bluuebunny/crossref_metadata_2025 huggingface dataset:
Joint Optimization–Encryption Model Based on Auto Encoder Neural Network, Variable-Length Dynamic S-Box and New Stream Encryption
Authors: Hu Anqi , Gong Xiaoxue , Guo Lei
Published: December 2022

The openness of the Internet makes information transmission prone to the risk of illegal eavesdropping, and data security and user privacy protection have become one of the key research hotspots. Aiming to solve the problem that a single encryption method cannot provide comprehensive security guarantees and the traditional one-time pad encryption method consumes a large amount of secure channel resource, this paper innovatively proposes a variable-length dynamic S-box and a cross-mapping sequence-based new type of stream encryption, and an improved method of synchronizing keys during a one-time pad transmission. On this basis, this paper innovatively proposes a joint optimization–encryption model based on the auto encoder neural network, variable-length dynamic S-box and new stream encryption. Finally, taking the optical fiber transmission as an example, the simulation results show that the joint optimization–encryption model proposed in this paper can save security channel resources and effectively resist common security attacks in the process of high-speed information transmission.
Underground research laboratories, an important support to the Belgian Regulatory Body's Research and Development programme and the management of uncertainties
Authors: Bernier Frédéric J. , Detilleux Valéry , Lemy Frank , Pochet Guillaume M. , Surkova Maryna , Volckaert Geert , Mommaert Chantal
Published: December 2022

In order to fulfil its mission, the Belgian Regulatory Body (constituted in Belgium by FANC and Bel V) has set up its own Research & Development programme, independent from the Waste Management Organisation's one. The Regulatory Body's Research & Development programme is mostly intended to investigate safety issues with the objective to be able to assess if the safety concept developed by the Waste Management Organisation fulfils the defined safety requirements. It includes the development and the follow-up of
in situ
experiments in underground research laboratories (e.g. in the Mont Terri project in Switzerland and at Tournemire in France). Such activities are essential to maintain and improve the scientific and technical skills, to strengthen the independence from the Waste Management Organisation and to build public confidence in the regulatory function.

More generally, Research & Development in underground research laboratories is important to the Regulatory Body as it allows investigating processes, technologies and activities important to safety under
in situ
conditions. In particular, it is essential to accurately identify and characterize the processes upon which the safety functions of a disposal system rely, as well as processes that may affect the system performance. It is also necessary to demonstrate that construction and operational methods and technologies are technically feasible and can be implemented as assumed in the safety case. Such research and development activities are of great importance to properly manage some of the key uncertainties associated with a disposal programme and in particular to identify, characterize and reduce them where needed.
YaConv: Convolution with Low Cache Footprint
Authors: Korostelev Ivan , L. De Carvalho João P. , Moreira José , Amaral José Nelson
Published: December 2022

This article introduces
YaConv
, a new algorithm to compute convolution using
GEMM
microkernels from a Basic Linear Algebra Subprograms library that is efficient for multiple CPU architectures. Previous approaches either create a copy of each image element for each filter element or reload these elements into cache for each
GEMM
call, leading to redundant instances of the image elements in cache. Instead,
YaConv
loads each image element once into the cache and maximizes the reuse of these elements. The output image is computed by scattering results of the
GEMM
microkernel calls to the correct locations in the output image. The main advantage of this new algorithm—which leads to better performance in comparison to the existing
im2col
approach on several architectures—is a more efficient use of the memory hierarchy. The experimental evaluation on convolutional layers from PyTorch, along with a parameterized study, indicates an average 24% speedup over
im2col
convolution. Increased performance comes as a result of 3× reduction in L3 cache accesses and 2× fewer branch instructions.
HCMS: Hierarchical and Conditional Modality Selection for Efficient Video Recognition
Authors: Weng Zejia , Wu Zuxuan , Li Hengduo , Chen Jingjing , Jiang Yu-Gang
Published: December 2022

Videos are multimodal in nature. Conventional video recognition pipelines typically fuse multimodal features for improved performance. However, this is not only computationally expensive but also neglects the fact that different videos rely on different modalities for predictions. This article introduces Hierarchical and Conditional Modality Selection (HCMS), a simple yet efficient multimodal learning framework for efficient video recognition. HCMS operates on a low-cost modality, i.e., audio clues, by default, and dynamically decides on-the-fly whether to use computationally expensive modalities, including appearance and motion clues, on a per-input basis. This is achieved by the collaboration of three LSTMs that are organized in a hierarchical manner. In particular, LSTMs that operate on high-cost modalities contain a gating module, which takes as inputs lower-level features and historical information to adaptively determine whether to activate its corresponding modality; otherwise, it simply reuses historical information. We conduct extensive experiments on two large-scale video benchmarks, FCVID and ActivityNet, and the results demonstrate the proposed approach can effectively explore multimodal information for improved classification performance while requiring much less computation.
Disentangled Representations Learning for Multi-target Cross-domain Recommendation
Authors: Guo Xiaobo , Li Shaoshuai , Guo Naicheng , Cao Jiangxia , Liu Xiaolei , Ma Qiongxu , Gan Runsheng , Zhao Yunan
Published: December 2022

Data sparsity has been a long-standing issue for accurate and trustworthy recommendation systems (RS). To alleviate the problem, many researchers pay much attention to cross-domain recommendation (CDR), which aims at transferring rich knowledge from related source domains to enhance the recommendation performance of sparse target domain. To reach the knowledge transferring purpose, recent CDR works always focus on designing different pairwise directed or undirected information transferring strategies between source and target domains. However, such pairwise transferring idea is difficult to adapt to multi-target CDR scenarios directly, e.g., transferring knowledge between multiple domains and improving their performance simultaneously, as such strategies may lead the following issues: (1) When the number of domains increases, the number of transferring modules will grow exponentially, which causes heavy computation complexity. (2) A single pairwise transferring module could only capture the relevant information of two domains, but ignores the correlated information of other domains, which may limit the transferring effectiveness. (3) When a sparse domain serves as the source domain during the pairwise transferring, it would easily leads the negative transfer problem, and the untrustworthy information may hurt the target domain recommendation performance. In this article, we consider the key challenge of the multi-target CDR task: How to identify the most valuable trustworthy information over multiple domains and transfer such information efficiently to avoid the negative transfer problem? To fulfill the above challenge, we propose a novel end-to-end model termed as
DR-MTCDR
, standing for
D
isentangled
R
epresentations learning for
M
ulti-
T
arget
CDR
. DR-MTCDR aims at transferring the trustworthy domain-shared information across domains, which has the two major advantages in both efficiency and effectiveness: (1) For efficiency, DR-MTCDR utilizes a unified module on all domains to capture disentangled domain-shared information and domain-specific information, which could support all domain recommendation and be insensitive to the number of domains. (2) For effectiveness, based on the disentangled domain-shared and domain-specific information, DR-MTCDR has the capability to lead positive effect and make trustworthy recommendation for each domain. Empirical evaluations on datasets from both public datasets and real-world large-scale financial datasets have shown that the proposed framework outperforms other state-of-the-art baselines.
Proteoglycans: a common portal for SARS-CoV-2 and extracellular vesicle uptake
Authors: Cerezo-Magaña Myriam , Bång-Rudenstam Anna , Belting Mattias
Published: December 2022

As structural components of the glycocalyx, heparan sulfate proteoglycans (HSPGs) are involved in multiple pathophysiological processes at the apex of cell signaling cascades, and as endocytosis receptors for particle structures, such as lipoproteins, extracellular vesicles, and enveloped viruses, including SARS-CoV-2. Given their diversity and complex biogenesis regulation, HSPGs remain understudied. Here we compile some of the latest studies focusing on HSPGs as internalizing receptors of extracellular vesicles (“endogenous virus”) and SARS-CoV-2 lipid-enclosed particles and highlight similarities in their biophysical and structural characteristics. Specifically, the similarities in their biogenesis, size, and lipid composition may explain a common dependence on HSPGs for efficient cell-surface attachment and uptake. We further discuss the relative complexity of extracellular vesicle composition and the viral mechanisms that evolve towards increased infectivity that complicate therapeutic strategies addressing blockade of their uptake.
Tissue inhibitor of metalloproteinase-4 deletion in mice impacts maternal cardiac function during pregnancy and postpartum
Authors: Thurstin Ashley A. , Egeli Allison N. , Goldsmith Edie C. , Spinale Francis G. , LaVoie Holly A.
Published: December 2022

Pregnancy and lactation increase volume load on the heart. Defects in cardiac remodeling during pregnancy and postpartum can result in peripartum cardiomyopathy. TIMPs participate in cardiac remodeling. The present study reports the cardiac function in Timp4 knockout adult female mice during pregnancy and lactation. Timp4 knockout females at many time points have higher ejection fraction to maintain stroke volume. Global deletion of Timp4 was not detrimental to maternal heart function during first pregnancies and lactation.
Perception of COVID-19 and Vaccine Acceptance among Healthcare Workers
Authors: Nemr Nader , Kishk Rania M. , Soliman Nourhan Hassan , Farghaly Rasha Mohammed , Kishk Safaa M. , Louis Nageh
Published: December 2022

Background. COVID-19 infection is more likely to be acquired and transmitted by healthcare workers (HCWs). Furthermore, they serve as role models for communities in terms of COVID-19 vaccination attitudes. As a result, HCWs’ reluctance to vaccinate could have a significant impact on pandemic containment efforts. Aim. To characterize the current COVID-19 vaccine approval situation among healthcare workers and to determine the most likely reason for agreement or disagreement with COVID-19 vaccination. Methods. This cross-sectional design included 451 HCWs from COVID-19 treatment institutions, with COVID-19 exposure risk changing depending on job function and working location. Results. The study recruited 156 physicians and 295 nurses, of whom 58.1% were female and 41.9% were male. Physicians had a significantly higher rate of participation in COVID-19 pandemic prevention and control, with a rate of 69.9% versus 55.3% of nurses. Acceptance of COVID-19 vaccination was reported by 40.8% of HCWs. The rate of acceptance was significantly higher among physicians (55.1%) than among nurses (33.2%) (

p
<
0.001

). Most HCWs (67.8%) believed the vaccine was not effective. Physicians showed more significant trust in the effectiveness of the vaccine than nurses (41% and 27.5, respectively) (

p
=
0.003

). Concerning vaccine safety, only 32.8% of HCWs believed it was safe. This was significantly higher in physicians (41.7%) than in nurses (28.1%) (

p
=
0.004

). Conclusion. Vaccination uncertainty is common among healthcare personnel in Egypt, and this could be a significant barrier to vaccine uptake among the public. Campaigns to raise vaccine knowledge are critically needed.
Random Forests in Count Data Modelling: An Analysis of the Influence of Data Features and Overdispersion on Regression Performance
Authors: Mushagalusa Ciza Arsène , Fandohan Adandé Belarmain , Glèlè Kakaï Romain
Published: December 2022

Machine learning algorithms, especially random forests (RFs), have become an integrated part of the modern scientific methodology and represent an efficient alternative to conventional parametric algorithms. This study aimed to assess the influence of data features and overdispersion on RF regression performance. We assessed the effect of types of predictors (100, 75, 50, and 20% continuous, and 100% categorical), the number of predictors (p = 816 and 24), and the sample size (N = 50, 250, and 1250) on RF parameter settings. We also compared RF performance to that of classical generalized linear models (Poisson, negative binomial, and zero-inflated Poisson) and the linear model applied to log-transformed data. Two real datasets were analysed to demonstrate the usefulness of RF for overdispersed data modelling. Goodness-of-fit statistics such as root mean square error (RMSE) and biases were used to determine RF accuracy and validity. Results revealed that the number of variables to be randomly selected for each split, the proportion of samples to train the model, the minimal number of samples within each terminal node, and RF regression performance are not influenced by the sample size, number, and type of predictors. However, the ratio of observations to the number of predictors affects the stability of the best RF parameters. RF performs well for all types of covariates and different levels of dispersion. The magnitude of dispersion does not significantly influence RF predictive validity. In contrast, its predictive accuracy is significantly influenced by the magnitude of dispersion in the response variable, conditional on the explanatory variables. RF has performed almost as well as the models of the classical Poisson family in the presence of overdispersion. Given RF’s advantages, it is an appropriate statistical alternative for counting data.
Vibration Reduction Design and Test of UAV Load Radar
Authors: Fu Jia , Fan Chaohui , Yu Changshuai , Liu Guangming , Luo Haitao
Published: December 2022

It is challenging to design complex structures and calculate their dimensions only on the basis of theory. This issue was resolved efficiently by the FEM. Using the applicable test, the mechanical properties of the materials used in the damper are estimated, effectively guiding the design of the device and boosting its vibration-reducing performance. A large-load unmanned aerial vehicle (UAV) airborne radar is the subject of this research article. According to particular use case, vibration dampers for the radar-mounting platform are designed using passive vibration isolation technology to reduce aircraft vibration transmission to the radar. A conference paper has already presented prior research on the same topic. Earlier research confirmed the vibration-isolating efficacy of the T-type rubber isolator, which can greatly improve the operational conditions of the airborne radar. In contrast, earlier studies relied more on experimental validation and did not use numerical simulations. This research’s primary objective is the development of a T-type rubber isolator, whose vibration reduction performance is proven using FEM and experiments. Utilizing a suitable simulation analysis method to investigate the vibration reduction of an unmanned aerial vehicle’s (UAV) airborne component is a crucial technical task. By comparing multiple material models, a more precise model for characterizing rubber was developed.
[Retracted] Global Value Chain Governance of the Apparel Design Industry under the Background of Global Sustainable Economic Development
Authors: Yu Dan , Zhao Peipei
Published: December 2022

In recent years, there has been a significant rise in the importance of sustainability in business and personal life. Unsustainable techniques are used throughout the whole lifecycle of the clothing and textiles business, from the time it is made to the time it is recycled or thrown away. The garment sector and its customers must be ready for a more sustainable future as the government, corporations, and society turn their focus toward it. An investigation of how the apparel design industry (ADI) might adhere to the global value chain (GVC) in light of sustainable economic development (SED) and their relevance in China. This study analyzes the ADI moves up from one functional level to another within the global apparel value chain and what factors allow them to break into the global market. Researchers demonstrate that the existing sustainable operations in the AD industry are a long way from attaining the aims of economic development going hand in hand with the goals of economic sustainability by studying both the relevant literature and the industrial practices in the sector. This study relies on various case studies, proposes the “apparel global value chain” of governance‐based sustainability, explores the implications of these models for areas with very varied degrees of development and diverse AD structures, and considers their implications for regional development and sustainability. As a development of a better knowledge of how fashion design contributes to economic sustainability. The study aims to examine how ADI uses economically sustainable techniques and how designers contribute to the creation of environmentally friendly goods.
Effects of Glycyrrhizin (GL) Supplementation on Survival, Growth Performance, Expression of Feeding-Related Genes, Activities of Digestive Enzymes, Antioxidant Capacity, and Expression of Inflammatory Factors in Large Yellow Croaker (Larimichthys crocea) Larvae
Authors: Wang Yuntao , Xu Wenxuan , Zhang Jianmin , Liu Jiahui , Wang Zhen , Liu Yongtao , Mai Kangsen , Ai Qinghui
Published: December 2022

A 30-day feeding trial was conducted to determine the effects of dietary glycyrrhizin (GL) on survival, growth performance, expression of feeding-related genes, activities of digestive enzymes, antioxidant capacity, and expression of inflammatory factors of large yellow croaker larvae with an initial weight of

3.78
±
0.27

 mg. Four 53.80% crude protein and 16.40% crude lipid diets were formulated with supplementation of 0%, 0.005%, 0.01%, and 0.02% GL, respectively. Results indicated that larvae fed diets with GL had higher survival rate and specific growth rate than the control (

P
<
0.05

). Compared with the control, the mRNA expression of orexigenic factor genes including neuropeptide Y (npy) and agouti-related protein (agrp) were significantly increased in larvae fed the diet with 0.005% GL, while the mRNA expression of anorexigenic factor genes including thyrotropin-releasing hormone (trh), cocaine and amphetamine regulated transcript (cart), and leptin receptor (lepr) were significantly decreased in larvae fed the diet with 0.005% GL (

P
<
0.05

). The trypsin activity in larvae fed the diet with 0.005% GL was significantly higher than the control (

P
<
0.05

). The alkaline phosphatase (AKP) activity in larvae fed the diet with 0.01% GL was significantly higher than the control (

P
<
0.05

). A clear increase of total glutathione (T-GSH) content, activities of superoxide dismutase (SOD), and glutathione peroxidase (GSH-Px) was observed in larvae fed the diet with 0.01% GL compared with the control (

P
<
0.05

). Moreover, the mRNA expression of interleukin-1β (il-1β) and interleukin-6 (il-6) (proinflammatory genes) in larvae fed the diet with 0.02% GL were significantly lower than the control (

P
<
0.05

). In conclusion, the supplementation of 0.005% -0.01% GL could enhance the expression of orexigenic factor genes, activities of digestive enzymes and antioxidant capacity, ultimately improving the survival, and growth performance of large yellow croaker larvae.
A Review on the Application of Circulating Fluidized Bed Fly Ash in Building Materials
Authors: Jia Guanhua , Wang Yanlin , Yang Fengling
Published: December 2022

Circulating fluidized bed (CFB)-based coal burning technology is a type of clean coal combustion technology. Owing to its characteristics of low temperature combustion and desulfurization in furnace, the CFB fly ash is quite different from ordinary fly ash. Based on the comprehensive understanding of physical and chemical characteristics of CFB fly ash, this review summarizes the research progress of the application of CFB fly ash in Portland cement, magnesium oxysulfate (MOS) cement, solid-waste–based cementitious material (SWCM), concrete, geopolymer, artificial aggregate, and other building materials. The study shows that CFB fly ash can improve the early physical and mechanical properties and reduce the drying shrinkage properties for cementitious materials. Its unique chemical composition endows it with good application performance in the MOS cement, geopolymer, and SWCM. However, unfavorable characteristics of CFB fly ash, such as loose and porous structure, self-hardening behavior, and expansibility greatly hinder its practical applications in building materials. Therefore, some measures, such as addition of rational water-reducing agent, ultrafine grinding, mixing of ordinary fly ash, controlling the dosage of CFB fly ash, and water-cement ratio, must be taken to improve the negative influence of CFB fly ash on the building materials, in order to expand its application range. This article summarizes the application and research status of CFB fly ash in building materials, aiming at further promoting the research studies on CFB fly ash resource utilization in building materials and improving the efficiency of CFB fly ash resource utilization.
Assessing the Short-Term Effects of No-Till on Crop Yield, Greenhouse Gas Emissions, and Soil C and N Pools in a Cover-Cropped, Biodynamic Mediterranean Vineyard
Authors: Lazcano Cristina , Gonzalez-Maldonado Noelymar , Yao Erika H. , Wong Connie T. F. , Falcone Mia , Dodson Peterson Jean , Casassa L. Federico , Malama Bwalya , Decock Charlotte
Published: December 2022

Background and Aims. No-till is considered a core practice of conservation and climate-smart agriculture. Nevertheless, recent evidence suggests that the benefits of this practice for climate change mitigation might be overestimated, particularly in the short term. Methods and Results. In a three-year field experiment, we investigated the environmental and agronomic performance of this practice by looking at changes in soil physical properties, C and N pools, as well as vine yield and grape quality. No-till increased stratification in the distribution of active soil C (POXC), further accentuating the already existing difference between top and subsoil. No-till also slightly reduced the daily efflux of CO2 from the soil during the rainy season, showing that these plots were less prone to lose C than tilled plots. Nonetheless, no-till did not increase total soil C stocks. This, together with the lack of differences in cumulative N2O emissions, resulted in similar global warming potential in till and no-till plots. Vine yield and grape quality remained unchanged in the no-till compared to the tilled plots. Conclusions. Even though no-till did not result in short-term climate change mitigation, results of this study suggest changes in the ecological processes leading to C accumulation and mineralization and that may result in future C sequestration. There were no deleterious effects of no-till on grape yield and quality. Significance of the Study. This study shows that reducing tillage intensity in vineyards is a feasible strategy from an agronomic standpoint.
A Combined Discrete Road Traffic State Prediction Model Based on GFD-ARMA-FISHER Analytical Framework
Authors: Yuan Pengcheng , Han Yin
Published: December 2022

With the popularization of the Internet and the widespread application of mobile terminal, travelers are increasingly dependent on traffic information. It is particularly important to construct and develop more accurate discrete traffic state prediction models. Considering that most of the previous studies about traffic state prediction are essentially a prediction of specific parameters, this paper proposes a generalized fractional difference (GFD)-auto regressive moving average (ARMA)-Fisher combined model that can directly predict the discrete traffic state. First, using the original historical traffic flow data as training samples for cluster analysis, we obtain different traffic state classification samples. Then, using the Fisher discriminant method, we develop a discrete traffic state recognition model based on the traffic state classification samples. The model can help us identify the discrete state of the future traffic flow when we input a set of predicted traffic parameters to it. In order to improve the accuracy of the model prediction, this paper creatively proposes to apply the GFD method to the stabilization of original traffic flow data and then develop an ARMA model to predict the traffic parameters processed by GFD (GFD-ARMA). Last, we obtain the predicted discrete traffic state using the calibrated Fisher discrimination model whose inputs are the parameters predicted by the GFD-ARMA. The proposed method is tested using field data from CHangzhou, China. The results suggest that the developed GFD-ARMA-FISHER method shows a higher accuracy for traffic state prediction and is better than other similar methods.
Petrochemical Equipment Detection by Improved Yolov5 with Multiscale Deep Feature Fusion and Attention Mechanism
Authors: Wei Zhenqiang , Dong Shaohua , Wang Xuchu
Published: December 2022

Petrochemical equipment detection technology plays important role in petrochemical industry security monitoring systems, equipment working status analysis systems, and other applications. In complex scenes, the accuracy and speed of petrochemical equipment detection would be limited because of the missing and false detection of equipment with extreme sizes, due to image quality, equipment scale, light, and other factors. In this paper, a one-stage attention mechanism-enhanced Yolov5 network is proposed to detect typical types of petrochemical equipment in industry scene images. The model considers the advantages of the channel and spatial attention mechanism and incorporates it into the three mainframes. Furthermore, the multiscale deep feature is fused with a bottom-up feature pyramid structure to learn the features of equipment with extreme sizes. Moreover, an adaptive anchor generation algorithm is proposed to handle objects with extreme sizes in a complex background. In addition, the data augmentation strategy is also introduced to handle the relatively small and extremely large sample and to enhance the robustness of the fused model. The proposed model was validated on the self-built petrochemical equipment image data set, and the experimental results show that it achieves a competitive performance in comparison with the related state-of-the-art detectors.
Miniaturized Wide Scanning Angle Phased Array Using EBG Structure for 5G Applications
Authors: Shen Xiumei , Xue Kun , Tang Xinggang , He Yuqi
Published: December 2022

This article presents a miniaturized wide-angle scanning phased array for fifth-generation (5G) application. A subarray of two closely packed patch antennas on electromagnetic bandgap structure (EBG) ground with the operating bandwidth of 26–29 GHz is used as the basic module of the linear array, which contains four equally spaced subarrays. The existence of the EBG ground enables the array to be compact in size (3.2 × 0.6 × 0.12λL3), yet the mutual coupling between each element can reach to more than 22 dB within the whole band of interest. The EBG structures also contribute to the wide element radiation pattern of the aperiodic array and consequently the wide scanning angle performance of the array. The range of the main beam scan with EBG structure can reach from −70° to 70° with more than 6 dB side lobe levels (SLLs) at 26.5 GHz with 3 dBi scanning gain loss. This proposed method enabling the array to be compact and wide in scanning angle is very attractive for 5G mobile terminal applications.
Identification of Diagnostic Markers in Infantile Hemangiomas
Authors: Huang Sicong , Chen Ruiqi , Gao Shihua , Shi Yongjie , Xiao Qiwen , Zhou Qiang , Wei Jie , Kang Jiale , Sun Weimin , Hu Yingyu , Shen Gang , Jia Hongyun
Published: December 2022

Background. Infantile Hemangiomas (IHs) are common benign vascular tumors of infancy that may have serious consequences. The research on diagnostic markers for IHs is scarce. Methods. The “limma” R package was applied to identify differentially expressed genes (DEGs) in developing IHs. Plugin ClueGO in Cytoscape software performed functional enrichment of DEGs. The Search Tool for Retrieving Interacting Genes (STRING) database was utilized to construct the PPI network. The least absolute shrinkage and selection operator (LASSO) regression model and support vector machine recursive feature elimination (SVM-RFE) analysis were used to identify diagnostic genes for IHs. The receiver operating characteristic (ROC) curve evaluated diagnostic genes’ discriminatory ability. Single-gene based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) was conducted by Gene Set Enrichment Analysis (GSEA). The chemicals related to the diagnostic genes were excavated by the Comparative Toxicogenomics Database (CTD). Finally, the online website Network Analyst was used to predict the transcription factors targeting the diagnostic genes. Results. A total of 205 DEGs were singled out from IHs samples of 6-, 12-, and 24-month-old infants. These genes principally participated in vasculogenesis and development-related, endothelial cell-related biological processes. Then we mined 127 interacting proteins and created a network with 127 nodes and 251 edges. Furthermore, LASSO and SVM-RRF algorithms identified five diagnostic genes, namely, TMEM2, GUCY1A2, ISL1, WARS, and STEAP4. ROC curve analysis results indicated that the diagnostic genes had a powerful ability to distinguish IHs samples from normal samples. Next, the results of GSEA for a single gene illustrated that all five diagnostic genes inhibited the “valine, leucine, and isoleucine degradation” pathway in the development of IHs. WARS, TMEM2, and STEAP4 activated the “blood vessel development” and “vasculature development” in IHs. Subsequently, inhibitors targeting TMEM2, GUCY1A2, ISL1, and STEAP4 were mined. Finally, 14 transcription factors regulating GUCY1A2, 14 transcription factors regulating STEAP4, and 26 transcription factors regulating ISL1 were predicted. Conclusion. This study identified five diagnostic markers for IHs and further explored the mechanisms and targeting drugs, providing a basis for diagnosing and treating IHs.
Meta-Analysis of Human Cancer Single-Cell RNA-Seq Datasets Using the IMMUcan Database
Authors: Camps Jordi , Noël Floriane , Liechti Robin , Massenet-Regad Lucile , Rigade Sidwell , Götz Lou , Hoffmann Caroline , Amblard Elise , Saichi Melissa , Ibrahim Mahmoud M. , Pollard Jack , Medvedovic Jasna , Roider Helge G. , Soumelis Vassili
Published: December 2022

The development of single-cell RNA sequencing (scRNA-seq) technologies has greatly contributed to deciphering the tumor microenvironment (TME). An enormous amount of independent scRNA-seq studies have been published representing a valuable resource that provides opportunities for meta-analysis studies. However, the massive amount of biological information, the marked heterogeneity and variability between studies, and the technical challenges in processing heterogeneous datasets create major bottlenecks for the full exploitation of scRNA-seq data. We have developed IMMUcan scDB (https://immucanscdb.vital-it.ch), a fully integrated scRNA-seq database exclusively dedicated to human cancer and accessible to nonspecialists. IMMUcan scDB encompasses 144 datasets on 56 different cancer types, annotated in 50 fields containing precise clinical, technological, and biological information. A data processing pipeline was developed and organized in four steps: (i) data collection; (ii) data processing (quality control and sample integration); (iii) supervised cell annotation with a cell ontology classifier of the TME; and (iv) interface to analyze TME in a cancer type–specific or global manner. This framework was used to explore datasets across tumor locations in a gene-centric (CXCL13) and cell-centric (B cells) manner as well as to conduct meta-analysis studies such as ranking immune cell types and genes correlated to malignant transformation. This integrated, freely accessible, and user-friendly resource represents an unprecedented level of detailed annotation, offering vast possibilities for downstream exploitation of human cancer scRNA-seq data for discovery and validation studies.

Significance:

The IMMUcan scDB database is an accessible supportive tool to analyze and decipher tumor-associated single-cell RNA sequencing data, allowing researchers to maximally use this data to provide new insights into cancer biology.
Spatially Resolved Single-Cell Assessment of Pancreatic Cancer Expression Subtypes Reveals Co-expressor Phenotypes and Extensive Intratumoral Heterogeneity
Authors: Williams Hannah L. , Dias Costa Andressa , Zhang Jinming , Raghavan Srivatsan , Winter Peter S. , Kapner Kevin S. , Ginebaugh Scott P. , Väyrynen Sara A. , Väyrynen Juha P. , Yuan Chen , Navia Andrew W. , Wang Junning , Yang Annan , Bosse Timothy L. , Kalekar Radha L. , Lowder Kristen E. , Lau Mai Chan , Elganainy Dalia , Morales-Oyarvide Vicente , Rubinson Douglas A. , Singh Harshabad , Perez Kimberly , Cleary James M. , Clancy Thomas E. , Wang Jiping , Mancias Joseph D. , Brais Lauren K. , Hill Emma R. , Kozak Margaret M. , Linehan David C. , Dunne Richard F. , Chang Daniel T. , Koong Albert C. , Hezel Aram F. , Hahn William C. , Shalek Alex K. , Aguirre Andrew J. , Nowak Jonathan A. , Wolpin Brian M.
Published: December 2022

Pancreatic ductal adenocarcinoma (PDAC) has been classified into classical and basal-like transcriptional subtypes by bulk RNA measurements. However, recent work has uncovered greater complexity to transcriptional subtypes than was initially appreciated using bulk RNA expression profiling. To provide a deeper understanding of PDAC subtypes, we developed a multiplex immunofluorescence (mIF) pipeline that quantifies protein expression of six PDAC subtype markers (CLDN18.2, TFF1, GATA6, KRT17, KRT5, and S100A2) and permits spatially resolved, single-cell interrogation of pancreatic tumors from resection specimens and core needle biopsies. Both primary and metastatic tumors displayed striking intratumoral subtype heterogeneity that was associated with patient outcomes, existed at the scale of individual glands, and was significantly reduced in patient-derived organoid cultures. Tumor cells co-expressing classical and basal markers were present in > 90% of tumors, existed on a basal-classical polarization continuum, and were enriched in tumors containing a greater admixture of basal and classical cell populations. Cell–cell neighbor analyses within tumor glands further suggested that co-expressor cells may represent an intermediate state between expression subtype poles. The extensive intratumoral heterogeneity identified through this clinically applicable mIF pipeline may inform prognosis and treatment selection for patients with PDAC.

Significance:

A high-throughput pipeline using multiplex immunofluorescence in pancreatic cancer reveals striking expression subtype intratumoral heterogeneity with implications for therapy selection and identifies co-expressor cells that may serve as intermediates during subtype switching.
Abstract PR006: Single-molecule and single-cell epigenetics: Decoding the epigenome for cancer research and diagnostics
Authors: Shema Efrat
Published: December 2022

Genes and genomic elements are packaged by chromatin structures that regulate their activity. We developed a novel high-throughput single-molecule imaging technology to decode combinatorial modifications on millions of individual nucleosomes. We apply this technology to image nucleosomes and delineate their combinatorial epigenetic patterns, and how these patterns are deregulated in cancer. In addition, we adapt single-cell technologies based on CyTOF to profile the global levels of multiple histone modifications in single cells, thus revealing epigenetic heterogeneity in cancer. Our research focuses on pediatric gliomas harboring lysine to methionine substitution of residue 27 on histone H3 (K27M). We provide evidence for widespread effects of the H3-K27M oncohistone on multiple core epigenetic pathways, and highlight the capability of single-molecule and single-cell tools to reveal mechanisms of chromatin deregulation and heterogeneity in cancer. We also harness the single-molecule technology as a novel liquid biopsy approach, by comprehensively profiling combinatorial epigenetic marks of plasma-isolated nucleosomes. Applying this analysis to a cohort of plasma samples detect colorectal cancer at high accuracy and sensitivity, even at early stages. Finally, combining this proteomic analysis with single-molecule DNA sequencing reveals the tissue-of-origin of the tumor.

Citation Format: Efrat Shema. Single-molecule and single-cell epigenetics: Decoding the epigenome for cancer research and diagnostics. [abstract]. In: Proceedings of the AACR Special Conference: Cancer Epigenomics; 2022 Oct 6-8; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2022;82(23 Suppl_2):Abstract nr PR006.
Abstract IA013: Early-onset colorectal cancer: Emerging risk factors and early detection
Authors: Cao Yin
Published: December 2022

Early-onset colorectal cancer (CRC) is increasing in the United States and globally. Compared to CRC diagnosed at older ages, patients with early-onset CRC experienced significant diagnostic delays, with tumors of advanced clinicopathological features and unknown etiology. While screening recommendations have been recently lowered to age 45 in the United States, about half of early-onset CRC cases are diagnosed before age 45, thus will not be detected via screening. This presentation will review emerging risk factors of early-onset CRC throughout the life course, as well as clinical signs and symptoms associated with early-onset CRC.

Citation Format: Yin Cao. Early-onset colorectal cancer: Emerging risk factors and early detection [abstract]. In: Proceedings of the AACR Special Conference on Colorectal Cancer; 2022 Oct 1-4; Portland, OR. Philadelphia (PA): AACR; Cancer Res 2022;82(23 Suppl_1):Abstract nr IA013.
Abstract IA015: Combined single cell atlasing and organoid modeling reveal progressive plasticity during human colorectal cancer metastasis
Authors: Ganesh Karuna
Published: December 2022

Metastatic cancers invariably relapse due to the emergence of therapy resistant subclones that are capable of self-renewal, slow cell-cycling, tumor re-initiation and therapy resistance. However, the molecular mechanisms that underpin the phenotypic plasticity of metastasis stem cells, their relationship to tumor initiating cancer stem cells and macrometastasis, and the co-evolution of the immune response to dynamically emerging tumor regenerative states are not understood. We are adopting a patient-derived functional biospecimen approach combining (1) hypothesis-generation using transcriptomics, epigenomics and spatial/histological analysis of patient samples of primary and metastatic gastrointestinal (GI) cancer with (2) mechanistic dissection in cutting-edge patient-derived organoid models, including co-cultures with immune/stromal cells, and (3) genetically engineered and orthotopic transplantation models of metastatic GI cancer. By profiling, lineage tracing and genetically interrogating the evolving transcriptomic and chromatin landscapes of regenerative metastatic states, we are defining the molecular mechanisms that underpin the phenotypic plasticity of metastatic cancer, and the co-evolution of the tumor microenvironmental response to dynamically emerging tumor regenerative states. Our approach is unveiling crucial signaling nodes required for metastatic plasticity that can be therapeutically targeted to improve outcomes for patients with advanced cancer.

Citation Format: Karuna Ganesh. Combined single cell atlasing and organoid modeling reveal progressive plasticity during human colorectal cancer metastasis [abstract]. In: Proceedings of the AACR Special Conference on Colorectal Cancer; 2022 Oct 1-4; Portland, OR. Philadelphia (PA): AACR; Cancer Res 2022;82(23 Suppl_1):Abstract nr IA015.
TNF Receptors Choose HSC Fate in SupportingDnmt3a-Mutant Clonal Hematopoiesis
Authors: Niño Katia E. , Pietras Eric M.
Published: December 2022

TNFα receptor signaling distinctly promotes self-renewal and lymphoid differentiation in Dnmt3a-mutant hematopoietic stem cells, contributing to clonal hematopoiesis of indeterminate potential.

See related article by SanMiguel et al., p. 2763 (3).
Inferring the Genetic Influences on Psychological Traits Using MRI Connectivity Predictive Models: Demonstration with Cognition
Authors: Hatoum Alexander S. , Reineberg Andrew E. , Kragel Philip A. , Wager Tor D. , Friedman Naomi P.
Published: December 2022

Introduction: Genetic correlations between brain and behavioral phenotypes in analyses from major genetic consortia have been weak and mostly nonsignificant. fMRI models of systems-level brain patterns may help improve our ability to link genes, brains, and behavior by identifying reliable and reproducible endophenotypes. Work using connectivity-based predictive modeling has generated brain-based proxies of behavioral and neuropsychological variables. If such models capture activity in inherited brain systems, they may offer a more powerful link between genes and behavior. Method: As a proof of concept, we develop models predicting intelligence (IQ) based on fMRI connectivity and test their effectiveness as endophenotypes. We link brain and IQ in a model development dataset of N = 3,000 individuals and test the genetic correlations between brain models and measured IQ in a genetic validation sample of N = 13,092 individuals from the UK Biobank. We compare an additive connectivity-based model to multivariate LASSO and ridge models phenotypically and genetically. We also compare these approaches to single “candidate” brain areas. Results: We found that predictive brain models were significantly phenotypically correlated with IQ and showed much stronger correlations than individual edges. Further, brain models were more heritable (h2 = 0.155–0.181) than single brain regions (h2 = 0.038–0.118) and captured about half of the genetic variance in IQ (rG = 0.422–0.576), while rGs with single brain measures were smaller and nonsignificant. For the different approaches, LASSO and ridge were similarly predictive, with slightly weaker performance of the additive model. LASSO model weights were highly theoretically interpretable and replicated known brain IQ associations. Finally, functional connectivity models trained in midlife showed genetic correlations with early life correlates of IQ, suggesting some stability in the prediction of fMRI models. Conclusion: Multisystem predictive models hold promise as imaging endophenotypes that offer complex and theoretically relevant conclusions for future imaging genetics research.
Comparative Studies of Karyotypes in the Cervidae Family
Authors: Proskuryakova Anastasia A. , Ivanova Ekaterina S. , Perelman Polina L. , Ferguson-Smith Malcolm A. , Yang Fentang , Okhlopkov Innokentiy M. , Graphodatsky Alexander S.
Published: December 2022

The family Cervidae is the second most diverse family in the infraorder Pecora and is characterized by a striking variability in the diploid chromosome numbers among species, ranging from 6 to 70. Chromosomal rearrangements in Cervidae have been studied in detail by chromosome painting. There are many comparative cytogenetic data for both subfamilies (Cervinae and Capreolinae) based on homologies with chromosomes of cattle and Chinese muntjac. Previously it was found that interchromosomal rearrangements are the major type of rearrangements occurring in the Cervidae family. Here, we build a detailed chromosome map of a female reindeer (Rangifer tarandus, 2n = 70, Capreolinae) and a female black muntjac (Muntiacus crinifrons, 2n = 8, Cervinae) with dromedary homologies to find out what other types of rearrangements may have underlined the variability of Cervidae karyotypes. To track chromosomal rearrangements and the distribution of nucleolus organizer regions not only during Cervidae but also Pecora evolution, we summarized new data and compared them with chromosomal maps of other already studied species. We discuss changes in the pecoran ancestral karyotype in the light of new painting data. We show that intrachromosomal rearrangements in autosomes of Cervidae are more frequent than previously thought: at least 13 inversions in evolutionary breakpoint regions were detected.
Potential Embolic Sources in Embolic Stroke of Undetermined Source Patients with Patent Foramen Ovale
Authors: Toh Keith Zhi Xian , Koh Ming Yi , Ho Jamie S.Y. , Ong Kathleen Hui Xin , Lee Yong Qin , Chen Xintong , Fang Jun Tao , Chong Elliot Yeung , Lim Isis Claire Z.Y. , Teo Yao Hao , Teo Yao Neng , Chua Christopher Y.K. , Lim Yinghao , Chan Bernard P.L. , Sharma Vijay K. , Yeo Leonard L.L. , Sia Ching Hui , Tan Benjamin Y.Q.
Published: December 2022

Introduction: A patent foramen ovale (PFO) may coexist with other potential embolic sources (PESs) in patients with embolic stroke of undetermined source (ESUS), leading to difficulty in attributing the stroke to either the PFO or other PESs. We aimed to investigate the prevalence and predictors of concomitant PESs in ESUS patients with PFOs. Methods: A retrospective cohort study was conducted in a tertiary stroke centre. Consecutive patients with ESUS and a concomitant PFO admitted between 2012 and 2021 were included in the study. Baseline characteristics and investigations as a part of stroke workup including echocardiographic and neuroimaging data were collected. PESs were adjudicated by 2 independent neurologists after reviewing the relevant workup. Results: Out of 1,487 ESUS patients, a total of 309 patients who had a concomitant PFO with mean age of 48.8 ± 13.2 years were identified during the study period. The median Risk of Paradoxical Embolism (RoPE) score for the study cohort was 6 (IQR 5–7.5). Of the 309 patients, 154 (49.8%) only had PFO, 105 (34.0%) patients had 1 other PES, 34 (11.0%) had 2 PES, and 16 (5.2%) had 3 or more PES. The most common PESs were atrial cardiopathy (23.9%), left ventricular dysfunction (22.0%), and cardiac valve disease (12.9%). The presence of additional PESs was associated with age ≥60 years (p < 0.001), RoPE score ≤6 (p ≤0.001), and the presence of comorbidities including diabetes mellitus (p = 0.004), hypertension (p≤ 0.001), and ischaemic heart disease (p = 0.011). Conclusion: A large proportion of ESUS patients with PFOs had concomitant PESs. The presence of concomitant PESs was associated with older age and a lower RoPE score. Further, large cohort studies are warranted to investigate the significance of the PES and their overlap with PFOs in ESUS.