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Background and Purpose : Several studies have explored premature atrial complexes (PACs) as high-risk factors for atrial fibrillation (AF) in ischemic stroke patients; however, the results were controversial. The aim of this systematic review and meta-analysis was to examine whether PACs can predict AF in ischemic stroke patients. Methods : We comprehensively searched the published literature in PubMed, Embase, and Wiley-Cochrane library databases from inception through 18th August 2020. Pooled odds ratios (ORs) and 95% confidence intervals (CIs) were analyzed by the fixed-effect model or the random-effect model based on heterogeneity. Results : We identified 12 eligible studies including 2340 stroke patients with a mean age of 65.9 years. PACs were highly associated with AF occurrence in stroke (pooled OR: 4.16, 95% CI: 3.06-5.65) and cryptogenic stroke patients (pooled OR: 3.72, 95% CI: 2.66-5.20). Subgroup analysis showed PAC presence and frequent PACs were correlated with stroke in AF patients (pooled OR: 3.72, 95% CI:1.65-8.36 and pooled OR: 5.12, 95% CI: 3.12-8.41, respectively). Frequent PACs were identified as the risks for asymptomatic AF (OR: 6.18, 95% CI: 3.23-11.83) and future AF occurrence (OR: 3.71, 95% CI: 2.62-5.26) in stroke patients. The definition of frequent PACs was inconsistent, and was more than 70 beats/24 hours based on Holter monitoring. Conclusions : PACs confer high risks for asymptomatic AF and future AF occurrence in stroke patients. This article is protected by copyright. All rights reserved

With the increasing severity of global water scarcity, a myriad of scientific activities is directed toward advancing brackish water desalination and wastewater remediation technologies. Flow-electrode capacitive deionization (FCDI), a newly developed electrochemically driven ion removal approach combining ion-exchange membranes and flowable particle electrodes, has been actively explored over the past seven years, driven by the possibility of energy-efficient, sustainable, and fully continuous production of high-quality fresh water, as well as flexible management of the particle electrodes and concentrate stream. Here, we provide a comprehensive overview of current advances of this interesting technology with particular attention given to FCDI principles, designs (including cell architecture and electrode and separator options), operational modes (including approaches to management of the flowable electrodes), characterizations and modeling, and environmental applications (including water desalination, resource recovery, and contaminant abatement). Furthermore, we introduce the definitions and performance metrics that should be used so that fair assessments and comparisons can be made between different systems and separation conditions. We then highlight the most pressing challenges (i.e., operation and capital cost, scale-up, and commercialization) in the full-scale application of this technology. We conclude this state-of-the-art review by considering the overall outlook of the technology and discussing areas requiring particular attention in the future.

In times of public health emergencies, health agencies need to engage and communicate with the public in real-time to share updates and accurate information. This is especially the case for the COVID-19 pandemic where public engagement can potentially save lives and flatten the curve. This paper considers how the use of interactive features and strategic network positions of health agencies on social media influenced their public engagement outcomes. Specifically, we analyzed 203 U.S. public health agencies' Twitter activity and the public engagement they received by extracting data from a large-scale Twitter dataset collected from January 21st to May 31st, 2020. Results show that health agencies' network position in addition to their two-way communication strategy greatly influenced the level of public engagement with their COVID-19 related content on Twitter. Findings highlight the benefits of strategic social media communication of public health agencies resides not only in how agencies use social media but also in their formation of network position to amplify their visibility. As official sources of health and risk information, public health agencies should coordinate their social media communication efforts to strategically position themselves in advantageous network positions to augment public engagement outcomes.

Subarachnoid hemorrhage (SAH) is considered a devastating disease, leaving survivors with lifelong neurological impairment. With increased knowledge that regulatory T cells (Tregs) provide protection against stroke, novel agents which could expand Treg populations have been assessed in terms of the potential clinical neuroprotection effect. Using a rat SAH model, we investigated the number variation of Tregs induced by SAH and the protective effect of low-dose interleukin-2 (IL-2) treatment on the SAH model. We observed that the number of peripheral Tregs significantly decreased soon after SAH, accompanying with reactivity recovery after 3 days. Our results also revealed that low-dose IL-2 treatment not only elevated Tregs numbers but significantly reduced neuronal injury and improved neurological functions up to 21 days (d) after SAH. Furthermore, compared with PBS-treatment group, cerebral proinflammatory factors and peripheral neutrophils were significantly suppressed by low-dose IL-2 after SAH. Therefore, the results suggest that low-dose IL-2 treatment is a novel and clinically feasible immunotherapy to improve long-term outcomes after SAH, perhaps via up-regulating Treg population to suppress neuroinflammation induced by SAH.

This paper takes a longitudinal perspective to explore China’s media organisations’ adoption of the WeChat public platform. Drawing from theories of organisation ecology and strategic exploitation/...

Our study examines Facebook posts containing nine prominent COVID-19 vaccine misinformation topics that circulated on the platform between March 1st, 2020 and March 1st, 2021. We first identify misinformation spreaders and fact checkers, further dividing the latter group into those who repeat misinformation to debunk the false claim and those who share correct information without repeating the misinformation. Our analysis shows that, on Facebook, there are almost as many fact checkers as misinformation spreaders. In particular, fact checkers’ posts that repeat the original misinformation received significantly more comments than posts from misinformation spreaders. However, we found that misinformation spreaders were far more likely to take on central positions in the misinformation URL co-sharing network than fact checkers. This demonstrates the remarkable ability of misinformation spreaders to coordinate communication strategies across topics.

Background It has been well established that the TMEM106B gene rs1990622 variant was a frontotemporal dementia (FTD) risk factor. Until recently, growing evidence highlights the role of TMEM106B in Alzheimer’s disease (AD). However, it remains largely unclear about the role of rs1990622 variant in AD. Methods Here, we conducted comprehensive analyses including genetic association study, gene expression analysis, eQTLs analysis, and colocalization analysis. In stage 1, we conducted a genetic association analysis of rs1990622 using large-scale genome-wide association study (GWAS) datasets from International Genomics of Alzheimer’s Project (21,982 AD and 41,944 cognitively normal controls) and UK Biobank (314,278 participants). In stage 2, we performed a gene expression analysis of TMEM106B in 49 different human tissues using the gene expression data in GTEx. In stage 3, we performed an expression quantitative trait loci (eQTLs) analysis using multiple datasets from UKBEC, GTEx, and Mayo RNAseq Study. In stage 4, we performed a colocalization analysis to provide evidence of the AD GWAS and eQTLs pair influencing both AD and the TMEM106B expression at a particular region. Results We found (1) rs1990622 variant T allele contributed to AD risk. A sex-specific analysis in UK Biobank further indicated that rs1990622 T allele only contributed to increased AD risk in females, but not in males; (2) TMEM106B showed different expression in different human brain tissues especially high expression in cerebellum; (3) rs1990622 variant could regulate the expression of TMEM106B in human brain tissues, which vary considerably in different disease statuses, the mean ages at death, the percents of females, and the different descents of the selected donors; (4) colocalization analysis provided suggestive evidence that the same variant contributed to AD risk and TMEM106B expression in cerebellum. Conclusion Our comprehensive analyses highlighted the role of FTD rs1990622 variant in AD risk. This cross-disease approach may delineate disease-specific and common features, which will be important for both diagnostic and therapeutic development purposes. Meanwhile, these findings highlight the importance to better understand TMEM106B function and dysfunction in the context of normal aging and neurodegenerative diseases.

To address time unfairness access problems in the dynamic channel, an underwater adaptive contention window (CW) adjustment backoff algorithm (QL-UACW) based on Q-learning is presented in this paper. That algorithm employs the concept of reinforcement learning to adaptively adjust the competition window by autonomously learning the network communication process. Based on QL-UACW and in order to address space unfairness access issues of nodes location, a load adaptive hybrid media access control (MAC) protocol with competition for the same directional nodes and time slot allocation for different directional nodes called SDHA is also presented. The protocol divides the nodes into co-nodes and counter-nodes according to the relative communication directions of the nodes. The co-nodes use competition protocol and the counter-nodes use time slot allocation protocol to solve the hidden terminal problem of node communication in order to prevent space access unfairness. Theoretical and simulation analysis show that the QL-UACW algorithm effectively improves the fairness of node access channels, reduces the collision rate of data frames, and increases network throughput. Additionally, the SDHA protocol is proven to provide advantages in normalized network throughput, average end-to-end delay, and the success rate of data frame transmission. The work is supported by “the National Key Research and Development Program, China under Grant No. 2018YFC0407900 ”, supported by “the National Natural Science Foundation of China under Grant No. 61971206 and 62072072 ”, supported by “the Open fund of State Key Laboratory of Acoustics, China under Grant No. SKLA201901 ”, supported by “ China Academy of Military Sciences Fund under Grant No. 2019 ”, supported by “ Liaoning BaiQianWan Talents Program, China under grant No. 2016 ”, supported by “ Natural Science Foundation of Liaoning Province Project, China under Grant No. 20170540793 ”.

Argument pair extraction (APE) is a new task in the field of argument mining, aiming at mining interactive argument pairs from peer review and rebuttal. Previous work framed APE as a combination of a sequence labeling task and a sentence pair classification task via multi-task learning. However, this method lacks explicit modeling of the relations between arguments, and the two subtasks may not cooperate well. Towards these issues, we propose a hierarchical sequence labeling model that can efficiently represent arguments and explicitly capture the correlations between them. Our method matches argument pairs from the perspective of review and rebuttal respectively, and merges the results, enabling a more comprehensive extraction of argument pairs. Also, we propose a series of improved models and fuse them using weighted voting. Our method achieved first place in the NLPCC 2021 Shared Task on APE, proving the effectiveness of our method.

This paper presents the winning system that participated in SemEval-2021 Task 5: Toxic Spans Detection. This task aims to locate those spans that attribute to the text’s toxicity within a text, which is crucial for semi-automated moderation in online discussions. We formalize this task as the Sequence Labeling (SL) problem and the Span Boundary Detection (SBD) problem separately and employ three state-of-the-art models. Next, we integrate predictions of these models to produce a more credible and complement result. Our system achieves a char-level score of 70.83%, ranking 1/91. In addition, we also explore the lexicon-based method, which is strongly interpretable and flexible in practice.

With the explosive growth of data traffic and the number of terminals in wireless networks, the scarcity contradiction of wireless spectrum resources is increasing. Deploying Long Term Evolution(LTE) technology to 5GHz unlicensed band of WiFi is an effective way to greatly improve spectrum efficiency. The core technology of existing broadband WiFi technology adopts traditional CSMA/CA mechanism, which has the problems of occupying WiFi channel resources for a long time, not fully utilizing network resources and blocking broadband channels when LTE accesses WiFi channels. In this paper, an optimization scheme of LTE and WiFi coexistence structure based on subcarrier nulling is proposed. The working mechanism of this scheme is as follows: the nodes of broadband WiFi perform spectrum sensing at subcarrier granularity. When detecting that there are LTE nodes working in the same frequency band around, only the subcarriers that do not overlap with the channels of the LTE nodes are used for communication, and the 20MHz occupied by LTE is set to null, so as to achieve the purpose of coexistence. Simulation results show that the optimization scheme based on subcarrier nulling can effectively improve the performance of coexistence system, and make the fairness up to more than 90%, which provides an realizable scheme for the problems existing in the coexistence structure of LTE and WiFi.

Background Severe traumatic brain injury (TBI) results in long-term neurological deficits associated with white matter injury (WMI). Ethyl pyruvate (EP) is a simple derivative of the endogenous energy substrate pyruvate with neuroprotective properties, but its role in recovery from WMI has not been explored. Aims This study examines the effect of EP treatment on rats following TBI using behavioral tests and white matter histological analysis up to 28 days post-injury. Materials and methods Anaesthetised adult rats were subjected to TBI by controlled cortical impact. After surgery, EP or Ringers solution (RS) was administrated intraperitoneally at 15 min after TBI and again at 12, 24, 36, 48, and 60 h after TBI. Sensorimotor deficits were evaluated up to day 21 after TBI by four independent tests. Immunofluorescence and transmission electron microscopy (TEM) were performed to assess white matter injury. Microglia activation and related inflammatory molecules were examined up to day 14 after TBI by immunohistochemistry or real-time PCR. Results Here, we demonstrate that EP improves sensorimotor function following TBI as well as improves white matter outcomes up to 28 d after TBI, as shown by reduced myelin loss. Furthermore, EP administration during the acute phase of TBI recovery shifted microglia polarization toward the anti-inflammatoryM2 phenotype, modulating the release of inflammatory-related factors. Conclusion EP treatment may protect TBI-induced WMI via modulating microglia polarization toward M2.

Mendelian randomization (MR) studies have reported the causal association between serum calcium levels and bone mineral density (BMD). The results showed that genetically increased serum calcium levels in individuals with normal calcium levels did not increase BMD and could even reduce BMD. However, whether there are differences in the association between serum calcium and BMD in different age strata remains unclear.We selected eight serum calcium genetic variants with genome-wide significance (P 5.00E-08) as the potential instrumental variables. We conducted an MR analysis to evaluate the impact of serum calcium levels on total body BMD in five age strata, 0-15, 15-30, 30-45, 45-60, and ≥60 years, using large-scale serum calcium (61,079 individuals) and total body BMD genome-wide association study (66,628 individuals) datasets. For pleiotropy analysis, we used a manual method and four common statistical methods, namely the MR-Egger intercept, MR-PRESSO, heterogeneity, and Steiger filtering tests. For MR analysis, we selected four MR methods, namely inverse-variance weighted, weighted median, MR-Egger, and MR-PRESSO. In addition to the univariable MR analysis, we conducted a multivariate MR analysis taking into account the effect of serum parathyroid hormone levels.Univariable MR analysis using the inverse-variance weighted method indicated that per 0.5-mg/dL increase (about 1 standard deviation) in serum calcium levels was statistically significantly associated with reduced total body BMD only in the ≥60 years stratum (effect estimate (beta) = -0.545, 95% confidence interval (CI): -0.892 to -0.198, P = 0.002). The weighted median regression (beta = -0.446, 95% CI: -0.821 to -0.094, P = 1.40E-02) and MR-PRESSO (beta = -0.545, 95% CI: -0.892 to -0.198, P = 0.022) MR methods further supported this suggestive association. The multivariable MR analysis also found a significant association between increased serum calcium levels and reduced total body BMD in the ≥60 years stratum (beta = -0.547, 95% CI: -0.934 to -0.16, P = 0.006).Our results provide genetic evidence that increased serum calcium levels did not improve BMD in the general population and that the elevated serum calcium levels in generally healthy populations, especially in adults older than 60 years, may even reduce the BMD. Our results are comparable with those of recent MR findings.

Due to the scaling of CMOS technology dimension, the occurrence of transient fault will be increased when the sensitive nodes are struck by high-energy neutrons and alpha particles. If the transient fault affects and changes the value of the nodes of a sequential element, it will cause single event upset (SEU). To overcome this problem, a reliable latch is proposed to protect both internal and output nodes in this paper. The simulation results of TSMC 28nm CMOS logic process show that the latch has good anti SEU, low power consumption and high-speed performance.

Korea’s automobile industry has not developed for a long time, but it has achieved a leading position in the world, and also occupied part of the Chinese market. What causes this result is worth studying. Based on the big data, this paper uses the result equation model to analyze the development of Korean automobile industry in China

Atrial natriuretic peptide (ANP) has a protective effect on allergic disorders of airway. It could inhibit transforming growth factor beta 1 (TGF-β1)-induced epithelial-mesenchymal transition (EMT) of human airway cells. In this study, we investigated the role and mechanism of ANP in airway EMT with asthma. Asthma model was established with BALB/c mice using ovalbumin. Asthmatic mice were respectively treated with ANP, NPRA antagonist (A71915) or ANP+A71915. The protein expressions of TGF-β1, ANP, epithelial cadherin (E-Cadherin), α-smooth muscle actin (α-SMA), Smad3 and phosphorylated-Smad3 in lung tissues were detected by immunohistochemistry and Western blotting. The mRNA expressions of E-Cadherin, α-SMA, Smad3 in lung tissues were measured using real-time PCR. The levels of TGF-β1, IL-4 and IL-5 in bronchoalveolar lavage fluid, cyclic guanosine monophosphate (cGMP) in lung tissues, and ANP in serum were assessed by ELISA. Compared with controls, mRNA and protein expressions of E-Cadherin were decreased, whereas α-SMA were increased in asthma group, which could be reversed by ANP. cGMP level was higher in ANP group than asthma group, whereas p-Smad3 expression was lower. TGF-β1, IL-4 and IL-5 levels were higher in asthma group than controls, which could be reversed by ANP. The effect of ANP in asthma group could be inhibited by A71915. Therefore, in asthma, ANP/NPRA could inhibit airway EMT by targeting Smad3 via attenuating phosphorylation of Smad3. cGMP signaling may be involved into this process. This may provide a potential of ANP in treatment on refractory asthma with airway remodeling.

Background To develop a modified transforaminal endoscopic spine system (TESSYS®) technique for treating intracanalicular combining foraminal and/or extraforaminal lumbar disc herniation (ICFE-LDH), and evaluate the technical efficacy and safety. Methods Twenty-three patients with ICFE-LDH underwent the modified TESSYS technique were enrolled. Magnetic resonance imaging (MRI) was used to verify the reduction of herniated disc. Pre- and post-operative neurological functions were compared by visual analogue scale (VAS) score, Oswestry disability index (ODI) and the modified MacNab criteria. The technical safety was evaluated by surgical complications. Results MRI demonstrated reductions of disc herniations in 22 patients (95.7%) after surgeries. The VAS scores were significantly improved at 1 year follow-up (low back: P=0.001, lower limbs: P

The authors take the quality of sensing data into consideration and design a reputation-aware incentive mechanism (RAIM) with the properties of truthfulness and individual rationality while maximising the weighted social welfare of the whole system. In addition, in order to reduce the computational complexity of RAIM and improve the system feasibility, the authors propose a heuristic algorithm RAIM-H, with the computational complexity of O ( n 2 ) . Simulation results show the nice performance of the proposed mechanisms RAIM and RAIM-H in terms of the weighted social welfare and the average reputation. Specifically, RAIM can improve the weighted social welfare by 8.65 and 48.16% compared with trustworthy sensing for crowd management (TSCM) and random selection, respectively, with the number of smartphone users n = 16 . Meanwhile, RAIM-H approaches to the maximum very well and can improve the weighted social welfare by 6.15% and 75% compared with TSCM and random selection, respectively, with the number of smartphone users n = 100 .

License Assisted Access (LAA) is a technology developed to coexist with WiFi in unlicensed band. LAA performs Listen Before Talk (LBT) and accesses the channel by sending a Reservation Signal (RS), which will occupy the channel for a duration and cause a waste of spectrum so that reduce system throughput. In this paper, we propose an adaptive algorithm for LAA and WiFi Coexistence based on Q-learning (LWCQ) to dynamically schedule LAA communication, and determine LAA transmission duration after accessing the channel according to the communication state. LWCQ intends to avoid LAA accessing the channel frequently to reduce throughput loss, and it can also adjust appropriately LAA communication duration according to WiFi traffic. We analyze the performance of LWCQ, simulation results show that LWCQ effectively improves LAA and system throughput and ensures the fairness of LAA and WiFi communication.

NGOs are important civil actors in societies' emergency and disaster responses, and they come together on social media to identify prominent issues and coordinate issue responses. This research explores how U.S. NGO form topic-driven communities on social media to discuss and build representational strategic networks around issues related to the COVID-19 pandemic. Drawing from Issue Niche Theory, we examined how NGOs' networks and discourse evolved before and after the general public paid great attention to the COVID-19 issue and how such patterns changed across the whole issue niche and sub-issue niches. We analyzed the evolution of Twitter-based networks and discourse of 2,588 NGOs in the first five months of the COVID-19 outbreak in the United States. Our analysis revealed important factors that shape tie formation patterns in the NGOs' communities in this novel issue niche. The findings show that NGOs’ discourses help to orient the organizational community to identify most salient issues. Finally, changes in the discourse patterns reflected changes in the communication networks in the NGO community.

Well-designed heterostructure semiconductor photocatalysts can improve the activity of photocatalytic reactions. In this work, we constructed a series of hierarchical ZnIn2S4/In2O3 heterostructures by growing ultrathin two-dimensional ZnIn2S4 nanosheets onto one-dimensional In2O3 electrospun nanofibers and used them as photocatalysts for the efficient photoreduction of toxic Cr(VI). This structural design increased the specific surface area, promoted the separation of photogenerated electrons and holes, and provided more active sites for the catalytic reactions. Under visible light irradiation, the optimized ZnIn2S4/In2O3 photocatalyst showed the highest photocatalytic performance with 100% reduction efficiency for Cr(VI) (50 mg/L) within 90 min, which is much higher than pure In2O3 and ZnIn2S4. Additionally, the recycling tests and X-ray diffraction (XRD) characterization indicated the stability of the ZnIn2S4/In2O3 photocatalyst, making it a promising candidate for environmental remediation applications. Finally, the two active species (e- and ·O2-) participating in the photoreduction process were determined via trapping experiments and electron spin resonance (ESR) spectroscopy. Finally, a possible mechanism for the ZnIn2S4/In2O3 heterojunction photocatalytic system was carefully determined.

In recent years, we have been witnessing the explosive growth of mobile users with sensor-embedded smartphones. Firstly, with the development of wireless communication technology and the increase in personal income, mobile devices (e.g., smartphone, ipad, PDA, etc.) have been becoming more and more popular. For instance, smartphones have become the central communication device in people’s daily lives [1]. Based on the latest Ericsson mobility report [2], the number of worldwide mobile subscriptions has reached 7.5 billion in 2017 and will approach 9.1 billion in 2022. Secondly, with the development of hardware technology, the size of sensors has become smaller and smaller. Moore’s law [3] tells that the number of transistors in a densely integrated circuit doubles about every 18 months. With the miniaturization of sensors, most mobile devices are embedded with various types of sensors (e.g., GPS, accelerometer, camera, digital compass, magnetometer, barometer, gyroscope) [4]. Thus, devices such as smartphones, smart wearable devices (e.g., Google glasses, Apple Watch and Mi Band), unmanned vehicle, and in-vehicle sensing devices (GPS, OBD) can be used to collect information from the surrounding environment. We can use these devices to sense the noise level, traffic situation, temperature, etc. Meanwhile, these devices can upload the sensed data to the server of a data collector through wireless access networks such as cellular system or WiFi at the convenience time and location.

Auction theory is a branch of economics which deals with how people sell or buy objects. The history of auction can be date back to 500 bc, at which time it was recorded that auctions of women for marriage were held annually in Babylon [1]. Nowadays, numerous kinds of commodities such as antiques, arts, bonds, wireless spectrum, and so on, are sold by means of auction. For instance, auctions are used to sale and disposal of assets such as the open bidding for government procurement, construction works, and right to natural resources (e.g., oil, natural gas, etc.). In recent years, auction has been widely used in spectrum resource allocation, e-commerce websites, online advertising, etc.

Credit card fraud resulted in the loss of $3 billion to North American financial institutions in 2017. The rise of digital payments systems such as Apple Pay, Android Pay, and Venmo has meant that loss due to fraudulent activity is expected to increase. Deep Learning presents a promising solution to the problem of credit card fraud detection by enabling institutions to make optimal use of their historic customer data as well as real-time transaction details that are recorded at the time of the transaction. In 2017, a study found that a Deep Learning approach provided comparable results to prevailing fraud detection methods such as Gradient Boosted Trees and Logistic Regression. However, Deep Learning encompasses a number of topologies. Additionally, the various parameters used to construct the model (e.g. the number of neurons in the hidden layer of a neural network) also influence its results. In this paper, we evaluate a subsection of Deep Learning topologies — from the general artificial neural network to topologies with built-in time and memory components such as Long Short-term memory — and different parameters with regard to their efficacy in fraud detection on a dataset of nearly 80 million credit card transactions that have been pre-labeled as fraudulent and legitimate. We utilize a high performance, distributed cloud computing environment to navigate past common fraud detection problems such as class imbalance and scalability. Our analysis provides a comprehensive guide to sensitivity analysis of model parameters with regard to performance in fraud detection. We also present a framework for parameter tuning of Deep Learning topologies for credit card fraud detection to enable financial institutions to reduce losses by preventing fraudulent activity.

Background End-stage renal disease and dialysis patients have a higher incidence of renal cell carcinoma (RCC) than the general population. Preoperatively evaluating the biological behavior of RCC plays an important role in treatment decision-making. Susceptibility-weighted imaging (SWI) can visualize the distribution of microvenous structures and hemorrhage without contrast materials. Purpose To evaluate the feasibility of SWI in grading clear cell RCCs (CRCC) and compare the ability of SWI and necrosis for grading CRCCs. Material and Methods Retrospective reviews of 35 patients with pathologically-proven CRCCs were performed. All patients underwent both conventional magnetic resonance imaging (MRI) and SWI examinations. The morphology of the intratumoral susceptibility signal intensities (ITSS) was classified into hemorrhage and microvessels. The differences of ITSSs on SWI and necrosis between low- and high-grade CRCCs were assessed. The diagnostic values of ITSSs and necrosis in differentiating low- from high-grade CRCCs were compared by receiver-operating characteristics. Results ITSSs were seen in 31 of 35 patients. No ITSSs were seen in four patients with low-grade CRCCs. Mean scores of ITSSs on SWI were significantly lower for low-grade CRCCs (1.24 ± 0.72) than that for the high-grade CRCCs (2.70 ± 0.48). No significant necrosis was seen in 10 patients with low-grade CRCCs. There was a significant difference of the presence of intratumoral necrosis between low- and high-grade CRCCs. Sensitivity, specificity, positive (PPV) and negative predictive values (NPV) were for ITSSs: 70%, 100%, 100%, and 89.3%, respectively; for necrosis: 100%, 40%, 40%, and 100%. Conclusion SWI can evaluate ITSSs without contrast materials and can be an alternative to grading CRCCs preoperatively for some special patients.

Internet of Things (IoT) aims to provide ubiquitous connectivity and services for individual persons and machines. The related applications cover various areas in industry, healthcare, city management, etc. The efficient information collection is very critical for the success of these applications. Without enough number of data, it is difficult for a system to provide high-quality services. Participatory sensing network (PSN) is a promising paradigm to efficiently collect information/sensing data. Meanwhile, the incentive mechanism design plays a key role in achieving the collection of enough number of sensing data in PSN, where the sensor-equipped mobile devices are owned and controlled by individual users. Most of existing works on incentive mechanism design focus on the participation of smartphone users, rather than the quality of sensing data. However, data quality is also an important factor for a data collector since smartphone users may submit the erroneous or unreliable data. Low-quality data will impact the accuracy of data analysis result and degrade the provided service. Therefore, data quality should be considered in the incentive mechanism design. Reputation is one way to evaluate the quality of data provided by a mobile user. In this chapter, we introduce the significance of incentive mechanism design for the applications of IoT, then we design a reputation-aware incentive mechanism. Taking the quality of sensing data into account, the proposed mechanism can maximize the weighted social welfare of the whole system and guarantee the nice features of truthfulness and individual rationality. Extensive simulations have been conducted to demonstrate the better performance of the proposed incentive mechanism compared with other existing methods in terms of the weighted social welfare and the average reputation.

To assess the incidence of new onset rapid atrial responses (RAR) in sick sinus syndrome (SSS) or high-degree atrioventricular block (AVB) patients after dual chamber pacemaker implantation, and analyze the influence factors of new onset RAR in patients after dual chamber pacemaker implantation. We

Introduction: Only a small proportion of patients with diarrhea are diagnosed with laboratory tests in China, and most are diagnosed based on clinical symptoms. Therefore, understanding the prevalence of different diarrheal pathogens and their specific symptoms is important. Methodology: Data from a prospective study in Beijing of acute diarrhea and the related pathogens were used to study the association between different pathogen groups and the infected patients’ characteristics. A total of 355 patients with acute diarrhea, clinically diagnosed with infectious or noninfectious diarrhea by general practitioners (GPs), were recruited from three districts. Results: Different species of diarrheal pathogens were detected in 133 (37.5%) patients. The most prevalent pathogen was calicivirus (42.9%), followed by rotavirus (30.1%), Vibrio parahaemolyticus (13.5%), and Salmonella spp. (10.5%). The detection rates in patients diagnosed with infectious or noninfectious diarrhea by GPs did not differ significantly (c2 = 0.026, p = 0.873). Abdominal pain correlated negatively with viral pathogens, whereas nausea, living in the suburbs, and winter infection correlated positively with viral infection. Abdominal pain and leukocytosis were positively associated with bacterial infections, whereas winter infection correlated negatively with them. Conclusion: In this study, we found that the detection rates in patients diagnosed with infectious or noninfectious diarrhea by GPs was the same. We also revealed the improper prescription of antibiotics by GPs based simply on clinical diagnoses. A further analysis of diagnostic accuracy and methods is required to assist GPs in improving their diagnoses when insufficient laboratory tests are available and budgets are limited.

In this paper analysis the traffic organization of two commercial centers : the Xiaoximen and Nanping Walking Street area of the Kunming, and discussion the practice forms and existing problems in China's urban transport development based on the concept of TOD development model. The paper proposes the concept of TOD model need throughout the entire process of urban planning and construction, and guides trip by the reasonable traffic and transit system design to achieve TOD development model. Only based on the origin of public transport and walking space design can achieve urban transport development strategies of public transport priority really.

Since the province of Yunnan carried out weight calculating program, We have achieved preliminary accomplishments on administering overload and over limit transportation, decreasing traffic accident happening, highway property protecting. But, as the result of our province implement this program still very short, there are many problems in practical experience and these problems needs to be resolved immediately. This essay, firstly by comparing on loading based toll collection model of some provinces in China, then to choose and contrast much reasonable weight estimating toll modal, Secondly, by analyzing and considering from this two aspects: normal load and overload transportation vehicles; Thirdly, to utilize the accumulated experience and data on investigation and studying of weight calculating toll, and on this basis of it to make evaluation after administering about this modal, to acquire the disadvantages of this existing model; Finally, to emphasize from differentiating normal load preferential interval, determining on overload extra coefficient, at the same time to integrate area theory, to make optimization for loading based toll collection model of our province. The aim is to consummate this theory of weight estimating toll, to satisfy speedy development, in addition to make the reference judgment of government policy making department.

Obtaining the quantitative positioning space-based demographic and socio-economic information has the significance on assessing resources, environment and disaster. This paper presents a dynamic modeling method for rural GDP statistics data spatialization based on neural network Selecting Fangshan District in Beijing, China as the study area and taking villages as studying unit, this paper analyzes spatial correlation between the rural GDP and different geographic elements, establishes the assessment system of key factors which influence the economic development, and uses BP neural network to simulate the spatial interaction between the rural GDP and the factors and build the rural GDP spatial quantitative distribution of 500m × 500m grids. The result shows that the result of simulation and distribution are approximately consistent. The results also indicate that, the spatialization method of socio-economic statistic data using neural network has advantages of intelligent modeling and automation, wide adaptability and high precision spatialization.

As the development of expressway in our country at high speed, the database of expressway toll collection matured gradually both in technique and theory, meanwhile established a foundation for collection and analysis of traffic volume information. Firstly, introduction have been made about general investigation method of link traffic volume and pointed out its deficiency in this paper. After that, provided a calculated algorithm in deal with link traffic volume based on the database of expressway loading based toll collection. Make use of export traffic flow, earned toll, information of loading based toll collection and calculation model of toll collection to established a whole-line link traffic volume digital model. Finally, tested and verified the calculation method with the application of Yunnan expressway loading based toll collection in this method. Provided a reliable scientific method in calculation of link traffic volume for the people engaged in traffic control, traffic planning, economic evaluation and so on, although it have guiding significance for the prediction of expressway traffic volume in the future.

To realize the traffic control at the crossroads is by the red and green signal lights. In order to improve capacity of intersection, the green wave band is a useful signal control mode. This paper proposes a new kind of green waves display and control mode. There are signal lights along the road form the moving light zones, and the moving speed and length of light zones are determined and made coordinative of the signal cycle of intersection by the computer system. The driver can adjust his speed in time to join the green light zone, remain consistent with the speed of light moving and will pass the next intersection without waiting.

Objective To investigate the feasibility of gadolinium (Gd) contrast-enhanced magnetic resonance lymphangiography (MRL) in breast cancer patients within a typical clinical setting, and to establish a Gd-MRL protocol and identify potential MRL biomarkers for differentiating metastatic from non-metastatic lymph nodes. Materials and Methods 32 patients with unilateral breast cancer were enrolled and divided into 4 groups of 8 patients. Groups I, II, and III received 1.0, 0.5, and 0.3 ml of intradermal contrast; group IV received two 0.5 ml doses of intradermal contrast. MRL images were acquired on a 3.0 T system and evaluated independently by two radiologists for the number and size of enhancing lymph nodes, lymph node contrast uptake kinetics, lymph vessel size, and contrast enhancement patterns within lymph nodes. Results Group III patients had a statistically significant decrease in the total number of enhancing axillary lymph nodes and lymphatic vessels compared to all other groups. While group IV patients had a statistically significant faster time to reach the maximum peak enhancement over group I and II (by 3 minutes), there was no other statistically significant difference between imaging results between groups I, II, and IV. 27 out of 128 lymphatic vessels (21%) showed dilatation, and all patients with dilated lymphatic vessels were pathologically proven to have metastases. Using the pattern of enhancement defects as the sole criterion for identifying metastatic lymph nodes during Gd-MRL interpretation, and using histopathology as the gold standard, the sensitivity and specificity were estimated to be 86% and 95%, respectively. Conclusion Gd-MRL can adequately depict the lymphatic system, can define sentinel lymph nodes, and has the potential to differentiate between metastatic and non-metastatic lymph nodes in breast cancer patients.