Xichuan Zhou

Tuberculosis Surveillance by Analyzing Google Trends

Tuberculosis (TB) is a major global health concern, causing nearly ten million new cases and over one million deaths every year. The early detection of possible epidemic is the first and important defense line against TB. However, traditional surveillance approaches, e.g., U.S. Centers for Disease Control and Prevention (CDC), publish the TB morbidity surveillance results on a quarterly basis, with months of reporting lag. Moreover, in some developing countries, where most infections occur, there may not be enough medical resources to build traditional surveillance systems. To improve early detection of TB outbreaks, we developed a syndromic approach to estimate the actual number of TB cases using Google search volume. Specifically, the search volume of 19 TB-related terms, obtained from January 2004 to April 2009, were examined for surveillance purpose. Contemporary TB surveillance data were extracted from the CDCs reports to build and evaluate the syndromic system. We estimate the actual TB occurrences using a nonstationary dynamic system. Respective models are built to monitor both national-level and state-level TB activities. The surveillance results of the syndromic system can be updated every day, which is 12 weeks ahead of CDCs reports.

Notifiable infectious disease surveillance with data collected by search engine

Notifiable infectious diseases are a major public health concern in China, causing about five million illnesses and twelve thousand deaths every year. Early detection of disease activity, when followed by a rapid response, can reduce both social and medical impact of the disease. We aim to improve early detection by monitoring health-seeking behavior and disease-related news over the Internet. Specifically, we counted unique search queries submitted to the Baidu search engine in 2008 that contained disease-related search terms. Meanwhile we counted the news articles aggregated by Baidu’s robot programs that contained disease-related keywords. We found that the search frequency data and the news count data both have distinct temporal association with disease activity. We adopted a linear model and used searches and news with 1–200-day lead time as explanatory variables to predict the number of infections and deaths attributable to four notifiable infectious diseases, i.e., scarlet fever, dysentery, AIDS, and tuberculosis. With the search frequency data and news count data, our approach can quantitatively estimate up-to-date epidemic trends 10–40 days ahead of the release of Chinese Centers for Disease Control and Prevention (Chinese CDC) reports. This approach may provide an additional tool for notifiable infectious disease surveillance.

Monitoring Epidemic Alert Levels by Analyzing Internet Search Volume

The prevention of infectious diseases is a global health priority area. The early detection of possible epidemics is the first and important defense line against infectious diseases. However, conventional surveillance systems, e.g., the Centers for Disease Control and Prevention (CDC), rely on clinical data. The CDC publishes the surveillance results weeks after epidemic outbreaks. To improve the early detection of epidemic outbreaks, we designed a syndromic surveillance system to predict the epidemic trends based on disease-related Google search volume. Specifically, we first represented the epidemic trend with multiple alert levels to reduce the noise level. Then, we predicted the epidemic alert levels using a continuous density HMM, which incorporated the intrinsic characteristic of the disease transmission for alert level estimation. Respective models are built to monitor both national and regional epidemic alert levels of the U.S. The proposed system can provide real-time surveillance results, which are weeks before the CDCs reports. This paper focusses on monitoring the infectious disease in the U.S., however, we believe similar approach may be used to monitor epidemics for the developing countries as well.

Experimental research on wild-type p53 plasmid transfected into retinoblastoma cells and tissues using an ultrasound microbubble intensifier.

The transfection efficiency of wild-type p53 (wtp53) was investigated in retinoblastoma (RB) Y79 cells using an ultrasound microbubble technique. A human RB nude mouse xenograft tumour model was also used to investigate whether this technique could deliver wtp53 into solid tumours. Reverse transcription–polymerase chain reaction (RT–PCR) demonstrated that wtp53 was successfully transfected into Y79 cells in the plasmid with microbubbles and ultrasound group and in the plasmid with liposomes group, but not in the plasmid with ultrasound group or in the untreated control group. Flow cytometry showed that apoptosis was highest in the microbubbles and ultrasound group (25.58%) compared with the plasmid with liposomes group (19.50%), and the other two groups (< 10%). RT–PCR also showed that the wtp53 gene was successfully transfected into solid tumours in the plasmid with microbubbles and ultrasound group. This study provides preliminary evidence in support of a potential new approach to RB gene therapy.

Deep Learning With Grouped Features for Spatial Spectral Classification of Hyperspectral Images

This letter presents a novel deep learning algorithm for feature extraction from the hyperspectral images. The proposed method takes advantage of the knowledge that the features of the spatial-spectral data naturally fall into an array of groups with respect to different spectral bands. Aiming to reduce the influence of redundant spectral bands adaptively using unlabeled hyperspectral data, we incorporate the group information in the training algorithm of the deep neural network via a regularized weight-decay process. Experiments over different benchmarks of hyperspectral images show that the proposed method provides competitive solution with the state-of-the-art approaches.

A Column-Parallel Inverter-Based Cyclic ADC for CMOS Image Sensor With Capacitance and Clock Scaling

This paper presents a low-power column-parallel inverter-based cyclic analog-to-digital converter (ADC) for CMOS image sensor readout circuit. By partially floating the capacitors inside the multiply digital-analog-converter during the least significant bit (LSB) quantization, the amplifier load capacitance could be significantly scaled down, which allows much higher settling speed and shorter cycle period. Since the signal-to-noise ratio for LSB cycle is relaxed due to the residual amplification, the proposed capacitance scaling only contributes ignorable input-referred quantization noise. Using the proposed techniques, a cyclic ADC can operate under 50% power consumption without suffering conversion rate, noise performance, and linearity. A 12-b quantization resolution test chip is fabricated using the TSMC 0.18-μm technology with 110 column-parallel ADC channels and 10.08-μm × 750-μm column pitch. The 3.5/-2 LSBs integral nonlinearity and 10.1-b effective-number-of-bit are measured under 2-μs sampling rate with 120-μW power consumption per channel.

A Linear 126-dB Dynamic Range Light-to-Frequency Converter With Dark Current Suppression Upto 125 °C for Blood Oxygen Concentration Detection

This paper presents a high linear dynamic range light-to-frequency converter (LFC) with integrated photodiodes (PDs). The high dynamic range with linear response is achieved by two techniques. At first, the NWell/PSubstrate PD is regulated to almost zero voltage drop, in order to minimize the bias-dependent leakage current under a low light irradiance. Secondly, a replica amplifier is implemented to monitor the offset voltage of the regulator against process-voltage-temperature variation. As the result, the systematic and layout-dependent input offset voltage of the regulator can be reduced. The proposed LFC chip is fabricated using 0.35-μm 2-poly-3-metal nonepitaxy CMOS technology with a die size of 1.02 × 0.83 mm2. According to the measurement results, the proposed LFC is much less sensitive to temperature variation and it exhibits more than six orders of magnitude linear response under the light irradiance as low as 0.5 nW/cm2 with a temperature range from -25 °C to 125 °C.

A Spatial-Temporal Method to Detect Global Influenza Epidemics Using Heterogeneous Data Collected from the Internet

The 2009 influenza pandemic teaches us how fast the influenza virus could spread globally within a short period of time. To address the challenge of timely global influenza surveillance, this paper presents a spatial-temporal method that incorporates heterogeneous data collected from the Internet to detect influenza epidemics in real time. Specifically, the influenza morbidity data, the influenza-related Google query data and news data, and the international air transportation data are integrated in a multivariate hidden Markov model, which is designed to describe the intrinsic temporal-geographical correlation of influenza transmission for surveillance purpose. Respective models are built for 106 countries and regions in the world. Despite that the WHO morbidity data are not always available for most countries, the proposed method achieves 90.26 to 97.10 percent accuracy on average for real-time detection of global influenza epidemics during the period from January 2005 to December 2015. Moreover, experiment shows that, the proposed method could even predict an influenza epidemic before it occurs with 89.20 percent accuracy on average. Timely international surveillance results may help the authorities to prevent and control the influenza disease at the early stage of a global influenza pandemic.

Low-Reverse Recovery Charge Superjunction MOSFET With a p-Type Schottky Body Diode

A novel low-reverse recovery charge superjunction MOSFET (SJ-MOSFET) with a p-type Schottky body diode is proposed in this letter. The device has a p-type Schottky contact on the p-pillar at the drain side. Electrons are prevented from injecting into the drain side by the p-type Schottky contact, and the total carrier concentration is greatly reduced. Compared with the conventional device, the proposed SJ-MOSFET has a lower reverse recovery charge and a larger soft factor. Simulated results show that the reverse recovery charge is reduced by 81.3% and 76.0% at 300 K and 400 K, respectively, with a metal work function of 4.5 eV. The optimized metal work function range is 4.3–4.6 eV.

Ultra-Low Specific On-Resistance Trench SOI LDMOS with a Floating Lateral Field Plate

ABSTRACT An ultra-low specific on-resistance (Ron,sp) trench silicon-on-insulator (SOI) LDMOS is proposed in this paper. In this novel structure, a floating lateral metal field plate (FLFP) is introduced into the oxide trench of the conventional SOI LDMOS (con-TLDMOS) and connected to the gate outside the device working region. The oxide trench causes multidirectional depletion, which leads to electric field reshaping. The FLFP causes an assistant depletion effect especially for the trench surface regions, which significantly increases the doping concentration of the drift region (Nd). Therefore, a novel structure (FLFP-TLDMOS) with a breakdown voltage (BV) of 188 V and an Ron,sp of 1.05 mΩ·cm2 is obtained on a 4.8-μm-long drift region. Compared with the con-TLDMOS, the Ron,sp of the FLFP-TLDMOS can be reduced by about 54.3%; furthermore, its BV is maintained the same class with the con-TLDMOS, and the figure of merit is increased by 118%. Furthermore, the dynamic performance and self-heating effect of the novel structure are slightly improved compared with the conventional trench structure.