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Abstract Background Plague is an acute infectious disease caused by the Yersinia pestis. Historically, it has been a major pandemic with high mortality rates, known as the “Black Death” in the 14th century, which resulted in millions of deaths in Europe. With increasing economic prosperity, more and more people are traveling to Xizang. However, this trend also hides significant safety hazards. Currently, there are few recent reports on plague, especially those with imaging manifestations available. In this study, we report the detailed clinical and radiological data of the patient with pneumonic plague in Xizang, China, in 2023. Case presentation We report a case of pneumonic plague in Xizang, which occurred in a herdsman living in an area where dead marmots were found. The patient presented with symptoms such as fever, hemoptysis, dyspnea and coma. Chest computed tomography (CT) scans showed multiple nodules distributed in the central regions of lung lobes, consolidation distributed in secondary pulmonary lobules, and had a gravity-dependent distribution pattern. These imaging findings were consistent with pulmonary hemorrhage and diffuse alveolar damage. Despite emergency treatment, the patient died within 48 h of admission. Through retrospective medical history investigation, laboratory examination and autopsy, the final diagnosis was confirmed as pneumonic plague. Conclusion Pneumonic plague is the most deadly infectious disease, and its pathological features mainly include damage to the alveoli, pulmonary hemorrhage, and pulmonary edema. Corresponding to CT, it manifests as acute and rapidly progressing pneumonia, alveolar damage, and pulmonary hemorrhage. The value of this article lies in the completeness and typicality of the imaging data, vivid hand-drawn illustrations of transmission pathways, and comprehensive literature review, all of which serve to enhance public understanding of plague and play an important warning role.

BackgroundDental implants have become an increasingly popular option for replacing missing teeth, and the prevalence of peri-implantitis has also increased, which is expected to become a public health problem worldwide and cause high economic and health burdens. This scenario highlights the need for new therapeutic options to treat peri-implantitis.MethodsIn this study, we proposed a novel sono-responsive antibacterial nanosystem co-loaded with metformin (Met) and bone morphogenetic protein-2 (BMP-2) to promote efficacy in treating peri-implantitis. We introduced the zeolitic imidazolate framework-8 (ZIF-8) as a carrier for hematoporphyrin monomethyl ether (HMME) to enhance the antibacterial effect of sonodynamic antibacterial therapy and tested its reactive oxygen species (ROS) production efficiency and bactericidal effect in vitro. Afterward, HMME-loaded ZIF-8, BMP-2-loaded polylactic acid-glycolic acid (PLGA), and Met were incorporated into gelatin methacryloyl (GelMA) hydrogels to form HMME@ZIF-8/Met/BMP-2@PLGA/GelMA composite hydrogels, and the biocompatibility of which was determined in vitro and in vivo. A bacterial-induced peri-implantitis model in the maxilla of rats was established to detect the effects of the composite hydrogels with adjunctive use of ultrasound on regulating inflammation and promoting bone tissue repair in vivo.ResultsThe results indicated that HMME@ZIF-8 with ultrasound stimulation demonstrated more better ROS production efficiency and antimicrobial efficacy. The composite hydrogels had good biocompatibility. Ultrasound-assisted application of the composite hydrogels reduced the release of the inflammatory factors IL-6 and TNF-α and reduced bone loss around the implant in rats with bacterial-induced peri-implantitis.ConclusionOur observations suggest that HMME@ZIF-8 may be a new good sonosensitizer material for sonodynamic antibacterial therapy. The use of HMME@ZIF-8/Met/BMP-2@PLGA/GelMA composite hydrogels in combination with ultrasound can provide a novel option for treating peri-implantitis in the future.

This perspective paper introduces a novel framework for container orchestration called CODECO. The CODECO orchestration framework relies on a data-network-computing approach to define the best infrastructure that can support the operation of next-generation Internet applications across a mobile, heterogeneous Edge-Cloud continuum. The selection of such an infrastructure is aligned with target performance profiles defined by the user, such as resilience or greenness. CODECO proposes to rely on decentralized Artificial Intelligence approaches to provide the most suitable infrastructure to an application deployment, considering infrastructural challenges, such as intermittent connectivity and node failure. This paper explains the current CODECO framework and gives insight into operational use-cases where CODECO is being deployed, as relevant examples of application for such a framework. Recent developments in the creation of the open-source CODECO framework are described and explained, allowing the use of the framework by the research community. The paper then provides a thorough analysis of CODECO’s features in comparison with existing orchestration frameworks, explaining the benefits introduced with this dynamic orchestration approach.

Abstract BackgroundRemovable partial denture (RPD) design is crucial to long-term success in dental treatment, but shortcomings in RPD design training and competency acquisition among dental students have persisted for decades. Digital production is increasing in prevalence in stomatology, and a digital RPD (D-RPD) module, under the framework of the certified Objective Manipulative Skill Examination of Dental Technicians (OMEDT) system reported in our previous work, may improve on existing RPD training models for students. ObjectiveWe aimed to determine the efficacy of a virtual 3D simulation–based progressive digital training module for RPD design compared to traditional training. MethodsWe developed a prospective cohort study including dental technology students at the Stomatology College of Chongqing Medical University. Cohort 1 received traditional RPD design training (7 wk). Cohort 2 received D-RPD module training based on text and 2D sketches (7 wk). Cohort 3 received D-RPD module pilot training based on text and 2D sketches (4 wk) and continued to receive training based on 3D virtual casts of real patients (3 wk). RPD design tests based on virtual casts were conducted at 1 month and 1 year after training. We collected RPD design scores and the time spent to perform each assessment. ResultsWe collected the RPD design scores and the time spent to perform each assessment at 1 month and 1 year after training. The study recruited 109 students, including 58 (53.2%) female and 51 male (56.8%) students. Cohort 1 scored the lowest and cohort 3 scored the highest in both tests (cohorts 1-3 at 1 mo: mean score 65.8, SD 21.5; mean score 81.9, SD 6.88; and mean score 85.3, SD 8.55, respectively; PPPPPPP ConclusionsCohort 3 obtained the highest score at both time points with retention of competency at 1 year, indicating that progressive D-RPD training including virtual 3D simulation facilitated improved competency in RPD design. The adoption of D-RPD training may benefit learning outcomes.

Changes in human behaviors, such as reductions of physical contacts and the adoption of preventive measures, impact the transmission of infectious diseases considerably. Behavioral adaptations may be the result of individuals aiming to protect themselves or mere responses to public containment measures, or a combination of both. What drives autonomous and policy-induced adaptation, how they are related and change over time is insufficiently understood. Here, we develop a framework for more precise analysis of behavioral adaptation, focusing on confluence, interactions and time variance of autonomous and policy-induced adaptation. We carry out an empirical analysis of Germany during the fall of 2020 and beyond. Subsequently, we discuss how behavioral adaptation processes can be better represented in behavioral-epidemiological models. We find that our framework is useful to understand the interplay of autonomous and policy-induced adaptation as a "moving target". Our empirical analysis suggests that mobility patterns in Germany changed significantly due to both autonomous and policy-induced adaption, with potentially weaker effects over time due to decreasing risk signals, diminishing risk perceptions and an erosion of trust in the government. We find that while a number of simulation and prediction models have made great efforts to represent behavioral adaptation, the interplay of autonomous and policy-induced adaption needs to be better understood to construct convincing counterfactual scenarios for policy analysis. The insights presented here are of interest to modelers and policy makers aiming to understand and account for behaviors during a pandemic response more accurately.

ABSTRACT Trimethylamine-N-oxide (TMAO) is a major pro-atherogenic and pro-thrombotic metaorganismal molecule produced through the initial conversion of the dietary L-carnitine and other precursors into trimethylamine (TMA). We recently identified a dual-microbe anaerobic pathway for the metabolism of L-carnitine into TMA, in which the widely distributed cai operon in Enterobacteriaceae converts L-carnitine into gamma-butyrobetaine (γBB), followed by the degradation of γBB into TMA by the relatively rare gamma-butyrobetaine utilization (gbu) gene cluster present in Emergencia timonensis and few other related microbes. Studies of this pathway in animal models, however, have been limited by the lack of single microbes harboring the whole L-carnitine→γBB→TMA transformation pathway. Such recombinant microbes would both serve as a tool to further prove the contribution of this pathway to gut microbial TMA production and for future in vivo studies investigating the diet linkage to cardiovascular disease and the involvement of the TMAO pathway in this linkage. Here, we recapitulate the whole pathway in a single microbe by cloning the E. timonensis gbu gene cluster into Escherichia fergusonii, which naturally harbors the cai operon. We then show that the native E. timonensis GroES/GroEL-like proteins are needed for the proper functioning of the gbu cluster at 37°C. Finally, we demonstrate that inoculating germ-free mice with this recombinant E. fergusonii strain is sufficient to raise serum TMAO to pathophysiological levels upon dietary L-carnitine supplementation. The recombinant E. fergusonii strain developed will be a useful tool to facilitate future studies on the role of anaerobic gut microbial L-carnitine metabolism in cardiovascular and metabolic diseases. IMPORTANCE The key atherosclerotic TMAO originates from the initial gut microbial conversion of L-carnitine and other dietary compounds into TMA. Developing therapeutic strategies to block gut microbial TMA production needs a detailed understanding of the different production mechanisms and their relative contributions. Recently, we identified a two-step anaerobic pathway for TMA production from L-carnitine through initial conversion by some microbes into the intermediate γBB which is then metabolized by other microbes into TMA. Investigational studies of this pathway, however, are limited by the lack of single microbes harboring the whole pathway. Here, we engineered E. fergusonii strain to harbor the whole two-step pathway and optimized the expression through cloning a specific chaperone from the original host. Inoculating germ-free mice with this recombinant E. fergusonii is enough to raise serum TMAO to pathophysiological levels upon L-carnitine feeding. This engineered microbe will facilitate future studies investigating the contribution of this pathway to cardiovascular disease.

A synthetic pathway to a novel 4-aryl-3,4-dihydro-2H-1,4-benzoxazine scaffold was developed and a series of compounds based on the scaffold were synthesised as potential anticancer agents. The 4-aryl-substituted compounds were prepared via Buchwald–Hartwig cross-coupling between substituted bromobenzenes and various 1,4-benzoxazines, which in turn were generated from a cascade hydrogenation and reductive amination one-pot reaction. These analogues exhibited moderate to good potency against various cancer cell lines. Structure–activity relationship analysis indicated that the inclusion of hydroxyl groups on ring A and ring B was beneficial to biological activity, while having a para-amino group on ring C significantly enhanced potency. Molecule 14f displayed the most potent anticancer activity (IC50 = 7.84–16.2 µM against PC-3, NHDF, MDA-MB-231, MIA PaCa-2, and U-87 MG cancer cell lines), indicating its potential as a lead compound for further structural optimisation. All the synthesised compounds were fully characterised with NMR, HMRS, and IR. The novel benzoxazine scaffold described in this study holds promise and deserves further in-depth studies.

Identifying an unfamiliar caller's profession is important to protect citizens' personal safety and property. Owing to the limited data protection of various popular online services in some countries, such as taxi hailing and ordering takeouts, many users presently encounter an increasing number of phone calls from strangers. The situation may be aggravated when criminals pretend to be such service delivery staff, threatening the user individuals as well as the society. In addition, numerous people experience excessive digital marketing and fraudulent phone calls because of personal information leakage. However, previous works on malicious call detection only focused on binary classification, which does not work for the identification of multiple professions. We observed that web service requests issued from users' mobile phones might exhibit their application preferences, spatial and temporal patterns, and other profession-related information. This offers researchers and engineers a hint to identify unfamiliar callers. In fact, some previous works already leveraged raw data from mobile phones (which includes sensitive information) for personality studies. However, accessing users' mobile phone raw data may violate the more and more strict private data protection policies and regulations (e.g., General Data Protection Regulation). We observe that appropriate statistical methods can offer an effective means to eliminate private information and preserve personal characteristics, thus enabling the identification of the types of mobile phone callers without privacy concerns. In this paper, we develop CPFinder —- a system that exploits privacy-preserving mobile data to automatically identify callers who are divided into four categories of users: taxi drivers, delivery and takeouts staffs, telemarketers and fraudsters, and normal users (other professions). Our evaluation of an anonymized dataset of 1,282 users over a period of 3 months in Shanghai City shows that the CPFinder can achieve accuracies of more than 75.0% and 92.4% for multiclass and binary classifications, respectively.

Location-Based Services (LBS) have been prosperous owing to technological advancements of smart devices. Analyzing location-based user-generated data is a helpful way to understand human mobility patterns, further fueling applications such as recommender systems and urban computing. This dataset documents user activities of location-based services through LBSLab, a smartphone-based system implemented as a mini-program in the WeChat app. The dataset contains activity data of multiple types including logins, profile viewing, weather checking, and check-ins with location information (latitude and longitude), POI and mood indicated, collected from 467 users over a period of 11 days. We also present some temporal and spatial data analysis and believe the reuse of the data will allow researchers to better understand user behaviors of LBS, human mobility, and also temporal and spatial characteristics of people's moods.

Objective: Side effects in the psychotherapy are sometimes unavoidable. Therapists play a significant role in the side effects of psychotherapy, but there have been few quantitative studies on the mechanisms by which therapists contribute to them. Methods: We designed the psychotherapy Side Effects Questionnaire-Therapist Version (PSEQ-T) and released it online through an official WeChat account, where 530 therapists participated in the cross-sectional analysis. The therapists were classified into groups with and without perceptions of clients’ side effects. A number of features were selected to distinguish the therapists by category. Six machine learning-based algorithms were selected and trained by our dataset to build classification models. We leveraged the Shapley Additive exPlanations (SHAP) method to quantify the importance of each feature to the therapist categories. Results: Our study demonstrated the following: (1) Of the therapists, 316 perceived clients’ side effects in psychotherapy, with a 59.6% incidence of side effects; the most common type was “make the clients or patients feel bad” (49.8%). (2) A Random Forest-based machine-learning classifier offered the best predictive performance to distinguish the therapists with and without perceptions of clients' side effects, with an F1 score of 0.722 and an AUC value of 0.717. (3) “Therapists’ psychological activity” was the most relevant feature for distinguishing the therapist category. Conclusions: Our study revealed that the therapist's mastery of the limitations of psychotherapy technology and theory, especially the awareness and construction of their psychological states, was the most critical factor in predicting the therapist's perception of the side effects of psychotherapy.

Transcriptional rates are often estimated by fitting the distribution of mature mRNA numbers measured using smFISH (single molecule fluorescence in situ hybridization) with the distribution predicted by the telegraph model of gene expression, which defines two promoter states of activity and inactivity. However, fluctuations in mature mRNA numbers are strongly affected by processes downstream of transcription. In addition, the telegraph model assumes one gene copy but in experiments, cells may have two gene copies as cells replicate their genome during the cell cycle. While it is often presumed that post-transcriptional noise and gene copy number variation affect transcriptional parameter estimation, the size of the error introduced remains unclear. To address this issue, here we measure both mature and nascent mRNA distributions of GAL10 in yeast cells using smFISH and classify each cell according to its cell cycle phase. We infer transcriptional parameters from mature and nascent mRNA distributions, with and without accounting for cell cycle phase and compare the results to live-cell transcription measurements of the same gene. We find that: (i) correcting for cell cycle dynamics decreases the promoter switching rates and the initiation rate, and increases the fraction of time spent in the active state, as well as the burst size; (ii) additional correction for post-transcriptional noise leads to further increases in the burst size and to a large reduction in the errors in parameter estimation. Furthermore, we outline how to correctly adjust for measurement noise in smFISH due to uncertainty in transcription site localisation when introns cannot be labelled. Simulations with parameters estimated from nascent smFISH data, which is corrected for cell cycle phases and measurement noise, leads to autocorrelation functions that agree with those obtained from live-cell imaging.

Cells are complex systems that make decisions biologists struggle to understand. Here, the authors use neural networks to approximate the solution of mathematical models that capture the history and randomness of biochemical processes in order to understand the principles of transcription control.

ABSTRACT To solve the problems of a large number of clods remaining in potatoes after mechanized harvesting in northern heavy clay soil planting areas in China and requiring much labor to separate clods from potatoes, which leads to a heavy workload, inefficiency and a low cleaning rate, an RGB-D-based Mask R-CNN dynamic potato identification model is established by using acquired RBG-D image data of untreated potatoes after mechanized harvesting, and a potato cleaning method is presented in this paper. This makes it possible to automatically separate clod impurities from potatoes. The experimental results showed that the prediction accuracy of the identification model is more than 97%. With the increase in cleaning conveyance speed, the prediction accuracy of the model and the actual cleaning precision show a downward trend. Comprehensively considering the potato cleaning efficiency and accuracy, when the speed is set to 0.4 m·s−1, the cleaning precision reaches as high as 96.35%. This research provides a method and theoretical reference for the further study of intelligent potato cleaning systems.

Metabolisms of field plankton community, including gross primary production (GPP), community respiration (CR), and net community production (NCP), usually indicate the status of the health, resource production, and carbon budget of marine ecosystems. In this study, we explored the regional variance and driving forces of plankton metabolism in coastal waters of the Guangdong-Hong Kong-Macao Greater Bay Area (GGBA), a fast-developed area with complex hydrological and environmental states. The results showed that the maximum GPP and CR occurred in the estuarine plume of the GGBA in summer, while in winter the more active metabolisms of plankton community occurred in the Daya Bay, with the GPP and CR being respectively mediated by the nutrient level and temperature. Moreover, four regional zones were divided on the base of the environments and biological factors in surface water of the GGBA i.e., the river-runoff zone, river-plume zone, nearshore and far-offshore zones. The metabolic states in these zones varied significantly due to the regional and seasonal variations of, for example, the nutrient level, temperature, and turbidity driven by multiple factors including land-derived runoffs, anthropogenic activities, the Yuedong Coastal Current, and offshore seawater-intrusions. On the whole, the GGBA areas exhibited the weak heterotrophic processes in both summer (NCP = -24.9 ± 26.7 mg C m-3 d-1) and winter (NCP = -51.2 ± 8.51 mg C m -3 d-1). In addition, we found that the higher CR occurred to the bottom layers of the river plume and nearshore zones wherein hypoxia happened, indicating a possible attribution of plankton community respiration to the hypoxia in the GGBA.

Dental anatomy education for dental technology students should be developed in alignment with digital dental laboratory practices. We hypothesized that a virtually assisted sketching-based dental anatomy teaching module could improve students' acquisition of skills essential for digital restoration design. The second-year dental technology curriculum included a novel virtual technology-assisted sketching-based module for dental anatomy education. Pre- and post-course assessments evaluated students' skill sets and knowledge bases. Computer-aided design (CAD) scores were analyzed after one year to assess how the skills students developed through this module impacted their subsequent CAD performance. Participants who undertook the dental sketching-based teaching module demonstrated significantly improved theoretical knowledge of dental anatomy, dental aesthetic perception, and spatial reasoning skills. A partial least squares structural equation model indicated that the positive effects of this module on subsequent CAD performance were indirectly mediated by dental aesthetic perception, spatial reasoning, and practice time. A virtually assisted sketching-based dental anatomy teaching module significantly improved students' acquisition of skills and knowledge and positively mediated dental technology students' CAD performance.

Abstract Background Giant vascular eccrine spiradenoma is a rare variant of eccrine spiradenoma. It is different from the eccrine spiradenoma in its larger size and greater degree of vascularity. It is often clinically confusedwith a vascular or malignant tumor. Case presentation Here, we report a case of a 67-year-old man who presented with a tumor in the scrotum scrotal region for 6 years. The tumor had begun as a small nodule and had grown slowly with over time. Surgical excision and pathological examination revealed that the lesion was the a giant vascular eccrine spiradenoma. Conclusion This study reports the first case of giant vascular eccrine spiradenoma in the scrotum.

Inferring people’s Socioeconomic Attributes (SEAs), including income, occupation, and education level, is an important problem for both social sciences and many networked applications like targeted advertising and personalized recommendation. Previous works mainly focus on estimating SEAs from peoples’ cyberspace behaviors and relationships, such as the content of tweets or the social networks between online users. Besides cyberspace data, alternative data sources about users’ physical behavior, like their home location, may offer new insights. More specifically, in this paper, we study how to predict a person’s income level, family income level, occupation type, and education level from his/her home location. As a case study, we collect people’s home locations and socioeconomic attributes through a survey involving 9 provinces and 85 cities in China. We further enrich home location with the knowledge from real estate websites, government statistics websites, online map services, etc. To learn a shared representation from input features as well as attribute-specific representations for different SEAs, we propose H2SEA, a factorization machine-based multi-task learning method with attention mechanism. Extensive experiment results show that: (1) Home location can clearly improve the estimation accuracy for all SEA prediction tasks (e.g., 80.2% improvement in terms of F1-score in estimating personal income level); (2) The proposed H2SEA model outperforms alternative models for SEA inference in terms of various evaluation metrics, such as Area Under Curve (AUC), F-measure, and specificity; (3) The performance of specific SEA prediction tasks (e.g., personal income) can be further improved if H2SEA only focuses on cities or villages due to urban-rural gap in China; (4) Compared with online crawled housing price data, the area-level average income and Points Of Interest (POI) are more important features for SEA inferences in China.

This paper poses a question: How many types of social relations can be categorized in the Chinese context? In social networks, the calculation of tie strength can better represent the degree of intimacy of the relationship between nodes, rather than just indicating whether the link exists or not. Previou research suggests that Granovetter measures tie strength so as to distinguish strong ties from weak ties, and the Dunbar circle theory may offer a plausible approach to calculating 5 types of relations according to interaction frequency via unsupervised learning (e.g., clustering interactive data between users in Facebook and Twitter). In this paper, we differentiate the layers of an ego-centered network by measuring the different dimensions of user's online interaction data based on the Dunbar circle theory. To label the types of Chinese guanxi, we conduct a survey to collect the ground truth from the real world and link this survey data to big data collected from a widely used social network platform in China. After repeating the Dunbar experiments, we modify our computing methods and indicators computed from big data in order to have a model best fit for the ground truth. At the same time, a comprehensive set of effective predictors are selected to have a dialogue with existing theories of tie strength. Eventually, by combining Guanxi theory with Dunbar circle studies, four types of guanxi are found to represent a four-layer model of a Chinese ego-centered network.

Pod phenotypic traits are closely related to grain yield and quality. Pod phenotype detection in soybean populations in natural environments is important to soybean breeding, cultivation, and field management. For an accurate pod phenotype description, a dynamic detection method is proposed based on an improved YOLO-v5 network. First, two varieties were taken as research objects. A self-developed field soybean three-dimensional color image acquisition vehicle was used to obtain RGB and depth images of soybean pods in the field. Second, the red–green–blue (RGB) and depth images were registered using an edge feature point alignment metric to accurately distinguish complex environmental backgrounds and establish a red–green–blue-depth (RGB-D) dataset for model training. Third, an improved feature pyramid network and path aggregation network (FPN+PAN) structure and a channel attention atrous spatial pyramid pooling (CA-ASPP) module were introduced to improve the dim and small pod target detection. Finally, a soybean pod quantity compensation model was established by analyzing the influence of the number of individual plants in the soybean population on the detection precision to statistically correct the predicted pod quantity. In the experimental phase, we analyzed the impact of different datasets on the model and the performance of different models on the same dataset under the same test conditions. The test results showed that compared with network models trained on the RGB dataset, the recall and precision of models trained on the RGB-D dataset increased by approximately 32% and 25%, respectively. Compared with YOLO-v5s, the precision of the improved YOLO-v5 increased by approximately 6%, reaching 88.14% precision for pod quantity detection with 200 plants in the soybean population. After model compensation, the mean relative errors between the predicted and actual pod quantities were 2% to 3% for the two soybean varieties. Thus, the proposed method can provide rapid and massive detection for pod phenotyping in soybean populations and a theoretical basis and technical knowledge for soybean breeding, scientific cultivation, and field management.

This paper aims to investigate whether online private supplementary education, also known as shadow education, can alleviate educational inequality and what types of mechanisms can help alleviate it. We investigate this using an online learning platform dataset (3,603 anonymous students from China) with additional data from multiple sources and employ geospatial analyses to measure students' socioeconomic, regional and rural/urban inequalities. We find that taking part in online education narrows the performance gap in mathematics between privileged and unprivileged students in terms of school status and regional disparity in China. Theoretically, two micro-level mechanisms explain the alleviating differences: (1) equal access mechanism: students from lower city tiers and low-status schools show greater score improvement when having equal access to online education; (2) equal quality mechanism: students from rural regions improve their in-class rankings more substantially if they receive equal quality online education with the same tutoring and learning environment alongside urban students. This study comprehensively looks at the different effects of two mechanisms of online education--equal access and equal quality--for alleviating various types of inequality. Thus we speak to both educational inequality and digital inequality theory, finding that equal access to online education is not enough for rural students, as they also need access to classes of equal educational quality with their urban counterparts.

Abstract Background Tooth loss caused by caries or injuries has a negative effect on human health; thus, it is important to develop a reliable method of tooth regeneration. Research on tooth regeneration has mainly focused on mouse pluripotent stem cells, mouse embryonic stem cells, and adult stem cells from various sources in mice, whereas little has examined the differentiation of human embryonic stem (hES) cells into dental epithelium (DE) and odontogenic potential in vivo. Methods In this study, we induced hES cells to differentiate into dental epithelium using different concentrations of bone morphogenetic protein 4 (BMP4). With 1 pM BMP4, the hES cells differentiated into oral ectoderm (OE). These cells were then stimulated with 30 pM BMP4. Quantitative reverse transcription-polymerase chain reaction (qRT-PCR) and immunofluorescence showed the differentiation of OE and DE. The DE generated was mixed with embryonic day 14.5 mouse dental mesenchyme (DM) and transplanted into the renal capsules of nude mice. Thirty days later, the resulting tooth-like structures were examined by micro-computed tomography and hematoxylin and eosin staining. Results After 4 days of 1 pM BMP4 stimulation, Pitx1-positive OE formed. qRT-PCR and immunofluorescence revealed that induction with 30 pM BMP4 for 2 days caused the OE to differentiate into Pitx2/Dlx2/AMBN-positive DE-like cells. These cells also expressed β-catenin and p-Smad1/5/8, which are key proteins in the Wnt/β-catenin and Bmp signaling pathways, respectively. Thirty days after in vivo transplantation, six teeth with enamel and dentin had formed on the kidney. Conclusions These results show that hES cells differentiated into DE after sequential stimulation with different concentrations of BMP4, and the DE thus generated showed odontogenic potential.

Abstract Computational social science has integrated social science theories and methodology with big data analysis. It has opened a number of new topics for big data analysis and enabled qualitative and quantitative sociological research to provide the ground truth for testing the results of data mining. At the same time, threads of evidence obtained by data mining can inform the development of theory and thereby guide the construction of predictive models to infer and explain more phenomena. Using the example of the Internet data of China’s venture capital industry, this paper shows the triadic dialogue among data mining, sociological theory, and predictive models and forms a methodology of big data analysis guided by sociological theories.

Implant-tooth-supported fixed dentures are designed clinically due to the limitation of the local anatomy in specific implant sites or following an implant failure. However, it is difficult to precisely simulate the post-surgical biomechanical behavior on alveolar bone tissue with daily mastication. This paper presents and validates a reliable system to predict alveolar bone material properties and to identify the best fit treatment. We randomly chose 23 patients who had been followed for at least three years after Implant-tooth-supported fixed dentures. A poroelastic finite element model (FEM) is designed to characterize the biomechanical stresses of the aortic tissue for each patient. The Kernel least mean square is used to model the relationship between the alveolar bone material properties and biomechanical stress features generated by the daily occlusal force. Therefore, the alveolar bone material properties and the optimal treatment can be predicted by this integrated model prior to the operation for a new patient. The prediction accuracy of our model is 91.5%, more accurate than linear finite element model-LFEM (72.8%), mass-spring model-MSM (78.5%), and mass tensor model-MTM (80.2%). That demonstrates the high value of this model as a decision-making assistant for surgical planning of the patients who are scheduled to undergo dentures. The results of this paper demonstrate that this integrated model can predict alveolar bone material properties postoperatively with high accuracy. It combines bio-mechanical and machine learning approach to create a surgical planning tool which may support the clinical decision in the future.

Given that the USA and Germany are the most populous countries in North America and Western Europe, understanding the behavioral differences between American and German users of online social networks is essential. In this work, we conduct a data-driven study based on the Yelp Open Dataset. We demonstrate the behavioral characteristics of both American and German users from different aspects, i.e., social connectivity, review styles, and spatiotemporal patterns. In addition, we construct a classification model to accurately recognize American and German users according to the behavioral data. Our model achieves high classification performance with an F1-score of 0.891 and AUC of 0.949.

Abstract Background The aim is to investigate the prevalence of dental anxiety and its association with pain and other related factors in adult patients with irreversible pulpitis. Methods One hundred and thirty patients with irreversible pulpitis were included in this cross-sectional study. Participants were asked to fill out an information table and a battery of questionnaires to assess their level of dental anxiety, pain at their first and most recent dental experience, and pain intensity before/during the present endodontic treatment. The level of anxiety that participants displayed during the present treatment was also evaluated by the dentists using an anxiety rating scale. Data were analyzed by t-test, ANOVA, and Spearman correlation tests. Results 83.1% of participants suffered from moderate or high dental anxiety, and 16.2% met criteria for specific phobia. Subjects who had higher MDAS scores were more likely to postpone their dental visits (P

Customers often suffer from the variability of data access time in (edge) cloud storage service, caused by network congestion, load dynamics, and so on. One efficient solution to guarantee a reliable latency-sensitive service (e.g., for industrial Internet of Things application) is to issue requests with multiple download/upload sessions which access the required data (replicas) stored in one or more servers, and use the earliest response from those sessions. In order to minimize the total storage costs, how to optimally allocate data in a minimum number of servers without violating latency guarantees remains to be a crucial issue for the cloud provider to deal with. In this paper, we study the latency-sensitive data allocation problem, the latency-sensitive data reallocation problem and the latency-sensitive workload consolidation problem for cloud storage. We model the data access time as a given distribution whose cumulative density function is known, and prove that these three problems are NP-hard. To solve them, we propose an exact integer nonlinear program (INLP) and a Tabu Search-based heuristic. The simulation results reveal that the INLP can always achieve the best performance in terms of lower number of used nodes and higher storage and throughput utilization, but this comes at the expense of much higher running time. The Tabu Search based heuristic, on the other hand, can obtain close-to-optimal performance, but in a much lower running time.

Big data has a strong demand for a network infrastructure with the capability to support data sharing and retrieval efficiently. Information-centric networking (ICN) is an emerging approach to satisfy this demand, where big data is cached ubiquitously in the network and retrieved using data names. However, existing authentication and authorization schemes rely mostly on centralized servers to provide certification and mediation services for data retrieval. This causes considerable traffic overhead for the secure distributed sharing of data. To solve this problem, we employ identity-based cryptography (IBC) to propose a Distributed Authentication and Authorization Scheme (DAAS), where an identity-based signature (IBS) is used to achieve distributed verifications of the identities of publishers and users. Moreover, Ciphertext-Policy Attribute-based encryption (CP-ABE) is used to enable the distributed and fine-grained authorization. DAAS consists of three phases: initialization, secure data publication, and secure data retrieval, which seamlessly integrate authentication and authorization with the interest/data communication paradigm in ICN. In particular, we propose trustworthy registration and Network Operator and Authority Manifest (NOAM) dissemination to provide initial secure registration and enable efficient authentication for global data retrieval. Meanwhile, Attribute Manifest (AM) distribution coupled with automatic attribute update is proposed to reduce the cost of attribute retrieval. We examine the performance of the proposed DAAS, which shows that it can achieve a lower bandwidth cost than existing schemes.

By means of finite volume method, the heat transfer characteristic of crude oil under the tubular heating in the floating roof tank is investigated by numerical simulation. The evolution of temperature profile and its relationship with the flow pattern is presented in detail. A noticeable finding is that there exists the transformation of the flow pattern which affects the temperature profile apparently during the heating process. Special concern is taken on the evolution of temperature distribution on the interface between oil and the inner wall of the tank. The temperature profile on the top wall, sidewall and base wall of the tank is investigated. It is concluded that the plume induced by natural convection takes most responsibility for the formation of temperature field in the tank. The second factor is the boundary condition of the tank.

The device-to-device (D2D) channels allow content sharing when two devices are in close proximity, which can help improve resource utilization and network capacity. The data caching is introduced in D2D networks to enable the quick data access in mobile networks. Due to the selfish nature of users, they wish to get as much replication as possible in the opportunistic connections, seeking to maximize their own profits. However, caching data for other nodes may result in additional cost to the node serving as cache, where the cost is invoked by the cache placement of cache nodes and accessing cost of other nodes. In this paper, we propose a social-aware caching game to incentivize nodes to cache data for others. In the game, we consider the social ties and physical distance as the factors to formulate the caching cost. We obtain the Nash equilibrium of the game and propose a social-aware algorithm to minimize the total cost of getting object data in the network. The extensive simulation results show that our algorithm gains significant cache benefit.

Abdominal aortic aneurysm (AAA) is an inflammatory vascular disorder with high mortality. Accumulating evidence shows that toll-like receptor 2 (TLR2) plays a critical role in the regulation of wound-repairing process after tissue injury. We wondered if TLR2 signaling contributed to the pathogenesis of AAA and that targeting TLR2 would attenuate AAA development and progression. In this study, enhanced expression of TLR2 and its ligands were observed in human AAA tissue. Neutralization of TLR2 protected against AAA development and caused established AAA to regress in mouse models of AAA. In addition, TLR2-deficient mice also failed to develop AAA. The prophylactic and therapeutic effects of blocking TLR2 were accompanied by a significant resolution of inflammation and vascular remodeling, as indicated by the decreased expression or activity of MMP-2/9, α-SMA, inflammatory cytokines, and transcription factors NF-κB, AP-1 and STAT1/3 in AAA tissue. Mechanistically, blocking TLR2 decreased the expression and interaction of TLR2 and several endogenous ligands, which diminished chronic inflammation and vascular remodeling in the vascular tissue of AAA. Our studies indicate that the interactions between TLR2 and its endogenous ligands contribute to the pathogenesis of AAA and that targeting TLR2 offers great potential toward the development of therapeutic agents against AAA.

To explore a new technique for reconstructing and measuring three-dimensional (3D) models of orthodontic plaster casts using multi-baseline digital close-range photogrammetry (MBDCRP) with a single-lens reflex camera.Thirty sets of orthodontic plaster casts that do not exhibit severe horizontal overlap (>2 mm) between any two teeth were recorded by a single-lens reflex camera with 72 pictures taken in different directions. The 3D models of these casts were reconstructed and measured using the open source software MeshLab. These parameters, including mesio-distal crown diameter, arch width, and arch perimeter, were recorded six times on both the 3D digital models and on plaster casts by two examiners. Statistical analysis was carried out using the Bland-Altman method to measure agreement between the novel method and the traditional calliper method by calculating the differences between mean values.The average differences between the measurements of the photogrammetric 3D models and the plaster casts were 0.011-0.402mm. The mean differences between measurements obtained by the photogrammetric 3D models and the dental casts were not significant except for the lower arch perimeter (P>0.05), and all the differences were regarded as clinically acceptable (

Aging is a multifactorial process that includes the lifelong accumulation of molecular damage, leading to age-related frailty, disability and disease, and eventually death. In this study, we report evidence of a significant correlation between the number of genes encoding the immunomodulatory CD33-related sialic acid-binding immunoglobulin-like receptors (CD33rSiglecs) and maximum lifespan in mammals. In keeping with this, we show that mice lacking Siglec-E, the main member of the CD33rSiglec family, exhibit reduced survival. Removal of Siglec-E causes the development of exaggerated signs of aging at the molecular, structural, and cognitive level. We found that accelerated aging was related both to an unbalanced ROS metabolism, and to a secondary impairment in detoxification of reactive molecules, ultimately leading to increased damage to cellular DNA, proteins, and lipids. Taken together, our data suggest that CD33rSiglecs co-evolved in mammals to achieve a better management of oxidative stress during inflammation, which in turn reduces molecular damage and extends lifespan.

Online social networks such as Facebook, Twitter and Gowalla allow people to communicate and interact across borders. In past years online social networks have become increasingly important for studying the behavior of individuals, group formation, and the emergence of online societies. Here we focus on the characterization of the average growth of online social networks and try to understand which are possible processes behind seemingly long-range temporal correlated collective behavior. In agreement with recent findings, but in contrast to Gibrat's law of proportionate growth, we find scaling in the average growth rate and its standard deviation. In contrast, Renren and Twitter deviate, however, in certain important aspects significantly from those found in many social and economic systems. Whereas independent methods suggest no significance for temporally long-range correlated behavior for Renren and Twitter, a scaling analysis of the standard deviation does suggest long-range temporal correlated growth in Gowalla. However, we demonstrate that seemingly long-range temporal correlations in the growth of online social networks, such as in Gowalla, can be explained by a decomposition into temporally and spatially independent growth processes with a large variety of entry rates. Our analysis thus suggests that temporally or spatially correlated behavior does not play a major role in the growth of online social networks.