Miracle-dz/UniFAR-FTUs huggingface dataset:
Variation in adverse drug events of opioids in the United States

Background: The United States (US) ranks high, nationally, in opioid consumption. The ongoing increase in the misuse and mortality amid the opioid epidemic has been contributing to its rising cost. The worsening health and economic impact of opioid use disorder in the US warrants further attention. We, therefore, assessed commonly prescribed opioids to determine the opioids that were over-represented versus under-represented for adverse drug events (ADEs) to better understand their distribution patterns using the Food and Drug Administration’s Adverse Event Reporting System (FAERS) while correcting for distribution using the Drug Enforcement Administration’s Automation of Reports and Consolidated Orders System (ARCOS). Comparing the ratio of the percentage of adverse drug events as reported by the FAERS relative to the percentage of distribution as reported by the ARCOS database is a novel approach to evaluate post-marketing safety surveillance and may inform healthcare policies and providers to better regulate the use of these opioids. Methods: We analyzed the adverse events for 11 prescription opioids, when correcting for distribution, and their ratios for three periods, 2006–2010, 2011–2016, and 2017–2021, in the US. The opioids include buprenorphine, codeine, fentanyl, hydrocodone, hydromorphone, meperidine, methadone, morphine, oxycodone, oxymorphone, and tapentadol. Oral morphine milligram equivalents (MMEs) were calculated by conversions relative to morphine. The relative ADEs of the selected opioids, opioid distributions, and ADEs relative to distribution ratios were analyzed for the 11 opioids. Results: Oxycodone, fentanyl, and morphine accounted for over half of the total number of ADEs (n = 667,969), while meperidine accounted for less than 1%. Opioid distributions were relatively constant over time, with methadone repeatedly accounting for the largest proportions. Many ADE-to-opioid distribution ratios increased over time, with meperidine (60.6), oxymorphone (11.1), tapentadol (10.3), and hydromorphone (7.9) being the most over-represented for ADEs in the most recent period. Methadone was under-represented (<0.20) in all the three periods. Conclusion: The use of the FAERS with the ARCOS provides insights into dynamic changes in ADEs of the selected opioids in the US. There is further need to monitor and address the ADEs of these drugs.
A Novel SiC Device Model With Power Stage and Gate Driver Uncoupled

Silicon Carbide (SiC) power transistors have fast switching speed but are more inclined to be affected by parasitics. Miller capacitor (<italic>C<sub>gd</sub></italic>) and common source inductor (<inline-formula><tex-math notation="LaTeX">$ L_{\textit{scom}} $</tex-math></inline-formula>) are two major cross coupled parameters across gate driver loop and power loop, which cause overshoots, crosstalk and decreased switching speed. To ensure a reliable and efficient switching, the impacts of <italic>C<sub>gd</sub></italic> and <inline-formula><tex-math notation="LaTeX">$ L_{\textit{scom}} $</tex-math></inline-formula> need to be identified and fully compensated in the gate driver. However, state-of-arts failed to provide a straightforward compensation approach with the coupling effects extracted and un-coupled model accurately represented. In this paper, a novel SiC device model with decoupled equivalent miller capacitance and common-mode inductance extracted for both gate driver loop and power loop is presented following the Miller theorem in micro-electronics. The decoupled equivalent circuit is verified to have decent accuracy in predicting the actual SiC switching transients. Furthermore, this paper proposed an improved resonant gate driver (RGD) design using the proposed decoupling model under the effects of common-mode inductance. The pre-charging current and driving pulse width are adjusted to ensure a full resonance driving transient with <inline-formula><tex-math notation="LaTeX">$ L_{\textit{scom}} $</tex-math></inline-formula>. The experimental results show that impact of <inline-formula><tex-math notation="LaTeX">$ L_{\textit{scom}} $</tex-math></inline-formula> is fully compensated, where SiC's <italic>di<sub>ds</sub>/dt</italic> is improved from 1.88 A/ns to 2.102 A/ns, which is close to the results without <inline-formula><tex-math notation="LaTeX">$ L_{\textit{scom}} $</tex-math></inline-formula>.
The application of deep learning for COVID-19 diagnosis and treatment

The coronavirus disease 2019 (COVID-19) pandemic has resulted in a considerable increase in hospitalizations, leading to an increasing demand for accurate and efficient techniques for diagnosis. The CT-based diagnosis can provide pathologic information to assist treatment but be restricted due to inefficient and relatively complicated implementation. With the advent of deep learning and advanced hardware, an AI-assisted method diagnosis and segmentation for COVID-19 are proposed. In this paper, many traditional machine learning methods for imaging classification and segmentation are discussed, such as k-Nearest Neighbours (KNN), support vector machines (SVM), edge-based or region-based segmentation. In addition, we proposed a ResNet-based model and an improved U-Net for medical tasks of classification and segmentation, respectively. Our proposed model achieved desirable accuracy in medical applications.
A SEMISMOOTH NEWTON-CG AUGMENTED LAGRANGIAN METHOD FOR LARGE SCALE LINEAR AND CONVEX QUADRATIC SDPS

2009 ii Acknowledgements I hope to express my gratitude towards a number of people who have supported and encouraged me in the work of this thesis. First and foremost, I would like to express my deepest respect and most sincere gratitude to my advisor Professor Toh Kim Chuan. He has done a lot for me in the last six years, since I signed up with him as a masters student in my first year, curious to learn more about optimization. With his endless supply of fresh ideas and openness to looking at new problems in different areas, his guidance has proved to be indispensable to my research. I will always remember the patient guidance, encouragement and advice he has provided throughout my time as his student. My deepest thanks to my co-advisor Professor Defeng Sun, for his patient introducing me into the field of convex optimization, for his enthusiasm about discussing mathematical issues and for the large amount of time he devoted to my concerns. This work would not have been possible without his amazing depth of knowledge and tireless enthusiasm. I am very fortunate to have had the opportunity to work with him. My grateful thanks also go to Professor Gongyun Zhao for his courses on numerical optimization. It was his unique style of teaching that enrich my knowledge in optimization algorithms and software. I am much obliged to my group members of optimization in mathematical department. Their enlightening suggestions and encouragements made me feel I was not isolated in my research. I feel very lucky to have been a part of this group, and I will cherish the memories of my time with them.gapore provided excellent working conditions and support that providing the Scholarship which allowed me to undertake this research and complete my thesis. I am as ever, especially indebted to my parents, for their unquestioning love and support encouraged me to complete this work. Last but not least, I am also greatly indebted to my husband not only for his constant encouragement but also for his patience and understanding throughout the years of my research.
Long-Term Oxidation of Zirconium Alloy in Simulated Nuclear Reactor Primary Coolant—Experiments and Modeling

Oxidation of Zr-1%Nb fuel cladding alloy in simulated primary coolant of a pressurized water nuclear reactor is followed by in-situ electrochemical impedance spectroscopy. In-depth composition and thickness of the oxide are estimated by ex-situ analytical techniques. A kinetic model of the oxidation process featuring interfacial reactions of metal oxidation and water reduction, as well as electron and ion transport through the oxide governed by diffusion-migration, is parameterized by quantitative comparison to impedance data. The effects of compressive stress on diffusion and ionic space charge on migration of ionic point defects are introduced to rationalize the dependence of transport parameters on thickness (or oxidation time). The influence of ex-situ and in-situ hydrogen charging on kinetic and transport parameters is also studied.
Segmentation of ultrasound breast images based on a neutrosophic method

Breast cancer is one of the leading cancers of women. Ultrasound is often used for breast cancer diagnosis because it is harmless, portable, and low-cost. However, the segmentation of breast ultrasound (BUS) images is a difficult task due to their low contrast and speckle noise. Neutrosophy studies the origin, nature, and scope of neutralities and their interactions with different ideational spectra. It is a new philosophy to extend fuzzy logic and is the basis of neutrosophic logic, neutrosophic probability theory, neutrosophic set theory, and neutrosophic statistics. In this paper, we employ neutrosophy and develop a fully automatic algorithm for BUS image segmentation. By using neutrosophy, we integrate two conflicting opinions about speckle in ultrasound image: speckle is noise and speckle includes pattern information. The experiments demonstrate that the proposed approach is accurate, effective, and robust.
PathwayKO: An integrated platform for deciphering the systems-level signaling pathways

Systems characterization of immune landscapes in health, disease and clinical intervention cases is a priority in modern medicine. High-throughput transcriptomes accumulated from gene-knockout (KO) experiments are crucial for deciphering target KO signaling pathways that are impaired by KO genes at the systems-level. There is a demand for integrative platforms. This article describes the PathwayKO platform, which has integrated state-of-the-art methods of pathway enrichment analysis, statistics analysis, and visualizing analysis to conduct cutting-edge integrative pathway analysis in a pipeline fashion and decipher target KO signaling pathways at the systems-level. We focus on describing the methodology, principles and application features of PathwayKO. First, we demonstrate that the PathwayKO platform can be utilized to comprehensively analyze real-world mouse KO transcriptomes (GSE22873 and GSE24327), which reveal systemic mechanisms underlying the innate immune responses triggered by non-infectious extensive hepatectomy (2 hours after 85% liver resection surgery) and infectious CASP-model sepsis (12 hours after CASP-model surgery). Strikingly, our results indicate that both cases hit the same core set of 21 KO MyD88-associated signaling pathways, including the Toll-like receptor signaling pathway, the NFκB signaling pathway, the MAPK signaling pathway, and the PD-L1 expression and PD-1 checkpoint pathway in cancer, alongside the pathways of bacterial, viral and parasitic infections. These findings suggest common fundamental mechanisms between these immune responses and offer informative cues that warrant future experimental validation. Such mechanisms in mice may serve as models for humans and ultimately guide formulating the research paradigms and composite strategies to reduce the high mortality rates of patients in intensive care units who have undergone successful traumatic surgical treatments. Second, we demonstrate that the PathwayKO platform model-based assessments can effectively evaluate the performance difference of pathway analysis methods when benchmarked with a collection of proper transcriptomes. Together, such advances in methods for deciphering biological insights at the systems-level may benefit the fields of bioinformatics, systems immunology and beyond.
Correction of distributed phase aberrations caused by propagation-speed and array-pitch errors

When an incorrectly assumed array pitch or propagation speed is used to form an image, phase aberrations are generated even in a homogeneous medium, and they are distributed. That is, at different image points the phase aberration profiles are different. Even though array pitch and propagation speed are usually known quite accurately, it is important and interesting to test whether a phase aberration correction algorithm is capable of correcting distributed phase aberrations caused by incorrect array pitch and propagation speed values. It was demonstrated that the Near Field Signal Redundancy (NFSR) algorithm is able to correct these kinds of phase aberrations with data from a linear array. However, only a single phase-aberration profile was measured and the image quality after correction was only improved in a small region. In this paper we present new results from a phased array where multiple phase aberration profiles have been measured and the whole image has been corrected. These results have further demonstrated that the NFSR algorithm is capable of correcting distributed phase aberrations caused by array pitch and propagation speed errors.
A Meta-Analysis of the Prevalence of Wheat Allergy Worldwide

Wheat allergy is a primary disease of food allergy, and its global prevalence is unclear. This study aimed to characterize the latest worldwide prevalence of wheat allergy based on five different diagnostic methods. Study searches were conducted in Web of Science, PubMed, Ovid LWW, and Cochrane database, with a time limit of 1 January 2007 to 1 September 2022. The review and screening of the articles was undertaken by two independent reviewers. The statistical analysis was conducted by R. A total of 56 articles were finally included. The prevalence of wheat allergy was 0.63% (95% CI: 0.43–0.87%) for self-reported, 0.70% (95% CI: 0.18–1.22%) for self-reported physician-diagnosed, 0.22% (95%CI: 0.07–0.65%) for skin prick test positive, 0.97% (95% CI: 0.43–2.20%) for specific immunoglobulin E positive, and 0.04% (95% CI: 0–0.16%) for food challenge. However, food challenge can be largely subjective, and the results were only based two countries, so the prevalence of wheat allergy confirmed by food challenge may be not entirely trustworthy. In conclusion, investigating the prevalence of wheat allergy in the real world as accurately as possible will contribute to the prevention, management, and risk assessment of wheat allergy.
Exploiting volume electron microscopy to investigate structural plasticity and stability of the postsynaptic compartment of central synapses

Volume reconstruction from electron microscopy datasets is a tool increasingly used to study the ultrastructure of the synapse in the broader context of neuronal network and brain organization. Fine modifications of synapse structure, such as activity-dependent dendritic spine enlargement and changes in the size and shape of the postsynaptic density, occur upon maturation and plasticity. The lack of structural plasticity or the inability to stabilize potentiated synapses are associated with synaptic and neuronal functional impairment. Mapping these rearrangements with the high resolution of electron microscopy proved to be essential in order to establish precise correlations between the geometry of synapses and their functional states. In this review we discuss recent discoveries on the substructure of the postsynaptic compartment of central excitatory synapses and how those are correlated with functional states of the neuronal network. The added value of volume electron microscopy analyses with respect to conventional transmission electron microscopy studies is highlighted considering that some limitations of volume-based methods imposed several adjustments to describe the geometry of this synaptic compartment and new parameters–that are good indicators of synapses strength and activity–have been introduced.
Optimal matching problems with costs given by Finsler distances

In this paper we deal with an optimal matching problem, that is, we want to transport two commodities (modeled by two measures that encode the spacial distribution of each commodity) to a given location, where they will match, minimizing the total transport cost that in our case is given by the sum of the two different Finsler distances that the two measures are transported. We perform a method to approximate the matching measure and the pair of Kantorovich potentials associated with this problem taking limit as $p\to \infty$ in a variational system of $p-$Laplacian type.
3-D eddy current analysis in permanent magnet of interior permanent magnet motors

Interior permanent magnet motors are widely used in various fields. However, in high-speed operations, it is important to decrease the eddy current loss in the permanent magnet. Therefore, we propose to divide the permanent magnet. This paper describes a practical technique to analyze the eddy current in the divided magnet. The effect of division of permanent magnet on eddy current loss is also quantitatively clarified.
Edge Preserved Low-Rank SAR Image Despeckling via Hierarchical Prior Knowledge Regulation

Synthetic aperture radar (SAR) image despeckling is a challenging task as speckle noise is spatially correlated and signal-dependent, and appears as a grainy texture superimposed on images. Although traditional low-rank SAR image despeckling methods have shown promising performance, they have the problem of producing oversmoothed images with blurred edges due to their low-rank characteristics. In this article, we propose a novel edge preserved SAR despeckling method named edge preserved nonlocal low-rank SAR image despeckling (EP-LRSID), which can keep rich edge details while reducing speckle noise. Specifically, EP-LRSID takes a fresh look at the low-rank model, i.e., we can obtain structural edge information from residuals, which is viewed as noise and simply disregarded by the traditional low-rank methods. To obtain discriminative edge information from residuals, the edge subspace is obtained in a manifold framework by using a dynamic affinity graph regularization. Moreover, a new hierarchical prior knowledge regulation is designed to make different kinds of pixels processed hierarchically, especially the strong scattering points in SAR images. By introducing this prior knowledge, our low-rank model can obtain more confidential low-rank parts and edge parts, and thus, structural information, including edges, can be better preserved in this way. Extensive experiments on several real and synthetic datasets demonstrate that EP-LRSID can achieve the highest despeckling performance with edge preservation than other state-of-the-art despeckling algorithms.
Is Omega Squared Less Biased? a Comparison of Three Major Effect Size Indices in One-Way Anova

The purpose of this study is to find less biased effect size index in one-way analysis of variance (ANOVA) by performing a thorough Monte Carlo study with 1,000,000 replications per condition. Our results show that contrary to common belief, epsilon squared is the least biased among the threemajorindices, while omega squared produces the least root mean squared errors, for all conditions. Although eta squared results in the least standard deviation, this does not necessarily make it a good estimator because a considerable amount of bias still occurs when the sample size is small.
Discriminant Analysis When the Initial Samples are Misclassified II: Non-Random Misclassification Models

Two models of non-random initial misclassifications are studied. In these models, observations which are closer to the mean of the “wrong” population have a greater chance of being misclassified than others. Sampling studies show that (a) the actual error rates of the rules from samples with initial misclassification are only slightly affected; (b) the apparent error rates, obtained by resubstituting the observations into the calculated discriminant function, are drastically affected, and cannot be used; and (c) the Mahalanobis D 2 is greatly inflated.
Predicting Undesired Treatment Outcomes With Machine Learning in Mental Health Care: Multisite Study

Background Predicting which treatment will work for which patient in mental health care remains a challenge. Objective The aim of this multisite study was 2-fold: (1) to predict patients’ response to treatment in Dutch basic mental health care using commonly available data from routine care and (2) to compare the performance of these machine learning models across three different mental health care organizations in the Netherlands by using clinically interpretable models. Methods Using anonymized data sets from three different mental health care organizations in the Netherlands (n=6452), we applied a least absolute shrinkage and selection operator regression 3 times to predict the treatment outcome. The algorithms were internally validated with cross-validation within each site and externally validated on the data from the other sites. Results The performance of the algorithms, measured by the area under the curve of the internal validations as well as the corresponding external validations, ranged from 0.77 to 0.80. Conclusions Machine learning models provide a robust and generalizable approach in automated risk signaling technology to identify cases at risk of poor treatment outcomes. The results of this study hold substantial implications for clinical practice by demonstrating that the performance of a model derived from one site is similar when applied to another site (ie, good external validation).
A Comprehensive Defense Framework Against Model Extraction Attacks

As a promising service, Machine Learning as a Service (MLaaS) provides personalized inference functions for clients through paid APIs. Nevertheless, it is vulnerable to model extraction attacks, in which an attacker can extract a functionally-equivalent model by repeatedly querying the APIs with crafted samples. While numerous works have been proposed to defend against model extraction attacks, existing efforts are accompanied by limitations and low comprehensiveness. In this article, we propose AMAO, a comprehensive defense framework against model extraction attacks. Specifically, AMAO consists of four interlinked successive phases: adversarial training is first exploited to weaken the effectiveness of model extraction attacks. Then, malicious query detection is used to detect malicious queries and mark malicious users. After that, we develop a label-flipping poisoning attack to instruct the adaptive query responses to malicious users. Besides, the image pHash algorithm is employed to ensure the indistinguishability of the query responses. Finally, the perturbed results are served as a backdoor to verify the ownership of any suspicious model. Extensive experiments demonstrate that AMAO outperforms existing defenses in defending against model extraction attacks and is also robust against the adaptive adversary who is aware of the defense.
CURVATURE BOUND AND TRAJECTORIES FOR MAGNETIC FIELDS ON A HADAMARD SURFACE

On acomplete oriented Riemannian manifold M, a closed 2-form B is called a magnetic field.Let Q denote the skew symmetric operator Q,:TM ―>TM definedby (u,Q(v)) = B(u, v) for every u, veTM. We calla smooth curve-7 a trajectory for J? if it satisfiesthe equation V^7 = Q(y). Since O is skew symmetric, we findthatevery trajectory has constant speed and is defined for ―00 R the strength of the magnetic fieldat the point x. For the trivialmagnetic field B = 0, the case without the force of a magnetic field, trajectoriesare nothing but geodesies. In term of physics itis a trajectoryof a charged particleunder theactionof the magnetic field.For a classicaltreatment and physical meaning of magnetic fieldssee[8].