BDS), Galileo navigation satellite system (Galileo), and GLObal NAvigation Satellite System (GLONASS) are provided to support multi-GNSS and multi-frequency Precise Point Positioning (PPP) Ambiguity Resolution (AR). Compared with dual-frequency PPP AR, the time to first fix of triple-frequency solution is improved by 30%. For RTS, the proposed orbit prediction strategy improves the three dimensional accuracy of predicted orbit by 1 cm. The multi-thread strategy and high-performance matrix library are employed to accelerate the real-time orbit and clock determination. The results with respect to the IGS precise products show the high accuracy of RTS orbits and clocks, 4–9 cm and 0.1–0.2 ns, respectively. Using real-time satellite corrections, real-time PPP solutions achieve satisfactory performance with horizontal and vertical positioning errors within 2 and 4 cm, respectively, and convergence time of 16.97 min.

Here the authors find that MUS81 mediates excess DNA double strand break (DSB) generation in TDP1 KO cells after camptothecin treatment. They show that TOP1 cleavage complexes can be either resolved directly by TDP1 or be converted into DSBs and repaired further by the Homologous Recombination pathway.

Abstract Background People live a long time in pre-diabetes/early diabetes without a formal diagnosis or management. Heterogeneity of progression coupled with deficiencies in electronic health records related to incomplete data, discrete events, and irregular event intervals make identification of pre-diabetes and critical points of diabetes progression challenging. Methods We utilized longitudinal electronic health records of 9298 patients with type 2 diabetes or prediabetes from 2005 to 2016 from a large regional healthcare delivery network in China. We optimized a generative Markov-Bayesian-based model to generate 5000 synthetic illness trajectories. The synthetic data were manually reviewed by endocrinologists. Results We build an optimized generative progression model for type 2 diabetes using anchor information to reduce the number of parameters learning in the third layer of the model from $$O\left(N\times W\right)$$ O N × W to $$O\left((N-C)\times W\right)$$ O ( N - C ) × W , where $$N$$ N is the number of clinical findings, $$W$$ W is the number of complications, $$C$$ C is the number of anchors. Based on this model, we infer the relationships between progression stages, the onset of complication categories, and the associated diagnoses during the whole progression of type 2 diabetes using electronic health records. Discussion Our findings indicate that 55.3% of single complications and 31.8% of complication patterns could be predicted early and managed appropriately to potentially delay (as it is a progressive disease) or prevented (by lifestyle modifications that keep patient from developing/triggering diabetes in the first place). Conclusions The full type 2 diabetes patient trajectories generated by the chronic disease progression model can counter a lack of real-world evidence of desired longitudinal timeframe while facilitating population health management.

BackgroundThe incidence of sleep disorders in children with autism spectrum disorder (ASD) is very high. Sleep disorders can exacerbate the development of ASD and impose a heavy burden on families and society. The pathological mechanism of sleep disorders in autism is complex, but gene mutations and neural abnormalities may be involved.MethodsIn this review, we examined literature addressing the genetic and neural mechanisms of sleep disorders in children with ASD. The databases PubMed and Scopus were searched for eligible studies published between 2013 and 2023.ResultsProlonged awakenings of children with ASD may be caused by the following processes. Mutations in the MECP2, VGAT and SLC6A1 genes can decrease GABA inhibition on neurons in the locus coeruleus, leading to hyperactivity of noradrenergic neurons and prolonged awakenings in children with ASD. Mutations in the HRH1, HRH2, and HRH3 genes heighten the expression of histamine receptors in the posterior hypothalamus, potentially intensifying histamine’s ability to promote arousal. Mutations in the KCNQ3 and PCDH10 genes cause atypical modulation of amygdala impact on orexinergic neurons, potentially causing hyperexcitability of the hypothalamic orexin system. Mutations in the AHI1, ARHGEF10, UBE3A, and SLC6A3 genes affect dopamine synthesis, catabolism, and reuptake processes, which can elevate dopamine concentrations in the midbrain. Secondly, non-rapid eye movement sleep disorder is closely related to the lack of butyric acid, iron deficiency and dysfunction of the thalamic reticular nucleus induced by PTCHD1 gene alterations. Thirdly, mutations in the HTR2A, SLC6A4, MAOA, MAOB, TPH2, VMATs, SHANK3, and CADPS2 genes induce structural and functional abnormalities of the dorsal raphe nucleus (DRN) and amygdala, which may disturb REM sleep. In addition, the decrease in melatonin levels caused by ASMT, MTNR1A, and MTNR1B gene mutations, along with functional abnormalities of basal forebrain cholinergic neurons, may lead to abnormal sleep–wake rhythm transitions.ConclusionOur review revealed that the functional and structural abnormalities of sleep–wake related neural circuits induced by gene mutations are strongly correlated with sleep disorders in children with ASD. Exploring the neural mechanisms of sleep disorders and the underlying genetic pathology in children with ASD is significant for further studies of therapy.

Classifying financial texts is a common task for identifying financial risks.Traditional financial news classification requires a large number of labeled texts to train the classifier.However, labeling financial news requires not only professional financial background knowledge, but also time-consuming and labor-intensive.In order to reduce the dependence on labeled text, a semi-supervised learning financial text classification algorithm- SSF (semi-supervised learning financial news classification algorithm) was proposed, which uses a consistent training method of supervised learning and unsupervised learning to improve the use of unlabeled data.And unsupervised data augmentation for financial texts was introduced, that is, use specific target data augmentation methods for specific tasks to generate more effective data.Experiments on multiple financial news data sets were conducted to verify that the proposed SSF algorithm has a significant improvement in effectiveness compared with other text classification algorithms.

Dryopteris sect. Diclisodon is a small section of ferns with about 12 species mainly distributed in East Asia. Here, we carried out morphological and phylogenetic analyses of this section. A new species from southwest China, D. gaoligongensis, is described and illustrated. Dryopteris gaoligongensis resembles D. indonesiana and D. sparsa, but differs by having a creeping rhizome and large 4-pinnate fronds. We also show that D. glabrior Ching & Z.Y. Liu is a distinct species; however, because it is a later homonym of D. glabrior Copel., it should be renamed D. renchangiana. We conclude that a species previously known as D. nitidula, also an illegitimate homonym, should be recognized with a new name, D. sinonepalensis. We resolve the phylogenetic position of D. yoroii as sister to other sampled species of D. sect. Diclisodon. Our phylogenetic analyses confirm the distinctiveness of D. gaoligongensis, D. renchangiana, and D. sinonepalensis. A key to species of D. sect. Diclisodon in China is provided.

The effects of incorporation of hempseed meal on the quality attributes of chicken sausage was explored. Hempseed meal was hydrated and added to chicken sausage in the proportion of 10%, 20%, 30%, and 40%, respectively, and the changes of the product quality attributes were analyzed. Results showed that with the increment of hempseed meal, moisture, protein, and lipid content of chicken sausage were significantly decreased, while ash and total dietary fiber content increased. The emulsion stability of the sausage was improved, and the cooking loss decreased. The addition of hempseed meal also increased the total phenolic content and DPPH radical scavenging activity; however, it promoted lipid oxidation (p

The new energy storage, referring to new types of electrical energy storage other than pumped storage, has excellent value in the power system and can provide corresponding bids in various types of electricity markets. As the scale of new energy storage continues to grow, China has issued several policies to encourage its application and participation in electricity markets. It is urgent to establish market mechanisms well adapted to energy storage participation and study the operation strategy and profitability of energy storage. Based on the development of the electricity market in a provincial region of China, this paper designs mechanisms for independent energy storage to participate in various markets. Then, its current and future operation strategies by division time or capacity for participation in each type of market are analyzed, and the profitability under various scenarios is calculated. Finally, based on the calculation results, the theoretical analysis basis for developing independent energy storage in the province and the policy formulation of participation in the market is provided.

Abstract Objective Our goal was to research and develop exploratory analysis tools for clinical notes, which now are underrepresented to limit the diversity of data insights on medically relevant applications. Results We characterize how exploratory analysis can affect representation learning on clinical narratives and present several self-developed tools to explore sepsis. Our experiments focus on patients with sepsis in the MIMIC-III Clinical Database or in our institution’s research patient data repository. We found that global embeddings assist in learning local representations of clinical notes. Second, aligning at any specific time facilitates the use of learning models by pooling more available clinical notes to form a training set. Furthermore, reconstruction of the timeline enhances downstream-processing techniques by emphasizing temporal expressions and temporal relationships in clinical documentation. We demonstrate that clustering helps plot various types of clinical notes against a scale, which conveys a sense of the range or spread of the data and is useful for understanding data correlations. Appropriate exploratory analysis tools provide keen insights into preprocessing clinical notes, thereby further enhancing downstream analysis capabilities, making data driven medicine possible. Our examples can help generate better data representation of clinical documentation for models with improved performance and interpretability.

Abstract Background The pathophysiological mechanism of recurrent miscarriage (RM) is unclear. The goals of this study were to determine the role of microRNA-4497 overexpression in placental villus tissues in early RM; To identify the potential target mRNAs of miRNA-4497; And to investigate the microRNA-4497-mediated regulatory mechanisms in placental trophoblasts. Methods Bioinformatics analysis was performed to identify the candidate target genes of miRNA-4497. The protein expression of Sp1 transcription factor (SP1), chemokine (C-X-C motif) receptor 5 (CXCR5) and bone morphogenetic protein 8a (BMP8A) were determined in the villus tissues of the RM and normal groups by Western blotting and immunohistochemistry. Cultured 293T cells were co-transfected with the miRNA-4497 agomir or luciferase reporter vectors containing the wild-type or mutant 3’-UTRs of the target mRNAs to verify the regulatory role of miRNA-4497. Results Bioinformatics analysis suggested that SP1, CXCR5 and BMP8A mRNAs are potential targets of miRNA-4497. The expression of SP1, CXCR5 and BMP8A proteins in the chorionic villus tissues of RM placentas were significantly decreased compared to those in the normal controls. Moreover, SP1 protein levels were inversely correlated with the levels of miRNA-4497 in the placentas of RM patients and normal controls. The expression of SP1 mRNA and protein were down-regulated in HTR-8/SVneo cells after forced overexpression of the miRNA-4497 agomir. The results of the co-transfection assay showed that mutation of the miRNA-4497-binding sites in the 3’-untranslated region (3’-UTR) of SP1 led to a recovery of luciferase activity upon overexpression of miRNA-4497, suggesting that SP1 could be a direct target of miRNA-4497. Conclusions An increased miRNA-4497 level in the placental villus tissues associated with recurrent miscarriage may down-regulate SP1 expression. The negative regulation of SP1 by miRNA-4497 may potentially contribute to the pathogenesis of recurrent miscarriage through promotion of trophoblast apoptosis. These findings provide novel information on the regulation of placental trophoblast apoptosis, and could be useful for the development of new therapeutic strategies for better management of recurrent miscarriage.

Organizational, administrative, and educational challenges in establishing and sustaining biomedical data science infrastructures lead to the inefficient use of Research Patient Data Repositories (RPDRs). The challenges, including but not limited to deployment, sustainability, cost optimization, collaboration, governance, security, rapid response, reliability, stability, scalability, and convenience, restrict each other and may not be naturally alleviated through traditional hardware upgrades or protocol enhancements. This article attempts to borrow data science thinking and practices in the business realm, which we call the data industry viewpoint, to improve RPDRs.

Currently, most of the personal health data (PHD) are managed and stored separately by individual medical institutions. When these data need to be shared, they must be transferred to a trusted management center and approved by data owners through the third-party endorsement technology. Therefore, it is difficult for personal health data to be shared and circulated over multiple medical institutions. On the other hand, the use of directly exchanging and sharing the original data has become inconsistent with the data rapid growth of medical institutions because of the need of massive data transferring across agencies. In order to secure sharing and managing the mass personal health data generated by various medical institutions, a federal personal health data management framework (PHDMF, https://hvic.biosino.org/PHDMF) has been developed, which had the following advantages: 1) the blockchain technology was used to establish a data consortium over multiple medical institutions, which could provide a flexible and scalable technical solution for member extension and solve the problem of third-party endorsement during data sharing; 2) using data distributed storage technology, personal health data could be majorly stored in their original medical institutions, and the massive data transferring process was of no further use, which could match up with the data rapid growth of these institutions; 3) the distributed ledger technology was utilized to record the hash value of data, given the anti-tampering feature of the technology, malicious modification of data could be identified by comparing the hash value; 4) the smart contract technology was introduced to manage users’ access and operation of data, which made the data transaction process traceable and solved the problem of data provenance; and 5) a trusted computing environment was provided for meta-analysis with statistic information instead of original data, the trusted computing environment could be further applied to more health data, such as genome sequencing data, protein expression data, and metabolic profile data through combining the federated learning and blockchain technology. In summary, the framework provides a convenient, secure, and trusted environment for health data supervision and circulation, which facilitate the consortium establish over medical institutions and help achieve the value of data sharing and mining.

Abstract When a protein is covalently and irreversibly bound to DNA (i.e., a DNA–protein cross-link [DPC]), it may obstruct any DNA-based transaction, such as transcription and replication. DPC formation is very common in cells, as it can arise from endogenous factors, such as aldehyde produced during cell metabolism, or exogenous sources like ionizing radiation, ultraviolet light, and chemotherapeutic agents. DPCs are composed of DNA, protein, and their cross-linked bonds, each of which can be targeted by different repair pathways. Many studies have demonstrated that nucleotide excision repair and homologous recombination can act on DNA molecules and execute nuclease-dependent DPC repair. Enzymes that have evolved to deal specifically with DPC, such as tyrosyl-DNA phosphodiesterases 1 and 2, can directly reverse cross-linked bonds and release DPC from DNA. The newly identified proteolysis pathway, which employs the proteases Wss1 and SprT-like domain at the N-terminus (SPRTN), can directly hydrolyze the proteins in DPCs, thus offering a new venue for DPC repair in cells. A deep understanding of the mechanisms of each pathway and the interplay among them may provide new guidance for targeting DPC repair as a therapeutic strategy for cancer. Here, we summarize the progress in DPC repair field and describe how cells may employ these different repair pathways for efficient repair of DPCs.

Abstract Background Chronic obstructive pulmonary disease (COPD) is a progressive lung disease that is classified into stages based on disease severity. We aimed to characterize the time to progression prior to death in patients with COPD and to generate a temporal visualization that describes signs and symptoms during different stages of COPD progression. Methods We present a two-step approach for visualizing COPD progression at the level of unstructured clinical notes. We included 15,500 COPD patients who both received care within Partners Healthcare’s network and died between 2011 and 2017. We first propose a four-layer deep learning model that utilizes a specially configured recurrent neural network to capture irregular time lapse segments. Using those irregular time lapse segments, we created a temporal visualization (the COPD atlas) to demonstrate COPD progression, which consisted of representative sentences at each time window prior to death based on a fraction of theme words produced by a latent Dirichlet allocation model. We evaluated our approach on an annotated corpus of COPD patients’ unstructured pulmonary, radiology, and cardiology notes. Results Experiments compared to the baselines showed that our proposed approach improved interpretability as well as the accuracy of estimating COPD progression. Conclusions Our experiments demonstrated that the proposed deep-learning approach to handling temporal variation in COPD progression is feasible and can be used to generate a graphical representation of disease progression using information extracted from clinical notes.

Abstract Background Unsatisfactory analgesia would occur frequently during repeated cesarean section under epidural anesthesia. The aim of this study is to observe the effects of intravenous remifentanil on maternal comfort, maternal and neonatal safety during repeated cesarean section under epidural anesthesia. Methods A total of 80 parturients undergoing repeated cesarean section were involved in the study. The patients were randomly divided into the intravenous remifentanil- assisted epidural group (group R) and epidural group (group E), respectively (n = 40). In group R, the remifentanil was continuously intravenously infused as an adjuvant to epidural anesthesia. In group E, 0.75% ropivacaine epidural or intravenous ketamine was administered as needed. Parturient baseline characteristics, vital signs, VAS scores, and comfort scores during surgery were recorded. Adverse effects were also recorded. Results A total of 80 patients were enrolled in the current study and the final analyses included 39 patients in group R and 38 patients in group E. No differences in patients’ baseline characteristics were found between the two groups (p > 0.05). Compared with group E, the comfort score was significantly higher in group R (9.1 ± 1.0 vs. 7.5 ± 1.3, p 0.05). Conclusions Continuous intravenous infusion of low-dose remifentanil can significantly improve the experience of parturients undergoing repeated cesarean section under epidural anesthesia, without noticeable maternal or neonatal adverse effects. Trial registration This study was pre-registered at http://www.chictr.org.cn/index.aspx (ChiCTR1800018423) on 17/09/2018.

Abstract Background It is significant to identificate complex biological mechanisms of various diseases in biomedical research. Recently, the growing generation of tremendous amount of data in genomics, epigenomics, metagenomics, proteomics, metabolomics, nutriomics, etc., has resulted in the rise of systematic biological means of exploring complex diseases. However, the disparity between the production of the multiple data and our capability of analyzing data has been broaden gradually. Furthermore, we observe that networks can represent many of the above-mentioned data, and founded on the vector representations learned by network embedding methods, entities which are in close proximity but at present do not actually possess direct links are very likely to be related, therefore they are promising candidate subjects for biological investigation. Results We incorporate six public biological databases to construct a heterogeneous biological network containing three categories of entities (i.e., genes, diseases, miRNAs) and multiple types of edges (i.e., the known relationships). To tackle the inherent heterogeneity, we develop a heterogeneous network embedding model for mapping the network into a low dimensional vector space in which the relationships between entities are preserved well. And in order to assess the effectiveness of our method, we conduct gene-disease as well as miRNA-disease associations predictions, results of which show the superiority of our novel method over several state-of-the-arts. Furthermore, many associations predicted by our method are verified in the latest real-world dataset. Conclusions We propose a novel heterogeneous network embedding method which can adequately take advantage of the abundant contextual information and structures of heterogeneous network. Moreover, we illustrate the performance of the proposed method on directing studies in biology, which can assist in identifying new hypotheses in biological investigation.

Thanks to the development of geographic information technology, geospatial representation learning based on POIs (Point-of-Interest) has gained widespread attention in the past few years. POI is an important indicator to reflect urban socioeconomic activities, widely used to extract geospatial information. However, previous studies often focus on a specific area, such as a city or a district, and are designed only for particular tasks, such as land-use classification. On the other hand, large-scale pre-trained models (PTMs) have recently achieved impressive success and become a milestone in artificial intelligence (AI). Against this background, this study proposes the first large-scale pre-training geospatial representation learning model called GeoBERT. First, we collect about 17 million POIs in 30 cities across China to construct pre-training corpora, with 313 POI types as the tokens and the level-7 Geohash grids as the basic units. Second, we pre-train GeoEBRT to learn grid embedding in self-supervised learning by masking the POI type and then predicting. Third, under the paradigm of “pre-training + fine-tuning”, we design five practical downstream tasks. Experiments show that, with just one additional output layer fine-tuning, GeoBERT outperforms previous NLP methods (Word2vec, GloVe) used in geospatial representation learning by 9.21% on average in F1-score for classification tasks, such as store site recommendation and working/living area prediction. For regression tasks, such as POI number prediction, house price prediction, and passenger flow prediction, GeoBERT demonstrates greater performance improvements. The experiment results prove that pre-training on large-scale POI data can significantly improve the ability to extract geospatial information. In the discussion section, we provide a detailed analysis of what GeoBERT has learned from the perspective of attention mechanisms.

Rapid detection of microorganisms in jet fuel can effectively prevent a series of problems caused by microbial contamination. In this paper, the application of the ATP bioluminescence method developed by the research group is systematically studied in the rapid detection of microorganisms in jet fuel. The results show that the ATP standard curve of the ATP bioluminescence method has good linear relationships with the standard curves of the colonies of five major contamination fungi and mixed bacteria (R2 is all above 0.96), and that the ATP content in each single colony is on the order of magnitude of 10-17 mol, indicating that this method can achieve rapid and effective measurement of the total number of microorganisms in jet fuel. The method also proves t have good measuring performance in various aspects – the method detection limit is 8.8x10-17 mol and the repeatability and reproducibility is 29.35%, which are within the acceptable range; the fluorescence reaction is highly stable, and a fluorescence decay of 5% can be guaranteed within 60s. This method has little difference in field detection with the HY-LITE® JET A1 fuel test method recommended by IATA, and what is more, it is accurate, stable and lost-cost, making it effective as an alternative method. It is an independent intellectual property of China and can be recommended as a method for rapid detection of microorganisms in jet fuel in national standards.

The adenosine triphosphate (ATP) bioluminescence assay is a feasible way to quantify the characteristic polluting microorganisms of the jet fuels. The effect of this method hinges on the release of ATP through the effective lysis of microorganisms. To achieve a good microbial lysis effect, this paper explores the fungi lysis effects of two quaternary ammonium salts, benzyldodecyldimethylammonium bromide (BAB) and benzalkonium chloride (BAC), and discusses the impacts of the two cationic surfactants on luciferase activity. The results show that BAB was more effective than BAC in the lysis of the characteristic polluting fungi, putting the optimal BAB concentration at 0.05%; traditional lysing agents like cetyl trimethyl ammonium bromide (CTAB) and trichloroacetic acid (TCA) had poor effects on fungi lysis; 0.025% Hibitine and 0.05% BAB exerted a synergistic effect on the lysis of characteristic polluting fungi, and were identified as the preliminary components of the lysing agent for the characteristic polluting microorganisms of jet fuels; the optimized luciferase-luciferin reaction system effectively mitigated the suppression effect of the lysis agent on enzymatic activity, and the ATP standard curves with or without lysing agent had no significant difference (p = 0.9768 > 0.05). The optimized luciferase-luciferin reaction system, coupled with the mixture of BAB and Hibitine, outshines the traditional plate culture method and HY-LiTE JET A1 Fuel Test in detection efficiency and cost.

Failure mode and effect analysis (FMEA) is an effective quality management technique widely used in various industries to improve the reliability and safety of systems, products, processes, and services. In traditional FMEA, the ranking of failure modes is carried out by the risk priority number (RPN), which is calculated by the product of severity (S), occurrence (O), and detection (D). Nevertheless, the normal FMEA has many inherent defects in assessing and ranking failure modes. Therefore, in this paper, we present a new FMEA model, which integrates probabilistic linguistic term sets (PLTSs) and fuzzy Petri nets (FPNs) for the risk assessment and prioritization of failure modes. Specifically, the PLTSs are used to capture the uncertainty of FMEA team members' subjective judgments, and the FPNs are established to acquire the risk priority of the identified failure modes. Besides, a technique for order preference by similarity to an ideal solution (TOPSIS)-based weighting method is proposed to determine the objective weight of each team member. Finally, a marine-ship system risk assessment example is provided to illustrate the suggested FMEA and a comparative analysis is conducted to assess its effectiveness and usefulness. The results show that the new FMEA approach can produce more reliable and reasonable risk ranking result of failure modes.

Sorghum (Sorghum bicolor) grain is a rich source of bioactive phenolic compounds and understanding the phenolic profile of different sorghum genotypes is an important step towards the selection of the most appropriate genotype for industrial applications. The free and bound phenolic compounds of sorghum bran and kernel fractions from five Australian-grown sorghum genotypes (1 white, 2 red, 1 brown and 1 black coloured grain) were identified/tentatively identified by HPLC-DAD-ESI-QTOF-MS/MS and quantified/semi-quantified by HPLC-DAD. Firstly, MS chromatograms of sorghum samples and standards and the MS/MS spectra of individual detected compounds and standards are presented. Then quantification data of these compounds is provided. This dataset is supplementary to the research paper “Comprehensive profiling of phenolic compounds by HPLC-DAD-ESI-QTOF-MS/MS to reveal their location and form of presence in different sorghum grain genotypes” [1].

Hospitalized geriatric patients are a highly heterogeneous group often with variable diseases and conditions. Physicians, and geriatricians especially, are devoted to seeking non-invasive testing tools to support a timely, accurate diagnosis. Chinese tongue diagnosis, mainly based on the color and texture of the tongue, offers a unique solution. To develop a non-invasive assessment tool using machine learning in supporting a timely, accurate diagnosis in the elderly, we created an annotated dataset of 15% of 688 (=100) tongue images collected from hospitalized geriatric patients in a tertiary hospital in Shanghai, China. Images were captured via a light-field camera using CIELAB color space (to simulate human visual perception) and then were manually labeled by a panel of subject matter experts after chart reviewing patients’ clinical information documented in the hospital's information system. We expect that the dataset can assist in implementing a systematic means of conducting Chinese tongue diagnosis, predicting geriatric syndromes using tongue appearance, and even developing an mHealth application to provide individualized health suggestions for the elderly.

Cephalotaxus lanceolata K. M. Feng ex C. Y. Cheng W. C. Cheng and L. K. Fu (Taxaceae) is a threatened plant species and a typical plant species with extremely small populations (PSESP) with only four individuals found in Gaoligong Mountain. With the aim of providing data for future conservation efforts, we sequenced the whole chloroplast (cp) genome of C. lanceolata. The results showed that the plastid genome is 136,404 bp in size. In total, 116 unique genes were annotated, including 83 protein-coding genes, 29 tRNA genes, and 4 rRNA genes. The total GC content was 35.1%. We performed phylogenetic analyses based on 12 cp genomes of Taxaceae, and we determined that the genus Cephalotaxus forms a sister group to Taxus and Pseudotaxus.

With the increasing awareness of the value of data, transborder data flow has attracted more and more attention. On one hand, transborder data flow is necessary for economic globalization and the development of the digital economy. On the other hand, it may cause damage to national data security without effective supervision. Therefore, it is necessary to distinguish between reasonable transborder data flow and malicious one, and formulate appropriate regulations. According to the analysis, two types of current transborder data flow and four transborder data flow channels were given. Furthermore, a classification supervision method for transborder data flow was proposed. This work provides the support for transborder data flow supervision and the legislation for transborder data flow.

With the rapid development of the digital economy for which data is the key elements,there are more and more disputes and cases involving data.Since the data element is a newly concept,it is necessary to clarify the types of the data-centric economic activity,and then what rights need to be authorized to whom executing these data-centric economic activities should be designed.The data-centric economic activities were divided into five categories according the characteristics of data:data production and reproduction,data right confirmation,data usage and service,data sharing,and scientific research.Correspondingly,four types of rights were proposed including data production right,data proprietary right,data access right,and data sharing right,to provide a reference for legislation of data use and development of dagital economy.

Background and objective: Type 2 diabetes mellitus (T2DM) is a serious public health problem, and the phenomenon of T2DM occurring in younger people has directed more attention to functional changes in the brain. In this study, the microstructural integrity of white matter (WM) was evaluated in three groups of middle-aged subjects: healthy controls (HCs) and T2DM patients with and without peripheral microvascular complications (T2DM-C and T2DM-NC patients, respectively). Methods: Diffusion tensor imaging (DTI) and related clinical examinations were performed in 66 subjects, including 20 T2DM-C patients, 20 T2DM-NC patients, 26 age- and sex-matched HCs. Magnetic resonance imaging (MRI) at 3 T was used to perform DTI; then, FSL and tract-based spatial statistics (TBSS) software were used to assess differences in the fractional anisotropy (FA) and mean diffusivity (MD) among the groups. The use of the FA and MD as parameters was evaluated by receiver operating characteristic (ROC) curve analysis. Results: There were no significant differences in sex or age among the groups, and the clinical data of the groups met the experimental requirements. There was no significant difference in the FA values between the HCs and T2DM-NC groups. Compared with the HCs, the T2DM-C patients showed decreased FA values and increased MD values in the corpus callosum, bilateral anterior limb of the internal capsule, right retrolenticular part of the internal capsule, bilateral posterior thalamic radiation, right superior longitudinal fasciculus, bilateral superior corona radiata and left middle frontal gyrus (P

Diabetes is a global health challenge. Currently, an effective treatment for diabetes is to reduce the postprandial hyperglycaemia by inhibiting the carbohydrate hydrolysing enzymes in the digestive system. In this study, we investigated the in vitro α-glucosidase and α-amylase inhibitory effects of free and bound phenolic extracts, from the bran and kernel fractions of five sorghum grain genotypes. The results showed that the inhibitory effect of sorghum phenolic extracts depended on the phenolic concentration and composition. Sorghum with higher phenolic contents generally had higher inhibitory activity. Among the tested extracts, the brown sorghum (IS131C)-bran-free extract (BR-bran-free, half-maximal inhibitory concentration (IC50) = 18 ± 11 mg sorghum/mL) showed the strongest inhibition against α-glucosidase which was comparable to that of acarbose (IC50 = 1.39 ± 0.23 mg acarbose/mL). The red sorghum (Mr-Buster)-kernel-bound extract (RM-kernel-bound, IC50 = 160 ± 12 mg sorghum/mL) was the most potent in inhibiting α-amylase but was much weaker compared to acarbose (IC50 = 0.50 ± 0.03 mg acarbose/mL).

In recent years, the development of new constellations including Galileo, BeiDou Navigation Satellite System (BDS) and Quasi-Zenith Satellite System (QZSS) have undergone dramatic changes. Since January 2018, about 30 satellites of the new constellations have been launched and most of the new satellites have been included in the precise orbit and clock products provided by the Multi Global Navigation Satellite System (Multi-GNSS) Experiment (MGEX). Meanwhile, critical issues including antenna parameters, yaw-attitude models and solar radiation pressure models have been continuously refined for these new constellations and updated into precise MGEX orbit determination and precise clock estimation solutions. In this context, MGEX products since 2018 are herein assessed by orbit and clock comparisons among individual analysis centers (ACs), satellite laser ranging (SLR) validation and precise point positioning (PPP) solutions. Orbit comparisons showed 3D agreements of 3–5 cm for Galileo, 8–9 cm for BDS-2 inclined geosynchronous orbit (IGSO), 12–18 cm for BDS-2 medium earth orbit (MEO) satellites, 24 cm for BDS-3 MEO and 11–16 cm for QZSS IGSO satellites. SLR validations demonstrated an orbit accuracy of about 3–4 cm for Galileo and BDS-2 MEO, 5–6 cm for BDS-2 IGSO, 4–6 cm for BDS-3 MEO and 5–10 cm for QZSS IGSO satellites. Clock products from different ACs generally had a consistency of 0.1–0.3 ns for Galileo, 0.2–0.5 ns for BDS IGSO/MEO and 0.2–0.4 ns for QZSS satellites. The positioning errors of kinematic PPP in Galileo-only mode were about 17–19 mm in the north, 13–16 mm in the east and 74–81 mm in the up direction, respectively. As for BDS-only PPP, positioning accuracies of about 14, 14 and 49 mm could be achieved in kinematic mode with products from Wuhan University applied.

Generally, big data is regarded as a term about data sets so large or complex that conventional data technologies cannot handle. This statement of big data leads to confusion: none of big data has been handled by existing data technologies;or none of current successful data applications can be called as big data. Therefore, what is the best way to define big data becomes a problem. Data, technology, and application were regarded as three associated key factors of big data by analyzing the state-of-the-art of big data. A comprehensive definition on big data was defined as the umbrella of big data set, big data technology, and big data application. Here, big data set means all data that can be acquired and were related to one decision-making application instead of all data in an area or an enterprise. In addition, the issues in big data applications and the main challenges in big data technologies were discussed. Finally, the future directions of big data research were presented including data science and the technologies of big data reservation and development.

In this survey, new recoverable sulfonated magnetic nanocatalysts (SCMNPs@imine/SO _3 H) was synthesized by covalent attachment of a Schiff base ligand on the surface of SCMNPs@APTES/sucanh through reaction with 4-[(Pyridin-2-ylmethylene)-amino]-phenol and subsequent reaction with chlorosulfonic acid. The synthesized nanocatalyst was characterized by several techniques such as FTIR, TGA, VSM, XRD, UV–vis and EDX analysis. The nanocatalyst was evaluated in the preparation of 12-aryl-8,9,10,12-tetrahydrobenzo[a]xanthen-11-one and 1,8-dioxooctahydroxanthene derivatives with pharmacologically and biologically remarkable activity. 12-aryl-8,9,10,12-tetrahydrobenzo[a]xanthen-11-one and 1,8-dioxooctahydroxanthene derivatives were respectively prepared via one-pot, three-component reactions of 2-naphthol, dimedone and aldehydes, as well as the reaction between dimedone and aldehydes in the presence of SCMNPs@imine/SO _3 H under solvent-free conditions. The merits of this synthesis are high-to-excellent yields, short reaction time, mild basic conditions and high catalytic activity. Also, the SCMNPs@imine/SO _3 H with suitable magnetic strength can be easily separated from the reaction solution using an external magnetic field.

Background/Aims: Water channels, also named aquaporins (AQPs), play crucial roles in cellular water homeostasis. Methods: RT-PCR indicated the mRNA expression of AQPs 1-5, 7, 9, and 11-12, but not AQPs 0, 6, 8, and 10 in the 2∼8-cell stage human embryos. AQP3 and AQP7 were further analyzed for their mRNA expression and protein expression in the oocyte, zygote, 2-cell embryo, 4-cell embryo, 8-cell embryo, morula, and blastocyst from both human and mouse using RT-PCR and immunofluorescence, respectively. Results: AQP3 and AQP7 were detected in all these stages. Knockdown of either AQP3 or AQP7 by targeted siRNA injection into 2-cell mouse embryos significantly inhibited preimplantation embryo development. However, knockdown of AQP3 in JAr spheroid did not affect its attachment to Ishikawa cells. Conclusion: These data demonstrate that multiple aquaporins are expressed in the early stage human embryos and that AQP3 and AQP7 may play a role in preimplantation mouse embryo development.

A Fe61Cr2Nb3Si12B22 amorphous alloy rod sample of 8.8 mm diameter has been successfully prepared through explosive consolidation. The structure and thermal stability of the as-synthesized sample have been analyzed through X-ray diffraction (XRD) and differential scanning calorimeter (DSC) analysis. The results demonstrate that the sample still retains an amorphous structure, and the glass transition temperature (Tg), the crystallization onset temperature (Tx), the supercooled liquid zone (ΔTx) (Tx − Tg) and the reduced glass transition temperatures (Trg) (Tg/Tm) are 784 K, 812 K, 28 K, and 0.556, respectively. Its microstructure has been investigated by optical microscopy (OM) and scanning electron microscopy (SEM). The average microhardness of the alumina compact is about 1069 HV.

The hospital admission data from medicine department and infectious disease department of a hospital was analyzed and a classify model between the number of patients and meteorological factors was built.High accuracy of prediction in abnormal number of patients by utilizing random forest classifier was achieved,and decision support to Public Health Department was provided so that the hospital can make a reasonable allocation of doctors.All experiments were conducted on real data from the hospital and the results show that the final trained model achieve relatively high accuracy and recall.

Study on the relationship between the chronic disease and the corresponding complications has great theoretical significance and applicable value for patients and clinical medicine.In order to utilize healthcare electronic record more reasonably,preprocessing was needed according to prior medical knowledge for chronic disease complication.The challenge of this work is that medical knowledge should be exploited to label the corresponding complications.To meet these challenges and assist physicians in labeling complications of a target chronic disease,a semi-supervised chronic disease complications clustering algorithm based on ICD-10 code for diagnoses was proposed.Experiments on a real dataset of diabetes electronic healthcare record show that the algorithms are practical and effective.