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Concurrent fNIRS and EEG for Brain Function Investigation: A Systematic, Methodology-Focused Review
Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) stand as state-of-the-art techniques for non-invasive functional neuroimaging. On a unimodal basis, EEG has poor spatial resolution while presenting high temporal resolution. In contrast, fNIRS offers better spatial resolution, though it is constrained by its poor temporal resolution. One important merit shared by the EEG and fNIRS is that both modalities have favorable portability and could be integrated into a compatible experimental setup, providing a compelling ground for the development of a multimodal fNIRS–EEG integration analysis approach. Despite a growing number of studies using concurrent fNIRS-EEG designs reported in recent years, the methodological reference of past studies remains unclear. To fill this knowledge gap, this review critically summarizes the status of analysis methods currently used in concurrent fNIRS–EEG studies, providing an up-to-date overview and guideline for future projects to conduct concurrent fNIRS–EEG studies. A literature search was conducted using PubMed and Web of Science through 31 August 2021. After screening and qualification assessment, 92 studies involving concurrent fNIRS–EEG data recordings and analyses were included in the final methodological review. Specifically, three methodological categories of concurrent fNIRS–EEG data analyses, including EEG-informed fNIRS analyses, fNIRS-informed EEG analyses, and parallel fNIRS–EEG analyses, were identified and explained with detailed description. Finally, we highlighted current challenges and potential directions in concurrent fNIRS–EEG data analyses in future research.
Journal Article
Integrated transcriptomic and proteomic analysis reveals the regulatory role of exogenous gibberellin in sugarcane internode maturation
IntroductionSugarcane cultivation is a vital component of the agricultural economy in southern China. Investigating internode development in sugarcane is crucial for optimizing cultivation management practices and improving cane yield.MethodsIn this study, transcriptome and proteome sequencing were performed on internode tissues of sugarcane cultivar Guitang 42 at 0, 6, and 12 days post-treatment, aiming to identify key molecular components and elucidate biological pathways through which exogenous gibberellic acid (GA3) regulates internode maturation.ResultsAccordingly, GA3 predominantly promoted internodal elongation, rather than nodal expansion. Following transcriptome and proteome sequencing, 3D principal component analysis (PCA) based on both datasets revealed a clear separation between the GA3-treated (GA) and control (CK) groups. The comparison of GA₆d vs. CK₀d identified the largest number of differentially expressed genes (DEGs, 34,541), followed by CK₁2d vs. CK₀d (27,898) and GA₁2d vs. CK₀d (22,709). Similarly, the pairwise comparison between GA₆d and CK₀d yielded the highest number of differentially expressed proteins (DEPs, 363). KEGG enrichment analysis based on DEGs, DEPs and their intersection revealed that GA3 treatment up-regulated the phenylpropanoid biosynthesis and phenylalanine metabolism pathways, thereby promoting lignin biosynthesis. Additionally, PPI analysis revealed high-confidence interactions between two hub proteins (PAL and 4CL). Finally, we elucidated the biosynthetic pathways that produce p-hydroxyphenyl lignin, guaiacyl lignin, and syringyl lignin using L-phenylalanine as the substrate.DiscussionThe results presented herein provide new insights into sugarcane internode maturation.
Journal Article
Estimating utilization distributions from fitted step‐selection functions
Habitat‐selection analyses are often used to link environmental covariates, measured within some spatial domain of assumed availability, to animal location data that are assumed to be independent. Step‐selection functions (SSFs) relax this independence assumption, by using a conditional model that explicitly acknowledges the spatiotemporal dynamics of the availability domain and hence the temporal dependence among successive locations. However, it is not clear how to produce an SSF‐based map of the expected utilization distribution. Here, we used SSFs to analyze virtual animal movement data generated at a fine spatiotemporal scale and then rarefied to emulate realistic telemetry data. We then compared two different approaches for generating maps from the estimated regression coefficients. First, we considered a naïve approach that used the coefficients as if they were obtained by fitting an unconditional model. Second, we explored a simulation‐based approach, where maps were generated using stochastic simulations of the parameterized step‐selection process. We found that the simulation‐based approach always outperformed the naïve mapping approach and that the latter overestimated home‐range size and underestimated local space‐use variability. Differences between the approaches were greatest for complex landscapes and high sampling rates, suggesting that the simulation‐based approach, despite its added complexity, is likely to offer significant advantages when applying SSFs to real data.
Journal Article
Students' Social-Cognitive Engagement in Online Discussions: An Integrated Analysis Perspective
Grounded on constructivism, mining a complex mix of social and cognitive interrelations is key to understanding collaborative discussion in online learning. A single examination of one of these factors tends to overlook the impact of the other factor on learning. In this paper, we innovatively constructed a social-cognitive engagement setting to jointly characterize social and cognitive aspects. In the online discussion forum, this study jointly characterized students' social and cognitive aspects to investigate interactive patterns of different social-cognitive engagements and social-cognitive engagement evolution across four periods (i.e., creation, growth, maturity, and death). Multi-methods including social network analysis, content analysis, epistemic network analysis, and statistical analysis was applied in this study. The results showed that the interactive patterns of social-cognitive engagement were affected by both social network position and cognitive level. In particular, students' social network position was a vital indicator for the contributions to cognitive level of students, and cognitive level affected the related interactions to some extent. In addition, this study found a nonlinear evolutionary development of students' social-cognitive engagement. Furthermore, maturity is a critical period on which teachers should focus, as the co-occurrence of social-cognitive engagement reaches a maximum level in this period. Based on the results, this multi-perspective analysis including social and cognitive aspects can provide insightful methodological implications and practical suggestions for teachers in conducting in-depth interactive discussions.
Journal Article
Bridging the gap between commercial fisheries and survey data to model the spatiotemporal dynamics of marine species
Monitoring and assessment of natural resources often require inputs from multiple data sources. In fisheries science, for example, the inference of a species’ abundance distribution relies on two main data sources, namely commercial fisheries and scientific survey data. Despite efforts to combine these data into an integrated statistical model, their coupling is frequently hampered due to differences in their sampling designs, which imposes distinct bias sources in the estimator of the abundance distribution. We developed a flexible species distribution model (SDM) that can integrate both data sources while filtering out their relative bias contributions. We applied the model on three different age groups of the western Baltic cod stock. For each age group, we tested the model on (1) survey data and (2) integrated data (survey + commercial) as a means to compare their differences and investigate how the inclusion of commercial fisheries data improved the spatiotemporal abundance estimator and parameter estimates. Moreover, we proposed a novel validation approach to evaluate whether the inclusion of commercial fisheries data in the integrated model is not in direct contradiction with the survey data. Following our approach, the results indicated that the use of commercial fisheries data is suitable for the integrated model. Across all age groups, our results demonstrated how commercial fisheries supplied additional information on cod’s spatiotemporal abundance dynamics, highlighting sometimes abundance hot spots that were not detected by the survey model alone. Additionally, the integrated model provided a reduction of up to 20% and 10% in the uncertainty (SE) of the predicted abundance fields and fixed-effect parameters, respectively. The proposed model represents thus a valuable benchmark for evaluating spatiotemporal dynamics of fish, and strengthens the science-based advice for marine policymakers.
Journal Article
Integrated transcriptome and metabolome analysis to investigate the mechanism of intranasal insulin treatment in a rat model of vascular dementia
Introduction: Insulin has an effect on neurodegenerative diseases. However, the role and mechanism of insulin in vascular dementia (VD) and its underlying mechanism are unknown. In this study, we aimed to investigate the effects and mechanism of insulin on VD. Methods: Experimental rats were randomly assigned to control (CK), Sham, VD, and insulin (INS) + VD groups. Insulin was administered by intranasal spray. Cognitive function was evaluated using the Morris's water maze. Nissl's staining and immunohistochemical staining were used to assess morphological alterations. Apoptosis was evaluated using TUNEL-staining. Transcriptome and metabolome analyses were performed to identify differentially expressed genes (DEGs) and differentially expressed metabolites (DEMs), respectively. Results: Insulin significantly improved cognitive and memory functions in VD model rats ( p < 0.05). Compared with the VD group, the insulin + VD group exhibited significantly reduced the number of Nissl's bodies numbers, apoptosis level, GFAP-positive cell numbers, apoptosis rates, and p-tau and tau levels in the hippocampal CA1 region ( p < 0.05). Transcriptomic analysis found 1,257 and 938 DEGs in the VD vs. CK and insulin + VD vs. VD comparisons, respectively. The DEGs were mainly enriched in calcium signaling, cAMP signaling, axon guidance, and glutamatergic synapse signaling pathways. In addition, metabolomic analysis identified 1 and 14 DEMs between groups in negative and positive modes, respectively. KEGG pathway analysis indicated that DEGs and DEMs were mostly enriched in metabolic pathway. Conclusion: Insulin could effectively improve cognitive function in VD model rats by downregulating tau and p-tau expression, inhibiting astrocyte inflammation and neuron apoptosis, and regulating genes involved in calcium signaling, cAMP signaling, axon guidance, and glutamatergic synapse pathways, as well as metabolites involved in metabolic pathway.
Journal Article
Using lineups to evaluate goodness of fit of animal movement models
Movement models are frequently fit to animal location data to understand how individuals respond to and interact with local environmental features. Several open‐source software packages are available for analysing animal movements and can facilitate parameter estimation, yet there are relatively few methods available for evaluating model goodness of fit. We describe how a simple graphical technique, the lineup protocol, can be used to evaluate goodness of fit of integrated step‐selection analyses and hidden Markov models, but the method can be applied much more broadly. We leverage the ability to simulate data from fitted models and demonstrate the approach using both an integrated step‐selection analysis and a hidden Markov model applied to fisher (Pekania pennanti) data. A variety of responses and movement metrics can be used to evaluate models, and the lineup protocol can be tailored to focus on specific model assumptions or movement features that are of primary interest. Although it is possible to evaluate statistical significance using a formal hypothesis test, the method can also be used in a more exploratory fashion (e.g. to explore variability in model behaviour across stochastic simulations or to identify areas where the model could be improved). We provide coded examples and vignettes to demonstrate the flexibility of the approach. We encourage movement ecologists to consider how their models will be applied when choosing appropriate graphical responses for evaluating goodness of fit.
Journal Article
Comprehensive Analysis of PANoptosis-Related Gene Signature of Ulcerative Colitis
Accumulating evidence shows that the abnormal increase in the mortality of intestinal epithelial cells (IECs) caused by apoptosis, pyroptosis, and necroptosis is closely related to the function of mucous membrane immunity and barrier function in patients with ulcerative colitis (UC). As a procedural death path that integrates the above-mentioned many deaths, the role of PANoptosis in UC has not been clarified. This study aims to explore the characterization of PANoptosis patterns and determine the potential biomarkers and therapeutic targets. We constructed a PANoptosis gene set and revealed significant activation of PANoptosis in UC patients based on multiple transcriptome profiles of intestinal mucosal biopsies from the GEO database. Comprehensive bioinformatics analysis revealed five key genes (ZBP1, AIM2, CASP1/8, IRF1) of PANoptosome with good diagnostic value and were highly correlated with an increase in pro-inflammatory immune cells and factors. In addition, we established a reliable ceRNA regulatory network of PANoptosis and predicted three potential small-molecule drugs sharing calcium channel blockers that were identified, among which flunarizine exhibited the highest correlation with a high binding affinity to the targets. Finally, we used the DSS-induced colitis model to validate our findings. This study identifies key genes of PANoptosis associated with UC development and hypothesizes that IRF1 as a TF promotes PANoptosome multicomponent expression, activates PANoptosis, and then induces IECs excessive death.
Journal Article
Molecular Characterization and Landscape of Breast cancer Models from a multi-omics Perspective
Breast cancer is well-known to be a highly heterogenous disease. This facet of cancer makes finding a research model that mirrors the disparate intrinsic features challenging. With advances in multi-omics technologies, establishing parallels between the various models and human tumors is increasingly intricate. Here we review the various model systems and their relation to primary breast tumors using available omics data platforms. Among the research models reviewed here, breast cancer cell lines have the least resemblance to human tumors since they have accumulated many mutations and copy number alterations during their long use. Moreover, individual proteomic and metabolomic profiles do not overlap with the molecular landscape of breast cancer. Interestingly, omics analysis revealed that the initial subtype classification of some breast cancer cell lines was inappropriate. In cell lines the major subtypes are all well represented and share some features with primary tumors. In contrast, patient-derived xenografts (PDX) and patient-derived organoids (PDO) are superior in mirroring human breast cancers at many levels, making them suitable models for drug screening and molecular analysis. While patient derived organoids are spread across luminal, basal- and normal-like subtypes, the PDX samples were initially largely basal but other subtypes have been increasingly described. Murine models offer heterogenous tumor landscapes, inter and intra-model heterogeneity, and give rise to tumors of different phenotypes and histology. Murine models have a reduced mutational burden compared to human breast cancer but share some transcriptomic resemblance, and representation of many breast cancer subtypes can be found among the variety subtypes. To date, while mammospheres and three- dimensional cultures lack comprehensive omics data, these are excellent models for the study of stem cells, cell fate decision and differentiation, and have also been used for drug screening. Therefore, this review explores the molecular landscapes and characterization of breast cancer research models by comparing recent published multi-omics data and analysis.
Journal Article