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Abstract Poorly identified tumor boundaries and nontargeted therapies lead to the high recurrence rates and poor quality of life of prostate cancer patients. Near-infrared-II (NIR-II) fluorescence imaging provides certain advantages, including high resolution and the sensitive detection of tumor boundaries. Herein, a cyanine agent (CY7-4) with significantly greater tumor affinity and blood circulation time than indocyanine green was screened. By binding albumin, the absorbance of CY7-4 in an aqueous solution showed no effects from aggregation, with a peak absorbance at 830 nm and a strong fluorescence emission tail beyond 1000 nm. Due to its extended circulation time (half-life of 2.5 h) and high affinity for tumor cells, this fluorophore was used for primary and metastatic tumor diagnosis and continuous monitoring. Moreover, a high tumor signal-to-noise ratio (up to ~ 10) and excellent preferential mitochondrial accumulation ensured the efficacy of this molecule for photothermal therapy. Therefore, we integrated NIR-II fluorescence-guided surgery and intraoperative photothermal therapy to overcome the shortcomings of a single treatment modality. A significant reduction in recurrence and an improved survival rate were observed, indicating that the concept of intraoperative combination therapy has potential for the precise clinical treatment of prostate cancer. Graphical Abstract

Abstract The second near infrared window is considered to be the optimal optical window for medical imaging and therapy as its capability of deep tissue penetration. The preparation of the gold nanorods with long wavelength absorption and low cytotoxicity is still a challenge. A series gold nanorods with large aspect ratio have been synthesized. Strong plasma absorption in the second near infrared window from 1000 to 1300 nm could be observed. The biocompatibility of the synthesized gold nanorods is dramatically improved via coating by bovine serum albumin (BSA), while the optical properties of which remains. The breast cancer tumor-bearing mouse could be well treated by the prepared gold nanorods with the NIR-II light intensity as low as 0.75 W/cm2. In summary, these results demonstrate the feasibility of using low illumination dose to treat tumor in the NIR-II region via the large aspect ratio gould nanoparticles.

The air temperature is a vital measuring variable widely applied in science and engineering as it is considered one vital part of the lives of plants and animals. From the point of view of time series analysis and data-driven modeling, changes in daily 24h air temperature can be described as one time-series data. Compared to relatively complicated models, simple, efficient and of even better performance are advantages of data-driven-based concise modeling methods, especially for embedded applications which usually have limited resources such as low RAM and low processing power. This study takes the perspective of data-driven modeling and time-series analysis to present one succinct and efficient solution based on discrete cosine transform (DCT)-extended modeling for the hourly air temperature forecast (HTF) in engineering and embedded applications. Characteristics of the DCT lie in optimal de-correlation and energy compaction. Orienting upon extended-DCT modeling, one concise least-squares (LS)-extended DCT predictive algorithm for the HTF is introduced. Then one Mbed-based embedded system design utilizing the proposed LS-extended DCT predictive algorithm as well as theoretical analyses and fast computation of arbitrary length DCT is presented. Verification results indicate the suitability of the proposed predictive algorithm. Only the temperature data without other parameters being straightly employed in forecast modeling is one major advantage of the proposed data-driven DCT-based HTF method, which benefits it to be suitable for engineering and embedded applications. [ABSTRACT FROM AUTHOR]

The objective of this study is to propose a position-distance-related model based on complex networks for the H1N1 epidemic simulation. Situation updates of the H1N1 prevalenced in 2009 show that spreading of the epidemic virus is highly correlative with its position and distance as well. Then in the proposed simulation model, each node in the network is described with not only its edges but also its position and distance. Accordingly, two mechanisms called "growth with position" and "degree and distance based preferential attachment" are introduced in the proposed model that it establishes one connection with likelihood proportional to node's degree and inversely proportional to the distance between two nodes. Beside the traditional node-growth mode, one called link-growth mode is also introduced. The main advantage of the proposed method is that it is one concise data-driven modeling based on complex networks. Simulation results utilizing the proposed link-growth mode and the traditional node-growth mode show that the two modes are equivalent to each other but from different perspectives. Moreover, compared to the traditional node-growth mode, the proposed link-growth mode is clear and concise. [ABSTRACT FROM AUTHOR]

Along with the rapid development of the Internet technology, social network sites (SNSs) are increasingly emerging. As various types of datasets are easily exposed to the network, privacy security in SNS becomes the focus of the study. Anonymization techniques, such as generalization and bucketization, and data perturbation techniques are usually employed in data publishing for privacy protection. However, in the meanwhile, the privacy protection methods reduce the utility of the data. Thus, the issue of privacy-utility trade-off becomes one very real problem. By considering the privacy-utility trade-off and combining the minimum spanning tree (MST) clustering technique and the sub-graph generalization technique, a privacy protection model is proposed for data publishing in weighted social networks. The MST-based clustering method is one representative and concise graph-based clustering algorithm. Meanwhile, as one MST of an edge-weighted graph is the minimum weight spanning tree, some important properties of the graph like the shortest path are preserved in the MST. Finally, on the basis of sub-graphs (clusters) divided by the MST-clustering, three sub-graph generalization approaches are designed in one reasonable way for data publishing to guarantee the privacy-utility trade-off of the proposed method. Experiments and result analysis indicate workability of the proposed method that the proposed model offers privacy protection for the data publishing by means of the sub-graph generalization approach while it can also improve the data utility especially for occasions like data mining, machine learning, and pattern recognition. [ABSTRACT FROM AUTHOR]