Dedicated COVID-19 Resource Center in Radiology Journals: Its Citation Metrics and Altmetrics Impact

Background: Since the emergence of coronavirus disease 2019 (COVID-19), several journals established dedicated resource center for all articles published on COVID-19. Our study compared the altmetric impact captured by articles published in journals having such COVID-19 resource center. Methods: We used Web of Science database to assess radiology journals publishing most common articles on COVID-19. We used Dimensions database to assess citations received and altmetric attention score for each article. For each article, we extracted number of citation received and altmetric attention score. To account for the the variation in strength and exposure between included journals, we adopted a normalization strategy and we used regression analysis in our statistical analysis. Results: A total of 494 articles included in the current assessment, including 334 (67.6%) articles published in journals with dedicated COVID-19 resource center including European radiology, American Journal of Roentgenology, Radiology, and Journal of the American college of radiology, while European Journal of Nuclear Medicine and Molecular Imaging, Academic Radiology did not have COVID-19 resource center. Journals with COVID-19 resource center had a mean normalized altmetric attention score of 0.38 higher (95% CI 0.25 to 0.50; p< 0.001) and a mean normalized citation count of 6.73 higher (95% CI 3.99 to 9.48; p< 0.001) than those without COVID-19 resource center. Conclusion: Radiology journals that provided COVID-19 articles in a dedicated resource center within its homepage had higher attention and citation for their COVID-19 articles compared to journals that did not have such dedicated resource center.

Mitochondrial mRNA is a stable and convenient reference for the normalization of RNA-seq data

The normalization of high-throughput RNA sequencing (RNA-seq) data is needed to accurately analyze gene expression levels. Traditional normalization methods can either correct the differences in sequencing depth, or correct both the sequencing depth and other unwanted variations introduced during sequencing library preparation through exogenous spike-ins1-4. However, the exogenous spike-ins are prone to variation5,6. Therefore, a better normalization approach with a more appropriate reference is an ongoing demand. In this study, we demonstrated that mitochondrial mRNA (mRNA encoded by mitochondria genome) can serve as a steady endogenous reference for RNA-seq data analysis, and performs better than exogenous spike-ins. We also found that using mitochondrial mRNA as a reference can reduce batch effects for RNA-seq data. These results provide a simple and practical normalization strategy for RNA-seq data, which will serve as a valuable tool widely applicable to transcriptomic studies.

NPALOSS: NEIGHBORING PIXEL AFFINITY LOSS FOR SEMANTIC SEGMENTATION IN HIGH-RESOLUTION AERIAL IMAGERY

The performance of semantic segmentation in high-resolution aerial imagery has been improved rapidly through the introduction of deep fully convolutional neural network (FCN). However, due to the complexity of object shapes and sizes, the labeling accuracy of small-sized objects and object boundaries still need to be improved. In this paper, we propose a neighboring pixel affinity loss (NPALoss) to improve the segmentation performance of these hard pixels. Specifically, we address the issues of how to determine the classifying difficulty of one pixel and how to get the suitable weight margin between well-classified pixels and hard pixels. Firstly, we convert the first problem into a problem that the pixel categories in the neighborhood are the same or different. Based on this idea, we build a neighboring pixel affinity map by counting the pixel-pair relationships for each pixel in the search region. Secondly, we investigate different weight transformation strategies for the affinity map to explore the suitable weight margin and avoid gradient overflow. The logarithm compression strategy is better than the normalization strategy, especially the common logarithm. Finally, combining the affinity map and logarithm compression strategy, we build NPALoss to adaptively assign different weights for each pixel. Comparative experiments are conducted on the ISPRS Vaihingen dataset and several commonly-used state-of-the-art networks. We demonstrate that our proposed approach can achieve promising results.

Effect of Objective Normalization and Penalty Parameter on Penalty Boundary Intersection Decomposition-Based Evolutionary Many-Objective Optimization Algorithms

An objective normalization strategy is essential in any evolutionary multiobjective or many-objective optimization (EMO or EMaO) algorithm, due to the distance calculations between objective vectors required to compute diversity and convergence of population members. For the decomposition-based EMO/EMaO algorithms involving the Penalty Boundary Intersection (PBI) metric, normalization is an important matter due to the computation of two distance metrics. In this article, we make a theoretical analysis of the effect of instabilities in the normalization process on the performance of PBI-based MOEA/D and a proposed PBI-based NSGA-III procedure. Although the effect is well recognized in the literature, few theoretical studies have been done so far to understand its true nature and the choice of a suitable penalty parameter value for an arbitrary problem. The developed theoretical results have been corroborated with extensive experimental results on three to 15-objective convex and non-convex instances of DTLZ and WFG problems. The article, makes important theoretical conclusions on PBI-based decomposition algorithms derived from the study.

Immune Normalization Strategy Against Suboptimal Health Status: Safe and Effective Mixed-Natural Killer Cell Therapy

Background: Immune normalization has emerged as a new paradigm in immunotherapy, which is proposed in cancer patients instead of the traditional immune enhancement therapy. The immune normalization strategy may also be implemented in cancer prevention of sub-healthy individuals.Methods: We established in vitro cultured mixed-Natural Killer cells (NKM), which could be used to achieve immune normalization. We defined the sub-healthy individuals after analyzing the PD-1 ratio in PBMCs from 95 donors over 50 years of age.Results: NKM were composed by approximately 20% NK cells (CD3-CD16+ or CD56+), 30% NKT-like cells (CD3+CD16+ or CD56+) and other T cells. The in vitro cytotoxicity of NKM was ten times higher than the peripheral blood mononuclear cells (PBMC). NKM cytotoxicity was negatively correlated with the ratio of regulatory T cells (CD3+CD4+ T), and positively correlated with the ratio of NK cells, especially CD56brightCD16bright NK cells. We found the sub-healthy individuals displayed significantly higher ratio of CD3+PD-1+ T cells in PBMC (Ratio > 4%) and higher ratio of CD3+CD8+PD-1+ T cells in CD3+CD8+ T cells (Ratio > 10%) than the healthy controls. Then, we evaluated the potential clinical application of NKM therapy in one pancreatic cancer patient and four sub-healthy individuals.Conclusions: NKM therapy showed good tolerance and no side effects were found. In sub-healthy individuals, the ratio of CD3+PD-1+ T cells and CD3+CD8+PD-1+ T cells was significantly reduced after NKM treatment, which indicated that NKM therapy could potentially be used for cancer prevention and health care, thereby achieving the immune normalization.Trial registration: International Research Center for Regenerative Medicine, BIH-2018-1001. 01 August 2018, BOAO International Hospital.International Research Center for Regenerative Medicine, BIH-2018-1002. 01 August 2018, BOAO International Hospital.

Estimating DOA and PPS parameters of signal received by ULA in heavy noise environment

Estimation of the direction-of-arrival (DOA) and parameters of polynomial phase signal (PPS) impinging on the uniform linear array (ULA) of sensors in heavy-tailed noise environments is considered in this paper. To estimate signal parameters, a recently proposed quasi maximum-likelihood (QML) estimator is adopted. The proposed algorithm consists of two successive steps: (1) noise influence mitigation by using the proposed normalization strategy and (2) signal parameters estimation using the DOA-QML approach. The algorithm performance is evaluated for both monocomponent and multicomponent signals.

A novel bioinformatics approach to identify the consistently well-performing normalization strategy for current metabolomic studies

Unwanted experimental/biological variation and technical error are frequently encountered in current metabolomics, which requires the employment of normalization methods for removing undesired data fluctuations. To ensure the ‘thorough’ removal of unwanted variations, the collective consideration of multiple criteria (‘intragroup variation’, ‘marker stability’ and ‘classification capability’) was essential. However, due to the limited number of available normalization methods, it is extremely challenging to discover the appropriate one that can meet all these criteria. Herein, a novel approach was proposed to discover the normalization strategies that are consistently well performing (CWP) under all criteria. Based on various benchmarks, all normalization methods popular in current metabolomics were ‘first’ discovered to be non-CWP. ‘Then’, 21 new strategies that combined the ‘sample’-based method with the ‘metabolite’-based one were found to be CWP. ‘Finally’, a variety of currently available methods (such as cubic splines, range scaling, level scaling, EigenMS, cyclic loess and mean) were identified to be CWP when combining with other normalization. In conclusion, this study not only discovered several strategies that performed consistently well under all criteria, but also proposed a novel approach that could ensure the identification of CWP strategies for future biological problems.