bluuebunny/crossref_metadata_2025 huggingface dataset:
Knowledge Domains and Emerging Trends of Osteoblasts-Osteoclasts in Bone Disease From 2002 to 2021: A Bibliometrics Analysis and Visualization Study
Authors: Hou Jun , Su Hongjie , Kuang Xiaocong , Qin Wencong , Liu Kaibing , Pan Kaixiang , Zhang Bokai , Yang Sijie , Yang Shenghui , Peng Xiao , Nie Xinyu , Hua Qikai
Published: July 2022

Background

Osteoblasts-Osteoclasts has been a major area in bone disease research for a long time. However, there are few systematic studies in this field using bibliometric analysis. We aimed to perform a bibliometric analysis and visualization study to determine hotspots and trends of osteoblasts-osteoclasts in bone diseases, identify collaboration and influence among authors, countries, institutions, and journals, and assess the knowledge base to develop basic and clinical research in the future.

Methods

We collected articles and reviews for osteoblasts-osteoclasts in bone diseases from the Web of Science Core Collection. In addition, we utilized scientometrics software (CiteSpace5.8 and VOSviewer1.6.18) for visual analysis of countries/regions, institutions, authors, references, and keywords in the field.

Results

In total, 16,832 authors from 579 institutions in 73 countries/regions have published 3,490 papers in 928 academic journals. The literature in this field is rapidly increasing, with Bone publishing the most articles, whereas Journal of Bone and Mineral Research had the most co-cited journals. These two journals mainly focused on molecular biology and the clinical medicine domain. The countries with the highest number of publications were the US and China, and the University of Arkansas for Medical Sciences was the most active institution. Regarding authors, Stavros C. Manolagas published the most articles, and Hiroshi Takayanagi had the most co-cited papers. Research in this field mainly includes molecular expression and regulatory mechanisms, differentiation, osteoprotection, inflammation, and tumors. The latest research hotspots are oxidative stress, mutation, osteocyte formation and absorption, bone metabolism, tumor therapy, and in-depth mechanisms.

Conclusion

We identified the research hotspots and development process of osteoblasts-osteoclasts in bone disease using bibliometric and visual methods. Osteoblasts-osteoclasts have attracted increasing attention in bone disease. This study will provide a valuable reference for researchers concerned with osteoblasts-osteoclasts in bone diseases.
Determinants of quality of life among elderly patients with type 2 diabetes in northwest of iran: based on problem areas in diabetes
Authors: Moghaddam Hamed Rezakhani , Sobhi Eslam , Soola Aghil Habibi
Published: July 2022

Background

Diabetes is a metabolic disease characterized by chronic hyperglycemia, leading to damage to various organs of the patients and a reduction of their life expectancy and quality of life (QOL). The aim of this study was to explore the determinants of the QOL based on the Problem Areas in Diabetes (PAID).

Methods

This cross-sectional study was carried out in an Iranian diabetic clinic in Ardabil. The PAID, the short form health survey (SF-12), and the sociodemographic questionnaire were all employed. Using the census sample method, 266 elderly people with type 2 diabetes from the lone diabetic clinic at Ardabil took part in this study. One-way ANOVA, t-test, one-sample Kolmogorov–Smirnov test, and multiple regression were used to analyze the data.

Results

Data analysis showed that there was a statistically negative significant relationship between the QOL dimensions and the triple domains of PAID (p < 0.01). In the final model of the predictors of the QOL, treatment barriers, psychological distress related to diabetes management, the type of treatment, age, and the duration of diabetes were statistically significant predictors of the QOL dimensions (p>0.05).

Conclusion

Individual characteristics and factors connected to health services should be prioritized in any intervention program aimed at improving the QOL of elderly patients with diabetes. Psychological distress should be considered in addition to regular physician visits.
Decreased intracellular IL-33 impairs endometrial receptivity in women with adenomyosis
Authors: He Bin , Teng Xiao-Ming , Hao Fan , Zhao Mei , Chen Zhi-Qin , Li Kun-Ming , Yan Qiang
Published: July 2022

Adenomyosis is a common benign uterine lesion that is associated with female infertility, reduced clinical pregnancy rate and high miscarriage risk. While it has been known that the impaired endometrial receptivity is implicated in infertility in patients with adenomyosis, the underlying mechanism remains unclear. In the present study, we showed that intracellular protein level of IL-33 was downregulated in the endometrium of patients with adenomyosis, and IL-33 expression status was shown to be positively correlated with that of HOXA10, an endometrial receptivity marker. The subsequent analysis indicated IL-33 overexpression led to the increase of HOXA10 expression and enhancement of embryo implantation in vitro, which was accompanied with induction of STAT3 phosphorylation. Meanwhile, cryptotanshinone, a potent STAT3 inhibitor, was found to significantly suppress the increase of HOXA10 expression and embryo implantation caused by IL-33 overexpression in vitro, revealing the critical role of STAT3 activity. Consistently, the positive relationship between IL33 and HOXA10 expression in the endometrium was verified in the analysis of adenomyosis mouse model.
Environmental Triggering of Type 1 Diabetes Autoimmunity
Authors: Houeiss Pamela , Luce Sandrine , Boitard Christian
Published: July 2022

Type 1 diabetes (T1D) is a chronic autoimmune disease in which pancreatic islet β cells are destroyed by immune cells, ultimately leading to overt diabetes. The progressive increase in T1D incidence over the years points to the role of environmental factors in triggering or accelerating the disease process which develops on a highly multigenic susceptibility background. Evidence that environmental factors induce T1D has mostly been obtained in animal models. In the human, associations between viruses, dietary habits or changes in the microbiota and the development of islet cell autoantibodies or overt diabetes have been reported. So far, prediction of T1D development is mostly based on autoantibody detection. Future work should focus on identifying a causality between the different environmental risk factors and T1D development to improve prediction scores. This should allow developing preventive strategies to limit the T1D burden in the future.
Sensitivity of Injection Characteristics to the Nozzle Hole Angle of a GDI Injector: A CFD Analysis
Authors: Li Xinhai , Shang Xianshang , Wang Lu , Cheng Yong
Published: July 2022

The increase in injection pressure makes it more challenging to accurately control the injection quantity of the injector of a gasoline direct injection (GDI), thus necessitating the optimization of the parameters of the nozzle holes and the clarification of such parameters in terms of their influence on the injection characteristics, so as to improve the injector’s consistency of injection characteristics. This article adopts the computational fluid dynamics (CFD) approach to investigate the influence of nozzle angle on the gas-liquid flow, cavitation state, and fuel injection rate in the hole. The results show that when the angle of concentric holes of the nozzle exceeds 65° and keeps rising further, it will lead to the gradual decrease of the injection rate during the stable period and the continuous rise of the sensitivity to the nozzle angle. The rising injection pressure would increase the sensitivity of the injection characteristics to the angle of the concentric holes, with the strongest level of sensitivity ranging between 70° and 75°. The negative pressure area on the upper inner wall of the hole would increase with the accretion of the hole angle. As the negative eccentricity rises, the injection rate would gradually drop in both the transition period and the stable period. In contrast, the increase of positive eccentricity would lead to the gradient escalation of the injection rate in the stable period. The impact of negative eccentricity is greater than that of positive eccentricity, implying that it is necessary to reduce the deviation of negative eccentricity as much as possible during the machining and positioning process so as to ensure positioning accuracy.
A Transformer-Based Multi-Entity Load Forecasting Method for Integrated Energy Systems
Authors: Wang Zhiyu , Zhu Zhen , Xiao Geyang , Bai Bing , Zhang Yinjie
Published: July 2022

Energy load forecasting is a critical component of energy system scheduling and optimization. This method, which is classified as a time-series forecasting method, uses prior features as inputs to forecast future energy loads. Unlike a traditional single-target scenario, an integrated energy system has a hierarchy of many correlated energy consumption entities as prediction targets. Existing data-driven approaches typically interpret entity indexes as suggestive features, which fail to adequately represent interrelationships among entities. This paper, therefore, proposes a neural network model named Cross-entity Temporal Fusion Transformer (CETFT) that leverages a cross-entity attention mechanism to model inter-entity correlations. The enhanced attention module is capable of mapping the relationships among entities within a time window and informing the decoder about which entity in the encoder to concentrate on. In order to reduce the computational complexity, shared variable selection networks are adapted to extract features from different entities. A data set obtained from 13 buildings on a university campus is used as a case study to verify the performance of the proposed approach. Compared to the comparative methods, the proposed model achieves the smallest error on most horizons and buildings. Furthermore, variable importance, temporal correlations, building relationships, and time-series patterns in data are analyzed with the attention mechanism and variable selection networks, therefore the rich interpretability of the model is verified.
Non-Pareto Genetic Algorithm for Optimal Planning of Multi-Type Energy Resources in Active Distribution Networks
Authors: Qin Huiling , Li Kui , Chen Zhijun
Published: July 2022

Based on summarizing and analyzing the typical applications of energy storage, the study established a model for an active distribution network, and analyzed the technical and economic benefits of its access to the distribution network. In addition, considering the economic and technical requirements of multiple types of energy, ensure the stable and continuous operation of multiple types of energy, and build an optimal configuration model for multiple types of energy. To achieve a reliable solution to the model, a non-Pareto genetic algorithm (NSGA-II) is designed to obtain the optimal Pareto solution set for multi-type energy location and volume schemes. The proposed solution algorithm has a rich individual update mechanism and an advanced Pareto solution set storage and screening mechanism, which can effectively solve the problem. Furthermore, idea point decision making (IPDM) has been designed to select the best compromise solution in Pareto non-dominated solution set. Finally, based on the IEEE-33 node standard test system, the input source-load uncertainty scenario set is used to construct the distribution network operation scenario, and the configuration model is solved. The results show that NSGA-II can obtain a Pareto front with better solution quality and a more uniform distribution. After accessing the battery energy storage systems (BESS), the annual total power fluctuation and peak-valley difference of daily maximum load have been reduced by 19.25% and 11.8% respectively.
An analysis of the characteristics of precipitation in the Northeast passage and its relationship with sea ice
Authors: Cheng Maoce , Qiu Yubao , Yang Meng , Huang Lin , Chen Jianguo , Li Yang
Published: July 2022

Precipitation is an important part of the atmospheric circulation in the Arctic and is of great significance to the energy budget and hydrological characteristics of the Arctic region. The distribution of precipitation affects the exchange of energy, which then affects the Arctic sea ice indirectly. Arctic precipitation impacts the sea surface albedo, which leads to changes in the sea ice concentration (SIC) and the energy exchange between the sea, ice, and air. In this study, GPM IMERG precipitation data, which have a spatial resolution of 0.1°, were used to analyze the characteristics of precipitation in the Northeast Passage (NEP) from May to December during the period 2011–2020. This analysis of the amount of precipitation and its distribution were performed for the Barents Sea, Kara Sea, Laptev Sea, and East Siberian Sea. The relationship between precipitation and sea ice was also explored. The results show that, during the study period, the average precipitation over the Barents Sea from May to December was 57–561 mm/year and that this area had the highest precipitation in the NEP. For the Kara Sea, the average precipitation for May to December was 50–386 mm/year and for the East Siberian Sea and the Laptev Sea it was 48–303 mm/year and 53–177 mm/year, respectively. For the NEP as a whole, September was found to be the month with the highest average precipitation. An analysis of the correlation between the precipitation and the SIC gave a correlation coefficient of −0.792 for the study period and showed that there is a 15-day delay between the precipitation increase and the decrease in SIC. The analysis of the precipitation data in these areas thus showed that precipitation is related to SIC and is of great importance to understanding and predicting the navigable capacity of the NEP.
Effects of Several Organic Fertilizers on Heavy Metal Passivation in Cd-Contaminated Gray-Purple Soil
Authors: Wang Luyao , Liu Siqi , Li Jianfeng , Li Shunqi
Published: July 2022

Soil heavy metal pollution has become a major ecological and environmental problem and a serious threat to global food security. Organic fertilizer can not only improve soil quality and provide nutrients for plants but also reduce the harm of heavy metal ions to a certain extent, meaning it has become a current research hotspot in the field of heavy metal passivation. In this paper, a completely combined experimental design was used to compare the effects of five organic fertilizers [nutshell organic fertilizer (NOF), pig manure organic fertilizer (PMOF), sludge organic fertilizer (SOF), humus soil organic fertilizer (HSOF) and earthworm soil organic fertilizer (ESOF)] on available Cd in soil with different pollution levels at different dosages, and the passivation mechanism of soil Cd was preliminarily discussed. The results showed that all kinds of organic fertilizers were passivated by reducing the Cd availability, and their effects on the Cd availability of purple soil were closely related to the degree of soil pollution and the amount of organic fertilizers. The passivation effect of moderate Cd-contaminated soil was the best, which increased with the increase of organic fertilizer application rate, and the effects of NOF and SOF were the best. However, the passivation effect of organic fertilizers on soil Cd was the worst in mild Cd-contaminated soil, especially ESOF. The results of Cd morphological correlation analysis showed that Exe-Cd and FeMnOx-Cd in the soil had significantly positive contributions to available Cd, while Res-Cd showed significantly negative contributions. And in moderate Cd contaminated soil, Exe-Cd content decreased by 7.12%–28.50%, while Res-Cd content increased by 19.74%–65.81%. In addition, the content of available Cd in soil decreased first and then increased with time after adding organic fertilizer, and reached the lowest value at 15 days and stabilized after 60 days. The conclusion of this paper can provide a theoretical basis for the rational use of organic fertilizer to reduce the bioavailability of cadmium in Gray-Purple soil.
Towards Green Innovation by China’s Industrial Policy: Evidence From Made in China 2025
Authors: Xu Lanxiang
Published: July 2022

Government policy is an effective strategy to encourage green innovation, but the effect of industrial policy on enterprise green innovation remains under-explored. Using China’s listed manufacturing enterprises data from 2010 to 2020, this study employs Made in China 2025 as a quasi-natural experiment as well as the DID method to explore the effect and mechanism of industrial policies on GI. The following key insights are obtained. 1) The Made in China 2025 has the potential to substantially improve the green innovation of manufacturing enterprises. This result shows that industrial policy can promote green innovation to boost the green transformation and upgrading of China’s manufacturing industry. 2) The policy effect is largely dependent on enterprise governance and regions. 3) Further mechanism considerations find that Made in China 2025 promotes green innovation through tax, environmental subsidies, and corporate social responsibility. In addition to enriching the literature on industrial policy and green innovation, this study offers valuable implications for government green governance and enterprise’s green transformation. Several suggestions for the implementation of Made in China 2025 are proposed.
Modeling the Impact of Cotton Production on Economic Development in Benin: A Technological Innovation Perspective
Authors: Chabi Simin Najib Dafia , Fei Chen , Dilanchiev Azer , Romaric Samson
Published: July 2022

This research provides an in-depth understanding of cotton production in Benin. The analysis explored the effects of cotton production on economic growth using a vector error correction model (VECM). Prior to the VECM, a descriptive analysis was conducted on a time series database collated over 56 years (1965–2021) from national and international organizations including World Bank, the United States Department of Agriculture, and the National Accounts. Depending on the availability of the information over this period, the data were trimmed down to enable a better overview of the trend for variable of interest. Thus, the review of the trend for agricultural land (hectares) for cotton observed from 1965 to 2021 revealed that the trend evolves over the years in three different patterns, while the yields demonstrate four patterns over the same period. For the VECM analysis that permits to understand the long- and short-term relation, the time bound covers 30 years from 1990 to 2019 given the availability of data for all the variables used for the model. The findings highlighted that a strong and positive connection is found between cotton export and economic growth and a long-term relation between the human capital and the economic growth in Benin. A rapid human capital development will increase quality of the employment generation, and the country’s economy will adjust upward. Furthermore, a short-run coefficient unveils feedback necessary in a relative level of investment to bring back the economic growth to equilibrium. From these findings discussed in this study, the government of Benin is commended to diversify the production of cotton through a town target policy.
Mapping of Pollution Distribution for Electric Power System Based on Satellite Remote Sensing
Authors: Ma Yi , Pei Zhipeng , Ma Yutang , Wu Bo , Zhai Shailei , Luo Jiqiang , Kong Peng
Published: July 2022

In recent years, the frequent fouling accidents have posed a serious threat to people’s life and property safety. Owing to the wide distribution of pollution sources and variable meteorological factors, it is a very time-consuming and labor-intensive task to map the pollution distribution using traditional methods. In this work, a study on the mapping of pollution distribution based on satellite remote sensing is carried out in Yunnan Province, China, as an example. Several machine learning methods (e.g. K-nearest neighbor, support vector machine) are used to analyze the effects of conditions such as multiple air pollution and meteorological data on pollution distribution map levels. The results indicate that the ensemble learning model has the highest accuracy of 72.32% in this application. The new pollution distribution map using this classifier has 5,506 more pixels in the most severe pollution level than the traditional map. Last, the remote sensing-based map and the manual measurement-based map were combined with corresponding experience weight to obtain a weighted pollution distribution map.
The Dilemma of Public Hearings in Land Expropriation in China Based on Farmers’ Satisfaction
Authors: Jiang Lin , Sun Li , Fu Zhenyi , Qi Rui , Tang Tao , Lin Qiaowen
Published: July 2022

Although public hearings have been introduced into Chinese land expropriation as an important democratic supervision and conflict resolution mechanism for more than 15 years, there is a dearth of research into its qualities. Taking the farmers’ satisfaction with the public hearings as the critical quality indicator, this article analyzes the dilemma of this special institution in Chinese land expropriation. Process tracing is employed to analyze the design defect of the public hearing institution. Farmers’ satisfactions with the public hearings are measured by a questionnaire, and the factors are examined by a structural equation model based on the theories of expectancy disconfirmation and procedural fairness. It is concluded that the distorted procedure and the inconsiderate arrangement affect farmers’ perceived procedural fairness and decrease their satisfaction with hearings. In order to solve the dilemma of public participation in land expropriation, the relevant authorities should start from the source of affecting farmers’ satisfaction.
Does institutional quality matter for environmental sustainability?
Authors: Li Dezhen , Bai Yuting , Yu Pingping , Meo Muhammad Saeed , Anees Alvena , Rahman Saif Ur
Published: July 2022

The prime objective of the study is to examine the asymmetric effect of institutional quality and other control variables on environmental sustainability in G7 economies. The study examined data from 1986 to 2020 using a nonlinear ARDL (NARDL) technique. The outcomes of the study show heterogeneous results for the sampled economies. The findings confirm the asymmetric relationship between institutional quality, foreign direct investment, trade openness, and economic growth, and environmental sustainability in G7 countries. Furthermore, the study finds that neglecting the series’ inherent nonlinearities may lead to misleading inferences. The findings suggest that policies should be based on individual country characteristics and that no single policy can be a good fit for devising environmental sustainability measures.
Towards sustainable energy: Factors affecting solar power system adoption by small and medium-sized businesses
Authors: Atchike Desire Wade , Zhenyu Zhao , Ali Tausif , Weishang Guo , Jabeen Gul
Published: July 2022

Climate change mitigation is the prime priority of businesses in the modern world, making them inclined to opt for clean energy acceptance. From the end-user perspective, acceptance of novel technologies is inevitably vital in promoting such technologies, and certain factors could play an influential role in shaping the acceptance behavior of businesses in embracing those technologies. Against this background, the purpose of this study is to determine the factors that influence the acceptance of solar panel systems (SPS) by small and medium businesses (SMBs) in Pakistan. A questionnaire survey was conducted in seven districts of Pakistan’s Punjab province from December 2021 to February 2022. The data were collected using a Five-point Likert scale. Based on the questionnaire survey, the variance-based structural equation model technique is utilized. The outcomes are: 1) size of the business, ease of using SPS, and credibility of SPS are the top three driving forces. 2) The cost of SPS, and the pressure of competitors are the bottlenecks to the acceptance of SPS, while the absence of technical skills is a neutral factor. 3) According to the effect size and path coefficients, the cost of SPS, and the size of the business are found to be the most significant factors, while the absence of technical skills is found to have less significance. As it is found that economic incentives majorly affect the acceptance of SPS thus, it is concluded that lowering the cost of SPS will increase its acceptance. Based on empirical results, it is suggested to promote public awareness about the environmentally-friendly nature of solar power systems to improve its acceptability at the individual as well as firm levels.
Bats adjust echolocation and social call design as a response to urban environments
Authors: Starik Nicole , Göttert Thomas
Published: July 2022

Behavioral traits play a major role in the successful adaptation of wildlife to urban conditions. We investigated and compared the acoustic behavior of free ranging bats in rural (Havelland, Brandenburg) and urban (Berlin city center) green areas (n = 6 sites) to assess possible effects of urbanization on bat vocalizations using automated real-time recordings from May to October 2020 and 2021. We show that foraging and social call activity of commonly occurring bat species was lower in urban areas compared to rural areas. We present data on rural-urban variation in acoustic parameters of echolocation and Type D social calls (produced during flight) using the example of the common pipistrelle Pipistrellus pipistrellus. Calls from urban sites revealed significantly higher end and peak frequencies compared to rural site calls. In addition, urban social calls present a higher degree of complexity as they structurally differed from rural social calls with regard to assemblage and number of call components. Moreover, urban social calls were emitted in a presumably different context than rural calls: antagonistic social calls in urban areas were detected throughout the year and in the acoustic absence of conspecifics and heterospecifics. Our results provide evidence for the ability of P. pipistrellus to modulate temporal and spectral features of echolocation and social calls, as well as patterns of social call production, in order to compensate for constraints imposed by the urban acoustic environment. We suggest that this acoustic behavioral plasticity plays a major role in the degree of adaptation of insectivorous bats to urban habitats.
Emerging Roles of Circ-ZNF609 in Multiple Human Diseases
Authors: Wang Songbo , Wu Jiajin , Wang Zhongyuan , Gong Zixuan , Liu Yiyang , Wang Zengjun
Published: July 2022

Circular RNAs (circRNAs) are a special type of endogenous RNAs with extensive roles in multiple human diseases. They are formed by back-splicing of partial sequences of the parental precursor mRNAs. Unlike linear RNAs, their covalently closed loop structure without a 5′ cap and a 3′ polyadenylated tail confers on them high stability and they are difficult to be digested by RNase R. Increasing evidence has proved that aberrant expressions of many circRNAs are detected and that circRNAs exert essential biological functions in disease development and progression via acting as a molecular sponge of microRNA, interacting with proteins as decoys or scaffolds, or self-encoding small peptides. Circular RNA zinc finger protein 609 (circ-ZNF609) originates from exon2 of ZNF609, which is located at chromosome 15q22.31, and it has recently been proved that it can translate into a protein. Being aberrantly upregulated in various diseases, it could promote malignant progression of human tumors, as well as tumor cell proliferation, migration, and invasion. Here in this review, we concluded the biological functions and potential mechanisms of circ-ZNF609 in multiple diseases, which could be further explored as a targetable molecule in future accurate diagnosis and prognosis.
Common Core Genes Play Vital Roles in Gastric Cancer With Different Stages
Authors: Yu Zhiyuan , Liang Chen , Tu Huaiyu , Qiu Shuzhong , Dong Xiaoyu , Zhang Yonghui , Ma Chao , Li Peiyu
Published: July 2022

Background: Owing to complex molecular mechanisms in gastric cancer (GC) oncogenesis and progression, existing biomarkers and therapeutic targets could not significantly improve diagnosis and prognosis. This study aims to identify the key genes and signaling pathways related to GC oncogenesis and progression using bioinformatics and meta-analysis methods.

Methods: Eligible microarray datasets were downloaded and integrated using the meta-analysis method. According to the tumor stage, GC gene chips were classified into three groups. Thereafter, the three groups’ differentially expressed genes (DEGs) were identified by comparing the gene data of the tumor groups with those of matched normal specimens. Enrichment analyses were conducted based on common DEGs among the three groups. Then protein–protein interaction (PPI) networks were constructed to identify relevant hub genes and subnetworks. The effects of significant DEGs and hub genes were verified and explored in other datasets. In addition, the analysis of mutated genes was also conducted using gene data from The Cancer Genome Atlas database.

Results: After integration of six microarray datasets, 1,229 common DEGs consisting of 1,065 upregulated and 164 downregulated genes were identified. Alpha-2 collagen type I (COL1A2), tissue inhibitor matrix metalloproteinase 1 (TIMP1), thymus cell antigen 1 (THY1), and biglycan (BGN) were selected as significant DEGs throughout GC development. The low expression of ghrelin (GHRL) is associated with a high lymph node ratio (LNR) and poor survival outcomes. Thereafter, we constructed a PPI network of all identified DEGs and gained 39 subnetworks and the top 20 hub genes. Enrichment analyses were performed for common DEGs, the most related subnetwork, and the top 20 hub genes. We also selected 61 metabolic DEGs to construct PPI networks and acquired the relevant hub genes. Centrosomal protein 55 (CEP55) and POLR1A were identified as hub genes associated with survival outcomes.

Conclusion: The DEGs, hub genes, and enrichment analysis for GC with different stages were comprehensively investigated, which contribute to exploring the new biomarkers and therapeutic targets.
Epigenomics as Potential Tools for Enhancing Magnitude of Breeding Approaches for Developing Climate Resilient Chickpea
Authors: Chandana B. S. , Mahto Rohit Kumar , Singh Rajesh Kumar , Ford Rebecca , Vaghefi Niloofar , Gupta Santosh Kumar , Yadav Hemant Kumar , Manohar Murli , Kumar Rajendra
Published: July 2022

Epigenomics has become a significant research interest at a time when rapid environmental changes are occurring. Epigenetic mechanisms mainly result from systems like DNA methylation, histone modification, and RNA interference. Epigenetic mechanisms are gaining importance in classical genetics, developmental biology, molecular biology, cancer biology, epidemiology, and evolution. Epigenetic mechanisms play important role in the action and interaction of plant genes during development, and also have an impact on classical plant breeding programs, inclusive of novel variation, single plant heritability, hybrid vigor, plant-environment interactions, stress tolerance, and performance stability. The epigenetics and epigenomics may be significant for crop adaptability and pliability to ambient alterations, directing to the creation of stout climate-resilient elegant crop cultivars. In this review, we have summarized recent progress made in understanding the epigenetic mechanisms in plant responses to biotic and abiotic stresses and have also tried to provide the ways for the efficient utilization of epigenomic mechanisms in developing climate-resilient crop cultivars, especially in chickpea, and other legume crops.
Prognostic Signature and Tumor Immune Landscape of N7-Methylguanosine-Related lncRNAs in Hepatocellular Carcinoma
Authors: Wei Wei , Liu Chao , Wang Meng , Jiang Wei , Wang Caihong , Zhang Shuqun
Published: July 2022

Despite great advances in the treatment of liver hepatocellular carcinoma (LIHC), such as immunotherapy, the prognosis remains extremely poor, and there is an urgent need to develop novel diagnostic and prognostic markers. Recently, RNA methylation-related long non-coding RNAs (lncRNAs) have been demonstrated to be novel potential biomarkers for tumor diagnosis and prognosis as well as immunotherapy response, such as N6-methyladenine (m6A) and 5-methylcytosine (m5C). N7-Methylguanosine (m7G) is a widespread RNA modification in eukaryotes, but the relationship between m7G-related lncRNAs and prognosis of LIHC patients as well as tumor immunotherapy response is still unknown. In this study, based on the LIHC patients’ clinical and transcriptomic data from TCGA database, a total of 992 m7G-related lncRNAs that co-expressed with 22 m7G regulatory genes were identified using Pearson correlation analysis. Univariate regression analysis was used to screen prognostic m7G-related lncRNAs, and the least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression were applied to construct a 9-m7G-related-lncRNA risk model. The m7G-related lncRNA risk model was validated to exhibit good prognostic performance through Kaplan–Meier analysis and ROC analysis. Together with the clinicopathological features, the m7G-related lncRNA risk score was found to be an independent prognostic factor for LIHC. Furthermore, the high-risk group of LIHC patients was unveiled to have a higher tumor mutation burden (TMB), and their tumor microenvironment was more prone to the immunosuppressive state and exhibited a lower response rate to immunotherapy. In addition, 47 anti-cancer drugs were identified to exhibit a difference in drug sensitivity between the high-risk and low-risk groups. Taken together, the m7G-related lncRNA risk model might display potential value in predicting prognosis, immunotherapy response, and drug sensitivity in LIHC patients.
Completing Single-Cell DNA Methylome Profiles via Transfer Learning Together With KL-Divergence
Authors: Dodlapati Sanjeeva , Jiang Zongliang , Sun Jiangwen
Published: July 2022

The high level of sparsity in methylome profiles obtained using whole-genome bisulfite sequencing in the case of low biological material amount limits its value in the study of systems in which large samples are difficult to assemble, such as mammalian preimplantation embryonic development. The recently developed computational methods for addressing the sparsity by imputing missing have their limits when the required minimum data coverage or profiles of the same tissue in other modalities are not available. In this study, we explored the use of transfer learning together with Kullback-Leibler (KL) divergence to train predictive models for completing methylome profiles with very low coverage (below 2%). Transfer learning was used to leverage less sparse profiles that are typically available for different tissues for the same species, while KL divergence was employed to maximize the usage of information carried in the input data. A deep neural network was adopted to extract both DNA sequence and local methylation patterns for imputation. Our study of training models for completing methylome profiles of bovine oocytes and early embryos demonstrates the effectiveness of transfer learning and KL divergence, with individual increase of 29.98 and 29.43%, respectively, in prediction performance and 38.70% increase when the two were used together. The drastically increased data coverage (43.80–73.6%) after imputation powers downstream analyses involving methylomes that cannot be effectively done using the very low coverage profiles (0.06–1.47%) before imputation.
Identification and Validation of Cuproptosis-Related Prognostic Signature and Associated Regulatory Axis in Uterine Corpus Endometrial Carcinoma
Authors: Chen Yun
Published: July 2022

Background: Uterine corpus endometrial carcinoma (UCEC) is a common gynecological malignancy globally with high recurrence and mortality rates. Cuproptosis is a new type of programmed cell death involved in tumor cell proliferation and growth, angiogenesis, and metastasis.

Methods: The difference in cuproptosis-related genes (CRGs) between UCEC tissues and normal tissues deposited in The Cancer Genome Atlas database was calculated using the “limma” R package. LASSO Cox regression analysis was conducted to construct a prognostic cuproptosis–related signature. Kaplan–Meier analysis was conducted to compare the survival of UCEC patients. A ceRNA network was constructed to identify the lncRNA–miRNA–mRNA regulatory axis. Quantitative reverse transcription-polymerase chain reaction (qRT-PCR) was performed to verify CRG expression in UCEC.

Results: The expression of FDX1, LIAS, DLAT, and CDKN2A were upregulated, whereas the expression of LIPT1, DLD, PDHB, MTF1, and GLS were downregulated in UCEC versus normal tissues. The genetic mutation landscape of CRGs in UCEC was also summarized. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses revealed that these CRGs were enriched in the tricarboxylic acid (TCA) cycle, glycolysis, and HIF-1 signaling pathway. LASSO Cox regression analysis was performed and identified a cuproptosis-related prognostic signature including these three prognostic biomarkers (CDKN2A, GLS, and LIPT1). UCEC patients with high risk scores had a poor prognosis with an area under the curve of 0.782 and 0.764 on 3- and 5-year receiver operating characteristic curves. Further analysis demonstrated a significant correlation between CDKN2A and pTNM stage, tumor grade, immune cell infiltration, drug sensitivity, tumor mutational burden (TMB) score, and microsatellite instable (MSI) score. The data validation of qRT-PCR further demonstrated the upregulation of CDKN2A and the downregulation of LIPT1 and GLS in UCEC versus normal tissues. The ceRNA network also identified lncRNA XIST/miR-125a-5p/CDKN2A regulatory axis for UCEC.

Conclusion: The current study identified a cuproptosis-related prognostic signature including these three prognostic biomarkers (CDKN2A, GLS, and LIPT1) for UCEC. The ceRNA network also identified that lncRNA XIST/miR-125a-5p/CDKN2A regulatory axis may be involved in the progression of UCEC. Further in vivo and in vitro studies should be conducted to verify these results.
Comprehensive Analysis of CRIP1 Expression in Acute Myeloid Leukemia
Authors: Gao Yan , Li Jin-Yuan , Mao Jia-Ying , Zhou Jia-Fan , Jiang Lu , Li Xue-Ping
Published: July 2022

Acute myeloid leukemia (AML) is a highly heterogeneous hematological malignancy that imposes great challenges in terms of drug resistance and relapse. Previous studies revealed heterogeneous leukemia cells and their relevant gene markers, such as CRIP1 as clinically prognostic in t (8;21) AML patients. However, the expression and role of CRIP1 in AML are poorly understood. We used the single-cell RNA sequencing and gene expression data from t (8;21) AML patients to analyze the immune and regulation networks of CRIP1. Two independent cohorts from GSE37642 and The Cancer Genome Atlas (TCGA) datasets were employed as validation cohorts. In addition, the methylation data from TCGA were used to analyze the methylation effect of the CRIP1 expression. Gene expression profile from t (8;21) AML patients showed that the CRIP1-high group exhibited an enrichment of immune-related pathways, including tumor necrosis factor (TNF)α signaling via nuclear factor kappa B (NFκB) pathways. Further studies using CIBERSORT showed that the CRIP1-high group had a significantly higher infiltration of exhausted CD8 T cells and activated mast cells. The CRIP1 expression was validated in the GSE37642-GPL96, GSE37642-GPL570, and TCGA datasets. In addition, with the methylation data, four CpG probes of CRIP1 (cg07065217, cg04411625, cg25682097, and 11763800) were identified as negatively associated with the CRIP1 gene expression in AML patients. Our data provide a comprehensive overview of the regulation of CRIP1 expression in AML patients. The evaluation of the TNFα-NFκB signaling pathway as well as the immune heterogeneity might provide new insights for exploring improvements in AML treatment.
Finding Lung-Cancer-Related lncRNAs Based on Laplacian Regularized Least Squares With Unbalanced Bi-Random Walk
Authors: Guo Zhifeng , Hui Yan , Kong Fanlong , Lin Xiaoxi
Published: July 2022

Lung cancer is one of the leading causes of cancer-related deaths. Thus, it is important to find its biomarkers. Furthermore, there is an increasing number of studies reporting that long noncoding RNAs (lncRNAs) demonstrate dense linkages with multiple human complex diseases. Inferring new lncRNA-disease associations help to identify potential biomarkers for lung cancer and further understand its pathogenesis, design new drugs, and formulate individualized therapeutic options for lung cancer patients. This study developed a computational method (LDA-RLSURW) by integrating Laplacian regularized least squares and unbalanced bi-random walk to discover possible lncRNA biomarkers for lung cancer. First, the lncRNA and disease similarities were computed. Second, unbalanced bi-random walk was, respectively, applied to the lncRNA and disease networks to score associations between diseases and lncRNAs. Third, Laplacian regularized least squares were further used to compute the association probability between each lncRNA-disease pair based on the computed random walk scores. LDA-RLSURW was compared using 10 classical LDA prediction methods, and the best AUC value of 0.9027 on the lncRNADisease database was obtained. We found the top 30 lncRNAs associated with lung cancers and inferred that lncRNAs TUG1, PTENP1, and UCA1 may be biomarkers of lung neoplasms, non-small–cell lung cancer, and LUAD, respectively.
Identification and functional analysis of novel SOX11 variants in Chinese patients with Coffin-Siris syndrome 9
Authors: Ding Yu , Chen Jiande , Tang Yijun , Chen Li-Na , Yao Ru-En , Yu Tingting , Yin Yong , Wang Xiumin , Wang Jian , Li Niu
Published: July 2022

SOX11 is a transcription factor belonging to the sex determining region Y-related high-mobility group box family that plays a vital role in early embryogenesis and neurogenesis. De novo variants in SOX11 have been initially reported to cause a rare neurodevelopmental disorder, mainly referred to Coffin-siris syndrome 9 (CSS9, OMIM# 615866) which is characterized with growth deficiency, intellectual disability (ID), microcephaly, coarse facies, and hypoplastic nails of the fifth fingers and/or toes. A recent large-scale cohort study suggests that SOX11 variation would result in a clinically and molecularly distinct disease from CSS. Here, we describe three unrelated Chinese cases with variable phenotype, mainly involving developmental delay, ID, short statute, microcephaly, facial deformities (i.e., prominent forehead, arched eye brow, flat nasal bridge, broad nose and short philtrum), and cryptorchidism. Whole-exome sequencing (WES) revealed three novel heterozygous variants in the SOX11 gene, including two missense variants of c.337T>C (p.Y113H) and c.425C>G (p.A142G), and one nonsense variant of c.820A>T (p. K142*). Luciferase reporting assay shows that the two missense variants impair the transcriptional activity of the SOX11 target gene GDF5. Additionally, WES uncovered a 4,300 kb deletion involving the region of 1q24.2-q25.1 (hg19,chr1:169,433,149-173,827,682) in patient 1, which also contributes to the condition of the patient. In summary, this is the first report of Chinese cases with de novo variants of SOX11. Our study partially supports the previous observation that the phenotype caused by SOX11 variants somewhat differs from classical CSS.
RNA m6A modification orchestrates the rhythm of immune cell development from hematopoietic stem cells to T and B cells
Authors: Zhao Chuanxiang , Xu Guoying , Zhang Xiaoxian , Ye Yunfeng , Cai Weili , Shao Qixiang
Published: July 2022

RNA, one of the major building blocks of the cell, participates in many essential life processes. RNA stability is well-established to be closely related to various RNA modifications. To date, hundreds of different RNA modifications have been identified. N6-methyladenosine (m6A) is one of the most important RNA modifications in mammalian cells. An increasing body of evidence from recently published studies suggests that m6A modification is a novel immune system regulator of the generation and differentiation of hematopoietic stem cells (HSCs) and immune cells. In this review, we introduce the process and relevant regulatory mechanisms of m6A modification; summarize recent findings of m6A in controlling HSC generation and self-renewal, and the development and differentiation of T and B lymphocytes from HSCs; and discuss the potential mechanisms involved.
Identification and validation of an immune-related gene prognostic signature for clear cell renal carcinoma
Authors: Hua Shan , Xie Zhiwen , Zhang Yongqing , Wu Lei , Shi Fei , Wang Xingjie , Xia Shujie , Dong Shengli , Jiang Juntao
Published: July 2022

Clear Cell Renal Carcinoma (ccRCC) accounts for nearly 80% of renal carcinoma cases, and immunotherapy plays an important role in ccRCC therapy. However, the responses to immunotherapy and overall survival for ccRCC patients are still hard to predict. Here, we constructed an immune-related predictive signature using 19 genes based on TCGA datasets. We also analyzed its relationships between disease prognosis, infiltrating immune cells, immune subtypes, mutation load, immune dysfunction, immune escape, etc. We found that our signature can distinguish immune characteristics and predict immunotherapeutic response for ccRCC patients with better prognostic prediction value than other immune scores. The expression levels of prognostic genes were determined by RT-qPCR assay. This signature may help to predict overall survival and guide the treatment for patients with ccRCC.
Human Variation in DNA Repair, Immune Function, and Cancer Risk
Authors: Cheong Ana , Nagel Zachary D.
Published: July 2022

DNA damage constantly threatens genome integrity, and DNA repair deficiency is associated with increased cancer risk. An intuitive and widely accepted explanation for this relationship is that unrepaired DNA damage leads to carcinogenesis due to the accumulation of mutations in somatic cells. But DNA repair also plays key roles in the function of immune cells, and immunodeficiency is an important risk factor for many cancers. Thus, it is possible that emerging links between inter-individual variation in DNA repair capacity and cancer risk are driven, at least in part, by variation in immune function, but this idea is underexplored. In this review we present an overview of the current understanding of the links between cancer risk and both inter-individual variation in DNA repair capacity and inter-individual variation in immune function. We discuss factors that play a role in both types of variability, including age, lifestyle, and environmental exposures. In conclusion, we propose a research paradigm that incorporates functional studies of both genome integrity and the immune system to predict cancer risk and lay the groundwork for personalized prevention.