bluuebunny/crossref_metadata_2025 huggingface dataset:
Mexico’s Higher Education Students’ Experience During the Lockdown due to the COVID-19 Pandemic
Authors: Zapata-Garibay Rogelio , González-Fagoaga Jesús Eduardo , Meza-Rodríguez Elsa B. , Salazar-Ramírez Edgar , Plascencia-López Ismael , González-Fagoaga Clara Judith
Published: June 2021

Transition from face-to-face to remote courses in Mexico represented a challenge for teachers, students, and parents from all education levels. The Mexican federal government declared phase two of the plan to reduce COVID-19 spread on March 24, 2020. In some states, mobility restriction measures started by March 17 included the education system. On April 13, educational activities began in the remote mode exclusively, and this situation could be extended until the end of the 2020–2021 cycle, if health conditions do not improve. Universities, teachers, and students were not ready to implement the emergency remote teaching (ERT) strategy because of the limited conditions of technological adaptation and digital connectivity in the country. The purpose of this cross-sectional study is to investigate the experiences of graduate and undergraduate students concerning the change from face-to-face to the ERT modality in the health emergency context due to the COVID-19 outbreak. We collected data from 660 students from 38 universities and 22 states across the country. The study investigated students' previous experience with online courses, technological tool use, barriers faced during the new teaching–studying modality, current use of educational and specialized tools, and some physical and mental health indicators. As a result, we found problems related to time management of their work activities, the balance of time between home and school activities, and perception about studying days, which they felt strenuous. More than half of all students reported Internet connection problems. Students from private universities declared a higher use of technological tools. Students from public universities expressed more significant difficulties following teachers' instructions, perceived less attention from teachers, considered the change to remote courses was difficult, and felt overwhelmed by the required technological skills. Students in the first two years of university reported a higher proportion having deemed the most strenuous study days and having had difficulties following the teachers' instructions and considered that the distance modality change was hard. Finally, all the students declared that the expenses increased in their home in the highest proportion were electricity, mobile phone data, and Internet service. Future research should focus on measuring the impact of ERT on students’ learning outcomes.
Holey Graphene–Enabled Solvent-Free Preparation of Ultrahigh Mass Loading Selenium Cathodes for High Areal Capacity Lithium–Selenium Batteries
Authors: Plaza-Rivera Christian O. , Viggiano Rocco P. , Dornbusch Donald A. , Wu James J. , Connell John W. , Lin Yi
Published: June 2021

Solvents and binders are typical requirements in conventional lithium ion battery electrode fabrication to enable intimate material mixing, mechanical robustness, and reproducibility. However, for high energy density conversion chemistry cathodes such as sulfur (S) and selenium (Se), the time-consuming solvent-based methods are proven unreliable to achieve high mass loading cathodes with sufficient quality. Here, we report a facile solvent-free and binder-free method to prepare high mass loading composite Se cathodes that is enabled by the use of holey graphene (hG) as a lightweight conductive scaffold. Holey graphene is a derivative of graphene and can be dry-pressed into robust discs by itself. It can also serve as a matrix to host materials such as Se for composite disc preparation in a mix-and-press process free of solvent and binder. The method allows the preparation of ultrahigh Se content cathodes (up to 90 wt% Se) and ultrahigh Se mass loading (up to 15.6 mg cm−2 in this work). These cathodes exhibit excellent Se utilization, high areal capacity (up to 9 mAh cm−2), and good rate performance. The dry-press approach also allows for the preparation of a layered composite cathode architecture, where a thin hG layer is inserted between the composite and the current collector to improve the electrical contact. A solvent-free approach is also used to prepare hG-based hybrids with metal sulfides to be incorporated into a composite cathode to help entrap soluble polyselenide intermediates. The hybrid material is compatible with the solvent-free mix-and-press electrode fabrication approach and shows promise in improving the Se retention. While further improvements are still required, this work demonstrates the outstanding potential of using this facile, solvent-free approach enabled by hG for fabrication of high-performance, high mass loading conversion chemistry cathodes.
Classification of Power Quality Disturbance Based on S-Transform and Convolution Neural Network
Authors: Li Jinsong , Liu Hao , Wang Dengke , Bi Tianshu
Published: June 2021

The accurate classification of power quality disturbance (PQD) signals is of great significance for the establishment of a real-time monitoring system of modern power grids, ensuring the safe and stable operation of the power system and ensuring the electricity safety of users. Traditional power quality disturbance signal classification methods are susceptible to noise interference, feature selection, etc. In order to further improve the accuracy of power quality disturbance signal classification methods, this paper proposes a power quality disturbance classification method based on S-transform and Convolutional Neural Network (CNN). Firstly, S-transform is used to extract disturbance signals to obtain the time-frequency matrix with characteristics of the disturbance signals. As an extension of wavelet transform and Fourier transform, S-transform can avoid the disadvantages of difficult window function selection and fixed window width. At the same time, the feature extracted by S-transform has better noise immunity. Secondly, CNN is used to perform secondary feature extraction on the obtained high-dimensional time-frequency modulus matrix to reduce data dimensions and obtain the main features of the disturbance signal, then the main features extracted are classified by using the SoftMax classifier. Finally, after a series of simulation experiments, the results show that the proposed algorithm can accurately classify single disturbance signals with different signal-to-noise ratios and composite disturbance signals composed of single disturbance signals, and it also has good noise immunity. Compared with other classification methods, the algorithm proposed in this paper has better timeliness and higher accuracy, and it is an efficient and feasible power quality disturbance signal classification method.
Detecting lncRNA–Cancer Associations by Combining miRNAs, Genes, and Prognosis With Matrix Factorization
Authors: Yan Huan , Chai Hua , Zhao Huiying
Published: June 2021

Motivation: Long non-coding RNAs (lncRNAs) play important roles in cancer development. Prediction of lncRNA–cancer association is necessary for efficiently discovering biomarkers and designing treatment for cancers. Currently, several methods have been developed to predict lncRNA–cancer associations. However, most of them do not consider the relationships between lncRNA with other molecules and with cancer prognosis, which has limited the accuracy of the prediction.

Method: Here, we constructed relationship matrices between 1,679 lncRNAs, 2,759 miRNAs, and 16,410 genes and cancer prognosis on three types of cancers (breast, lung, and colorectal cancers) to predict lncRNA–cancer associations. The matrices were iteratively reconstructed by matrix factorization to optimize low-rank size. This method is called detecting lncRNA cancer association (DRACA).

Results: Application of this method in the prediction of lncRNAs–breast cancer, lncRNA–lung cancer, and lncRNA–colorectal cancer associations achieved an area under curve (AUC) of 0.810, 0.796, and 0.795, respectively, by 10-fold cross-validations. The performances of DRACA in predicting associations between lncRNAs with three kinds of cancers were at least 6.6, 7.2, and 6.9% better than other methods, respectively. To our knowledge, this is the first method employing cancer prognosis in the prediction of lncRNA–cancer associations. When removing the relationships between cancer prognosis and genes, the AUCs were decreased 7.2, 0.6, and 5% for breast, lung, and colorectal cancers, respectively. Moreover, the predicted lncRNAs were found with greater numbers of somatic mutations than the lncRNAs not predicted as cancer-associated for three types of cancers. DRACA predicted many novel lncRNAs, whose expressions were found to be related to survival rates of patients. The method is available at https://github.com/Yanh35/DRACA.
Visualization and Analysis of Gene Expression in Stanford Type A Aortic Dissection Tissue Section by Spatial Transcriptomics
Authors: Li Yan-Hong , Cao Ying , Liu Fen , Zhao Qian , Adi Dilare , Huo Qiang , Liu Zheng , Luo Jun-Yi , Fang Bin-Bin , Tian Ting , Li Xiao-Mei , Liu Di , Yang Yi-Ning
Published: June 2021

Background: Spatial transcriptomics enables gene expression events to be pinpointed to a specific location in biological tissues. We developed a molecular approach for low-cell and high-fiber Stanford type A aortic dissection and preliminarily explored and visualized the heterogeneity of ascending aortic types and mapping cell-type-specific gene expression to specific anatomical domains.

Methods: We collected aortic samples from 15 patients with Stanford type A aortic dissection and a case of ascending aorta was randomly selected followed by 10x Genomics and spatial transcriptomics sequencing. In data processing of normalization, component analysis and dimensionality reduction analysis, different algorithms were compared to establish the pipeline suitable for human aortic tissue.

Results: We identified 19,879 genes based on the count level of gene expression at different locations and they were divided into seven groups based on gene expression trends. Major cell that the population may contain are indicated, and we can find different main distribution of different cell types, among which the tearing sites were mainly macrophages and stem cells. The gene expression of these different locations and the cell types they may contain are correlated and discussed in terms of their involvement in immunity, regulation of oxygen homeostasis, regulation of cell structure and basic function.

Conclusion: This approach provides a spatially resolved transcriptome− and tissue-wide perspective of the adult human aorta and will allow the application of human fibrous aortic tissues without any effect on genes in different layers with low RNA expression levels. Our findings will pave the way toward both a better understanding of Stanford type A aortic dissection pathogenesis and heterogeneity and the implementation of more effective personalized therapeutic approaches.
A Cluster-Based Approach for the Discovery of Copy Number Variations From Next-Generation Sequencing Data
Authors: Liu Guojun , Zhang Junying
Published: June 2021

The next-generation sequencing technology offers a wealth of data resources for the detection of copy number variations (CNVs) at a high resolution. However, it is still challenging to correctly detect CNVs of different lengths. It is necessary to develop new CNV detection tools to meet this demand. In this work, we propose a new CNV detection method, called CBCNV, for the detection of CNVs of different lengths from whole genome sequencing data. CBCNV uses a clustering algorithm to divide the read depth segment profile, and assigns an abnormal score to each read depth segment. Based on the abnormal score profile, Tukey’s fences method is adopted in CBCNV to forecast CNVs. The performance of the proposed method is evaluated on simulated data sets, and is compared with those of several existing methods. The experimental results prove that the performance of CBCNV is better than those of several existing methods. The proposed method is further tested and verified on real data sets, and the experimental results are found to be consistent with the simulation results. Therefore, the proposed method can be expected to become a routine tool in the analysis of CNVs from tumor-normal matched samples.
The Immune Subtypes and Landscape of Gastric Cancer and to Predict Based on the Whole-Slide Images Using Deep Learning
Authors: Chen Yan , Sun Zepang , Chen Wanlan , Liu Changyan , Chai Ruoyang , Ding Jingjing , Liu Wen , Feng Xianzhen , Zhou Jun , Shen Xiaoyi , Huang Shan , Xu Zhongqing
Published: June 2021

Background

Gastric cancer (GC) is a highly heterogeneous tumor with different responses to immunotherapy. Identifying immune subtypes and landscape of GC could improve immunotherapeutic strategies.

Methods

Based on the abundance of tumor-infiltrating immune cells in GC patients from The Cancer Genome Atlas, we used unsupervised consensus clustering algorithm to identify robust clusters of patients, and assessed their reproducibility in an independent cohort from Gene Expression Omnibus. We further confirmed the feasibility of our immune subtypes in five independent pan-cancer cohorts. Finally, functional enrichment analyses were provided, and a deep learning model studying the pathological images was constructed to identify the immune subtypes.

Results

We identified and validated three reproducible immune subtypes presented with diverse components of tumor-infiltrating immune cells, molecular features, and clinical characteristics. An immune-inflamed subtype 3, with better prognosis and the highest immune score, had the highest abundance of CD8+ T cells, CD4+ T–activated cells, follicular helper T cells, M1 macrophages, and NK cells among three subtypes. By contrast, an immune-excluded subtype 1, with the worst prognosis and the highest stromal score, demonstrated the highest infiltration of CD4+ T resting cells, regulatory T cells, B cells, and dendritic cells, while an immune-desert subtype 2, with an intermediate prognosis and the lowest immune score, demonstrated the highest infiltration of M2 macrophages and mast cells, and the lowest infiltration of M1 macrophages. Besides, higher proportion of EVB and MSI of TCGA molecular subtyping, over expression of CTLA4, PD1, PDL1, and TP53, and low expression of JAK1 were observed in immune subtype 3, which consisted with the results from Gene Set Enrichment Analysis. These subtypes may suggest different immunotherapy strategies. Finally, deep learning can predict the immune subtypes well.

Conclusion

This study offers a conceptual frame to better understand the tumor immune microenvironment of GC. Future work is required to estimate its reference value for the design of immune-related studies and immunotherapy selection.
Recent Advances and Future Perspective of DC-Based Therapy in NSCLC
Authors: van der Hoorn Iris A. E. , Flórez-Grau Georgina , van den Heuvel Michel M. , de Vries I. Jolanda M. , Piet Berber
Published: June 2021

Current treatment for patients with non-small-cell lung cancer (NSCLC) is suboptimal since therapy is only effective in a minority of patients and does not always induce a long-lasting response. This highlights the importance of exploring new treatment options. The clinical success of immunotherapy relies on the ability of the immune system to mount an adequate anti-tumor response. The activation of cytotoxic T cells, the effector immune cells responsible for tumor cell killing, is of paramount importance for the immunotherapy success. These cytotoxic T cells are primarily instructed by dendritic cells (DCs). DCs are the most potent antigen-presenting cells (APCs) and are capable of orchestrating a strong anti-cancer immune response. DC function is often suppressed in NSCLC. Therefore, resurrection of DC function is an interesting approach to enhance anti-cancer immune response. Recent data from DC-based treatment studies has given rise to the impression that DC-based treatment cannot induce clinical benefit in NSCLC by itself. However, these are all early-phase studies that were mainly designed to study safety and were not powered to study clinical benefit. The fact that these studies do show that DC-based therapies were well-tolerated and could induce the desired immune responses, indicates that DC-based therapy is still a promising option. Especially combination with other treatment modalities might enhance immunological response and clinical outcome. In this review, we will identify the possibilities from current DC-based treatment trials that could open up new venues to improve future treatment.
Early Prediction of Mortality, Severity, and Length of Stay in the Intensive Care Unit of Sepsis Patients Based on Sepsis 3.0 by Machine Learning Models
Authors: Su Longxiang , Xu Zheng , Chang Fengxiang , Ma Yingying , Liu Shengjun , Jiang Huizhen , Wang Hao , Li Dongkai , Chen Huan , Zhou Xiang , Hong Na , Zhu Weiguo , Long Yun
Published: June 2021

Background: Early prediction of the clinical outcome of patients with sepsis is of great significance and can guide treatment and reduce the mortality of patients. However, it is clinically difficult for clinicians.

Methods: A total of 2,224 patients with sepsis were involved over a 3-year period (2016–2018) in the intensive care unit (ICU) of Peking Union Medical College Hospital. With all the key medical data from the first 6 h in the ICU, three machine learning models, logistic regression, random forest, and XGBoost, were used to predict mortality, severity (sepsis/septic shock), and length of ICU stay (LOS) (>6 days, ≤ 6 days). Missing data imputation and oversampling were completed on the dataset before introduction into the models.

Results: Compared to the mortality and LOS predictions, the severity prediction achieved the best classification results, based on the area under the operating receiver characteristics (AUC), with the random forest classifier (sensitivity = 0.65, specificity = 0.73, F1 score = 0.72, AUC = 0.79). The random forest model also showed the best overall performance (mortality prediction: sensitivity = 0.50, specificity = 0.84, F1 score = 0.66, AUC = 0.74; LOS prediction: sensitivity = 0.79, specificity = 0.66, F1 score = 0.69, AUC = 0.76) among the three models. The predictive ability of the SOFA score itself was inferior to that of the above three models.

Conclusions: Using the random forest classifier in the first 6 h of ICU admission can provide a comprehensive early warning of sepsis, which will contribute to the formulation and management of clinical decisions and the allocation and management of resources.
The Impact of Portal Vein Thrombosis on the Prognosis of Patients With Cirrhosis: A Retrospective Propensity-Score Matched Study
Authors: Chen Zhiji , Ran Tao , Cao Haiyan , Xu Feng , Zhou Zhi-hang , He Song
Published: June 2021

Objectives: To investigate the impact of portal vein thrombosis (PVT) on cirrhosis decompensation and survival of cirrhosis.

Methods: In this retrospective observational study between January 2012 and August 2020, 117 patients with cirrhotic PVT and 125 patients with cirrhosis were included. Propensity score matching (PSM) was applied to reduce the bias. The clinical characteristics of non-tumoral PVT in cirrhosis and its influence on cirrhosis decompensation and survival were analyzed.

Results: The median follow-up for the PVT group was 15 (8.0–23.0) months and for the non-thrombosis group 14 (8.0–23.5) months. The presence of PVT was related with esophageal varices, higher Child-Pugh score and MELD score (P < 0.05). Most PVTs were partial (106/117). Non-occlusive PVT disappeared on later examinations in 32/106 patients (30.19%), of which six patients reappeared. All the 11 patients with occlusive PVT remained occlusive, among which five patients (45.45%) developed portal cavernoma. There was no significant correlation between PVT and decompensation or survival before or after PSM. Multivariate analysis identified only Child-Pugh score (HR = 2.210, 95% CI: 1.332–3.667) and serum sodium level (HR = 0.818, 95% CI: 0.717–0.933) as independent factors for death.

Conclusion: Though PVT is associated with greater Child-Pugh score and MELD score, it has no significant impact on the progression of cirrhosis.
Nucleotide Composition and Codon Usage Across Viruses and Their Respective Hosts
Authors: Simón Diego , Cristina Juan , Musto Héctor
Published: June 2021

The genetic material of the three domains of life (Bacteria, Archaea, and Eukaryota) is always double-stranded DNA, and their GC content (molar content of guanine plus cytosine) varies between ≈ 13% and ≈ 75%. Nucleotide composition is the simplest way of characterizing genomes. Despite this simplicity, it has several implications. Indeed, it is the main factor that determines, among other features, dinucleotide frequencies, repeated short DNA sequences, and codon and amino acid usage. Which forces drive this strong variation is still a matter of controversy. For rather obvious reasons, most of the studies concerning this huge variation and its consequences, have been done in free-living organisms. However, no recent comprehensive study of all known viruses has been done (that is, concerning all available sequences). Viruses, by far the most abundant biological entities on Earth, are the causative agents of many diseases. An overview of these entities is important also because their genetic material is not always double-stranded DNA: indeed, certain viruses have as genetic material single-stranded DNA, double-stranded RNA, single-stranded RNA, and/or retro-transcribing. Therefore, one may wonder if what we have learned about the evolution of GC content and its implications in prokaryotes and eukaryotes also applies to viruses. In this contribution, we attempt to describe compositional properties of ∼ 10,000 viral species: base composition (globally and according to Baltimore classification), correlations among non-coding regions and the three codon positions, and the relationship of the nucleotide frequencies and codon usage of viruses with the same feature of their hosts. This allowed us to determine how the base composition of phages strongly correlate with the value of their respective hosts, while eukaryotic viruses do not (with fungi and protists as exceptions). Finally, we discuss some of these results concerning codon usage: reinforcing previous results, we found that phages and hosts exhibit moderate to high correlations, while for eukaryotes and their viruses the correlations are weak or do not exist.
A Novel Diagnostic Method for Invasive Fungal Disease Using the Factor G Alpha Subunit From Limulus polyphemus
Authors: Cui Fang , Luo Peng , Bai Yao , Meng Jiangping
Published: June 2021

Deaths due to invasive fungal disease (IFD) have been increasing every year. Early and rapid detection is important to reduce the mortality rate associated with IFD. In this study, we explored a novel diagnostic method for detecting IFD, which involves the G Factor α subunit (GFαSub) from Limulus polyphemus. The GFαSub double-sandwich method was developed to detect (1,3)-β-D-glucans in human serum using purified GFαSub and horseradish peroxidase-labeled GFαSub. The GFαSub double-sandwich method and the G test were performed and compared. Using GFαSub sequence analysis, the expression plasmid pET30a-GFαSub252-668 was synthesized, and GFαSub252-668 was expressed and purified via isopropyl-β-d-thiogalactoside induction and nickel-nitrilotriacetic acid affinity. The optimization method was established via the orthogonal method. Using this method, the sera of 36 patients with IFD and 92 volunteers without IFD underwent detection, and the receiver operating characteristic curve of the GFαSub252-668 double-sandwich method was described. The sensitivity and specificity of the GFαSub252-668 double-sandwich method were 91.67 and 82.61%, respectively, and there was good correlation with the G test for the serum specimens of 36 patients with pulmonary IFD (R2 = 0.7592). In conclusion, our study suggests that the GFαSub252-668 double-sandwich method was satisfactory at detecting IFD cases. This method can be promoted and further developed as a novel method for diagnosing IFD.
Rumen Ciliated Protozoa of the Free-Living European Bison (Bison bonasus, Linnaeus)
Authors: Kišidayová Svetlana , Durkaj Dominik , Mihaliková Katarína , Váradyová Zora , Puchalska Julia , Szumacher-Strabel Małgorzata , Cieślak Adam , Gizejewski Zygmunt
Published: June 2021

This study aims to perform population analysis of the rumen ciliated protozoa of the free-living European bison (wisent, Bison bonasus, Linnaeus). The samples of the rumen fluid from the 18 bison subjected to the controlled culls within the free-ranging population in the Bialowieza primeval forest in Poland were collected and examined. The examined ciliates population consisted of the species of the families Isotrichidae and Ophryoscolecidae. There were 12 genera (Isotricha, Dasytricha, Diplodinium, Elytroplastron, Entodinium, Eodinium, Epidinium, Eremoplastron, Eudiplodinium, Metadinium, Ophryoscolex, and Ostracodinium) and 32 morphospecies of the ciliates. We observed the prevalence of a type B protozoan population (56% animals) with the typical Epidinium and Eudiplodinium genera members. Other examined animals possessed the mixed A–B population with Ophryoscolex genus, distinct for type A ciliate population. The average total ciliates count was 2.77 ± 1.03 × 105/ml (mean ± SD). The most abundant genera were Entodinium, 83%, and Dasytricha, 14%. The abundance of other genera was <1% of the total count. Within the 16 Entodinium species determined, the most abundant species was Entodinium nanellum (16.3% of total ciliates count). The average Shannon–Wiener diversity index was 2.1 ± 0.39, evenness was 0.7 ± 0.11, and species richness was 24 ± 3.0 (mean ± SD). Our study is the first report on the population composition and diversity of rumen ciliates of European bison. The composition and counts of ciliate genera and species were similar to the composition and counts of the rumen ciliated protozoa of American bison and many other kinds of free-living and domestic ruminants. Our European bison ciliate population analysis has shown medium ciliate density and high diversity typical for large free-living ruminants with mixed feeding behavior.
Substitutions at Loop Regions of TMUV E Protein Domain III Differentially Impair Viral Entry and Assembly
Authors: Hu Tao , Wu Zhen , Wu Shaoxiong , Wang Mingshu , Jia Renyong , Zhu Dekang , Liu Mafeng , Zhao Xinxin , Yang Qiao , Wu Ying , Zhang Shaqiu , Huang Juan , Mao Sai , Ou Xumin , Gao Qun , Sun Di , Liu Yunya , Zhang Ling , Yu YanLing , Chen Shun , Cheng Anchun
Published: June 2021

Flavivirus envelope protein (E) plays an important role in cellular infection, especially in virulence and antigenicity. E domain III of Tembusu virus (TMUV) is highly conserved among flaviviruses and contains four loop regions. However, the functions of the loop regions of TMUV E domain III in the viral life cycle have not yet been discovered. In this study, using a reverse genetics system, we performed site-directed mutagenesis on loops I, II, III, and IV of TMUV E domain III. Mutant 6 (S388A.G389A.K390A) showed better proliferation than the wild-type virus, while mutants 1–5 exhibited decreased in vitro infectivity, as determined by immunofluorescence assay (IFA). Based on a TMUV replicon system, the mutations exhibited no apparent effect on TMUV RNA replication. Subcellular fractionation assays and packaging system assays indicated that mutations in loops II–IV (T332A, T332S, S365A.S366A.T367A, and S388A.G389A.K390A, respectively) disrupted virion assembly. Moreover, loops I–IV played an important role in virus binding and entry, while mutant 6 (S388A.G389A.K390A) exhibited robust activity in virus entry. Taken together, our findings indicated the critical role of the loop regions in TMUV E domain III in the virus entry and assembly process.
Spatial Virome Analysis of Zanthoxylum armatum Trees Affected With the Flower Yellowing Disease
Authors: Cao Mengji , Zhang Song , Liao Ruiling , Wang Xiaoru , Xuan Zhiyou , Zhan Binhui , Li Zhiqi , Zhang Jie , Du Xinnian , Tang Zhengsen , Li Shifang , Zhou Yan
Published: June 2021

Zanthoxylum armatum is an important woody crop with multiple applications in pharmaceutics, cosmetics, and food industries. With continuous increases in the plantation area, integrated pest management is required for scale production when diseases caused by biotic factors such as pests and pathogens have become new problems, one of which is the infectious flower yellowing disease (FYD). Here, isolates of a new illarvirus (3) and a new nepovirus-associated subviral satellite RNA (12) were identified in Z. armatum, in addition to 38 new isolates of four previously reported RNA viruses. Sequence variation can be observed in viral/subviral quasispecies and among predominant isolates from the same or different samples and geographic origins. Intriguingly, RNA sequencing of different diseased trees invariably showed an extraordinary pattern of particularly high reads accumulation of the green Sichuan pepper-nepovirus (GSPNeV) and the satellite RNA in symptomatic tissues. In addition, we also examined small RNAs of the satellite RNA, which show similar patterns to those of coinfecting viruses. This study provides further evidence to support association of the FYD with viral/subviral infections and deepens our understanding of the diversity and molecular characteristics of the viruses and satellite, as well as their interactions with the host.
Quantitative Modeling of Protein Synthesis Using Ribosome Profiling Data
Authors: Yadav Vandana , Ullah Irshad Inayat , Kumar Hemant , Sharma Ajeet K.
Published: June 2021

Quantitative prediction on protein synthesis requires accurate translation initiation and codon translation rates. Ribosome profiling data, which provide steady-state distribution of relative ribosome occupancies along a transcript, can be used to extract these rate parameters. Various methods have been developed in the past few years to measure translation-initiation and codon translation rates from ribosome profiling data. In the review, we provide a detailed analysis of the key methods employed to extract the translation rate parameters from ribosome profiling data. We further discuss how these approaches were used to decipher the role of various structural and sequence-based features of mRNA molecules in the regulation of gene expression. The utilization of these accurate rate parameters in computational modeling of protein synthesis may provide new insights into the kinetic control of the process of gene expression.
Angiogenesis-Related Immune Signatures Correlate With Prognosis, Tumor Microenvironment, and Therapeutic Sensitivity in Hepatocellular Carcinoma
Authors: Yang Yuan , Wu Guozhi , Li Qiang , Zheng Ya , Liu Min , Zhou Lingshan , Chen Zhaofeng , Wang Yuping , Guo Qinghong , Ji Rui , Zhou Yongning
Published: June 2021

Background: Hepatocellular carcinoma (HCC) is one of the highly heterogeneous cancers that lacks an effective risk model for prognosis prediction. Therefore, we searched for angiogenesis-related immune genes that affected the prognosis of HCC to construct a risk model and studied the role of this model in HCC.

Methods: In this study, we collected the transcriptome data of HCC from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) database. Pearson correlation analysis was performed to identify the association between immune genes and angiogenesis-related genes. Consensus clustering was applied to divide patients into clusters A and B. Subsequently, we studied the differentially expressed angiogenesis-related immune genes (DEari-genes) that affected the prognosis of HCC. The most significant features were identified by least absolute shrinkage and selection operator (LASSO) regression, and a risk model was constructed. The reliability of the risk model was evaluated in the TCGA discovery cohort and the ICGC validation cohort. In addition, we compared the novel risk model to the previous models based on ROC analysis. ssGSEA analysis was used for function evaluation, and pRRophetic was utilized to predict the sensitivity of administering chemotherapeutic agents.

Results: Cluster A patients had favorable survival rates. A total of 23 DEari-genes were correlated with the prognosis of HCC. A five-gene (including BIRC5, KITLG, PGF, SPP1, and SHC1) signature-based risk model was constructed. After regrouping the HCC patients by the median score, we could effectively discriminate between them based on the adverse survival outcome, the unique tumor immune microenvironment, and low chemosensitivity.

Conclusion: The five-gene signature-based risk score established by ari-genes showed a promising clinical prediction value.
Frontal White Matter Hyperintensities and Executive Functioning Performance in Older Adults
Authors: Boutzoukas Emanuel M. , O'Shea Andrew , Albizu Alejandro , Evangelista Nicole D. , Hausman Hanna K. , Kraft Jessica N. , Van Etten Emily J. , Bharadwaj Pradyumna K. , Smith Samantha G. , Song Hyun , Porges Eric C. , Hishaw Alex , DeKosky Steven T. , Wu Samuel S. , Marsiske Michael , Alexander Gene E. , Cohen Ronald , Woods Adam J.
Published: June 2021

Frontal lobe structures decline faster than most other brain regions in older adults. Age-related change in the frontal lobe is associated with poorer executive function (e.g., working memory, switching/set-shifting, and inhibitory control). The effects and presence of frontal lobe white matter hyperintensities (WMH) on executive function in normal aging is relatively unknown. The current study assessed relationships between region-specific frontal WMH load and cognitive performance in healthy older adults using three executive function tasks from the NIH Toolbox (NIHTB) Cognition Battery. A cohort of 279 healthy older adults ages 65–88 completed NIHTB and 3T T1-weighted and FLAIR MRI. Lesion Segmentation Toolbox quantified WMH volume and generated lesion probability maps. Individual lesion maps were registered to the Desikan-Killiany atlas in FreeSurfer 6.0 to define regions of interest (ROI). Independent linear regressions assessed relationships between executive function performance and region-specific WMH in frontal lobe ROIs. All models included age, sex, education, estimated total intracranial volume, multi-site scanner differences, and cardiovascular disease risk using Framingham criteria as covariates. Poorer set-shifting performance was associated with greater WMH load in three frontal ROIs including bilateral superior frontal (left β = −0.18, FDR-p = 0.02; right β = −0.20, FDR-p = 0.01) and right medial orbitofrontal (β = −0.17, FDR-p = 0.02). Poorer inhibitory performance associated with higher WMH load in one frontal ROI, the right superior frontal (right β = −0.21, FDR-p = 0.01). There were no significant associations between working memory and WMH in frontal ROIs. Our study demonstrates that location and pattern of frontal WMH may be important to assess when examining age-related differences in cognitive functions involving switching/set-shifting and inhibition. On the other hand, working memory performance was not related to presence of frontal WMH in this sample. These data suggest that WMH may contribute selectively to age-related declines in executive function. Findings emerged beyond predictors known to be associated with WMH presence, including age and cardiovascular disease risk. The spread of WMH within the frontal lobes may play a key role in the neuropsychological profile of cognitive aging. Further research should explore whether early intervention on modifiable vascular factors or cognitive interventions targeted for executive abilities may help mitigate the effect of frontal WMH on executive function.
Antisense Oligonucleotide-Mediated Reduction of HDAC6 Does Not Reduce Tau Pathology in P301S Tau Transgenic Mice
Authors: Valencia Antonio , Bieber Veronica L. Reinhart , Bajrami Bekim , Marsh Galina , Hamann Stefan , Wei Ru , Ling Karen , Rigo Frank , Arnold H. Moore , Golonzhka Olga , Hering Heike
Published: June 2021

Acetylation of tau protein is dysregulated in Alzheimer's Disease (AD). It has been proposed that acetylation of specific sites in the KXGS motif of tau can regulate phosphorylation of nearby residues and reduce the propensity of tau to aggregate. Histone deacetylase 6 (HDAC6) is a cytoplasmic enzyme involved in deacetylation of multiple targets, including tau, and it has been suggested that inhibition of HDAC6 would increase tau acetylation at the KXGS motifs and thus may present a viable therapeutic approach to treat AD. To directly test the contribution of HDAC6 to tau pathology, we intracerebroventricularly injected an antisense oligonucleotide (ASO) directed against HDAC6 mRNA into brains of P301S tau mice (PS19 model), which resulted in a 70% knockdown of HDAC6 protein in the brain. Despite a robust decrease in levels of HDAC6, no increase in tau acetylation was observed. Additionally, no change of tau phosphorylation or tau aggregation was detected upon the knockdown of HDAC6. We conclude that HDAC6 does not impact tau pathology in PS19 mice.
Effective Stimulation Type and Waveform for Force Control of the Motor Unit System: Implications for Intraspinal Microstimulation
Authors: Kim Hojeong , Ju Youngchang
Published: June 2021

The input–output properties of spinal motoneurons and muscle fibers comprising motor units are highly non-linear. The goal of this study was to investigate the stimulation type (continuous versus discrete) and waveform (linear versus non-linear) controlling force production at the motor unit level under intraspinal microstimulation. We constructed a physiological model of the motor unit with computer software enabling virtual experiments on single motor units under a wide range of input conditions, including intracellular and synaptic stimulation of the motoneuron and variation in the muscle length under neuromodulatory inputs originating from the brainstem. Continuous current intensity and impulse current frequency waveforms were inversely estimated such that the motor unit could linearly develop and relax the muscle force within a broad range of contraction speeds and levels during isometric contraction at various muscle lengths. Under both continuous and discrete stimulation, the stimulation waveform non-linearity increased with increasing speed and level of force production and with decreasing muscle length. Only discrete stimulation could control force relaxation at all muscle lengths. In contrast, continuous stimulation could not control force relaxation at high contraction levels in shorter-than-optimal muscles due to persistent inward current saturation on the motoneuron dendrites. These results indicate that non-linear adjustment of the stimulation waveform is more effective in regard to varying the force profile and muscle length and that the discrete stimulation protocol is a more robust approach for designing stimulation patterns aimed at neural interfaces for precise movement control under pathological conditions.
To Explore the Predictive Power of Visuomotor Network Dysfunctions in Mild Cognitive Impairment and Alzheimer’s Disease
Authors: Staal Justine , Mattace-Raso Francesco , Daniels Hennie A. M. , van der Steen Johannes , Pel Johan J. M.
Published: June 2021

Background

Research into Alzheimer’s disease has shifted toward the identification of minimally invasive and less time-consuming modalities to define preclinical stages of Alzheimer’s disease.

Method

Here, we propose visuomotor network dysfunctions as a potential biomarker in AD and its prodromal stage, mild cognitive impairment with underlying the Alzheimer’s disease pathology. The functionality of this network was tested in terms of timing, accuracy, and speed with goal-directed eye-hand tasks. The predictive power was determined by comparing the classification performance of a zero-rule algorithm (baseline), a decision tree, a support vector machine, and a neural network using functional parameters to classify controls without cognitive disorders, mild cognitive impaired patients, and Alzheimer’s disease patients.

Results

Fair to good classification was achieved between controls and patients, controls and mild cognitive impaired patients, and between controls and Alzheimer’s disease patients with the support vector machine (77–82% accuracy, 57–93% sensitivity, 63–90% specificity, 0.74–0.78 area under the curve). Classification between mild cognitive impaired patients and Alzheimer’s disease patients was poor, as no algorithm outperformed the baseline (63% accuracy, 0% sensitivity, 100% specificity, 0.50 area under the curve).

Comparison with Existing Method(s)

The classification performance found in the present study is comparable to that of the existing CSF and MRI biomarkers.

Conclusion

The data suggest that visuomotor network dysfunctions have potential in biomarker research and the proposed eye-hand tasks could add to existing tests to form a clear definition of the preclinical phenotype of AD.
Identification of Electroencephalogram Signals in Alzheimer's Disease by Multifractal and Multiscale Entropy Analysis
Authors: Ando Momo , Nobukawa Sou , Kikuchi Mitsuru , Takahashi Tetsuya
Published: June 2021

Alzheimer's disease (AD) is the most common form of dementia and is a progressive neurodegenerative disease that primarily develops in old age. In recent years, it has been reported that early diagnosis of AD and early intervention significantly delays disease progression. Hence, early diagnosis and intervention are emphasized. As a diagnostic index for AD patients, evaluating the complexity of the dependence of the electroencephalography (EEG) signal on the temporal scale of Alzheimer's disease (AD) patients is effective. Multiscale entropy analysis and multifractal analysis have been performed individually, and their usefulness as diagnostic indicators has been confirmed, but the complemental relationship between these analyses, which may enhance diagnostic accuracy, has not been investigated. We hypothesize that combining multiscale entropy and fractal analyses may add another dimension to understanding the alteration of EEG dynamics in AD. In this study, we performed both multiscale entropy and multifractal analyses on EEGs from AD patients and healthy subjects. We found that the classification accuracy was improved using both techniques. These findings suggest that the use of multiscale entropy analysis and multifractal analysis may lead to the development of AD diagnostic tools.
Suppression of Luminance Contrast Sensitivity by Weak Color Presentation
Authors: Negishi Ippei , Shinomori Keizo
Published: June 2021

The results of psychophysical studies suggest that color in a visual scene affects luminance contrast perception. In our brain imaging studies we have found evidence of an effect of chromatic information on luminance information. The dependency of saturation on brain activity in the visual cortices was measured by functional magnetic resonance imaging (fMRI) while the subjects were observing visual stimuli consisting of colored patches of various hues manipulated in saturation (Chroma value in the Munsell color system) on an achromatic background. The results indicate that the patches suppressed luminance driven brain activity. Furthermore, the suppression was stronger rather than weaker for patches with lower saturation colors, although suppression was absent when gray patches were presented instead of colored patches. We also measured brain activity while the subjects observed only the patches (on a uniformly black background) and confirmed that the colored patches alone did not give rise to differences in brain activity for different Chroma values. The chromatic information affects the luminance information in V1, since the effect was observed in early visual cortices (V2 and V3) and the ventral pathway (hV4), as well as in the dorsal pathway (V3A/B). In addition, we conducted a psychophysical experiment in which the ability to discriminate luminance contrast on a grating was measured. Discrimination was worse when weak (less saturated) colored patches were attached to the grating than when strong (saturated) colored patches or achromatic patches were attached. The results of both the fMRI and psychophysical experiments were consistent in that the effects of color were greater in the conditions with low saturation colors.
Ethanol-Dependent Synthesis of Salsolinol in the Posterior Ventral Tegmental Area as Key Mechanism of Ethanol’s Action on Mesolimbic Dopamine
Authors: Bassareo Valentina , Frau Roberto , Maccioni Riccardo , Caboni Pierluigi , Manis Cristina , Peana Alessandra T. , Migheli Rossana , Porru Simona , Acquas Elio
Published: June 2021

Abnormal consumption of ethanol, the ingredient responsible for alcoholic drinks’ addictive liability, causes millions of deaths yearly. Ethanol’s addictive potential is triggered through activation, by a still unknown mechanism, of the mesolimbic dopamine (DA) system, part of a key motivation circuit, DA neurons in the posterior ventral tegmental area (pVTA) projecting to the ipsilateral nucleus accumbens shell (AcbSh). The present in vivo brain microdialysis study, in dually-implanted rats with one probe in the pVTA and another in the ipsilateral or contralateral AcbSh, demonstrates this mechanism. As a consequence of the oral administration of a pharmacologically relevant dose of ethanol, we simultaneously detect a) in the pVTA, a substance, 1-methyl-6,7-dihydroxy-1,2,3,4-tetrahydroisoquinoline (salsolinol), untraceable under control conditions, product of condensation between DA and ethanol’s first by-product, acetaldehyde; and b) in the AcbSh, a significant increase of DA release. Moreover, such newly generated salsolinol in the pVTA is responsible for increasing AcbSh DA release via μ opioid receptor (μOR) stimulation. In fact, inhibition of salsolinol’s generation in the pVTA or blockade of pVTA μORs prevents ethanol-increased ipsilateral, but not contralateral, AcbSh DA release. This evidence discloses the long-sought key mechanism of ethanol’s addictive potential and suggests the grounds for developing preventive and therapeutic strategies against abnormal consumption.
High-Resolution EEG Source Localization in Segmentation-Free Head Models Based on Finite-Difference Method and Matching Pursuit Algorithm
Authors: Moridera Takayoshi , Rashed Essam A. , Mizutani Shogo , Hirata Akimasa
Published: June 2021

Electroencephalogram (EEG) is a method to monitor electrophysiological activity on the scalp, which represents the macroscopic activity of the brain. However, it is challenging to identify EEG source regions inside the brain based on data measured by a scalp-attached network of electrodes. The accuracy of EEG source localization significantly depends on the type of head modeling and inverse problem solver. In this study, we adopted different models with a resolution of 0.5 mm to account for thin tissues/fluids, such as the cerebrospinal fluid (CSF) and dura. In particular, a spatially dependent conductivity (segmentation-free) model created using deep learning was developed and used for more realist representation of electrical conductivity. We then adopted a multi-grid-based finite-difference method (FDM) for forward problem analysis and a sparse-based algorithm to solve the inverse problem. This enabled us to perform efficient source localization using high-resolution model with a reasonable computational cost. Results indicated that the abrupt spatial change in conductivity, inherent in conventional segmentation-based head models, may trigger source localization error accumulation. The accurate modeling of the CSF, whose conductivity is the highest in the head, was an important factor affecting localization accuracy. Moreover, computational experiments with different noise levels and electrode setups demonstrate the robustness of the proposed method with segmentation-free head model.