bluuebunny/crossref_metadata_2025_split huggingface dataset:
Emerging roles of microRNA‐208a in cardiology and reverse cardio‐oncology
Authors: Wang Xiang , Chen Xinxin , Xu Hui , Zhou Shanshan , Zheng Yang , Keller Bradley B. , Cai Lu 
Published: February 2021

AbstractCardiovascular diseases (CVDs) and cancer, which are the leading causes of mortality globally, have been viewed as two distinct diseases. However, the fact that cancer and CVDs may coincide has been noted by cardiologists when taking care of patients with CVDs caused by cancer chemotherapy; this entity is designated cardio‐oncology. More recently, patients with CVDs have also been found to have increased risk of cancers, termed reverse cardio‐oncology. Although reverse cardio‐oncology has been highlighted as an important disease state in recent studies, how the diseased heart affects cancer and the potential mediators of the crosstalk between CVDs and cancer are largely unknown. Here, we focus on the roles of cardiac‐specific microRNA‐208a (miR‐208a) in cardiac and cancer biology and explore its essential roles in reverse cardio‐oncology. Accumulating evidence has shown that within the heart, increased miR‐208a promotes myocardial injury, arrhythmia, cardiac remodeling, and dysfunction and that secreted miR‐208a in the circulation may have novel roles in promoting tumor proliferation and invasion. This review, therefore, provides insights into the novel roles of miR‐208a in reverse cardio‐oncology and strategies to prevent secondary carcinogenesis in patients with early‐ or late‐stage heart failure.
A report on seven fetal cases associated with 15q11‐q13 microdeletion and microduplication
Authors: Huang Xiuzhu , Chen Jieping , Hu Wenlong , Li Lu , He Huiyan , Guo Hui , Liao Qiuyan , Ye Mei , Tang Donge , Dai Yong 
Published: February 2021

AbstractBackgroundThe 15q11‐q13 region contains three breakpoints (BP1 to BP3), and copy number variations often occur in the region.Aims15q11‐q13 microdeletion and microduplication are usually associated with Prader‐Willi and Angelman syndromes, respectively. It is not yet clear to what extent microdeletion and microduplication affect the physical health of the fetus and the child. In this study, we examined seven fetuses ranging in gestational age from 15 to 27 weeks.Materials & MethodsDetailed prenatal screening and laboratory examinations were performed, while karyotype analysis and chromosomal microarray analysis (CMA) of the amniotic fluid and umbilical cord blood were applied for genetic analysis.ResultsCMA analysis showed that four fetuses harbored a microdeletion and one fetus showed a microduplication at 15q11.2 BP1‐BP2, two fetuses had a microdeletion at 15q11‐q13 BP2‐BP3, and one fetus had an additional microdeletion at 16p13.11.DiscussionThere is no clear standard for the clinical diagnosis of 15q11‐q13 microdeletion and microduplication, some of them have clinical phenotypes or are clinically affected.ConclusionTherefore, parents of such fetuses should be informed of the possibility of microdeletions or microduplications to mitigate the psychological burden, and medical consultation and assistance should be provided when communicating the results of the mid‐gestation screening.
A novel SPINK5 donor splice site variant in a child with Netherton syndrome
Authors: Mintoff Dillon , Borg Isabella , Vornweg Julia , Mercieca Liam , Merdzanic Rijad , Numrich Johannes , Aquilina Susan , Pace Nikolai Paul , Fischer Judith 
Published: February 2021

AbstractBackgroundNetherton syndrome (NS) is a genodermatosis caused by loss‐of‐function mutations in SPINK5, resulting in aberrant LEKTI expression.MethodNext‐generation sequencing of SPINK5 (NM_001127698.1) was carried out and functional studies were performed by immunofluorescence microscopy of a lesional skin biopsy using anti‐LEKTI antibodies.ResultsWe describe a novel SPINK5 likely pathogenic donor splice site variant (NM_001127698.1:c.2015+5G>A) in a patient with NS and confirm its functional significance by demonstrating complete loss of LEKTI expression in lesional skin by immunofluorescence analysis.ConclusionThe 2015+5G>A is a novel, likely pathogenic variant in NS. Herein we review and assimilate documented SPINK5 pathogenic variants and discuss possible genotype–phenotype associations in NS.
The Metabolism and Potential Bioactivity of Chlorophyll and Metallo‐chlorophyll Derivatives in the Gastrointestinal Tract
Authors: Zhong Siqiong , Bird Amanda , Kopec Rachel E. 
Published: February 2021

AbstractChlorophyll is the vivid chromophore which imparts the green color to plant leaves, and is consumed by humans through green vegetables. The basic porphyrin structure of chlorophyll binds magnesium in plants, but can bind different divalent metals (e.g., copper, zinc, iron) facilitated by food processing techniques and/or chemical synthesis. This review covers the known elements of chlorophyll and metallo‐chlorophyll absorption, distribution, metabolism, excretion in vitro and in vivo. The review discusses what is understood about the ability of these novel metallo‐chlorophyll derivatives to deliver essential metals. This review also detail chlorophyll and metallo‐chlorophyll toxin binding properties which largely occur during digestion, focusing on toxins including dioxins, heterocyclic aromatic amines, polyaromatic hydrocarbons, and aflatoxin. Finally, the article highlights the gaps in the understanding of the metabolism and metal and toxin‐binding bioactivity of this family of molecules.
Is There a Carcinogenic Risk Attached to Vitamin B12 Deficient Diets and What Should We Do About It? Reviewing the Facts
Authors: Loedin Alexandra K. , Speijer Dave 
Published: February 2021

AbstractThe number of individuals partaking in veganism has increased sharply in the last decade. Therefore, it is critical to look at the implications of vegan diets for public health. Although there are multiple health benefits of a vegan diet, studies have also linked the diet with deficiencies in various micronutrients. This study focuses on vitamin B12, because of its critical role in DNA synthesis and methylation. In light of these connections, a critical review of recent scientific literature is conducted to understand the effects of a B12 deficient diet on the genome and epigenome, and whether it can give rise to cancer. It is observed that a B12 deficiency leads to increased uracil misincorporation, leading to impaired DNA synthesis and genomic instability. The deficiency also leads to global hypomethylation of DNA, a hallmark of early carcinogenesis. The findings of this study highlight the need for increased awareness among vegans to ensure adequate B12 intake through supplementation or consumption of fortified products as a preventative measure. Additionally, the biofortification of staple crops and an improved version of fermented products with increased B12 content can be developed when inadequate intake seems otherwise inevitable.
Strategic method of determining parameter values in frequency modulated continuous wave radar for low noise floor over middle‐long range
Authors: Park Junhyeong , Jung Dae‐Hwan , Park Seong‐Ook 
Published: February 2021

AbstractHeterodyne architecture has been frequently chosen to resolve the problem of dc offset in frequency modulated continuous wave (FMCW) radar. However, heterodyne FMCW radars use different local oscillators, resulting in uncorrelated phase noise (UPN) in beat signals. Therefore, the inherent leakage signal in the heterodyne FMCW radar also has UPN and raises the noise floor in the power spectrum. In this letter, we propose a strategic decision method for parameter values in the heterodyne FMCW radar to achieve a low noise floor over the middle‐long range. In addition, we experimentally discover the relation between the UPN and the processing gain for the first time. Based on this relation, we devise an exact formula for the degree of improvement achieved due to the proposed strategic decision method. Experimental results confirm the devised formula and show that the proposed method significantly reduces the noise floor over the middle‐long range.
End‐to‐end domain knowledge‐assisted automatic diagnosis of idiopathic pulmonary fibrosis (IPF) using computed tomography (CT)
Authors: Yu Wenxi , Zhou Hua , Goldin Jonathan G. , Wong Weng Kee , Kim Grace Hyun J. 
Published: February 2021

PurposeDomain knowledge (DK) acquired from prior studies is important for medical diagnosis. This paper leverages the population‐level DK using an optimality design criterion to train a deep learning model in an end‐to‐end manner. In this study, the problem of interest is at the patient level to diagnose a subject with idiopathic pulmonary fibrosis (IPF) among subjects with interstitial lung disease (ILD) using a computed tomography (CT). IPF diagnosis is a complicated process with multidisciplinary discussion with experts and is subject to interobserver variability, even for experienced radiologists. To this end, we propose a new statistical method to construct a time/memory‐efficient IPF diagnosis model using axial chest CT and DK, along with an optimality design criterion via a DK‐enhanced loss function of deep learning.MethodsFour state‐of‐the‐art two‐dimensional convolutional neural network (2D‐CNN) architectures (MobileNet, VGG16, ResNet‐50, and DenseNet‐121) and one baseline 2D‐CNN are implemented to automatically diagnose IPF among ILD patients. Axial lung CT images are retrospectively acquired from 389 IPF patients and 700 non‐IPF ILD patients in five multicenter clinical trials. To enrich the sample size and boost model performance, we sample 20 three‐slice samples (triplets) from each CT scan, where these three slices are randomly selected from the top, middle, and bottom of both lungs respectively. Model performance is evaluated using a fivefold cross‐validation, where each fold was stratified using a fixed proportion of IPF vs non‐IPF.ResultsUsing DK‐enhanced loss function increases the model performance of the baseline CNN model from 0.77 to 0.89 in terms of study‐wise accuracy. Four other well‐developed models reach satisfactory model performance with an overall accuracy >0.95 but the benefits brought on by the DK‐enhanced loss function is not noticeable.ConclusionsWe believe this is the first attempt that (a) uses population‐level DK with an optimal design criterion to train deep learning‐based diagnostic models in an end‐to‐end manner and (b) focuses on patient‐level IPF diagnosis. Further evaluation of using population‐level DK on prospective studies is warranted and is underway.
Joint calibrationless reconstruction of highly undersampled multicontrast MR datasets using a low‐rank Hankel tensor completion framework
Authors: Yi Zheyuan , Liu Yilong , Zhao Yujiao , Xiao Linfang , Leong Alex T. L. , Feng Yanqiu , Chen Fei , Wu Ed X. 
Published: February 2021

PurposeTo jointly reconstruct highly undersampled multicontrast two‐dimensional (2D) datasets through a low‐rank Hankel tensor completion framework.MethodsA multicontrast Hankel tensor completion (MC‐HTC) framework is proposed to exploit the shareable information in multicontrast datasets with respect to their highly correlated image structure, common spatial support, and shared coil sensitivity for joint reconstruction. This is achieved by first organizing multicontrast k‐space datasets into a single block‐wise Hankel tensor. Subsequent low‐rank tensor approximation via higher‐order singular value decomposition (HOSVD) uses the image structural correlation by considering different contrasts as virtual channels. Meanwhile, the HOSVD imposes common spatial support and shared coil sensitivity by treating data from different contrasts as from additional k‐space kernels. The missing k‐space data are then recovered by iteratively performing such low‐rank approximation and enforcing data consistency. This joint reconstruction framework was evaluated using multicontrast multichannel 2D human brain datasets (T1‐weighted, T2‐weighted, fluid‐attenuated inversion recovery, and T1‐weighted‐inversion recovery) of identical image geometry with random and uniform undersampling schemes.ResultsThe proposed method offered high acceleration, exhibiting significantly less residual errors when compared with both single‐contrast SAKE (simultaneous autocalibrating and k‐space estimation) and multicontrast J‐LORAKS (joint parallel‐imaging–low‐rank matrix modeling of local k‐space neighborhoods) low‐rank reconstruction. Furthermore, the MC‐HTC framework was applied uniquely to Cartesian uniform undersampling by incorporating a novel complementary k‐space sampling strategy where the phase‐encoding direction among different contrasts is orthogonally alternated.ConclusionThe proposed MC‐HTC approach presents an effective tensor completion framework to jointly reconstruct highly undersampled multicontrast 2D datasets without coil‐sensitivity calibration.
Comparison between surface electrodes and ultrasound monitoring to measure TMS evoked muscle contraction
Authors: Kaczmarczyk Isabella , Rawji Vishal , Rothwell John C. , Hodson‐Tole Emma , Sharma Nikhil 
Published: February 2021

AbstractIntroductionTranscranial magnetic stimulation (TMS) is widely used to explore cortical physiology in health and disease. Surface electromyography (sEMG) is appropriate for superficial muscles, but cannot be applied easily to less accessible muscles. Muscle ultrasound (mUS) may provide an elegant solution to this problem, but fundamental questions remain. We explore the relationship between TMS evoked muscle potentials and TMS evoked muscle contractions measured with mUS.MethodsIn 10 participants, we performed a TMS recruitment curve, simultaneously measuring motor evoked potentials (MEPs) and mUS in biceps (BI), first dorsal interosseous (FDI), tibialis anterior (TA), and the tongue (TO).ResultsResting motor threshold (RMT) measurements and recruitment curves were found to be consistent across sEMG and mUS.DiscussionThis work supports the use of TMS‐US to study less accessible muscles. The implications are broad but could include the study of a new range of muscles in disorders such as amyotrophic lateral sclerosis.
Performance of Subyearling Fall Chinook Salmon Tagged with 8-, 9-, and 12-mm Passive Integrated Transponder Tags in the Snake River
Authors: Tiffan Kenneth F. , Rhodes Tobyn N. , Bickford Brad K. , Lebeda Dalton D. , Connor William P. , Mullins Frank L. 
Published: February 2021

Abstract
Inferences based on tagged individuals from a population are limited in part by the minimum size of fish that can be tagged. Smaller tags allow a greater proportion of a population to be represented by tagging and should reduce potential tag effects on fish performance. We evaluated different performance metrics of juvenile fall Chinook Salmon Oncorhynchus tshawytscha tagged with 8-, 9-, and 12-mm PIT tags in the Snake River. We did not find evidence that posttagging mortality of 45–49-mm-FL fish tagged with 8-mm tags was higher than the posttagging mortality of larger fish tagged with 9- and 12-mm tags. The incisions of fish tagged with 8-mm tags using 14-guage needles healed faster than those of fish tagged with larger tags using 12-guage needles. For individuals that received 8-mm tags, growth in length and mass was higher for 45–49-mm fish than for 50–59-mm fish and 60-mm and larger fish. Growth of the larger size-classes (50–59 and ≥60 mm) was also generally higher for those tagged with 8-mm tags compared to those tagged with 9- and 12-mm tags, respectively. There were no strong relationships between tag burden (i.e., tag weight expressed as a percentage of fish weight) at the time of tagging and growth metrics for any tag size or fish size-class. Releases made to compare the detection efficiency of the three tag types in the juvenile fish bypass at Lower Granite Dam, Washington, showed that 99–100% of all fish were detected. Survival of fish from rearing areas to Lower Granite Dam generally increased with fish size and varied by year, but there was no strong evidence of a tag size effect. The 8-mm PIT tag allowed us to represent a larger portion (i.e., 6.2–24.1%) of the subyearling fall Chinook Salmon population in the Snake River without compromising fish performance or detectability at the dam.
A constitutive model for the accumulated strain of unsaturated soil under high‐cycle traffic loading
Authors: Cao Zhigang , Chen Jingyu , Alonso Eduardo E. , Tarragona Anna Ramon , Cai Yuanqiang , Gu Chuan , Zhang Qi 
Published: February 2021

AbstractThe road base is normally situated above the water table and thus in unsaturated state. Experimental results show that the accumulated strains of the unsaturated road base aggregate under high‐cycle traffic loads are significantly influenced by the matric suction. To predict the accumulated strain of unsaturated road base aggregate under high‐cycle traffic loads, a constitutive model was developed based on the Barcelona Basic Model (BBM) and the shakedown concept. In this model, the shakedown and plastic creep boundaries of the aggregate under cyclic loads were supposed to exist and to have the same shape as the “static” yield surface in BBM. The strain accumulation rates were described as an exponential function of the distance between the peak cyclic stress point and the conjugated point at the current cyclic yield surface. An explicit calculation methodology was adopted to avoid large calculation errors and to improve the calculation efficiency of the model. Comparison between model predictions and testing results proved the accuracy of the proposed model, which can be used as a basic model to predict the long‐term deformation of unsaturated road base aggregate under high‐cycle traffic loads.
A privacy‐focused approach for anomaly detection in IoT networks
Authors: Martins Pedro , Reis André B. , Salvador Paulo , Sargento Susana 
Published: February 2021

SummaryIoT devices present a series of liabilities to administrators that aim for fully secure networks. Owing to their heterogeneity and limited processing power, administrators often implement monitoring mechanisms to prevent unauthorized use of resources and data exfiltration. However, most approaches allow for easy discrimination of private behavioral patterns of its users or nodes by their MAC or IP addresses. Inferring data exchange patterns at the physical layer is a more complex task, as a single signal power indicator may correspond to a mixture of simultaneous data transmissions. This article proposes privacy‐focused mechanisms by resorting to physical layer data analysis and one‐class classification models to perform anomaly detection in IoT networks. We present a full processing pipeline that considers the signal that identifies and models patterns on the channel activity and silence periods. We train our models with data captured from interactions with an Amazon Echo with devices generating background noise and test them against a similar scenario with a tampered network node periodically uploading data. Our data show that the best performing model, kernel density estimation, is able to detect anomalies with a 99% precision rate, even surpassing the tested neural networks approaches. We also propose a framework that aims to deploy validated models into production IoT environments. We designed an end‐to‐end data flow that autonomously extracts data and classifies them at the anomaly detection server. The envisioned components were designed to be horizontally scaled for a myriad of data streams and machine learning algorithms working in parallel.
A real‐time optimization algorithm for the fixed‐stress splitting scheme
Authors: Honório Hermínio T. , Martins Bruno , Maliska Clovis R. 
Published: February 2021

AbstractThe efficient solution of the systems of equations arising from coupled consolidation problems is a matter of concern worldwide. The fixed‐stress splitting scheme is an effective approach for solving these equations either in a sequential manner or as a preconditioner for a fully coupled approach. Recent studies show that it is possible to improve the convergence rate of the fixed‐stress approach by choosing a proper tuning parameter. In this work, we present an optimization algorithm that dynamically searches for the optimal tuning parameter for each time step of the simulation, hence the denomination of real‐time optimization. The numerical examples show that the optimal tuning parameter indeed can change during the simulation, which highlights the importance of the dynamic search provided by the optimization algorithm. In some situations, the optimized fixed‐stress algorithm proposed in this work is able to reduce the total number of iterations by up to 70% compared to the non‐optimized version.
Thermo‐elastic topology optimization with stress and temperature constraints
Authors: Meng Qingxuan , Xu Bin , Wang Chao , Zhao Lei 
Published: February 2021

AbstractA thermo‐elastic topology optimization with stress and temperature constraints is proposed to attack the complex multiphysics problem in this paper. Based on the rational approximation of material properties (RAMP), the coupled equations of mechanic and temperature field are solved. Two optimization problems, volume minimization with temperature and stress constraints, and traditional compliance minimization with volume and temperature constraints, are discussed for comparison. The stress stabilizing control scheme (SSCS) combined with global stress measure is presented to tackle highly nonlinear and local nature of stress with thermal expansion in varying temperature field. The adjoint method is applied to achieve the sensitivity of multiphysics field and the density function is updated utilizing the method of moving asymptotes (MMA). Representative examples are investigated to demonstrate the effectiveness and utility of the proposed method. Clear topology and stable iterative process can be obtained for complex coupled problem by means of the SSCS. Meanwhile, the topology with stress and temperature constraints has obvious sensitivity to even subtle change in temperature. The optimization design considering several stress constraints under multithermal conditions can work well in different temperature ranges.
Multiscale design of nonlinear materials using a Eulerian shape optimization scheme
Authors: Najafi Ahmad R. , Safdari Masoud , Tortorelli Daniel A. , Geubelle Philippe H. 
Published: February 2021

AbstractMotivated by recent advances in manufacturing, the design of materials is the focal point of interest in the material research community. One of the critical challenges in this field is finding optimal material microstructure for a desired macroscopic response. This work presents a computational method for the mesoscale‐level design of particulate composites for an optimal macroscale‐level response. The method relies on a custom shape optimization scheme to find the extrema of a nonlinear cost function subject to a set of constraints. Three key “modules” constitute the method: multiscale modeling, sensitivity analysis, and optimization. Multiscale modeling relies on a classical homogenization method and a nonlinear NURBS‐based generalized finite element scheme to efficiently and accurately compute the structural response of particulate composites using a nonconformal discretization. A three‐parameter isotropic damage law is used to model microstructure‐level failure. An analytical sensitivity method is developed to compute the derivatives of the cost/constraint functions with respect to the design variables that control the microstructure's geometry. The derivation uncovers subtle but essential new terms contributing to the sensitivity of finite element shape functions and their spatial derivatives. Several structural problems are solved to demonstrate the applicability, performance, and accuracy of the method for the design of particulate composites with a desired macroscopic nonlinear stress‐strain response.