Primary hepatocellular carcinoma (PHC) is a major health problem worldwide and is one of the 10 most commonly diagnosed cancers in China. Heat shock protein 27 (HSP27) were found to be overexpressed in a wide range of malignancies including PHC, however, post-translational modification of HSP27 still needs exploration in PHC. Recently, SUMOylation, an important post-translational modification associating with the development of many kinds of cancers has been intensively studied. In the current study, mRNA and protein level of HSP27 in archived tumor samples representing various pathological characteristics of PHC were examined, and modification of HSP27 by SUMO2/3 was investigated. HSP27 were expressed abundantly in patients' tumor tissues, and found to be associated with pathological progression. Besides, HSP27 was also elevated significantly in liver cancer cell lines Huh7 and HepG2 compared with human hepatocyte cells L02. Furthermore, knockdown of HSP27 was found to be associated with the decreased proliferation and invasion ability in Huh7 and HepG2 cells. Immunofluorescence assay showed that HSP27 and SUMO2/3 were co-localized in the subcellular, and co-immunoprecipitation verified the interaction between HSP27 and SUMO2/3. Overexpression of SUMO2/3 upregulated the HSP27 protein level and promotes Huh7 and HepG2 cell proliferation and invasion, and vice versa when the SUMO2/3 was knockdown. Taken together, increased protein level of HSP27 through SUMO2/3-mediated SUMOylation plays crucial roles in the progression of PHC, and this finding may shed light on developing potential therapeutic targets for PHC.

Big Data in the area of Remote Sensing has been growing rapidly. Remote sensors are used in surveillance, security, traffic, environmental monitoring, and autonomous sensing. Real-time detection of small moving targets using a remote sensor is an ongoing, challenging problem. Since the object is located far away from the sensor, the object often appears too small. The object’s signal-to-noise-ratio (SNR) is often very low. Occurrences such as camera motion, moving backgrounds (e.g., rustling leaves), low contrast and resolution of foreground objects makes it difficult to segment out the targeted moving objects of interest. Due to the limited appearance of the target, it is tough to obtain the target’s characteristics such as its shape and texture. Without these characteristics, filtering out false detections can be a difficult task. Detecting these targets, would often require the detector to operate under a low detection threshold. However, lowering the detection threshold could lead to an increase of false alarms. In this paper, the author will introduce a new method that improves the probability to detect low SNR objects, while decreasing the number of false alarms as compared to using the traditional baseline detection technique.

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The amount of data produced by sensors, social and digital media, and Internet of Things (IoTs) are rapidly increasing each day. Decision makers often need to sift through a sea of Big Data to utilize information from a variety of sources in order to determine a course of action. This can be a very difficult and time-consuming task. For each data source encountered, the information can be redundant, conflicting, and/or incomplete. For near-real-time application, there is insufficient time for a human to interpret all the information from different sources. In this project, we have developed a near-real-time, data-agnostic, software architecture that is capable of using several disparate sources to autonomously generate Actionable Intelligence with a human in the loop. We demonstrated our solution through a traffic prediction exemplar problem.

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Neural network language model (NNLM) is an essential component of industrial ASR systems. One important challenge of training an NNLM is to leverage between scaling the learning process and handling big data. Conventional approaches such as block momentum provides a blockwise model update filtering (BMUF) process and achieves almost linear speedups with no performance degradation for speech recognition. However, it needs to calculate the model average from all computing nodes (e.g., GPUs) and when the number of computing nodes is large, the learning suffers from the severe communication latency. As a consequence, BMUF is not suitable under restricted network conditions. In this paper, we present a decentralized BMUF process, in which the model is split into different components, each of which is updated by communicating to some randomly chosen neighbor nodes with the same component, followed by a BMUF-like process. We apply this method to several LSTM language modeling tasks. Experimental results show that our approach achieves consistently better performance than conventional BMUF. In particular, we obtain a lower perplexity than the single-GPU baseline on the wiki-text-103 benchmark using 4 GPUs. In addition, no performance degradation is observed when scaling to 8 and 16 GPUs.