Human dehydrated normal and cancerous gastric tissues were measured using transmission time-domain terahertz spectroscopy. Based on the obtained terahertz absorption spectra, the contrasts between the two kinds of tissue were investigated and techniques for automatic identification of cancerous tissue were studied. Distinctive differences were demonstrated in both the shape and amplitude of the absorption spectra between normal and tumor tissue. Additionally, some spectral features in the range of 0.2~0.5 THz and 1~1.5 THz were revealed for all cancerous gastric tissues. To systematically achieve the identification of gastric cancer, principal component analysis combined with t-test was used to extract valuable information indicating the best distinction between the two types. Two clustering approaches, K-means and support vector machine (SVM), were then performed to classify the processed terahertz data into normal and cancerous groups. SVM presented a satisfactory result with less false classification cases. The results of this study implicate the potential of the terahertz technique to detect gastric cancer. The applied data analysis methodology provides a suggestion for automatic discrimination of terahertz spectra in other applications.

PFS (Prime Focus Spectrograph), a next generation facility instrument on the 8.2-meter Subaru Telescope, is a very wide-field, massively multiplexed, optical and near-infrared spectrograph. Exploiting the Subaru prime focus, 2394 reconfigurable fibers will be distributed over the 1.3 deg field of view. The spectrograph has been designed with 3 arms of blue, red, and near-infrared cameras to simultaneously observe spectra from 380nm to 1260nm in one exposure at a resolution of ∼1.6−2.7Å. An international collaboration is developing this instrument under the initiative of Kavli IPMU. The project is now going into the construction phase aiming at undertaking system integration in 2017-2018 and subsequently carrying out engineering operations in 2018-2019. This article gives an overview of the instrument, current project status and future paths forward.

The heterogeneities of colorectal cancer (CRC) lead to staging inadequately of patients' prognosis. Here, we performed a prognostic analysis based on the tumor mutational profile and explored the characteristics of the high-risk tumors. We sequenced 338 colorectal carcinomas as the training dataset, constructed a novel five-gene (SMAD4, MUC16, COL6A3, FLG and LRP1B) prognostic signature, and validated it in an independent dataset from The Cancer Genome Atlas (TCGA). Kaplan-Meier and Cox regression analyses confirmed that the five-gene signature is an independent predictor of recurrence and prognosis in patients with Stage III colon cancer. The mutant signature translated to an increased risk of death (hazard ratio = 2.45, 95% confidence interval = 1.15-5.22, p = 0.016 in our dataset; hazard ratio = 4.78, 95% confidence interval = 1.33-17.16, p = 0.008 in TCGA dataset). RNA and bacterial 16S rRNA sequencing of high-risk tumors indicated that mutations of the fivegene signature may lead to intestinal barrier integrity, translocation of gut bacteria and deregulation of immune response and extracellular related genes. The high-risk tumors overexpressed IL23A and IL1RN genes and enriched with cancer-related bacteria (Bacteroides fragilis, Peptostreptococcus, Parvimonas, Alloprevotella and Gemella) compared to the low-risk tumors. The signature identified the high-risk group characterized by gut bacterial translocation and upregulation of interleukins of the tumor microenvironment, which was worth further researching.

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This study presents a method for detecting contamination events of sources of drinking water based on the Dempster-Shafer (D-S) evidence theory. The detection method has the purpose of protecting water supply systems against accidental and intentional contamination events. This purpose is achieved by first predicting future water-quality parameters using an autoregressive (AR) model. The AR model predicts future water-quality parameters using recent measurements of these parameters made with automated (on-line) water-quality sensors. Next, a probabilistic method assigns probabilities to the time series of residuals formed by comparing predicted water-quality parameters with threshold values. Finally, the D-S fusion method searches for anomalous probabilities of the residuals and uses the result of that search to determine whether the current water quality is normal (that is, free of pollution) or contaminated. The D-S fusion method is extended and improved in this paper by weighted averaging of water-contamination evidence and by the analysis of the persistence of anomalous probabilities of water-quality parameters. The extended D-S fusion method makes determinations that have a high probability of being correct concerning whether or not a source of drinking water has been contaminated. This paper's method for detecting water-contamination events was tested with water-quality time series from automated (on-line) water quality sensors. In addition, a small-scale, experimental, water-pipe network was tested to detect water-contamination events. The two tests demonstrated that the extended D-S fusion method achieves a low false alarm rate and high probabilities of detecting water contamination events.