A Study of Machine Learning Models in Epidemic Surveillance: Using the Query Logs of Search Engines

Epidemics inevitably result in a large number of deaths and always cause considerable social and economic damage. Epidemic surveillance has thus become an important healthcare research issue. In 2009, Ginsberg et al. observed that the query logs of search engines can be used to estimate the status of epidemics in a timely manner. In this paper, we model epidemic surveillance as a classification problem and employ query statistics from Google to classify the status of a dengue fever epidemic. The query logs of twenty-three dengue-related keywords serve as observations for machine learning and testing, and a number of machine learning models are investigated to evaluate their surveillance performance. Evaluations based on a 5-year real world dataset demonstrate that search engine query logs can be used to construct accurate epidemic status classifiers. Moreover, the learned classifiers generally outperform conventional regression approaches. We also apply various machine learning models, including generative, discriminative, sequential, and non-sequential classification models, to demonstrate their applicability to epidemic surveillance

A Microcantilever-based Gas Flow Sensor for Flow Rate and Direction Detection

The purpose of this paper is to apply characteristics of residual stress that causes cantilever beams to bend for manufacturing a micro-structured gas flow sensor. This study uses a silicon wafer deposited silicon nitride layers, reassembled the gas flow sensor with four cantilever beams that perpendicular to each other and manufactured piezoresistive structure on each micro-cantilever by MEMS technologies, respectively. When the cantilever beams are formed after etching the silicon wafer, it bends up a little due to the released residual stress induced in the previous fabrication process. As air flows through the sensor upstream and downstream beam deformation was made, thus the airflow direction can be determined through comparing the resistance variation between different cantilever beams. The flow rate can also be measured by calculating the total resistance variations on the four cantilevers.Comment: Submitted on behalf of EDA Publishing Association (http://irevues.inist.fr/handle/2042/16838

An integrated analysis tool for analyzing hybridization intensities and genotypes using new-generation population-optimized human arrays

The cross-sample plot of the multipoint LOH/LCSH analyses of the three samples used in Fig. 5. The plot comprises four panels: (a) The top-left panel is a cross-sample and cross-chromosome plot. The vertical axis is the index of study samples, and the horizontal axis is the physical position (Mb) on each of the 23 chromosomes. The blue and red bars represent SNPs without and with LOH/LSCH, respectively. (b) The top-right panel is a histogram of cross-chromosome aberration frequency. The vertical axis is the index of study samples, and the horizontal axis is the cross-chromosome aberration frequency of the corresponding samples. The pink (skyblue) background represents that the genetic gender of a sample is female (male). The histogram represents the aberration frequency of LOH/LCSH SNPs across the chromosomes of the corresponding samples. (c) The bottom-left panel is a histogram of the cross-sample aberration frequency. The vertical axis is the cross-sample aberration frequency of a SNP, and the horizontal axis is the physical position (Mb) on each of the 23 chromosomes. The purple line represents the aberration proportion of samples carrying the SNPs with LOH/LCSH. (d) The bottom-right panel is the legend of the genetic gender that is used in panel (b), where the pink (skyblue) background represents that the genetic gender of a sample is female (male). (TIFF 1656 kb

7-Hy­droxy­indan-1-one

In the title compound, C9H8O2, an intra­molecular O—H⋯O hydrogen bond generates an S(6) ring. The dihedral angle between the mean plane of the S(6) ring and the benzene ring is 1.89 (2)°. In the crystal, inversion-related mol­ecules are linked by pairs of O—H⋯O hydrogen bonds, forming a cyclic dimers with R 2 2(12) graph-set motif. Weak inter­molecular C—H⋯Ocarbon­yl and C—H⋯Ohy­droxy hydrogen bonds link the dimers into chains along [010], generating two C(6) motifs that overlap three C atoms, forming R 2 2(8) ring motifs

Missing Teeth and Restoration Detection Using Dental Panoramic Radiography Based on Transfer Learning With CNNs

Common dental diseases include caries, periodontitis, missing teeth and restorations. Dentists still use manual methods to judge and label lesions which is very time-consuming and highly repetitive. This research proposal uses artificial intelligence combined with image judgment technology for an improved efficiency on the process. In terms of cropping technology in images, the proposed study uses histogram equalization combined with flat-field correction for pixel value assignment. The details of the bone structure improves the resolution of the high-noise coverage. Thus, using the polynomial function connects all the interstitial strands by the strips to form a smooth curve. The curve solves the problem where the original cropping technology could not recognize a single tooth in some images. The accuracy has been improved by around 4% through the proposed cropping technique. For the convolutional neural network (CNN) technology, the lesion area analysis model is trained to judge the restoration and missing teeth of the clinical panorama (PANO) to achieve the purpose of developing an automatic diagnosis as a precision medical technology. In the current 3 commonly used neural networks namely AlexNet, GoogLeNet, and SqueezeNet, the experimental results show that the accuracy of the proposed GoogLeNet model for restoration and SqueezeNet model for missing teeth reached 97.10% and 99.90%, respectively. This research has passed the Research Institution Review Board (IRB) with application number 202002030B0

Tooth Position Determination by Automatic Cutting and Marking of Dental Panoramic X-ray Film in Medical Image Processing

This paper presents a novel method for automatic segmentation of dental X-ray images into single tooth sections and for placing every segmented tooth onto a precise corresponding position table. Moreover, the proposed method automatically determines the tooth’s position in a panoramic X-ray film. The image-processing step incorporates a variety of image-enhancement techniques, including sharpening, histogram equalization, and flat-field correction. Moreover, image processing was implemented iteratively to achieve higher pixel value contrast between the teeth and cavity. The next image-enhancement step is aimed at detecting the teeth cavity and involves determining the segment and points separating the upper and lower jaw, using the difference in pixel values to cut the image into several equal sections and then connecting each cavity feature point to extend a curve that completes the description of the separated jaw. The curve is shifted up and down to look for the gap between the teeth, to identify and address missing teeth and overlapping. Under FDI World Dental Federation notation, the left and right sides receive eight-code sequences to mark each tooth, which provides improved convenience in clinical use. According to the literature, X-ray film cannot be marked correctly when a tooth is missing. This paper utilizes artificial center positioning and sets the teeth gap feature points to have the same count. Then, the gap feature points are connected as a curve with the curve of the jaw to illustrate the dental segmentation. In addition, we incorporate different image-processing methods to sequentially strengthen the X-ray film. The proposed procedure had an 89.95% accuracy rate for tooth positioning. As for the tooth cutting, where the edge of the cutting box is used to determine the position of each tooth number, the accuracy of the tooth positioning method in this proposed study is 92.78%

Demonstrating a superconducting dual-rail cavity qubit with erasure-detected logical measurements

A critical challenge in developing scalable error-corrected quantum systems is the accumulation of errors while performing operations and measurements. One promising approach is to design a system where errors can be detected and converted into erasures. A recent proposal aims to do this using a dual-rail encoding with superconducting cavities. In this work, we implement such a dual-rail cavity qubit and use it to demonstrate a projective logical measurement with erasure detection. We measure logical state preparation and measurement errors at the $0.01\%$-level and detect over $99\%$ of cavity decay events as erasures. We use the precision of this new measurement protocol to distinguish different types of errors in this system, finding that while decay errors occur with probability $\sim 0.2\%$ per microsecond, phase errors occur 6 times less frequently and bit flips occur at least 170 times less frequently. These findings represent the first confirmation of the expected error hierarchy necessary to concatenate dual-rail erasure qubits into a highly efficient erasure code