Machine Learning Methods for Mood Prediction Using Data from Smartphones and Wearables

HonorsStatisticsUniversity of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/162673/1/smengjie.pd

Entanglement-enhanced dual-comb spectroscopy

Dual-comb interferometry harnesses the interference of two laser frequency comb to provide unprecedented capability in spectroscopy applications. In the past decade, the state-of-the-art systems have reached a point where the signal-to-noise ratio per unit acquisition time is fundamentally limited by shot noise from vacuum fluctuations. To address the issue, we propose an entanglement-enhanced dual comb spectroscopy protocol that leverages quantum resources to significantly improve the signal-to-noise ratio performance. To analyze the performance of real systems, we develop a quantum model of dual-comb spectroscopy that takes practical noises into consideration. Based on this model, we propose quantum combs with side-band entanglement around each comb lines to suppress the shot noise in heterodyne detection. Our results show significant quantum advantages in the uW to mW power range, making this technique particularly attractive for biological and chemical sensing applications. Furthermore, the quantum comb can be engineered using nonlinear optics and promises near-term experimentation.Comment: 10+2 pages, 5 figures; typos corrected in v

Improving needle visibility in LED-based photoacoustic imaging using deep learning with semi-synthetic datasets

Photoacoustic imaging has shown great potential for guiding minimally invasive procedures by accurate identification of critical tissue targets and invasive medical devices (such as metallic needles). The use of light emitting diodes (LEDs) as the excitation light sources accelerates its clinical translation owing to its high affordability and portability. However, needle visibility in LED-based photoacoustic imaging is compromised primarily due to its low optical fluence. In this work, we propose a deep learning framework based on U-Net to improve the visibility of clinical metallic needles with a LED-based photoacoustic and ultrasound imaging system. To address the complexity of capturing ground truth for real data and the poor realism of purely simulated data, this framework included the generation of semi-synthetic training datasets combining both simulated data to represent features from the needles and in vivo measurements for tissue background. Evaluation of the trained neural network was performed with needle insertions into blood-vessel-mimicking phantoms, pork joint tissue ex vivo and measurements on human volunteers. This deep learning-based framework substantially improved the needle visibility in photoacoustic imaging in vivo compared to conventional reconstruction by suppressing background noise and image artefacts, achieving 5.8 and 4.5 times improvements in terms of signal-to-noise ratio and the modified Hausdorff distance, respectively. Thus, the proposed framework could be helpful for reducing complications during percutaneous needle insertions by accurate identification of clinical needles in photoacoustic imaging

Spatial distribution of job opportunities in China: Evidence from the opening of the high-speed rail

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The provision of sufficient job opportunities has traditionally been a primary objective for both local and central governments. In response to this concern, we investigate spatial dependence of job opportunities among 30 Chinese provincial capital cities (PCCs) from 2002 to 2016, giving special attention to the spatial spillovers of the opening of the high-speed rail (HSR). Using appropriate spatial panel data models, our findings suggest the presence of significant spatial autocorrelation of job opportunities among PCCs. Whilst the HSR has been found to increase job opportunities at the national level, which, however, is not confirmed at the regional level. The spatial spillover effects of the HSR are significant and positive only in the eastern/northeastern region. These findings can help the central government to more fully understand spatial dependence of job opportunities, better plan future HSR networks, and efficiently allocate transportation resources, encouraging cross-regional collaboration to promote regional employment

Enhanced Photoacoustic Visualisation of Clinical Needles by Combining Interstitial and Extracorporeal Illumination of Elastomeric Nanocomposite Coatings

Ultrasound (US) image guidance is widely used for minimally invasive procedures, but the invasive medical devices (such as metallic needles), especially their tips, can be poorly visualised in US images, leading to significant complications. Photoacoustic (PA) imaging is promising for visualising invasive devices and peripheral tissue targets. Light-emitting diodes (LEDs) acting as PA excitation sources facilitate the clinical translation of PA imaging, but the image quality is degraded due to the low pulse energy leading to insufficient contrast with needles at deep locations. In this paper, photoacoustic visualisation of clinical needles was enhanced by elastomeric nanocomposite coatings with superficial and interstitial illumination. Candle soot nanoparticle-polydimethylsiloxane (CSNP-PDMS) composites with high optical absorption and large thermal expansion coefficients were applied onto the needle exterior and the end-face of an optical fibre placed in the needle lumen. The excitation light was delivered at the surface by LED arrays and through the embedded optical fibre by a pulsed diode laser to improve the visibility of the needle tip. The performance was validated using an ex-vivo tissue model. An LED-based PA/US imaging system was used for imaging the needle out-of-plane and in-plane insertions over approach angles of 20 deg to 55 deg. The CSNP-PDMS composite conferred substantial visual enhancements on both the needle shaft and the tip, with an average of 1.7- and 1.6-fold improvements in signal-to-noise ratios (SNRs), respectively. With the extended light field involving extracorporeal and interstitial illumination and the highly absorbing coatings, enhanced visualisation of the needle shaft and needle tip was achieved with PA imaging, which could be helpful in current US-guided minimally invasive surgeries

Novi VP2/VP3 rekombinantni senekavirus A izoliran u sjevernoj Kini

Senecavirus A (SVA), previously called the Seneca Valley virus, is the only member of the genus Senecavirus within the family Picornaviridae. This virus was discovered as a serendipitous finding in 2002 and named Seneca Valley virus 001 (SVV-001). SVA is an emerging pathogen that can cause vesicular lesions and epidemic transient neonatal a sharp decline in swine. In this study, an SVA strain was isolated from a pig herd in Shandong Province in China and identified as SVA-CH-SDFX-2022. The full-length genome was 7282 nucleotides (nt) in length and contained a single open reading frame (ORF), excluding the poly (A) tails of the SVA isolates. Phylogenetic analysis showed that the isolate shares its genomic organization, resembling and sharing high nucleotide identities of 90.5% to 99.6%, with other previously reported SVA isolates. The strain was proved by in vitro characterization and the results demonstrate that the virus has robust growth ability in vitro. The recombination event of the SVA-CH-SDFX-2022 isolate was found and occurred between nts 1836 and 2710, which included the region of the VP2 (partial), and VP3 (partial) genes. It shows the importance of faster vaccine development and a better understanding of virus infection and spread because of increased infection rates and huge economic losses. This novel incursion has substantial implications for the regional control of vesicular transboundary diseases, and will be available for further study of the epidemiology of porcine SVA. Our findings provide useful data for studying SVA in pigs.Senekavirus A (SVA), prije nazivan virusom doline Seneca Valley, jedini je pripadnik roda senekavirusa u porodici Picornaviridae. Virus je slučajno otkriven 2002. i nazvan virusom doline Seneca 001 (SVV-001). SVA je novi patogen koji može uzrokovati vezikularne lezije i prolaznu epidemiju novorođene prasadi s naglim gubicima u proizvodnji. U ovom je istraživanju soj SVA izoliran u populaciji svinja iz provincije Shandong u Kini i identificiran kao SVA-CHSDFX-2022. Kompletni genom izolata SVA imao je 7282 nukleotida (nt) u dužini i sadržavao je jedan otvoreni okvir za očitavanje (ORF), bez poli-A repova. Filogenetska je analiza pokazala da izolat u velikoj mjeri sadržava genomsku organizaciju i nukleotidne identitete, od 90,5 % do 99,6 %, s drugim poznatim SVA izolatima. Karakterizacija virusa je pokazala da ima veliku sposobnost rasta in vitro. Pronađena je rekombinacija izolata SVA-CH-SDFX-između nukleotida 1836 i 2710 što je uključilo regiju gena VP2 (parcijalno) i gena VP3 (parcijalno). Zbog visoke stope infektivnosti i golemih ekonomskih gubitaka važan je brži razvoj cjepiva i bolje razumijevanje zaraze. Rezultati ovog istraživanja pružaju korisne podatke za proučavanje SVA virusa, posebno s obzirom na njegovu epidemiologiju u svinja i regionalnu prekograničnu kontrolu vezikularnih bolesti

Health care costs of cardiovascular disease in China: a machine learning-based cross-sectional study

BackgroundCardiovascular disease (CVD) causes substantial financial burden to patients with the condition, their households, and the healthcare system in China. Health care costs for treating patients with CVD vary significantly, but little is known about the factors associated with the cost variation. This study aims to identify and rank key determinants of health care costs in patients with CVD in China and to assess their effects on health care costs.MethodsData were from a survey of patients with CVD from 14 large tertiary grade-A general hospitals in S City, China, between 2018 and 2020. The survey included information on demographic characteristics, health conditions and comorbidities, medical service utilization, and health care costs. We used re-centered influence function regression to examine health care cost concentration, decomposing and estimating the effects of relevant factors on the distribution of costs. We also applied quantile regression forests—a machine learning approach—to identify the key factors for predicting the 10th (low), 50th (median), and 90th (high) quantiles of health care costs associated with CVD treatment.ResultsOur sample included 28,213 patients with CVD. The 10th, 50th and 90th quantiles of health care cost for patients with CVD were 6,103 CNY, 18,105 CNY, and 98,637 CNY, respectively. Patients with high health care costs were more likely to be older, male, and have a longer length of hospital stay, more comorbidities, more complex medical procedures, and emergency admissions. Higher health care costs were also associated with specific CVD types such as cardiomyopathy, heart failure, and stroke.ConclusionMachine learning methods are useful tools to identify determinants of health care costs for patients with CVD in China. Findings may help improve policymaking to alleviate the financial burden of CVD, particularly among patients with high health care costs

Chronic Myeloid Leukemia Patients Sensitive and Resistant to Imatinib Treatment Show Different Metabolic Responses

The BCR-ABL tyrosine kinase inhibitor imatinib is highly effective for chronic myeloid leukemia (CML). However, some patients gradually develop resistance to imatinib, resulting in therapeutic failure. Metabonomic and genomic profiling of patients' responses to drug interventions can provide novel information about the in vivo metabolism of low-molecular-weight compounds and extend our insight into the mechanism of drug resistance. Based on a multi-platform of high-throughput metabonomics, SNP array analysis, karyotype and mutation, the metabolic phenotypes and genomic polymorphisms of CML patients and their diverse responses to imatinib were characterized. The untreated CML patients (UCML) showed different metabolic patterns from those of healthy controls, and the discriminatory metabolites suggested the perturbed metabolism of the urea cycle, tricarboxylic acid cycle, lipid metabolism, and amino acid turnover in UCML. After imatinib treatment, patients sensitive to imatinib (SCML) and patients resistant to imatinib (RCML) had similar metabolic phenotypes to those of healthy controls and UCML, respectively. SCML showed a significant metabolic response to imatinib, with marked restoration of the perturbed metabolism. Most of the metabolites characterizing CML were adjusted to normal levels, including the intermediates of the urea cycle and tricarboxylic acid cycle (TCA). In contrast, neither cytogenetic nor metabonomic analysis indicated any positive response to imatinib in RCML. We report for the first time the associated genetic and metabonomic responses of CML patients to imatinib and show that the perturbed in vivo metabolism of UCML is independent of imatinib treatment in resistant patients. Thus, metabonomics can potentially characterize patients' sensitivity or resistance to drug intervention