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Evidence has suggested the potential of tumor-educated platelets as a biomarker trove for cancer diagnostics, but the difficulty in isolation limits its application. Since most of the circulating RNAs are derived from platelets, the change of RNA profile in platelets may lead to altered RNA expression in serum. Here, we identified a panel of platelet-associated long non-coding RNAs (lncRNAs) and evaluated its diagnostic capacity in serum of colorectal cancer (CRC) patients. Four lncRNAs, LNCAROD, SNHG20, LINC00534, and TSPOAP-AS1, were upregulated in both platelets and serum of CRC patients. A binary logistic model derived from them has validated area under roc curve of 0.78 indicating great performance. Furthermore, the expression levels of LNCAROD and TSPOAP-AS1 were correlated with cancer staging and tumor location. Together, our results add novel lncRNA biomarkers to the list of blood tests for CRC diagnostics and provide molecular evidence for the cross-talk between CRC platelets and serum.

N6-methyladenosine (m6A), the most prevalent internal modification in eukaryotic mRNAs, regulates gene expression at the post-transcriptional level. The reader proteins of m6A, mainly YTH domain-containing proteins, specifically recognize m6A-modified mRNAs and regulate their metabolism. Recent studies have highlighted essential roles of m6A readers in the initiation and development of human cancers. In this review, we summarize recent findings about the biological functions of YTH domain proteins in cancers, the underlying mechanisms, and clinical implications. Gene expression reprogramming by dysregulated m6A reader proteins offers potential targets for cancer treatment, while targeted m6A editors and readers provide tools to manipulate m6A metabolism in cancers.

We studied the fracture strain of polycrystal ɑ-iron at three different hydrogen concentrations and for three crystal sizes with molecular dynamics simulations (MDs). As the hydrogen concentration increases, a fine crystal model's fracture resistance is prone to below a comparatively coarse grain model. This finding elucidates that the most vulnerable area can alter from coarse grain zone to fine-grain zone in the welding heat-affected zone (HAZ) with the effect of hydrogen. A simplified model is thus built to predict the strain energy increment in different crystal size systems caused by the introduction of hydrogen atoms. This strain energy increment is not equal to the fracture energy reduction induced by the same amount of hydrogen insertion, demonstrating that elastic volume expansion of grain boundary (GB) caused by hydrogen is not the determining mechanism of intergranular failure. The density of triple or multi-junctions of GBs, which is typically dependent on the volume fraction of GBs, is the crucial factor for intergranular failure caused by hydrogen embrittlement.

Background Anatomy is a required course for all medicine-related industries. In recent decades, the teaching quality and effect of anatomy have been compromised by factors including a decrease in human body specimens, dampened enthusiasm for the discipline, reduced teaching hours of anatomy, scale expansion of medical education, and obstacles in performing field autopsies and observations. Methods Based on China's digitalized visible human research achievements, this article extracts the boundary information of anatomic structures from tomographic images, constructs threedimensional (3D) digital anatomical models with authentic texture information, and develops an anatomy assistive teaching system for teachers and students based on the knowledge points of anatomy, to meet the anatomy teaching requirements of different majors at various levels. Results This scientific, complete, and holistic system has produced over 6000 3D digital anatomical models, 5000 anatomy knowledge points, 50 anatomical operation videos, and 150 micro demonstration classes, with teaching contents for different majors and levels, such as systematic anatomy, topographic anatomy, sectional anatomy, anatomy of motion, and virtual anatomical operation table. Ranging from network terminals, desktops, touchscreen 3D displays, desktops, and projection 3D volumetric displays to augmented reality, its diversified interactive forms meet the requirements for a learning environment in different settings. Conclusions With multiple teaching and learning links covered, such as teaching environment, teaching resources, instructional slides, autonomous learning, and learning effect evaluation, this novel teaching system serves as a vital component and a necessary resource in anatomy teaching and functions as an important supplement to traditional anatomy teaching. Applied and promoted in most medical colleges and schools in China, this system has been recognized and approved by anatomy teachers and students, and plays a positive role in guaranteeing the effect and quality of anatomy teaching.

The prognostic and health management (PHM) of rolling bearings has been a popular research area. Since bearing fault is inevitable during degradation, how to improve the PHM performance based on both degradation states and fault types is still an open problem. In this study, two multilabel learning algorithms are proposed for PHM of rolling bearings, named personalized binary relevance (PBR) and hierarchical multilabel K-nearest neighbor (HML-KNN), respectively. Degradation states and fault types are used as the labels of the bearing data so that each sample has a corresponding label sequence, that is to say, the PHM problem is converted to a multilabel learning problem. Both algorithms have a personalized search process, which can not only help samples build a personalized model to improve classification accuracy but also solve the problem of data imbalance between labels. At the same time, the two algorithms also have their own characteristics and focus on different application situations. The PBR algorithm has faster modeling speed, more flexible use, and replaceable subclassifiers. HML-KNN is a high-order algorithm with global information analysis capabilities through the hierarchical processing of data and the conversion of label information. Both methods have achieved good enough results in the XJTU-SY bearing dataset. In order to illustrate the practicality of the algorithm, the experimental part further increases the difficulty. Using only a single faulty sample as the training set to determine the type of compound fault, the algorithms also show high performance. In the era of industrial big data, the depth of data mining and the design of algorithm models will help us better manage equipment health.

BackgroundIn this study, we enrolled 862 patients with Crohn’s disease (CD) in China to investigate the correlation between serum vitamin D (SVD) and serum lipids, inflammatory biomarkers, and important clinical parameters.Materials and Methods25(OH)D was measured by LS/MS/MS. Correlation analysis, chi-square tests, and logistic regression analysis were performed to determine the correlations between vitamin D and potential risk factors when vitamin D levels were lower than 10 ng/mL or 20 ng/mL.ResultsThe incidence of severe vitamin D deficiency (SVD < 10 ng/mL) in patients with CD was significantly higher than that in healthy controls (28.9 vs. 9.5%). Multinomial logistic regression analysis showed that penetrating disease [odds ratio (OR) = 2.18], low levels of high-density lipoprotein cholesterol (HDL) (OR = 1.91), high erythrocyte sedimentation rate (OR = 1.73), and platelet count (PLT) (OR = 2.71) were regarded as predictors of severe vitamin D deficiency, while only PLT (OR = 1.90) and HDL (OR = 1.76) were considered as predictors of mild vitamin D deficiency (SVD 10–20 ng/mL).ConclusionOur results confirm a higher incidence of severe vitamin D deficiency in patients with CD in China and show that vitamin D deficiency could result from the combined effects of penetrating disease, inflammation, and low levels of HDL.

The aim of this study was to assess the spatial variation in the concentrations of total arsenic (Astotal), arsenite (As(III)), arsenate (As(V)), monomethylarsonic acid (MMAs(V)), and dimethylarsinic acid (DMAs(V)) in fine particulate matter (PM2.5) and to explore the influence of local emission sources based on the monitoring data from 18 sampling sites in Nanjing, China. The results showed that the average concentration of Astotal in the PM2.5 was 6.81 ng/m3 in Nanjing, which exceeded the standard limit of 6 ng/m3 in China. As(V) was the dominant species and varied between 71 and 81% of water-extractible As in the PM2.5. The results of the spatial variation coefficients (CVs) showed that Astotal, As(III), and As(V) displayed moderate levels of spatial heterogeneity (CV = 0.23), while DMAs(V) a considerably high level (CV = 0.60). The concentrations of Astotal and As species can be arranged in the following order: urban background ~ urban street < suburban < rural < industrial sampling sites. This pattern was connected to the influence of three local emission sources (industrial source, road traffic, and biovolatilization), which were quantified by multiple linear regression. Results showed that local road traffic sources had the smallest value of standardized regression coefficient (0.26) among these three sources, indicating that local road traffic sources contributed less to the concentration of As in PM2.5 than industrial source emissions and biovolatilization. Our findings indicate that the spatial heterogeneity of As species should be considered in exposure assessments and As biovolatilization is linked to the high heterogeneity.

Traumatic brain injury (TBI) is a leading cause of morbidity and mortality among military service members and civilians in the United States. Despite significant advances in the understanding of TBI pathophysiology, several clinical reports indicate that multiple genetic and epigenetic factors can influence outcome. Homocysteine (HCY) is a non-proteinogenic amino acid, the catabolism of which can be dysregulated by stress, lifestyle, aging, or genetic abnormalities leading to hyperhomocysteinemia (HHCY). HHCY is a neurotoxic condition and a risk factor for multiple neurological and cardiovascular disorders that occurs when HCY levels is clinically > 15 µM. Although the deleterious impact of HHCY has been studied in human and animal models of neurological disorders such as stroke, Alzheimer’s disease and Parkinson's disease, it has not been addressed in TBI models. This study tested the hypothesis that HHCY has detrimental effects on TBI pathophysiology. Moderate HHCY was induced in adult male Sprague Dawley rats via daily administration of methionine followed by impact-induced traumatic brain injury. In this model, HHCY increased oxidative stress, upregulated expression of proteins that promote blood coagulation, exacerbated TBI-associated blood–brain barrier dysfunction and promoted the infiltration of inflammatory cells into the cortex. We also observed an increase of brain injury-induced lesion size and aggravated anxiety-like behavior. These findings show that moderate HHCY exacerbates TBI outcomes and suggest that HCY catabolic dysregulation may be a significant biological variable that could contribute to TBI pathophysiology heterogeneity.

Boundary pixel blur and category imbalance are common problems that occur during semantic segmentation of urban remote sensing images. Inspired by DenseU-Net, this paper proposes a new end-to-end network—SiameseDenseU-Net. First, the network simultaneously uses both true orthophoto (TOP) images and their corresponding normalized digital surface model (nDSM) as the input of the network structure. The deep image features are extracted in parallel by downsampling blocks. Information such as shallow textures and high-level abstract semantic features are fused throughout the connected channels. The features extracted by the two parallel processing chains are then fused. Finally, a softmax layer is used to perform prediction to generate dense label maps. Experiments on the Vaihingen dataset show that SiameseDenseU-Net improves the F1-score by 8.2% and 7.63% compared with the Hourglass-ShapeNetwork (HSN) model and with the U-Net model. Regarding the boundary pixels, when using the same focus loss function based on median frequency balance weighting, compared with the original DenseU-Net, the small-target “car” category F1-score of SiameseDenseU-Net improved by 0.92%. The overall accuracy and the average F1-score also improved to varying degrees. The proposed SiameseDenseU-Net is better at identifying small-target categories and boundary pixels, and it is numerically and visually superior to the contrast model.

In this paper, we show the existence and multiplicity of solutions for the following fourth-order Kirchhoff type elliptic equations Δ 2 u − M ( ‖ ∇ u ‖ 2 2 ) Δ u + V ( x ) u = f ( x , u ) , x ∈ R N , where M ( t ) : R → R is the Kirchhoff function, f ( x , u ) = λ k ( x , u ) + h ( x , u ) , λ ≥ 0 , k ( x , u ) is of sublinear growth and h ( x , u ) satisfies some general 3-superlinear growth conditions at infinity. We show the existence of at least one solution for above equations for λ = 0 . For λ > 0 small enough, we obtain at least two nontrivial solutions. Furthermore, if f ( x , u ) is odd in u , we show that above equations possess infinitely many solutions for all λ ≥ 0 . Our theorems generalize some known results in the literatures even for λ = 0 and our proof is based on the variational methods.

Empathy involves both empathic ability and empathic motivation. An important topic has been how to measure empathic ability and motivation simultaneously in both clinical and non-clinical samples and across different cultures. The Empathy Components Questionnaire (ECQ) is a self-report questionnaire that measures empathic ability and motivation in a questionnaire. The current study aimed to validate the Mandarin Chinese version of the ECQ (ECQ-Chinese) in three Chinese samples. In study 1, a total of 538 Chinese participants (Sample 1) completed the ECQ-Chinese via an online survey, and existing measures of empathy and related constructs which were used for criterion validity. In study 2, a total of 104 participants (Sample 2) were recruited again from sample 1 and completed the ECQ-Chinese three weeks later to investigate test-retest reliability. In study 3, a further 324 participants (Sample 3) completed the ECQ-Chinese for confirmatory factor analysis. The results showed that the ECQ-Chinese has a good internal consistency reliability, split-half reliability, and criterion validity (Study 1), and a good test-retest reliability (Study 2). Further, Study 3 found that a 22-item ECQ-Chinese consisting of five subscales had a good construct validity, convergence validity and discriminate validity, demonstrating it to be a suitable tool for the measurement of empathic ability and motivation in Chinese samples and to carry out cross-cultural studies of empathy and its components.

In image retrieval tasks, the single-layer convolutional feature has insufficient image semantic representation ability. A new image description algorithm ML-RCroW based on multilayer multiregion cross-weighted aggregational deep convolutional features is proposed. First, the ML-RCroW algorithm inputs an image into the VGG16 (a deep convolutional neural network developed by researchers at Visual Geometry Group and Google DeepMind) network model in which the fully connected layer is discarded. The visual feature information in the convolutional neural network (CNN) is extracted, and the target response weight map is generated by combining with the spatial weighting algorithm of the target fuzzy marker. Then, visual features in the CNN are divided into multiple regions, and the pixels of each region are weighted by regional spatial weight, regional channel weight, and regional weight. The image global vector is generated by aggregating and encoding every region in the weighted feature map. Finally, features of each layer of the VGG16 network model are extracted and then aggregated and dimensionally reduced to obtain the final feature vector of the image. The experiments are carried out on the Oxford5k and Paris6k datasets provided by Oxford VGG. The experimental results show that the average accuracy of image retrieval based on the image feature description algorithm ML-RCroW is better than that achieved by the other commonly used algorithms such as SPoC, R-MAC, and CroW.

In recent years, the sustainable development of education has become an increasing concern, and new technology characterized by intelligence has played an important role in promoting it. However, facing the endless stream of teaching platforms, learning platforms, student management platforms and learning APPs, teachers and students are tired of coping. Meanwhile, there has been a serious lack of real information about student growth, especially in the fields of electronic graphics, and audio and video materials. At present, there is no continuous student growth system that can be used over the course of their life, which is very unfavorable to their individual development. Graphic code is a technology with the potential to solve these problems; however, the existing graphic code technology suffers from clear deficiencies in the realization of a personalized student growth system that incorporates intelligence, security and sustainability. In response to this, this paper proposes a new generation of graphic code technology, namely intelligent graph element technology (IGET). Further, a new sustainable personalized student growth system model is designed based on artificial intelligence, big data analysis and intelligent graph element technologies, and the architecture and implementation of this system platform are completed. Finally, a student growth system based on intelligent code is verified through by an analysis of the results of a questionnaire survey. The research results show that, compared with the traditional student management system, the student growth system based on an intelligent graph element code has obvious advantages in convenience, intelligence, precision, security, and sustainability.

Aim: To investigate the value of galectin-3 in the diagnosis of acute coronary syndrome (ACS) and the assessment of coronary artery lesions. Methodology: This study recruited 157 patients with coronary artery disease where 102 and 55 of them were subsequently grouped as ACS and non-ACS, respectively. The severity of coronary artery lesions was evaluated by Gensini score and the number of vessels involved. Results: Receiver operator characteristics analyses of galectin-3 yielded an area under the curve of 0.679 in diagnosing ACS. The galectin-3 levels were correlated with Gensini score and the number of vessels involved. Conclusion: Our study demonstrated that galectin-3 is an effective auxiliary biomarker for the diagnosis of ACS and assessment of coronary artery lesions.

A joint operation of renewable energy with energy storage devices and the development of multi-energy market transactions are effective ways to accommodate renewable energy such as wind power. This paper addresses issues of joint equilibrium analysis of the wholesale electricity market (WEM) and the gas market (GM) in which wind power producers (WPPs), power-to-gas (P2G) equipment, natural gas generating unit (NGGU) and gas storage device (GSD) constitute a joint operator (JO) to participate in bidding competition. First, a new coupled electricity and gas markets trading framework is presented, in which WPP can use P2G equipment, NGGU and GSD to tackle the surplus and deficit of its actual output. In addition, P2G equipment and NGGU have arbitrage behavior in the multi-energy market. Second, based on this framework, a multi-period stochastic game equilibrium model in which the JO, conventional generators (CGs) and gas companies participate in the multi-energy market bidding competition is established, and the equilibrium model is solved by the nonlinear complementarity method. Finally, simulation results show that the presented multi-energy trading framework can tackle the deviation of the WPP participating in the multi-period WEM competition, reduce the market power of CGs, and improve the market competitiveness of WPP and the social welfare.

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NOXA, a BH3-only proapoptotic protein involved in regulating cell death decisions, is highly expressed but short-lived in colorectal cancer (CRC). Neddylated cullin-5 (CUL5)-mediated ubiquitination and degradation of NOXA is crucial to prevent its overaccumulation and maintain an appropriate action time. However, how this process is manipulated by CRC cells commonly exposed to oxidative stress remain unknown. The peroxiredoxin PRDX1, a conceivable antioxidant overexpressed in CRC tissues, has been shown to inhibit apoptosis and TRAF6 ubiquitin-ligase activity. In this study, we found that PRDX1 inhibits CRC cell apoptosis by downregulating NOXA. Mechanistically, PRDX1 promotes NOXA ubiquitination and degradation, which completely depend on CUL5 neddylation. Further studies have demonstrated that PRDX1 oligomers bind with both the Nedd8-conjugating enzyme UBE2F and CUL5 and that this tricomplex is critical for CUL5 neddylation, since silencing PRDX1 or inhibiting PRDX1 oligomerization greatly dampens CUL5 neddylation and NOXA degradation. An increase in reactive oxygen species (ROS) is not only a hallmark of cancer cells but also the leading driving force for PRDX1 oligomerization. As shown in our study, although ROS play a role in upregulating NOXA mRNA transcription, ROS scavenging in CRC cells by N-acetyl-L-cysteine (NAC) can significantly reduce CUL5 neddylation and extend the NOXA protein half-life. Therefore, in CRC, PRDX1 plays a key role in maintaining intracellular homeostasis under conditions of high metabolic activity by reinforcing UBE2F-CUL5-mediated degradation of NOXA, which is also evidenced in the resistance of CRC cells to etoposide treatment. Based on these findings, targeting PRDX1 could be an effective strategy to overcome the resistance of CRC to DNA damage-inducing chemotherapeutics.

The classical Mountain Pass Lemma of Ambrosetti-Rabinowitz has been studied, extended and modified in several directions. Notable examples would certainly include the generalization to locally Lipschitz functionals by K.C. Chang, analyzing the structure of the critical set in the mountain pass theorem in the works of Hofer, Pucci-Serrin and Tian, and the extension by Ghoussoub-Preiss to closed subsets in a Banach space with recent variations. In this paper, we utilize the generalized gradient of Clarke and Ekeland's variatonal principle to generalize the Ghoussoub-Preiss's Theorem in the setting of locally Lipschitz functionals. We give an application to periodic solutions of Hamiltonian systems., Comment: arXiv admin note: text overlap with arXiv:1405.6004

This paper proposes a sustainable management and decision-making model for COVID-19 control in schools, which makes improvements to current policies and strategies. It is not a case study of any specific school or country. The term one-size-fits-all has two meanings: being blind to the pandemic, and conducting inflexible and harsh policies. The former strategy leads to more casualties and does potential harm to children. Conversely, under long-lasting strict policies, people feel exhausted. Therefore, some administrators pretend that they are working hard for COVID-19 control, and people pretend to follow pandemic control rules. The proposed model helps to alleviate these problems and improve management efficiency. A customized queue model is introduced to control social gatherings. An indoor–outdoor tracking system is established. Based on tracing data, we can assess people’s infection risk, and allocate medical resources more effectively in case of emergency. We consider both social and technical feasibility. Test results demonstrate the improvements and effectiveness of the model. In conclusion, the model has patched up certain one-size-fits-all strategies to balance pandemic control and normal life.

Population ageing becomes a perplexing conundrum with social and economic development. Many senior citizens are now empty nesters because the younger generation prefer to stay in metropolises for a better life. Therefore, living in a nursing home is a popular choice for the aged. This objective-oriented paper proposes a sustainable elderly healthcare system for nursing homes. The main work is the design and implementation of a new rapid and interactive assistance service. Based on cost-effective fingerprint indoor-positioning technology, the alert message that a person is at risk will be immediately sent to nearby people before professionals arrive. Warning messages are available when nearing marked areas (e.g., slippery floors). The parallel path-finding algorithm plays a significant role in finding nearby people and alerting people who approach specific areas. Furthermore, this system provides application programming interfaces to connect to health devices, such as smart bracelets, watches, and glasses. In general, the system is designed to ensure the safety of the elderly and improve management efficiency, which corresponds to present smart elderly care proposals from governments.

Prognostics and health management (PHM) is an important topic of rolling bearing, so partition of healthy stage and different degree of fault states are equally important as fault types. This study uses a multi-label learning method, Binary Relevance (BR) algorithm, for fault diagnosis of bearing data based on both of states partition and fault types judgment. The Binary Relevance algorithm simplifies the classification process by transforming the multi-classification problem into multiple binary classification problems. Using extreme learning machine ensures the classification speed and effect. By comparative experiments on XJTU-SY bearing datasets, the effectiveness of our method is proved to be superior.

When wind power producers (WPPs) bid in the day-ahead market, how to address the deviation between the bid output and the actual output is one of the important topics in the design of electricity markets with WPPs. Under the background of the Energy Internet, the complementarity between power system and natural gas system provides an alternative solution for mitigating the adverse effects of random fluctuations in renewable energy sources. First, a multi-energy trading framework is presented to tackle WPP’s bid deviations. In the framework, the WPP is considered to bid in the day-ahead electricity market (DAEM). In order to compensate the bid deviations of WPP, power to gas (P2G) equipment, natural gas generating unit (NGGU) and gas storage devices can be used by the WPP when it has excess/deficit electricity, and the rest deviation can be compensated by the gas market (GM). Second, based on the trading framework, a multi-period joint game equilibrium model of the DAEM and GM is proposed. In this model, the Cournot bid form is applied by both WPP and conventional power producers in the DAEM and gas producers in the GM. Furthermore, a nonlinear complementary method is applied to solve the equilibrium model. Finally, the numerical examples show that the proposed multi-energy trading model can completely compensate the WPP’s bid deviation in the DAEM, help to improve the market competitiveness of WPP and the accommodation of wind power, and also smooth the price fluctuations in the DAEM and GM.

Background Interleukin-17A (IL-17) is an evolutionary conserved cytokine and best known for its role in boosting immune response. However, recent clinical researches showed that abundant IL-17 in tumor microenvironment was often associated with poor prognosis and reduced cytotoxic T cell infiltration. These contradictory phenomena suggest that IL-17 may have unique target cells in tumor microenvironment which switch its biological consequences from pro-inflammatory to anti-inflammatory. Mesenchymal stem/stromal cells (MSCs) are a major component of the tumor microenvironment. Upon cytokine stimulation, MSCs can express a plenary of inhibitory molecules, playing a critical role in tumor development and progression. Therefore, we aim to investigate the role of IL-17 in MSC-mediated immunosuppression. Results We found IFNγ and TNFα, two major cytokines in tumor microenvironment, could induce programmed death-ligand 1 (PD-L1) expression in MSCs. Interestingly, IL-17 has a synergistic effect with IFNγ and TNFα in elevating PD-L1 expression in MSCs. The presence of IL-17 empowered MSCs with strong immunosuppression abilities and enabled MSCs to promote tumor progression in a PD-L1 dependent manner. The upregulated PD-L1 expression in MSCs was due to the accumulation of nitric oxide (NO). On one hand, NO donor could mimic the effects of IL-17 on MSCs; on the other hand, IL-17 failed to enhance PD-L1 expression in inducible nitric oxide synthase (iNOS) deficient MSCs or with iNOS inhibitor presence. Conclusions Our study demonstrates that IL-17 can significantly increase the expression of PD-L1 by MSCs through iNOS induction. This IL-17-MSCs-PD-L1 axis shapes the immunosuppressive tumor microenvironment and facilitates tumor progression.

The aim of the present study was to explore whether Chinese learners could implicitly learn the semantic preferences of novel English words. In training, participants learned four novel verbs and were exposed to a set of verb-noun phrases that included these new words. What the participants were not told was that the use of the verbs depended on the concreteness of the nouns (i.e., the semantic preference rule). In testing, participants were required to choose between two possible verbs (one of which violated the semantic preference rule) for nouns that never occurred in training. The results showed that participants acquired unconscious knowledge of semantic preferences under incidental learning conditions, as measured by verbal reports and structural knowledge attributions. Our results provide further evidence for implicit learning of semantic preferences, suggesting that implicit learning is an important mechanism in the acquisition of L2 collocations.

Additional file 1.

Aiming at the problems of low accuracy and less prediction time step in traditional statistical model for PM2.5 concentration prediction, a PM2.5 concentration prediction method based on deep learning in Internet of Things air monitoring system is proposed. Firstly, the spatiotemporal correlation of each station data in the Internet of Things monitoring system is analyzed, and the cubic spline interpolation method is used to fill in the missing data. Then, the temporal attention of the input data is obtained by attention mechanism, and the feature encoder is used to encode the data to obtain the intermediate features. Finally, the intermediate feature is fused with the historical information of PM2.5 concentration, and the predicted value is obtained through the feature decoder. Using the proposed model to predict the PM2.5 concentration in Beijing, the experimental results show that the long-term PM2.5 predicted value is very close to the real value, and the RMSE and MAE are 17.93 μg/m3 and 11.52 μg/m3, respectively, which are better than other comparison models. So, this model is suitable for multivariable long time series forecasting scenarios.

Respiratory viral infection increases the number of lung-resident T lymphocytes, which enhance cough sensitivity by producing interferon-γ (IFN-γ). It is poorly understood why IFN-γ-secreting T lymphocytes persist for a long time when the respiratory viruses have been removed.Repeated pulmonary administration of IFN-γ and intraperitoneal injection with different inhibitors were used to study the effects of pulmonary IFN-γ in mice and guinea pigs.IFN-γ administration caused the increasing of IFN-γ-secreting T lymphocytes in both lung and blood, followed by the elevated physiological level of IFN-γ in the lung, the airway inflammation and the airway epithelial damage. IFN-γ administration also enhanced the cough sensitivity of guinea pigs. IFN-γ activated the STAT1 and extracellular signal-regulated kinase (ERK) pathways in lung tissues, released IFN-γ-inducible protein 10 (IP-10), and resulted in F-actin accumulation in lung-resident lymphocytes. The CXC chemokine receptor 3 (CXCR3) inhibitor potently suppressed all the IFN-γ-induced inflammatory changes. The STAT1 inhibitor mitigated IFN-γ-secreting T lymphocytes infiltration by inhibiting T lymphocytes proliferation. F-actin accumulation and the ERK1/2 pathway contributed to pulmonary IFN-γ-induced augmentation of the airway inflammation and increasing of IFN-γ-secreting T lymphocytes in blood.High physiological levels of IFN-γ in the lung may cause pulmonary lymphocytic inflammation and cough hypersensitivity by increasing the number of IFN-γ-secreting T lymphocytes through the IP-10 and CXCR3 pathways.

Laboratory analysis of trace metals using inductively coupled plasma (ICP) spectroscopy is not cost effective, and the complex spatial distribution of soil trace metals makes their spatial analysis and prediction problematic. Thus, for the health risk assessment of exposure to trace metals in soils, portable X-ray fluorescence (PXRF) spectroscopy was used to replace ICP spectroscopy for metal analysis, and robust geostatistical methods were used to identify spatial outliers in trace metal concentrations and to map trace metal distributions. A case study was carried out around an industrial area in Nanjing, China. The results showed that PXRF spectroscopy provided results for trace metal (Cu, Ni, Pb and Zn) levels comparable to ICP spectroscopy. The results of the health risk assessment showed that Ni posed a higher non-carcinogenic risk than Cu, Pb and Zn, indicating a higher priority of concern than the other elements. Sampling locations associated with adverse health effects were identified as ‘hotspots’, and high-risk areas were delineated from risk maps. These ‘hotspots’ and high-risk areas were in close proximity to and downwind from petrochemical plants, indicating the dominant role of industrial activities as the major sources of trace metals in soils. The approach used in this study could be adopted as a cost-effective methodology for screening ‘hotspots’ and priority areas of concern for cost-efficient health risk management.

Two cAMP signaling compartments centered on adenylyl cyclase (AC) exist in human airway smooth muscle (HASM) cells, one containing β2-adrenergic receptor AC6 and another containing E prostanoid receptor AC2. We hypothesized that different PDE isozymes selectively regulate cAMP signaling in each compartment. According to RNA-sequencing data, 18 of 24 PDE genes were expressed in primary HASM cells derived from age- and sex-matched donors with and without asthma. PDE8A was the third most abundant of the cAMP-degrading PDE genes, after PDE4A and PDE1A. Knockdown of PDE8A using shRNA evoked twofold greater cAMP responses to 1 μM forskolin in the presence of 3-isobutyl-1-methylxanthine. Overexpression of AC2 did not alter this response, but overexpression of AC6 increased cAMP responses an additional 80%. We examined cAMP dynamics in live HASM cells using a fluorescence sensor. PF-04957325, a PDE8-selective inhibitor, increased basal cAMP concentrations by itself, indicating a significant basal level of cAMP synthesis. In the presence of an AC inhibitor to reduce basal signaling, PF-04957325 accelerated cAMP production and increased the inhibition of cell proliferation induced by isoproterenol, but it had no effect on cAMP concentrations or cell proliferation regulated by prostaglandin E2. Lipid raft fractionation of HASM cells revealed PDE8A immunoreactivity in buoyant fractions containing caveolin-1 and AC5/6 immunoreactivity. Thus, PDE8 is expressed in lipid rafts of HASM cells, where it specifically regulates β2-adrenergic receptor AC6 signaling without effects on signaling by the E prostanoid receptors 2/4-AC2 complex. In airway diseases such as asthma and chronic obstructive pulmonary disease, PDE8 may represent a novel therapeutic target to modulate HASM responsiveness and airway remodeling.

Comprehensive bioinformatics analyses were performed to explore the key biomarkers in response to HIV infection of CD4+ and CD8+ T cells. The numbers of CD4+ and CD8+ T cells of HIV infected individuals were analyzed and the GEO database (GSE6740) was screened for differentially expressed genes (DEGs) in HIV infected CD4+ and CD8+ T cells. Gene Ontology enrichment, KEGG pathway analyses, and protein-protein interaction (PPI) network were performed to identify the key pathway and core proteins in anti-HIV virus process of CD4+ and CD8+ T cells. Finally, we analyzed the expressions of key proteins in HIV-infected T cells (GSE6740 dataset) and peripheral blood mononuclear cells(PBMCs) (GSE511 dataset). 1) CD4+ T cells counts and ratio of CD4+ /CD8+ T cells decreased while CD8+ T cells counts increased in HIV positive individuals; 2) 517 DEGs were found in HIV infected CD4+ and CD8+ T cells at acute and chronic stage with the criterial of P-value

Motivated by the paper of Ward [5] , we use variational minimizing methods and Rabinowitz's Saddle Point theorem [1] , [2] , [3] , [4] to study the existence of the solution for the boundary value problem (1.2) with a parameter a, we notice that two cases: a

Aniline oligomer-functionalized graphene oxide (AOFG) and reduced graphene oxide (AOFrG) were prepared via a liquid-phase chemical modification route and incorporated into the polyurethane (PU) by an in-situ polymerization to obtain both wear-resistant and anticorrosive composite coatings. GO/PU and rGO/PU composite coatings were prepared in the same manners and used for comparative studies. Results indicate that the functional modification improves the dispersion of GO and rGO in organic solvents significantly. As a result, the introduction of AOFG and AOFrG into the PU enhance the mechanical and thermal properties of PU prominently, a reinforcement in the friction durability, lubrication and anticorrosive performance ensued correspondingly, and the enhancement efficient was more much stronger than those of the raw GO and rGO. Furthermore, all properties of AOFG/PU composite coating are marginally superior to those of AOFrG/PU composite coating. The analysis results indicate that AOFG shows the most outstanding enhancement effect mainly due to its excellent dispersion homogeneity and interaction led by higher reactivity with the matrix, along with improved permeability resistance and proton erosion endurance.

Flow electrode capacitive deionization (FCDI) possesses continuous desalination capability by using a flowable electrode instead of a fixed electrode, which greatly increases the desalination capacity. However, the poor conductivity of the flow electrode slurry and the high blocking risk of the flow channel limit the larger scale FCDI in deionization. Herein, we report a flow electrode by introducing anion-/cationic compounds as conductive and dispersing agents into activated carbon (AC). A small amount of anion/cationic compounds formed electrostatic repulsion force, which improved the utilization rate and dosage of AC and reduced the co-ion effect in electrode slurry. Through investigation, we found that 20 wt% AC with anion-/cationic compounds (0.13 wt% polyetherimide and 0.25 wt% sulfosuccinic) had a higher salt ion removal rate (2.35 to 5.97 mmol/(m2·s)) and current efficiency (96.23%) than AC alone (0.82 mmol/(m2·s)) at 1.0 V in 0.59–5.06 g/L NaCl solution, which was also higher than that in previous studies. Meanwhile, electrochemical and physical characterization analyses further revealed that polymers can effectively improve the conductivity of the electrode solution and stabilize the pH change of the electrode slurry during the charging process. Ultimately, all effects work together to achieve an efficient desalination system.

Ultralow friction and corrosion resistant polymer composite coatings were prepared by introducing diverse terminated silicone oils (TSOs) into polyurethane (PU) resin and the graphene oxide (GO) functionalized by aniline oligomer (AOFG) was inducted to improve performances of PU composite coatings. CH3-TSO modified PU coating with the content of 5% had the optimal friction-reduced effect due to the weakest binding force with PU framework. The CH3-TSO/AOFG/PU trinary coating (GMSOPU) showed further-elevated enhanced resistance to high load and speed because that AOFG crosslinked with PU skeleton and achieved the denser and stronger coating structure to withstand the friction stress on the basis of CH3-TSO liquid lubrication. Moreover, CH3-TSO increased the contact angle of the PU composite coatings and effectively inhibited erosion. Combining with the synergistic effect of the CH3-TSO and AOFG, compact GMSOPU coating had the most excellent anti-corrosion properties due to the enhanced permeability resistance and extended corrosion path.

Impaired pancreatic β-cell function is the primary defect in type 2 diabetes. Glucose is an important regulator of β-cell growth and function; however, the mechanisms that are involved in the chronic adaptation of β cells to hyperglycemia remain largely unknown. In the present study, global gene expression patterns revealed that tryptophan hydroxylase 1 (Tph1) was the most profound of genes that are up-regulated in rat islets exposed to high glucose. Calcium and cAMP signals synergistically mediated glucose-stimulated Tph1 transcription in β cells by activating cAMP-responsive element-binding protein and promoting its binding with a Tph1 promoter. Similar to in vitro results, in vivo infusion of high glucose also strongly induced Tph1 expression and serotonin production in rat islets, along with enhanced islet function. Inhibition or knockdown of Tph1 markedly decreased glucose-potentiated insulin secretion. In contrast, overexpression of Tph1 augmented glucose-stimulated insulin secretion in rat islets by up-regulating the expression of genes that are related to islet function. In addition, the long-acting glucagon-like peptide 1 receptor agonist, exendin-4, stimulated Tph1 expression in a glucose-dependent manner. Knockdown of Tph1 inhibited exendin-4-potentiated insulin secretion in rat islets. These findings suggest that Tph1 mediates the compensation of islet function induced by glucose, and that promoting Tph1 expression in pancreatic β cells will provide a new strategy for the treatment of type 2 diabetes mellitus.-Zhang, Y., Deng, R., Yang, X., Xu, W., Liu, Y., Li, F., Zhang, J., Tang, H., Ji, X., Bi, Y., Wang, X., Zhou, L., Ning, G. Glucose potentiates β-cell function by inducing Tph1 expression in rat islets.

Size-fractionated heavy metal concentrations were observed in airborne particulate matter (PM) samples collected from 2014 to 2015 (spanning all four seasons) from suburban (Xianlin) and industrial (Pukou) areas in Nanjing, a megacity of southeast China. Rapid prediction models of size-fractionated metals were established based on multiple linear regression (MLR), back propagation artificial neural network (BP-ANN) and support vector machine (SVM) by using meteorological factors and PM concentrations as input parameters. About 38% and 77% of PM2.5 concentrations in Xianlin and Pukou, respectively, were beyond the Chinese National Ambient Air Quality Standard limit of 75 μg/m3. Nearly all elements had higher concentrations in industrial areas, and in winter among the four seasons. Anthropogenic elements such as Pb, Zn, Cd and Cu showed larger percentages in the fine fraction (o≤2.5 μm), whereas the crustal elements including Al, Ba, Fe, Ni, Sr and Ti showed larger percentages in the coarse fraction (o > 2.5 μm). SVM showed a higher training correlation coefficient (R), and lower mean absolute error (MAE) as well as lower root mean square error (RMSE), than MLR and BP-ANN for most metals. All the three methods showed better prediction results for Ni, Al, V, Cd and As, whereas relatively poor for Cr and Fe. The daily airborne metal concentrations in 2015 were then predicted by the fully trained SVM models and the results showed the heaviest pollution of airborne heavy metals occurred in December and January, whereas the lightest pollution occurred in June and July.

PM2.5 concentration have received considerable attention from meteorologists, who are able to notify the public and take precautionary measures to prevent negative effects on health. Therefore, establishing an efficient early warning system plays a critical role in fostering public health in heavily polluted areas. In this study, ensemble empirical mode decomposition and least square support vector machine (EEMD-LSSVM) based on Phase space reconstruction (PSR) is proposed for day-ahead PM2.5 concentration prediction, according to the application of a decomposition-ensemble learning paradigm. The main methods of the proposed model mainly include: first, EEMD is presented to decompose the original data of PM2.5 concentration into some intrinsic model functions (IMFs); second, PSR is applied to determine the input form of each extracted component; third, LSSVM, an effective forecasting tool, is used to predict all reconstructed components independently; finally, another LSSVM is employed to aggregate all predicted components into ensemble results for the final prediction. The empirical results show that this proposed model can outperform the comparison models and can significantly improve the prediction performance in terms of higher predictive and directional accuracy.

Information on chemical partitioning and associated risk of airborne metals, particularly during a haze–fog episode, is limited. Fine particulate matter (PM2.5) was collected during a severe haze–fog event in winter and non-haze–fog periods in summer and fall from an urban region of a typical Chinese mega-city, Nanjing. The particulate-bound metals (Al, As, Ba, Ca, Cd, Co, Cr, Cu, Fe, K, Mg, Mn, Mo, Na, Ni, Pb, Sr, Ti, V, and Zn) were chemically fractionated in a four-step sequential extraction procedure and human health risk was assessed. During the haze–fog episode, PM2.5 was extremely elevated with a mean concentration of 281 μg/m3 (range: 77–431 μg/m3), whereas the mean PM2.5 concentrations in summer and fall periods were 86 μg/m3 (range: 66–111 μg/m3) and 77 μg/m3 (range: 42–131 μg/m3), respectively. All elements had significantly higher concentrations and many metals exceeded relevant limits on haze–fog days. K, Na, Sr, Zn, Mo, Ca, Cd, Mg, Mn, Cu, Ba, Cr and As all showed relatively high proportions of the soluble and exchangeable fraction and strong bio-accessible potential. High temperature and humidity may increase the bio-accessible fraction of many airborne metals. The hazard index for potential toxic metals was 0.115, which was lower than the safe limit (1). However, the combined carcinogenic risk was 1.32 × 10− 6 for children and 5.29 × 10− 6 for adults, with both values being higher than the precautionary criterion (10− 6). Results of this study provide information for the behavior and risk mitigation of airborne metals.

In this paper, we propose a conditional privacy-preserving authentication scheme based on pseudonyms and (t,n) threshold secret sharing, named CPPT, for vehicular communications. To achieve conditional privacy preservation, our scheme implements anonymous communications based on pseudonyms generated by hash chains. To prevent bad vehicles from conducting framed attacks on honest ones, CPPT introduces Shamir (t,n) threshold secret sharing technique. In addition, through two one-way hash chains, forward security and backward security are guaranteed, and it also optimize the revocation overhead. The size of certificate revocation list (CRL) is only proportional to the number of revoked vehicles and irrelated to how many pseudonymous certificates are held by the revoked vehicles. Extensive simulations demonstrate that CPPT outperforms ECPP, DCS, Hybrid and EMAP schemes in terms of revocation overhead, certificate updating overhead and authentication overhead.

Evaluating the reliability of weighted networks is an NP-hard problem. In this paper, a symbolic algorithm evaluating the reliability of network under capacity and delay constraints is proposed. First, the weighted network is converted to a symbolic algebraic decision diagrams (ADD) by encoding the vertices of the network, and the variable ordering are computed by breadth-first search and priority function in order to reduce the size of ADD. Then, edges in minimal paths (MPs) which does not meet the constraint formula of quickest path problem are not visited, leading to a low complexity of the network traversal. Finally, ADD constructed by two custom operators is traversed to compute the network reliability. The computational performance of the proposed algorithm is illustrated through some experimentation with different weighted networks.

ObjectivesOur aim was to assess the release level of heparin-binding protein (HBP) in sepsis and septic shock under theThird International Consensus Definitions for Sepsis and Septic Shock(Sepsis-3).DesignProspective cohort study.SettingA general teaching hospital in China.ParticipantsAdult infected patients with suspected sepsis and people who underwent physical examination were included. According to the health status and severity of illness, the research subjects were divided into healthy, local infection, sepsis non-shock and septic shock under Sepsis-3 definitions.Main outcome measuresPlasma levels of HBP, procalcitonin (PCT), C reactive protein (CRP) and complete blood count were detected in all subjects. Single-factor analysis of variance was used to compare the biomarker levels of multiple groups. A receiver operating characteristic (ROC) curve was used to assess the diagnostic capacity of each marker.ResultsHBP levels were significantly higher in patients with sepsis non-shock than in those with local infections (median 49.7ng/mL vs 11.8 ng/mL, pConclusionsA high level of HBP in plasma is associated with sepsis, which might be a useful diagnostic marker in patients with suspected sepsis.

IoT is an important part of the new generation of information technologies and the next big thing in the IT industry after the computer and the internet. The IoT has great development potential and a wide range of possible applications, especially commercial applications. And information security of the IoT is the key to the long-term development of the whole industry. Currently, the two most significant factors in the development of the IoT are user identity authentication and privacy protection. This paper contains an analysis on the current picture of inter-device user identity authentication in the IoT and proposes an inter-device biometric authentication solution for the IoT that’s designed to work with larger devices, addressing the shortcomings of the traditional user identity authentication technologies including security and efficiency problems. A strategy for further solution optimization is also included. This paper elaborates on the specific process of user identity authentication carried out by users on devices and between devices making use of fingerprints. We’ll demonstrate the security of this solution against existing attack methods and in the last part, we enumerate various possible applications of this solution in smart homes.