Constructing probability density function of net-proton multiplicity distributions using Pearson curve method

The probability density functions of net-proton multiplicity distributions are constructed from the Beam Energy Scan results of the STAR experiment using the Pearson curve method for two different transverse momentum windows. The $6^{th}$ and $8^{th}$ order cumulants of net-proton multiplicity distributions are estimated from the constructed probability density functions. The beam energy dependence of $C_{6}/C_{2}$ and $C_{8}/C_{2}$ are found to be sensitive to the acceptance window. This method provides a unique opportunity to study the O(4) criticality near the chiral crossover transition and estimating the higher-order cumulants. In general, it is useful to determine the probability density function uniquely of a frequency data if the first four cumulants are known.Comment: 8 pages, 6 figures, text modifie

Development of Optical Devices for Digital Medicine

Department of Biomedical EngineeringAdvances of technology have made a revolution that interconnects industrial devices and fuses the boundaries of digital, physical and biological spaces. These technologies such as cloud computing, 3D printing technology, big data, internet of things (IOT), artificial intelligence (AI), and maturity of system integrations have been improved every year, changing our daily life quickly in intelligent and convenient ways. In this days, these explosions of technology, changing the way we live and think, is referred to 4th industrial revolution. As we know, every industry is affected by the new waves of technologies, digitalization and connectivity, and the biomedical or medical field is no exception. Healthcare fields have benefited mostly from recent technical improvements, revolutionizing the medical systems in many terms in cost-effective ways. Particularly, ???digital medicine??? has been recently came into the limelight as one of the uprising fields. In digital medicine, traditional medical devices and diagnostic programs have become miniaturized, digitalized, and automated. As taking advantages of digital medicine, specific fields related to digital pathology, point-of-care (POC) diagnostics, and application of deep learning or machine learning technologies have shown the great potentials not only in biomedical academia but also in the revenues of their markets. It allows to connect devices, hospital equipment, and to accelerate efficiencies in health service such as diagnosis, and to reduce the cost of services. Moreover, interconnection between advanced technologies has been improved the access of healthcare to the places where hospital or medical services are limited. Furthermore, artificial intelligence has shown promising results related to disease screening especially using medical images. Although fields in digital medicine are prospering, still there are limitations that needs to be overcome in order to provide further advanced health services to patients in the various situations. In digital pathology, improvements of microscopic technologies, internets, and storage capabilities have reduced the time-consuming processes. The simple transformation of microscopic image to digital have successfully alternated many limitations in the analogue histopathology workflow to efficient and cost saving ways. However, tissue staining is currently referred as one of the bottleneck that makes workflow still lengthy, labor-intensive, and costly. In the POC diagnostic fields, various digitalized portable smartphone-based diagnostic devices have been introduced as alternatives to conventional medical services. These devices have provided the quality assurance of diagnostics by taking advantages of sharing, and quantitative analysis of digital information. However, most of these works have been focused on replacing diagnostic process which mostly done in laboratory settings. As medical imaging devices and trained clinicians or practitioners are limited, there are also high demands on clinical imaging-based diagnostics in developing countries. In this thesis, computational microscope using patterned NIR illumination was developed for label-free quantitative differential phase tissue imaging to bypass the staining process of the pathology workflow. This system overcame the limitations found in the conventional quantitative differential phase contrast in a LED array microscope, allowing to captured light scattering and absorbing specimen while maintaining weak object approximation. Moreover, portable endoscope system was developed integrating the additive production technologies (3D printing), ICT, and optics for POC diagnostics. This innovative POC endoscope demonstrated comparable imaging capability to that of commercialized clinical endoscope system. Furthermore, deep learning and machine learning models have been trained and applied to each devices, respectively. Generative adversarial network (GAN) was applied to our NIR-based QPI system to virtually stain the label-free QPI which look comparable to image that is captured from bright field microscope using labeled tissue. Lastly, POC automated cervical cancer screening system was developed utilizing smartphone-based endoscope system as well as training the machine learning algorithm. 3-5% of acetic acid was applied to the suspicious lesion and its reaction was captured before and after application using smartphone endoscope. This screening system enables to extract the features of cancers and informs the possibility of cancer from endoscopic images.clos

Quantitative Screening of Cervical Cancers for Low-Resource Settings: Pilot Study of Smartphone-Based Endoscopic Visual Inspection After Acetic Acid Using Machine Learning Techniques

Background: Approximately 90% of global cervical cancer (CC) is mostly found in low- and middle-income countries. In most cases, CC can be detected early through routine screening programs, including a cytology-based test. However, it is logistically difficult to offer this program in low-resource settings due to limited resources and infrastructure, and few trained experts. A visual inspection following the application of acetic acid (VIA) has been widely promoted and is routinely recommended as a viable form of CC screening in resource-constrained countries. Digital images of the cervix have been acquired during VIA procedure with better quality assurance and visualization, leading to higher diagnostic accuracy and reduction of the variability of detection rate. However, a colposcope is bulky, expensive, electricity-dependent, and needs routine maintenance, and to confirm the grade of abnormality through its images, a specialist must be present. Recently, smartphone-based imaging systems have made a significant impact on the practice of medicine by offering a cost-effective, rapid, and noninvasive method of evaluation. Furthermore, computer-aided analyses, including image processing-based methods and machine learning techniques, have also shown great potential for a high impact on medicinal evaluations

Discriminative Feature Learning for Unsupervised Video Summarization

In this paper, we address the problem of unsupervised video summarization that automatically extracts key-shots from an input video. Specifically, we tackle two critical issues based on our empirical observations: (i) Ineffective feature learning due to flat distributions of output importance scores for each frame, and (ii) training difficulty when dealing with long-length video inputs. To alleviate the first problem, we propose a simple yet effective regularization loss term called variance loss. The proposed variance loss allows a network to predict output scores for each frame with high discrepancy which enables effective feature learning and significantly improves model performance. For the second problem, we design a novel two-stream network named Chunk and Stride Network (CSNet) that utilizes local (chunk) and global (stride) temporal view on the video features. Our CSNet gives better summarization results for long-length videos compared to the existing methods. In addition, we introduce an attention mechanism to handle the dynamic information in videos. We demonstrate the effectiveness of the proposed methods by conducting extensive ablation studies and show that our final model achieves new state-of-the-art results on two benchmark datasets.Comment: Accepted to AAAI 2019 !!

Hide-and-Tell: Learning to Bridge Photo Streams for Visual Storytelling

Visual storytelling is a task of creating a short story based on photo streams. Unlike existing visual captioning, storytelling aims to contain not only factual descriptions, but also human-like narration and semantics. However, the VIST dataset consists only of a small, fixed number of photos per story. Therefore, the main challenge of visual storytelling is to fill in the visual gap between photos with narrative and imaginative story. In this paper, we propose to explicitly learn to imagine a storyline that bridges the visual gap. During training, one or more photos is randomly omitted from the input stack, and we train the network to produce a full plausible story even with missing photo(s). Furthermore, we propose for visual storytelling a hide-and-tell model, which is designed to learn non-local relations across the photo streams and to refine and improve conventional RNN-based models. In experiments, we show that our scheme of hide-and-tell, and the network design are indeed effective at storytelling, and that our model outperforms previous state-of-the-art methods in automatic metrics. Finally, we qualitatively show the learned ability to interpolate storyline over visual gaps.Comment: AAAI 2020 pape

Lamellar keratoplasty using position-guided surgical needle and M-mode optical coherence tomography

Deep anterior lamellar keratoplasty (DALK) is an emerging surgical technique for the restoration of corneal clarity and vision acuity. The big-bubble technique in DALK surgery is the most essential procedure that includes the air injection through a thin syringe needle to separate the dysfunctional region of the cornea. Even though DALK is a well-known transplant method, it is still challenged to manipulate the needle inside the cornea under the surgical microscope, which varies its surgical yield. Here, we introduce the DALK protocol based on the position-guided needle and M-mode optical coherence tomography (OCT). Depth-resolved 26-gage needle was specially designed, fabricated by the stepwise transitional core fiber, and integrated with the swept source OCT system. Since our device is feasible to provide both the position information inside the cornea as well as air injection, it enables the accurate management of bubble formation during DALK. Our results show that real-time feedback of needle end position was intuitionally visualized and fast enough to adjust the location of the needle. Through our research, we realized that position-guided needle combined with M-mode OCT is a very efficient and promising surgical tool, which also to enhance the accuracy and stability of DALK

Comparison of Internal and Total Optical Aberrations for 2 Aberrometers: iTrace and OPD Scan

PURPOSE: To compare and evaluate the total and internal aberrations measured by two aberrometers: the laser ray tracing aberrometer (iTrace, Tracey Technology) and the automatic retinoscope aberrometer (OPD Scan, Nidek). METHODS: A total of 54 healthy eyes were enrolled in the study. Following pupil dilation, aberrations were measured with the iTrace and OPD Scan. We compared the aberrations obtained from measurements obtained at pupillary diameters of 4 mm and 6 mm with the OPD Scan and iTrace. Aberrations of internal optics and total aberrations were compared for the two aberrometers. For each aberrometer and each eye, the averaged Zernike data were used to calculate various root-mean-square (RMS) data. These parameters, together with the refractive parameters, were then analyzed and complimented by paired t-tests. RESULTS: At a pupil diameter of 4 mm, the number of total aberrations in the entire eye showed significant differences for the mean values of spherical aberrations (Z4,0) obtained with the OPD Scan and iTrace aberrometers (p=0.001). Aberrations of the internal optics showed significant differences in the mean values of total RMS, coma (Z3,-1), and trefoil (Z3,3) between the iTrace and OPD Scan (p<0.001, p=0.01, p<0.001) for the same pupil diameter of 4 mm. At a pupil diameter of 6 mm, the two instruments showed a similar number of total aberrations. Aberrations of the internal optics showed significant differences in the mean values of total RMS, spherical aberration (Z4,0), and coma (Z3,-1) between the two devices (p<0.001, p=0.01, p<0.001). CONCLUSIONS: The iTrace and OPD Scan showed the largest number of differences for aberrations of internal optics rather than total aberrations for both pupil diameters. These results suggest that in healthy eyes, the two aberrometers may vary in some details. The aberrometers showed more agreement at a pupil diameter of 6 mm compared to 4 mmope

Isolation and characterization of an arsenate-reducing bacterium and its application for arsenic extraction from contaminated soil

A Gram-negative anaerobic bacterium, Citrobacter sp. NC-1, was isolated from soil contaminated with arsenic at levels as high as 5,000 mg As kg−1. Strain NC-1 completely reduced 20 mM arsenate within 24 h and exhibited arsenate-reducing activity at concentrations as high as 60 mM. These results indicate that strain NC-1 is superior to other dissimilatory arsenate-reducing bacteria with respect to arsenate reduction, particularly at high concentrations. Strain NC-1 was also able to effectively extract arsenic from contaminated soils via the reduction of solid-phase arsenate to arsenite, which is much less adsorptive than arsenate. To characterize the reductase systems in strain NC-1, arsenate and nitrate reduction activities were investigated using washed-cell suspensions and crude cell extracts from cells grown on arsenate or nitrate. These reductase activities were induced individually by the two electron acceptors. This may be advantageous during bioremediation processes in which both contaminants are present