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Thymocytes bearing autoreactive T cell receptors (TCRs) are agonist-signaled by TCR/co-stimulatory molecules to either undergo clonal deletion or to differentiate into specialized regulatory T (Treg) or effector T (Teff) CD4+ cells. How these different fates are achieved during development remains poorly understood. We now document that deletion and differentiation are agonist-signaled at different times during thymic selection and that Treg and Teff cells both arise after clonal deletion as alternative lineage fates of agonist-signaled CD4+CD25+ precursors. Disruption of agonist signaling induces CD4+CD25+ precursors to initiate Foxp3 expression and become Treg cells, whereas persistent agonist signaling induces CD4+CD25+ precursors to become IL-2+ Teff cells. Notably, we discovered that transforming growth factor-β induces Foxp3 expression and promotes Treg cell development by disrupting weaker agonist signals and that Foxp3 expression is not induced by IL-2 except under non-physiological in vivo conditions. Thus, TCR signaling disruption versus persistence is a general mechanism of lineage fate determination in the thymus that directs development of agonist-signaled autoreactive thymocytes.

Fungal segmented negative-sense RNA viruses (SNSRVs) have been frequently found. Only the large segment encoding RNA-dependent RNA polymerase (RdRp) has been reported in most fungal SNSRVs, except for a few fungal SNSRVs reported to encode nucleocapsids, nonstructural proteins, or movement proteins.

With the fast development of Sensor Network, Internet of Things, mobile devices, and pervasive computing, enormous amounts of sensor devices are deployed in physical world. Data streams produced by these sensor devices, deployed broadly, can be used to create various value-added applications. Facing continuous, real-time, high-frequency, low-valued data streams, how to flexibly and efficiently cooperate them for creating valuable application is very crucial. In this study, we propose a service-oriented manner to realize flexible streams integration. It considers data stream produced by one sensor data as a stream data service and utilizes composing multiple services to realize the cooperation among sensor devices. Firstly, we propose a stream data service model based on Event-Condition-Action rules, which can encapsulate steam data as services and continuously and timely process stream data into value-added events. Then, we propose a declarative method which can dynamically compose stream data services. Based on two kinds of declarative rules, that is, sink-rules and connect-rules, multiple data streams can be dynamically integrated through flexible service composition. To ensure the performance of service composition, we also employ a sensor partition strategy and process multiple service compositions in parallel. Through comprehensive evaluations by experiments, our service composition method shows both good efficiency and effectiveness.

Chimeric antigen receptor (CAR) T cell therapies targeting single antigens have performed poorly in clinical trials for solid tumors due to heterogenous expression of tumor-associated antigens (TAAs), limited T cell persistence, and T cell exhaustion. Here, we aimed to identify optimal CARs against glypican 2 (GPC2) or CD276 (B7-H3), which were highly but heterogeneously expressed in neuroblastoma (NB), a lethal extracranial solid tumor of childhood. First, we examined CAR T cell expansion in the presence of targets by digital droplet PCR. Next, using pooled competitive optimization of CAR by cellular indexing of transcriptomes and epitopes by sequencing (CITE-Seq), termed P-COCC, we simultaneously analyzed protein and transcriptome expression of CAR T cells to identify high-activity CARs. Finally, we performed cytotoxicity assays to identify the most effective CAR against each target and combined the CARs into a bicistronic "OR" CAR (BiCisCAR). BiCisCAR T cells effectively eliminated tumor cells expressing GPC2 or CD276. Furthermore, the BiCisCAR T cells demonstrated prolonged persistence and resistance to exhaustion when compared with CARs targeting a single antigen. This study illustrated that targeting multiple TAAs with BiCisCAR may overcome heterogenous expression of target antigens in solid tumors and identified a potent, clinically relevant CAR against NB. Moreover, our multimodal approach integrating competitive expansion, P-COCC, and cytotoxicity assays is an effective strategy to identify potent CARs among a pool of candidates.

Background Chimeric antigen receptor T cells (CAR T) are potential therapies for rhabdosarcoma (RMS), the most common soft tissue sarcoma in children, where patients with relapsed or refractory disease have a dismal cure rate, and effective therapies are urgently needed. RMS tumors express high levels of the cell surface receptors FGFR4 and CD276, and both are direct targets of the PAX3-FOXO1 chimeric oncogene. However, CAR T have shown poor performance in solid tumors due to T cell exhaustion, limited persistence, and heterogeneous expression of target antigens. We hypothesize that engineering CAR constructs targeting FGFR4 and CD276 will enhance their activity and dual targeting will overcome the heterogenous target expression to effectively eliminate RMS tumors. Methods We modified and tested different hinge and transmembrane domains (HTM) or intracellular domain (ICD) of FGFR4 CAR constructs and developed multiple dual targeting Bicistronic CARs (BiCisCARs) against FGFR4 and CD276. We tested these CARs in aggressive mouse models and performed Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) of tumor infiltrating CAR T cells to functionally characterize these constructs. Results FGFR4 targeting CAR construct with CD8HTM and 4-1BB ICD could eliminate low burden RMS but was less effective against orthotopic tumors. Replacing CD8HTM with CD28HTM improved the efficacy in a moderate burden RMS model but did not show any benefit against a more aggressive RMS559 orthotopic model. Further modification using the CD28 ICD significantly enhanced the activity in a large burden RMS model but was unable to eradicate all tumors. Similarly, CAR T cells targeting CD276 alone, showed significant anti-tumor activity in moderate burden RMS models but could not eliminate all tumors. The BiCisCAR with FGFR4.CD28HTM.CD28ICD and CD276.CD8HTM.4-1BBICD was the most potent construct, eradicating 100% tumors in all tested orthotopic models, and those with heterogenous expression of target antigens. CITE-seq and flow cytometry assays demonstrated that this BiCisCAR showed the most significant tumor infiltration, and persistence, with limited exhaustion. This BiCisCAR exhibited a synergistic effect on cytokine production and anti-tumor activity, compared to single targeting CARs. Moreover, biochemical characterization revealed that the combined use of both CD28 and 4-1BB ICDs in the BiCisCAR resulted in the activation of three TCR downstream signaling pathways including AKT, Erk1/2 and p65. Conclusions and Future DirectionsThus, we have developed a potent BiCisCAR with dual targeting of FGFR4 and CD276 that shows optimal biochemical activity, persistence, and limited exhaustion, and addresses heterogenous expression of target antigens. This BiCisCAR will be further developed for future clinical trials in patients with high-risk RMS. Citation Format: Meijie Tian, Jun S. Wei, Adam Cheuk, David Milewski, Zhongmei Zhang, Yong Yuan Kim, Can Liu, Sherif Badr, Michael C. Kelly, Jerry T. Wu, Abdelrahman Rahmy, Hsien-Chao Chou, Xinyu Wen, Javed Khan. FGFR4 and CD276 dual targeting CAR T cells demonstrate synergistic antitumor activity in childhood rhabdomyosarcoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1784.

The dynamic interventions among sensor streams bring new challenges for the Internet of Things applications to derive meaningful information from a large amount of sensor streams. This paper proposes a declarative service-based method for capturing meaningful events from dynamic sensor streams based on our previous service abstraction, which can increase the value density of primitive sensor streams and raise the accessibility for higher level business applications. For aggregating related events from different sources, we set up declarative rules and compose cascaded events through service collaboration. In addition, for dealing with dynamic interventions among sensor streams, we propose a declarative rule evolution method to realize adaptive service collaboration at runtime. This paper also reports the tryout use of our method in the China Power Grid for detecting abnormal situations of power quality. Through a series of experiments based on real sensor data in the Power Grid, we verified that our method can capture events with high and steady precision.

Information service is always a hot topic especially when the Web is accessible anywhere. In university, lecture information is very important for students and teachers who want to take part in academic meetings. Therefore, lecture news extraction is an important and imperative task. Many open information extraction methods have been proposed, but due to the high heterogeneity of websites, this task is still a challenge. In this paper, we propose a method based on fusing multiple features to locate lecture news on the university website. These features include the linked relationship between parent webpage and child webpages, the visual similarity, and the semantics of webpages. Additionally, this paper provides an information service based on a main content extraction algorithm for extracting the lecture information. Stable and invariant features enable the proposed method to adapt to various kinds of campus websites. The experiments conducted on 50 websites show the effectiveness and efficiency of the provided service.

Many anomaly detection algorithms have been provided as services for more convenient and efficient utilization in IoT era. Due to concept drift existed in dynamic IoT stream data, it is a changeling task to apply proper anomaly detection services at run time. For effective on-line anomalous data discovery, this paper proposes a service selection framework to dynamically select and configure anomaly detection services. A fast classification model based on XGBoost is trained to identify the pattern of various stream data, so that suitable service can be selected and configured according to the pattern of stream data. Extensive experiments on real and synthetic data sets show that our framework can select suitable service for different scenarios, and the accuracy of the chosen services outperforms state-of-the-art methods.

Background: Ruling out distant metastases, non-small cell lung cancer (NSCLC)treatment depends on the results of mediastinal node staging (N staging). Several diagnostic methods play central roles in mediastinal N staging. This study is intended to evaluate the existing diagnostic methods and report quality, and to search for the best method for staging mediastinal lymph nodes. Methods: We searched PubMed, Embase, and the Cochrane Library to identify relevant studies, including randomized controlled trials and retrospective studies. These studies report the application of computed tomography, positron emission tomography-computed tomography, magnetic resonance imaging, endobronchial ultrasound, and mediastinoscopy in the diagnosis of mediastinal lymph node staging of NSCLC. The quality of the literature was assessed using the Quality Assessment of Diagnostic Accuracy Study 2. The true positive, false positive, true negative, and false negative of each study was extracted. The corresponding sensitivity, specificity, and other indicators were calculated and the Summary Receiver Operating curve was established. Then, head-to-head and indirect comparison meta-analyses will be conducted. Results: The results of this study will be published in a peer-reviewed journal. Conclusion: This study will provide basis for mediastinal lymph node staging of non-small cell lung cancer. PROSPERO registration number: CRD42019145667

Stream data from devices and sensors is considered a typical kind of big data. Though being promising, they have a good prospect only when we can reasonably correlate and effectively use them. Herein, services come back to the spotlight. The paper reports some of the authors' efforts in promoting service-based fusion and correlation of such stream data in a real setting – monitoring and optimized coordination of individual devices in a power plant. This paper advocates a decentralized and service-based approach to dynamically correlating the sensor data and proactively generating higher-level events between sensors and applications. A novel service model for transforming and correlating massive stream data is proposed. This service model shows potential in realizing various middle-way programmable nodes to form larger-granularity and software-defined ‘sensors' in an IoT context.

Regulatory T cells (Tregs) are small subsets of CD4 T cells that play a central role in the controlling of immune tolerance. Tregs are either generated in the thymus (tTregs) or the periphery (pTregs), and both express the master transcription factor Foxp3. Stable expression of Foxp3 is important for the maintenance of Tregs identity and their suppressive function. Similar to conventional T cells, Tregs can recognize both self- and non-self-antigens, and TCR engagement leads to Treg activation and the generation of effector Tregs. Emerging shreds of evidence suggest Tregs are not always stable, even fully committed mature tTregs, and can lose foxp3 expression and programming to effector-like T cells. In this review, we summarize recent findings in Treg instability and the intrinsic and extrinsic mechanisms in controlling the Foxp3 expression. Finally, we propose a new hypothesis that Foxp3 instability might help tTregs distinguish between self and non-self-antigens.

The complex interventions among sensor streams bring new challenges for IoT applications to derive meaningful information from large amounts of sensor streams. This paper aims to provide a data-driven service creation method for effectively capturing events based on our previous service abstraction – proactive data service. For improving the effectiveness of proactive data service, we consider the potential correlations among sensor streams besides user's pre-definitions when creating service. Based on the assumption that events frequently co-occurred in history have high probability to co-occur again, we regard frequent event sets as one kind of correlations among sensor streams, and propose an algorithm called FP-MFIM to efficiently find the maximum frequent event sets co-occurred in multiple sensor streams. For providing more effective information, we create PD-services with frequent co-occurred event types besides user-defined event types. This paper reports the tryout use of the method in China power grid for power quality event detection and location. Through a series of experiments based on real sensor data from power grid, we verified the efficiency of FP-MFIM algorithm and the effectiveness of our PD-services in real-world scenario.

Although IoT service and service composition provide effective means to develop IoT applications, the dynamic and time-varying correlation among massive sensors rises up new challenges to the traditional model-based approaches, and the extra uncertainty and complexity of service composition become apparent. This paper proposes a data-driven service composition method based on our previous proactive data service model. We utilize real-time correlation analysis of sensor data to refine model-based service composition at runtime. The correlation among sensor data is usually asynchronous. In this paper, we adopt and improve a Dynamic Time Warping (DTW) algorithm to obtain one-way lag-correlation, and realize dynamic sensor data correlation through refining existing service composition. Based on the real sensor data set in a coal-fired power plant, a series of experiments demonstrate the effectiveness of our service composition method.

With the increasing abundance of traffic data from sensors and devices, the integration and analysis of such streaming data are gaining importance in many application scenarios. This paper proposes a service-based approach for integrating and analyzing the traffic sensor data to support green commute in China by automatically discovering carpooling companions within a community. Our focuses are on modeling and design of the carpooling discovery services, algorithms for implementing the services, and performance enhancement when the data volume scales up. The proposed approach is verified with experiments using real-world data.

Though the existing data service mashup tools are gaining acceptance, it is still challenging for developers with no or little programming skills to develop data service mashups for dealing with situational and ad-hoc business problems. The paper focuses on interactive recommendation in which assistance is provided in a context-sensitive manner when the mashup plan can't be determined in advance. The paper analyzes the problem with a motivating scenario of mashup building for criminal investigation. Inspired by the observation that there exist dataflow patterns for certain integration functionalities, a dataflow-pattern-based recommendation framework is proposed to solve the problems. The framework can not only recommend data services by discovering similar situations, but also recommend mashup patterns and target data services. We propose a method to analyze the relationships between data services and dataflow patterns through both mining history logs and matching the input/output parameters. Further, to recommend target data services, we propose a method to transform the data mashup plans into mixed graphs and apply the graph-based substructure pattern mining (gSpan) algorithm on them. Experiments show that the dataflow-pattern-based recommendation approach for data service mashup is effective and efficient.

The instability of regulatory T (Treg) cells is involved in the pathogenesis of autoimmune diseases and also highlights safety concerns with regard to clinical Treg cell therapy. Cell-intrinsic molecular events linked to this Treg cell instability in vivo cells, which leads to safety concerns regardingare still obscure. Here we developed a novel luciferase-based reporter system and performed an unbiased screening for kinases that potentially modulate Foxp3 function. We found that the active form of COT/Tpl2 specifically inhibits the DNA binding activity of Foxp3 through a MEK-ERK-dependent pathway. Moreover, Treg cell-specific expression of activated MEK1 led to dysregulation of Treg function and instability of Foxp3 expression in vivo. Our results support the hypothesis that outside inflammatory signals act through the COT/Tpl2-MEK-ERK signaling pathway to destabilize the Treg lineage.

IoT service and service composition provide an effective means to develop IoT applications based on correlating multiple sensor data. The change of specific sensor data can cause others' changes under uncertain situations. It makes difficult for defining service composition plan in advance to build IoT application. This paper proposes a data-driven service composition method based on our previous proactive data service model. We regard service events frequently happen together with given service event as its situation, and the service events happen next as reacted actions under the situation. We analyze two kinds of correlation among service events via an improved FP-tree algorithm, and realize the service composition at runtime based on the real-time service events. Based on the real sensor data set in a coal-fired power plant, a series of experiments demonstrate that our method can effectively detect new service events based on current service events.

Accumulating evidence suggests that Foxp3+ cells can downregulate the expression of Foxp3, but whether thymically derived regulatory T cells (tTregs; especially committed tTregs) are capable of downregulating Foxp3 expression and being reprogrammed into other T effector cells remains controversial. Using a novel tTreg lineage-tracing mouse line, we were able to label epigenetically stable Foxp3+ cells derived from the thymus and demonstrate that mature tTregs are stable under homeostatic conditions. However, TCR engagement and sequential functional specialization of tTregs led to the generation of Foxp3 instability and reprogramming into the Th lineage. We further demonstrated that the signal switch from IL-2 to ICOS during Treg activation induced Treg instability and reprogramming. By using a dual lineage tracing model, we demonstrated that effector Tregs can revert to central Tregs, and this reversion is associated with increasing Foxp3 stability in vivo.

The current Sensor Networks are generally domain-specific and task-oriented, tailored for particular applications with little possibility of sharing and reusing sensor data for different applications. The servitization of stream sensor data is an effective solution for sharing and reusing sensor data resources. Considering the limitations of existing methods on processing large-scale stream data and concurrent requests, this paper proposes a lightweight model for stream sensor data service, which processes sensor stream data with service modeling operations, and distributes data based on Pub/Sub mechanism. This paper fulfills the encapsulation by utilizing event-driven mechanism for stream data service processing, using SparkStreaming framework to process sensor events, and improving traditional matching-tree algorithm to distribute stream data efficiently. Finally, we evaluate our approach through experiments, and the stream data service can handle multiple requests and deliver data to corresponding applications in milliseconds level.

With the social development and vehicle increasing, traffic congestion becomes a pressing problem. Carpooling is one solution for traffic congestion. This paper begins with a motivation case and proposes a service-based method to automatically identify vehicles which travel together (i.e., Traveling companion) and provide carpooling candidates for users in heavy urban traffic. This paper focuses on services design and implementation of the carpooling functions in a distributed parallel computing environment. Through a comprehensive evaluation by experiments, our proposal is shown to deliver good efficiency and effectiveness.

Data service mashup provides a development fashion that integrates heterogeneous data from multiple data sources into a single Web application. This paper focuses on the problem of recommending useful suggestions for developing data service mashups based on the association relationship of data services. Firstly the data service association relationship is analyzed from three angles: the data dependence, inheritance and the potential association between data services. Based on the analysis, a measure of the data service association relationship called connectivity is proposed to assess the relationship of any two data services. Then a recommendation method is proposed to suggest the next useful data services based on the connectivity. The experimental evaluation demonstrates the utility of our method.

In 2007, flipped class model was firstly employed in class by Jonathan Bergmann and Aaron Sams, chemical teachers of Woodland Park High School, Colorado. Along with the popularity and development of internet, flipped class model of Khan Academy has drawn wide attention from global educators.Since Khan Academy was founded by Salman in 2011to provide free learning platform for learners by means of flipped class video and teacher’s personalized question-answering system. In this paper, taking for example the elective course of general education-Graph and Image Processing, focus is put on online construction of flipped class model based on FCM concept. Keywords-FCM; Online Course; Teaching Design I. FLIPPED CLASS MODEL In 2007, flipped class model was firstly employed in class by Jonathan Bergmann and Aaron Sams, chemical teachers of Woodland Park High School, Colorado. Along with the popularity and development of internet, flipped class model of Khan Academy has drawn wide attention from global educators. [1] In 2011, Jiao Jianli, Professor of South China Normal University, firstly presented flipped class model in his blog, which was seeing rapid development in 2012. In April 2013, Professor Gao Song, vice president of Peking University and Academician of China Academy of Sciences mentioned that advanced placement program should be established for junior high students to acquire admission qualification by dint of MOOC platform and flipped class model. [2]In September 2013, Nanjing Primary School was determined as the pilot school of flipped class model. In this paper, FCM-based online course is constructed, with Graph and Image Processing, the elective course of general education (hereinafter referred to as the Course), taken as research object. II. ANALYSIS ON COURSE DEMAND A. Student’s huge difference in basic knowledge The teaching about the elective course of general education is facing severe challenges, for there is a huge difference in regional development level of teaching informatization and students’ application ability of information technology, and besides, universities attach importance to information technology to varying degrees. This is obviously embodied by the fact that teaching activities always encounter difficulties in meeting the requirements of all students. Some students perceive that the teaching contents are too simple and thus hope that teachers can increase the difficulty, while other students argue that it is difficult to catch up with the progress because of the fast teaching progress and the excessive software application knowledge. Teaching activities encounter difficulties in satisfying the demands of learners at different levels, and meanwhile, learner’s dissatisfaction with teaching directly influences teaching enthusiasm, which will further impact the implementation of teaching objectives.[3] B. Student’s huge difference in basic knowledgeThe frequent emergence of new technologies and software versions as well as large quantities of course contents Along with the emergence of new technologies and software versions, information technology has exerted an important influence on various fields such as education, economy, life and entertainment. Meanwhile, the teaching concept and methods have been turned into methodologies, which is of great 2nd International Conference on Information, Electronics and Computer (ICIEAC 2014) © 2014. The authors Published by Atlantis Press 157 significance for the construction and development of other subjects. The concepts, such as computational thinking and methodologies of educational technology, have already been listed as course objectives by Teaching Instruction Committee of Ministry of Education. Therefore, the class given in computer room is filled with more and more teaching contents. III. Analysis on course investigation Elective course of general education is oriented to all students. Questionnaires are distributed for the purpose of adjusting the teaching emphasis in each semester, because students of different specialties show a huge difference in basic computer skills and interests. The result of partial questionnaires in 2013 fall semester can be seen as below: A. The overall evaluation on elective course of general education (Fig.1) shows that 5% of respondents are very satisfied, and “satisfied” means that the overall quality of the course needs to be improved. Fig. 1 B. Concerning what kinds of improvements need to be made in elective course of general education (multiple choices available), Fig. 2 shows that school’s introduction to all courses occupies 50.94%. This suggests that most of the time, students rely on sense or the words of schoolmates to select courses online rather than on the understanding of the teaching contents. Of all respondents, 39.12% argue the elective course shall be adjusted; 35.84% agree the teaching management of elective course shall be improved; 33.96 % hold that

As an open source software platform, Hadoop has been applied widely. Aim at the need of Hadoop resource monitoring, this paper introduces Hadoop and two distributed monitoring systems, which are Ganglia and Nagios. We design a resource monitoring system of Hadoop, which can monitor Hadoop comprehensively, and provide real-time monitoring, warning messages and reports. It makes use of the advantages of Ganglia and Nagios, and resolves the problem that Ganglia can't store data permanently. Finally, an effective solution is provided for resource monitoring of Hadoop.

Robustness is the important issue in watermarking, robustness at the same time with blind watermark recovering algorithm remains especially challenging. This paper presents a combined DWT and DCT still image blind watermarking algorithm. The two-level DWT are performed on the original image, the low-frequency sub-band is divided into blocks, the DCT is performed on the every block, the DCT coefficients of every block are sorted using Zig-Zag order, the DCT low-frequency coefficient is selected as embedding watermarking. The watermarking signals are embedded into the selected embedding points using the modular arithmetic. The watermark recovering is the inverse process of the watermark embedding, according to the answer of the modular arithmetic, we can estimate the value of embedded the watermark. The algorithm is compared with a pure DWT-based scheme Experiment results shown that the proposed algorithm is robust to many attacks such as JPEG compression, addition noise, cropping, JPEG compression, median filter, rotation, and resize etc. Proposed algorithm is shown to provide good results in term of image imperceptibility, too.

In this paper, a wavelet packets watermarking algorithm based on chaos encryption for still digital images is presented. The watermarking is changed into bit sequence by modular arithmetic, at the same time, a random sequence is gotten by using a chaos map and deal with it also by modular arithmetic. After getting these two sequences, we do XOR arithmetic, then getting a new encrypted watermarking sequence. At the embedding stage, wavelet packets decomposition is used for original image, then embedding encrypted watermarking sequence into some frequency bands of original image by repeatedly embedding four times. At the extracting stage, wavelet packets decomposition for original image and watermarked image are used to inverse process. The experiment results demonstrated the new algorithm is robust for scaling, cropping, JPEG compression and noise attack.

A novel dual watermark technology based on digital copyright technology is proposed, which, making full use of the advances of the web, stores the correlation information including the keys and the dual watermarks in an XML document. A new image watermarking technology to spread a digital image with copyright protection is realised successfully on the Internet. The arithmetic has very good robustness against the most common, non-malevolent data manipulations, including digital-to-analogue conversion and digital format conversion. Finally, the experimental results confirm that the two watermarks embedded by the proposed algorithm are invisible and robust against commonly used image-processing manipulations such as JPEG compression, adding noise, cropping, and rescaling and soon. The proposed algorithm is shown to provide very good results in term of image imperceptibility too.