bluuebunny/crossref_metadata_2025_split huggingface dataset:
Fine-grained multi-focus image fusion based on edge features
Authors: Tian Bin , Yang Lichun , Dang Jianwu 
Published: February 2023

AbstractMulti-focus image fusion is a process of fusing multiple images of different focus areas into a total focus image, which has important application value. In view of the defects of the current fusion method in the detail information retention effect of the original image, a fusion architecture based on two stages is designed. In the training phase, combined with the polarized self-attention module and the DenseNet network structure, an encoder-decoder structure network is designed for image reconstruction tasks to enhance the original information retention ability of the model. In the fusion stage, combined with the encoded feature map, a fusion strategy based on edge feature map is designed for image fusion tasks to enhance the attention ability of detail information in the fusion process. Compared with 9 classical fusion algorithms, the proposed algorithm has achieved advanced fusion performance in both subjective and objective evaluations, and the fused image has better information retention effect on the original image.
Meta-analysis of NAD(P)(H) quantification results exhibits variability across mammalian tissues
Authors: Azouaoui Dassine , Choinière Michael René , Khan Momtafin , Sayfi Shahab , Jaffer Simran , Yousef Selvia , Patten David A. , Green Alexander E. , Menzies Keir J. 
Published: February 2023

AbstractNicotinamide Adenine Dinucleotide (NAD+) plays an important role in energy metabolism and signaling pathways controlling crucial cellular functions. The increased interest in NAD+ metabolism and NAD+-boosting therapies has reinforced the necessity for accurate NAD+ quantification. To examine the published NAD(P)(H) measures across mammalian tissues, we performed a meta-analysis of the existing data. An Ovid MEDLINE database search identified articles with NAD(P)(H) quantification results obtained from mammalian tissues published between 1961 and 2021. We screened 4890 records and extracted quantitative data, as well as the quantification methods, pre-analytical conditions, and subject characteristics. The extracted physiological NAD(P)(H) concentrations in various tissues from mice, rats, and humans, revealed an important inter- and intra-method variability that extended to recent publications. This highlights the relatively poor potential for cross-experimental analyses for NAD(P)(H) quantitative data and the importance of standardization for NAD(P)(H) quantification methods and pre-analytical procedures for future preclinical and clinical studies.
DeepInsight-3D architecture for anti-cancer drug response prediction with deep-learning on multi-omics
Authors: Sharma Alok , Lysenko Artem , Boroevich Keith A. , Tsunoda Tatsuhiko 
Published: February 2023

AbstractModern oncology offers a wide range of treatments and therefore choosing the best option for particular patient is very important for optimal outcome. Multi-omics profiling in combination with AI-based predictive models have great potential for streamlining these treatment decisions. However, these encouraging developments continue to be hampered by very high dimensionality of the datasets in combination with insufficiently large numbers of annotated samples. Here we proposed a novel deep learning-based method to predict patient-specific anticancer drug response from three types of multi-omics data. The proposed DeepInsight-3D approach relies on structured data-to-image conversion that then allows use of convolutional neural networks, which are particularly robust to high dimensionality of the inputs while retaining capabilities to model highly complex relationships between variables. Of particular note, we demonstrate that in this formalism additional channels of an image can be effectively used to accommodate data from different omics layers while implicitly encoding the connection between them. DeepInsight-3D was able to outperform other state-of-the-art methods applied to this task. The proposed improvements can facilitate the development of better personalized treatment strategies for different cancers in the future.
Gut microbiome composition associates with corticosteroid treatment, morbidity, and senescence in Chinook salmon (Oncorhynchus tshawytscha)
Authors: Couch Claire E. , Neal William T. , Herron Crystal L. , Kent Michael L. , Schreck Carl B. , Peterson James T. 
Published: February 2023

AbstractPacific salmon experience prolonged elevation in corticosteroid hormones during important life history events including migration, reproduction, and senescence. These periods of elevated corticosteroids correspond with changes to immunity and energy metabolism; therefore, fish may be particularly vulnerable to mortality at these times. Recent studies found that stress-induced cortisol release associated with microbial community shifts in salmonids, raising the question of how longer-term corticosteroid dynamics that accompany life history transitions affect salmonid microbiomes. In this work, we experimentally evaluated the relationships between gut microbiome composition, chronically elevated corticosteroids, and mortality in juvenile Chinook salmon (Oncorhynchus tshawytscha). We found that treatment with slow-release implants of the corticosteroids cortisol or dexamethasone resulted in changes to the gut microbiome. Morbidity was also associated with microbiome composition, suggesting that the gut microbiome reflects individual differences in susceptibility to opportunistic pathogens. Additionally, we analyzed a small number of samples from adult fish at various stages of senescence. Results from these samples suggest that microbiome composition associated with gut integrity, and that the microbial communities of corticosteroid treated juveniles shift in composition toward those of senescent adults. Overall, findings from this work indicate that the gut microbiome correlates with mortality risk during periods of chronic corticosteroid elevation.
Bioinformatics construction and experimental validation of a cuproptosis-related lncRNA prognostic model in lung adenocarcinoma for immunotherapy response prediction
Authors: Li Linfeng , Cai Qidong , Wu Zeyu , Li Xizhe , Zhou Wolong , Lu Liqing , Yi Bin , Chang Ruimin , Zhang Heng , Cheng Yuanda , Zhang Chunfang , Zhang Junjie 
Published: February 2023

AbstractCuproptosis is a newly form of cell death. Cuproptosis related lncRNA in lung adenocarcinoma (LUAD) has also not been fully elucidated. In the present study, we aimed to construct a prognostic signature based on cuproptosis-related lncRNA in LUAD and investigate its association with immunotherapy response. The RNA-sequencing data, clinical information and simple nucleotide variation of LUAD patients were obtained from TCGA database. The LASSO Cox regression was used to construct a prognostic signature. The CIBERSORT, ESTIMATE and ssGSEA algorithms were applied to assess the association between risk score and TME. TIDE score was applied to reflect the efficiency of immunotherapy response. The influence of overexpression of lncRNA TMPO-AS1 on A549 cell was also assessed by in vitro experiments. The lncRNA prognostic signature included AL606834.1, AL138778.1, AP000302.1, AC007384.1, AL161431.1, TMPO-AS1 and KIAA1671-AS1. Low-risk group exhibited much higher immune score, stromal score and ESTIMATE score, but lower tumor purity compared with high-risk groups. Also, low-risk group was associated with a much higher score of immune cells and immune related function sets, indicating an immune activation state. Low-risk patients had relative higher TIDE score and lower TMB. External validation using IMvigor210 immunotherapy cohort demonstrated that low-risk group had a better prognosis and might more easily benefit from immunotherapy. Overexpression of lncRNA TMPO-AS1 promoted the proliferation, migration and invasion of A549 cell line. The novel cuproptosis-related lncRNA signature could predict the prognosis of LUAD patients, and helped clinicians stratify patients appropriate for immunotherapy and determine individual therapeutic strategies.
Development and application of a new biological nano-selenium fermentation broth based on Bacillus subtilis SE201412
Authors: Huang Sisi , Yu Kan , Wen Liang , Long Xiaoling , Sun Jin , Liu Quxiao , Zheng Zhuo , Zheng Wei , Luo Hongmei , Liu Jinlong 
Published: February 2023

AbstractIn order to improve the functionality and additional value of agricultural products, this study developing nano-selenium fermentation broth and established a new application strategy of bio-nano-selenium by screening and identifying selenium-rich microorganisms. We isolated a new strain from tobacco waste and named it Bacillus subtilis SE201412 (GenBank accession no. OP854680), which could aerobically grow under the condition of 66,000 mg L−1 selenite concentration, and could convert 99.19% of selenite into biological nano-selenium (BioSeNPs) within 18 h. Using strain SE201412, we industrially produced the different concentrations of fermentation broth containing 5000–3000 mg L−1 pure selenium for commercial use. The synthesized selenium nanoparticles (SeNPs) were characterized by scanning electron microscopy (SEM), transmission electron microscopy (TEM), and nanoparticle tracking analysis (NTA). TEM and SEM results showed that SeNPs were distributed outside cells. NTA assay of fermentation broth indicated that the nanoparticles were spherical with an average particle size of 126 ± 0.5 nm. Toxicity test revealed that the median lethal dose (LD50) of the fermentation broth to mice was 2710 mg kg−1, indicating its low toxicity and high safety. In addition, we applied BioSeNP fermentation broth to rice and wheat through field experiments. The results showed that the application of fermentation broth significantly increased the total selenium content and organic selenium percentage in rice and wheat grains. Our findings provide valuable reference for the development of BioSeNPs with extensive application prospects.
Reversal of aging-associated increase in myelopoiesis and expression of alarmins by angiotensin-(1–7)
Authors: Chittimalli Kishore , Jahan Jesmin , Sakamuri Anil , Weyrick Hope , Winkle Wink , Adkins Steven , Vetter Stefan W. , Jarajapu Yagna P. R. 
Published: February 2023

AbstractAging is associated with chronic systemic inflammation largely due to increased myelopoiesis, which in turn increases risk for vascular disease. We have previously shown evidence for the therapeutic potential of Angiotensin-(1–7) (Ang-(1–7)) in reversing vasoreparative dysfunction in aging. This study tested the hypothesis that ischemic vascular repair in aging by Ang-(1–7) involves attenuation of myelopoietic potential in the bone marrow and decreased mobilization of inflammatory cells. Young or Old male mice of age 3–4 and 22–24 months, respectively, received Ang-(1–7) (1 µg/kg/min, s.c.) for four weeks. Myelopoiesis was evaluated in the bone marrow (BM) cells by carrying out the colony forming unit (CFU-GM) assay followed by flow cytometry of monocyte-macrophages. Expression of pro-myelopoietic factors and alarmins in the hematopoietic progenitor-enriched BM cells was evaluated. Hindlimb ischemia (HLI) was induced by femoral ligation, and mobilization of monocytes into the blood stream was determined. Blood flow recovery was monitored by Laser Doppler imaging and infiltration of inflammatory cells was evaluated by immunohistochemistry. BM cells from Old mice generated a higher number of monocytes (Ly6G-CD11b+Ly6Chi) and M1 macrophages (Ly6ChiF4/80+) compared to that of Young, which was reversed by Ang-(1–7). Gene expression of selected myelopoietic factors, alarmins (S100A8, S100A9, S100A14 and HMGb1) and the receptor for alarmins, RAGE, was higher in the Old hematopoietic progenitor-enriched BM cells compared to the Young. Increased expressions of these factors were decreased by Ang-(1–7). Ischemia-induced mobilization of monocytes was higher in Old mice with decreased blood flow recovery and increased infiltration of monocyte-macrophages compared to the Young, all of which were reversed by Ang-(1–7). Enhanced ischemic vascular repair by Ang-(1–7) in aging is largely by decreasing the generation and recruitment of inflammatory monocyte-macrophages to the areas of ischemic injury. This is associated with decreased alarmin signaling in the BM-hematopoietic progenitor cells.
Effects of percutaneous transluminal angioplasty and associated factors in access hand oxygenation in patients undergoing hemodialysis
Authors: Sugiyama Tomoko , Ito Kiyonori , Ookawara Susumu , Shimoyama Hirofumi , Shindo Mitsutoshi , Hirata Momoko , Shimoyama Hiromi , Nakazato Yuichi , Morishita Yoshiyuki 
Published: February 2023

AbstractIn hemodialysis (HD) patients with arteriovenous fistula (AVF), changes in systemic or peripheral tissue circulation occur non-physiologically via the presence of AVF; however, associations between blood flow and tissue oxygenation in the brain and access hand are uncertain. In this study, 85 HD patients with AVF were included and evaluated for changes in flow volume (FV) and regional oxygen saturation (rSO2) in the brain and hands with AVF before and after percutaneous transluminal angioplasty (PTA). Furthermore, we evaluated the factors that determine access hand rSO2 without stenosis after PTA. Brachial arterial FV increased after PTA (p < 0.001), and carotid FV decreased (p = 0.008). Access hand rSO2 significantly decreased after PTA (p < 0.001), but cerebral rSO2 did not significantly change (p = 0.317). In multivariable linear regression analysis of factors associated with access hand rSO2, serum creatinine (standardized coefficient: 0.296) and hemoglobin (standardized coefficient: 0.249) were extracted as independent factors for access hand rSO2. In conclusion, a decrease in access hand oxygenation and maintenance of cerebral oxygenation were observed throughout PTA. To maintain access hand oxygenation, it is important to adequately manage Hb level and maintain muscle mass, in addition to having an AVF with appropriate blood flow.
Evolving scattering networks for engineering disorder
Authors: Yu Sunkyu 
Published: February 2023

AbstractNetwork science provides a powerful tool for unraveling the complexities of social, technological and biological systems. Constructing networks using wave phenomena is also of great interest in devising advanced hardware for machine learning, as shown in optical neural networks. Although most wave-based networks have employed static network models, the impact of evolving models in network science provides strong motivation to apply dynamical network modeling to wave physics. Here the concept of evolving scattering networks for scattering phenomena is developed. The network is defined by links, node degrees and their evolution processes modeling multi-particle interferences, which directly determine scattering from disordered materials. I demonstrate the concept by examining network-based material classification, microstructure screening and preferential attachment in evolutions, which are applied to stealthy hyperuniformity. The results enable independent control of scattering from different length scales, revealing superdense material phases in short-range order. The proposed concept provides a bridge between wave physics and network science to resolve multiscale material complexities and open-system material design.
Dynamic adaptive engineering pathways for mitigating flood risks in Shanghai with regret theory
Authors: Tian Zhan , Ramsbottom David , Sun Laixiang , Huang Yijing , Zou Huan , Liu Junguo 
Published: February 2023

AbstractUncertainty in sea level rise and future extreme climate events presents a great planning challenge for flood defence in coastal mega cities like Shanghai. While academic literature has largely focused on uncertainty analysis, engineering solution design requires effective uncertainty management. Here we incorporate the regret theory of economics and decision science into the dynamic-adaptation-pathways framework and assess the impacts of high rates of changes on the flood defence systems in Shanghai. Specific options are developed to manage flooding on the Huangpu River from tidal water levels, river flows, rainfall, drainage inflows and combinations of these flood sources including sea level rises of up to 3 m. Dynamic adaptation pathways are developed where the timing of tipping points from one intervention to the next depends on the actual changes in sea level, rainfall and other variables that affect the future design. This framework is potentially applicable for planning ‘no regrets’ flood-defence systems in other low-lying coastal cities.
Double integral‐enhanced Zeroing neural network with linear noise rejection for time‐varying matrix inverse
Authors: Liao Bolin , Han Luyang , Cao Xinwei , Li Shuai , Li Jianfeng 
Published: February 2023

AbstractIn engineering fields, time‐varying matrix inversion (TVMI) issue is often encountered. Zeroing neural network (ZNN) has been extensively employed to resolve the TVMI problem. Nevertheless, the original ZNN (OZNN) and the integral‐enhanced ZNN (IEZNN) usually fail to deal with the TVMI problem under unbounded noises, such as linear noises. Therefore, a neural network model that can handle the TVMI under linear noise interference is urgently needed. This paper develops a double integral‐enhanced ZNN (DIEZNN) model based on a novel integral‐type design formula with inherent linear‐noise tolerance. Moreover, its convergence and robustness are verified by derivation strictly. For comparison and verification, the OZNN and the IEZNN models are adopted to resolve the TVMI under multiple identical noise environments. The experiments proved that the DIEZNN model has excellent advantages in solving TVMI problems under linear noises. In general, the DIEZNN model is an innovative work and is proposed for the first time. Satisfyingly, the errors of DIEZNN are always less than 1 × 10−3 under linear noises, whereas the error norms of OZNN and IEZNN models are not convergent to zero. In addition, these models are applied to the control of the controllable permanent magnet synchronous motor chaotic system to indicate the superiority of the DIEZNN.
Inferring adversarial behaviour in cyber‐physical power systems using a Bayesian attack graph approach
Authors: Sahu Abhijeet , Davis Katherine 
Published: February 2023

AbstractHighly connected smart power systems are subject to increasing vulnerabilities and adversarial threats. Defenders need to proactively identify and defend new high‐risk access paths of cyber intruders that target grid resilience. However, cyber‐physical risk analysis and defense in power systems often requires making assumptions on adversary behaviour, and these assumptions can be wrong. Thus, this work examines the problem of inferring adversary behaviour in power systems to improve risk‐based defense and detection. To achieve this, a Bayesian approach for inference of the Cyber‐Adversarial Power System (Bayes‐CAPS) is proposed that uses Bayesian networks (BNs) to define and solve the inference problem of adversarial movement in the grid infrastructure towards targets of physical impact. Specifically, BNs are used to compute conditional probabilities to queries, such as the probability of observing an event given a set of alerts. Bayes‐CAPS builds initial Bayesian attack graphs for realistic power system cyber‐physical models. These models are adaptable using collected data from the system under study. Then, Bayes‐CAPS computes the posterior probabilities of the occurrence of a security breach event in power systems. Experiments are conducted that evaluate algorithms based on time complexity, accuracy and impact of evidence for different scales and densities of network. The performance is evaluated and compared for five realistic cyber‐physical power system models of increasing size and complexities ranging from 8 to 300 substations based on computation and accuracy impacts.
Multi‐directional feature refinement network for real‐time semantic segmentation in urban street scenes
Authors: Zhou Yan , Zheng Xihong , Yang Yin , Li Jianxun , Mu Jinzhen , Irampaye Richard 
Published: February 2023

AbstractEfficient and accurate semantic segmentation is crucial for autonomous driving scene parsing. Capturing detailed information and semantic information efficiently through two‐branch networks has been widely utilised in real‐time semantic segmentation. This study proposes a network named MRFNet based on two‐branch strategy to solve the problem of accuracy and speed of segmentation in urban scenes. Many real‐time networks do not comprehensively consider contextual information from sub‐regions in different directions and at different scales. To handle this problem, a Multi‐directional Feature Refinement Module (MFRM) which has three sub‐paths to capture information at different scales and directions is proposed. And MFRM reduces computation by using strip pooling and dilated convolution operations. In particular, the authors propose a Feature Cross‐guide Aggregation Module to aggregate detailed information and contextual information through the mutual guidance of detailed information and semantic information. This module guides the extraction of feature maps in a more precise direction. Experiments on Cityscapes and CamVid datasets demonstrate the effectiveness of our method by achieving a balance between accuracy and inference speed. Specially, on single 1080Ti GPU, our method yields 78.9% mean intersection over union (mIoU) and 77.4% mIoU at speed of 144.5 frames per second (FPS) and 120.8 FPS on Cityscapes and CamVid datasets respectively.
Enhanced road information representation in graph recurrent network for traffic speed prediction
Authors: Chang Lei , Ma Cheng , Sun Kai , Qu Zhijian , Ren Chongguang 
Published: February 2023

AbstractCorrectly capturing the spatial‐temporal correlation of traffic sequences will benefit to make accurate predictions of the future traffic states. In the paper, the methods of enhancing road spatial and temporal information representation are proposed. Firstly, the parameter matrix of each road is constructed to represent the road‐specific traffic patterns for the graph convolution neural network and the recurrent neural network. Then, the node embedding, and matrix factorization are used to reduce the scale of the parameter matrix. Secondly, the node embedding‐based Data Adaptive Graph Generation model was introduced to infer the indirect relationship of each node, and the gating mechanism is designed to control the weights of the direct spatial information and the indirect spatial information. Thirdly, to enhance the traffic sequence representation, the time tag and peak tag for the sequences are designed at each sampling moment. At last, the Enhanced Road Information Representation in Graph Recurrent Network (En‐GRN) is proposed to predict traffic speed, and the prediction performance is tested on SZ‐taxi and Los‐loop dataset. The experimental results show that the presented works are effective for improving traffic prediction accuracy.