Quantitative signal quality assessment for large-scale continuous scalp electroencephalography from a big data perspective.

Objective. Despite electroencephalography (EEG) being a widely used neuroimaging technique with an excellent temporal resolution, in practice, the signals are heavily contaminated by artifacts masking responses of interest in an experiment. It is thus essential to guarantee a prompt and effective detection of artifacts that provides quantitative quality assessment (QA) on raw EEG data. This type of pipeline is crucial for large-scale EEG studies. However, current EEG QA studies are still limited.Approach. In this study, combined from a big data perspective, we therefore describe a quantitative signal quality assessment pipeline, a stable and general threshold-based QA pipeline that automatically integrates artifact detection and new QA measures to assess continuous resting-state raw EEG data. One simulation dataset and two resting-state EEG datasets from 42 healthy subjects and 983 clinical patients were utilized to calibrate the QA pipeline.Main Results. The results demonstrate that (1) the QA indices selected are sensitive: they almost strictly and linearly decrease as the noise level increases; (2) stable, replicable QA thresholds are valid for other experimental and clinical EEG datasets; and (3) use of the QA pipeline on these datasets reveals that high-frequency noises are the most common noises in EEG practice. The QA pipeline is also deployed in the WeBrain cloud platform (https://webrain.uestc.edu.cn/, the Chinese EEG Brain Consortium portal).Significance. These findings suggest that the proposed QA pipeline may be a stable and promising approach for quantitative EEG signal quality assessment in large-scale EEG studies.

Win-win-win pathway for ecological restoration by balancing hydrological, ecological, and agricultural dimensions: Contrasting lessons from highly eroded agroforestry.

Ecological restoration projects (ERP) can effectively reverse ecosystem degradation. However, some ERPs have failed to restore ecosystems under environmental constraints, and they were unable to achieve the desired ecological and economic benefits. To achieve a win-win-win target that balances the hydrological, ecological, and agricultural dimensions, we introduced the contrasting lessons from hotspots of ecosystem restoration in the arid Loess Plateau (LP) and the humid Karst Plateau (KP) in China, and discussed a novel strategy for coordinating ecosystem restoration, water and food security, and residents' livelihoods. The biophysical models and related statistical records showed that aggressive ERPs and soil and water conservation projects (SWCPs) significantly promoted vegetation restoration and reduced soil erosion and sediment yield in both areas. However, excessive afforestation in the arid LP exhausted water resources and threatened ecosystem sustainability. The accelerated replacement of cropland since 1999 in the LP aggravated cropland shortage which led to carbon sequestration and grain productivity declines in the initial years. However, the construction of terrace and check-dam fields and improvements in the conditions of agricultural production reconciled the cropland shortage and stabilized food security. The positive involvement of stakeholders in ERPs effectively minimized land degradation through economic development and the improved livelihoods of local residents. Therefore, based on the evidence from the KP and LP, the proposed win-win-win strategy is potentially applicable in other global regions that suffer from land degradation. This strategy can achieve considerable success if the planners have a good understanding of local environmental conditions as well as the social and economic needs of residents affected by ERPs.

The assessment of epigenetic diversity, differentiation, and structure in the 'Fuji' mutation line implicates roles of epigenetic modification in the occurrence of different mutant groups as well as spontaneous mutants.

The 'Fuji' line includes many varieties with a similar genetic background and consistent inducement factors with epigenetic occurrence, thus it may be considered an ideal candidate for epigenetic research. In this study, 91 bud mutations of 'Fuji' apple were used as the test materials. Using the genetic variation within 'Fuji' as the control, the characteristics of epigenetic variation at different levels in both varieties and mutant groups were examined. The results showed that: (1) the global genomic DNA methylation level of the 91 bud mutants of 'Fuji' ranged from 29.120%-45.084%, with an average of 35.910%. Internal cytosine methylation was the main DNA methylation pattern. Regarding the variation of methylation patterns of 'Fuji' mutants, the vast majority of loci maintained the original methylation pattern existed in 'Fuji'. CHG methylation variation was the main type of variation; (2) the variation in methylation patterns between the mutant groups was greater than that of methylation levels. Among these patterns, the variation in CHG methylation patterns (including CHG hypermethylation and CHG demethylation) was expected to be dominant. The observed variation in methylation levels was more important in the Color mutant group; however, the variation in methylation patterns was more obvious in both the early maturation and Spur mutant groups. Moreover, the range of variation in the Early-maturation group was much wider than that in the Spur mutant group; (3) epigenetic diversity and genetic diversity were both low between the mutant groups. In the 'Fuji' mutant groups, there was few correlation between genetic and epigenetic variation, and epigenetic differentiation resulted in more loci with moderate or greater differentiation; (4) the purifying selection seemed to play a major role in the differentiation of different groups of 'Fuji' mutants (65.618%), but epigenetic diversity selection still occurred at nearly 35% of loci. Sixteen epigenetic outlier loci were detected.

Neutrophil-to-Lymphocyte Ratio Predicts Length of Stay and Acute Hospital Cost in Patients with Acute Ischemic Stroke.

BACKGROUND: Although several risk factors for prolonged length of stay (LOS) and increased hospital cost have been identified, the association between LOS, hospital cost, and neutrophil-to-lymphocyte ratio (NLR) has not yet been investigated. We aimed to investigate the influence of NLR on LOS and hospital cost in patients with acute ischemic stroke. METHODS: Patients with acute ischemic stroke diagnosed within 24 hours of symptom onset were included. Univariate analysis and stepwise multiple regression analysis were used to identify independent predictors of LOS and hospital cost. RESULTS: A total of 346 patients were included in the final analysis. The median LOS was 11 days (range 8-13 days). The median acute hospital cost per patient was 19,030.6 RMB (U.S. $ 3065.8) (range 14,450.8 RMB-25,218.2 RMB). Neutrophil count to lymphocyte count (NLR) (P < .001), diabetes mellitus (P = .034), stroke subtype (P = .005), and initial stroke severity (P < .001) were significantly associated with prolonged LOS in the univariate analysis. NLR (P < .001), smoking (P = .04), stroke subtype (P < .001), initial stroke severity (P < .001), and LOS (P < .001) were significantly associated with increased hospital cost in the univariate analysis. Multivariate regression analysis showed that NLR was an independent predictor of both LOS and acute hospital cost. In addition, high NLR was significantly correlated with poor outcome at discharge, prolonged LOS, and increased hospital cost. CONCLUSIONS: NLR is significantly associated with LOS and acute hospital cost in patients presenting with acute ischemic stroke. It is a simple, inexpensive, and readily available biomarker and may serve as a clinically practical indicator for assessing the economic burden of stroke.