Activation network improves spatiotemporal modelling of human brain communication processes.

Dynamic functional networks (DFN) have considerably advanced modelling of the brain communication processes. The prevailing implementation capitalizes on the system and network-level correlations between time series. However, this approach does not account for the continuous impact of non-dynamic dependencies within the statistical correlation, resulting in relatively stable connectivity patterns of DFN over time with limited sensitivity for communication dynamic between brain regions. Here, we propose an activation network framework based on the activity of functional connectivity (AFC) to extract new types of connectivity patterns during brain communication process. The AFC captures potential time-specific fluctuations associated with the brain communication processes by eliminating the non-dynamic dependency of the statistical correlation. In a simulation study, the positive correlation (r=0.966,p<0.001) between the extracted dynamic dependencies and the simulated "ground truth" validates the method's dynamic detection capability. Applying to autism spectrum disorders (ASD) and COVID-19 datasets, the proposed activation network extracts richer topological reorganization information, which is largely invisible to the DFN. Detailed, the activation network exhibits significant inter-regional connections between function-specific subnetworks and reconfigures more efficiently in the temporal dimension. Furthermore, the DFN fails to distinguish between patients and healthy controls. However, the proposed method reveals a significant decrease (p<0.05) in brain information processing abilities in patients. Finally, combining two types of networks successfully classifies ASD (83.636 % ± 11.969 %,mean±std) and COVID-19 (67.333 % ± 5.398 %). These findings suggest the proposed method could be a potential analytic framework for elucidating the neural mechanism of brain dynamics.

The up-regulation of long non-coding RNA CCAT2 indicates a poor prognosis for prostate cancer and promotes metastasis by affecting epithelial-mesenchymal transition.

Recently, long noncoding RNAs (lncRNAs) have been shown to have critical regulatory roles in human cancer biology. LncRNA CCAT2 is a novel identified lncRNA that was previously reported to be up-regulated in different cancers, however, its role in prostate cancer remains unclear. The aim of this study was to investigate the expression and role of lncRNA CCAT2 in prostate cancer. The expression levels of lncRNA CCAT2 in PCa tissues and cell lines (DU145 and 22RV1) were evaluated by quantitative real-time PCR (qRT-PCR), and its association with prognosis of patients was analyzed by statistical analysis. Furthermore, the effect of CCAT2 on proliferation, migration, and invasion was studied in PCa cells. We found that the expression level of CCAT2 was higher in PCa tissues and cells compared to adjacent non-tumor tissues and normal prostate stromal immortalized cells WPMY-1. Kaplan-Meier survival analysis revealed that patients with high CCAT2 expression level had poorer overall survival and progression-free survival than those with low CCAT2 expression. Furthermore, multivariate analysis showed that the status of CCAT2 expression was an independent prognostic indicator for this disease. We also found that knockdown of CCAT2 could inhibit cell growth, migration, and invasion in vitro. In addition, knockdown of CCAT2 stimulated epithelial-mesenchymal transition (EMT) through abrogating N-cadherin, vimentin expression and intensifing the expression levels of E-cadherin. In conclusion, our data suggested that lncRNA CCAT2 was a novel molecule involved in PCa progression, which provided a potential prognostic biomarker and therapeutic target for new therapies in patients with prostate cancer.

An EEG study of human trust in autonomous vehicles based on graphic theoretical analysis.

With the development of autonomous vehicle technology, human-centered transport research will likely shift to the interaction between humans and vehicles. This study focuses on the human trust variation in autonomous vehicles (AVs) as the technology becomes increasingly intelligent. This study uses electroencephalogram data to analyze human trust in AVs during simulated driving conditions. Two driving conditions, the semi-autonomous and the autonomous, which correspond to the two highest levels of automatic driving, are used for the simulation, accompanied by various driving and car conditions. The graph theoretical analysis (GTA) is the primary method for data analysis. In semi-autonomous driving mode, the local efficiency and cluster coefficient are lower in car-normal conditions than in car-malfunction conditions with the car approaching. This finding suggests that the human brain has a strong information processing ability while facing predictable potential hazards. However, when it comes to a traffic light with a car malfunctioning under the semi-autonomous driving mode, the characteristic path length is higher for the car malfunction manifesting a weak information processing ability while facing unpredictable potential hazards. Furthermore, in fully automatic driving conditions, participants cannot do anything and need low-level brain function to take emergency actions as lower local efficiency and small worldness for car malfunction. Our results shed light on the design of the human-machine interaction and human factor engineering on the high level of an autonomous vehicle.

Toward practical driving fatigue detection using three frontal EEG channels: a proof-of-concept study.

Objective. Although various driving fatigue detection strategies have been introduced, the limited practicability is still an obstacle for the real application of these technologies. This study is based on the newly proposed non-hair-bearing (NHB) method to achieve practical driving fatigue detection with fewer channels from NHB areas and more efficient electroencephalogram (EEG) features.Approach. EEG data were recorded from 20 healthy subjects (15 males, age = 22.2 ± 3.2 years) in a 90 min simulated driving task using a remote wireless cap. Behaviorally, subjects demonstrated a salient fatigue effect, as reflected by a monotonic increase in reaction time. Using a sliding-window approach, we determined the vigilant and fatigued states at individual level to reduce the inter-subject differences in behavioral impairment and brain activity. Multiple EEG features, including power-spectrum density (PSD), functional connectivity (FC), and entropy, were estimated in a pairwise manner, which were set as input for fatigue classification.Main results. Intriguingly, this data-driven approach showed that the best classification performance was achieved using three EEG channel pairs located in the NHB area. The mixed features of the frontal NHB area lead to the high within-subject detection rate of driving fatigue (92.7% ± 0.92%) with satisfactory generalizability for fatigue classification across different subjects (77.13% ± 0.85%). Moreover, we found the most prominent contributing features were PSD of different frequency bands within the frontal NHB area and FC within the frontal NHB area and between frontal and parietal areas.Significance. In summary, the current work provided objective evidence to support the effectiveness of the NHB method and further improved the performance, thereby moving a step forward towards practical driving fatigue detection in real-world scenarios.

Dynamic Reorganization of Functional Connectivity Unmasks Fatigue Related Performance Declines in Simulated Driving.

Although driving fatigue has long been recognized as one of the leading causes of fatal accidents worldwide, the underlying neural mechanisms remain largely unknown that impedes the developments of automatic detection techniques. This study investigated the effects of driving fatigue on the reorganization of dynamic functional connectivity (FC) through our newly developed temporal brain network analysis framework. EEG data were recorded from 20 healthy subjects (male/female = 15/5, age = 22.2 ± 3.2 years) using a remote wireless cap with 24 channels. Temporal brain networks in the theta, alpha and beta were estimated using a sliding window approach and quantitatively compared between the most vigilant and fatigue states during a 90-min simulated driving experiment. Behaviorally, subjects demonstrated a salient driving fatigue effect as reflected by a monotonic increase of reaction time and speed variation. Furthermore, we found a significantly disintegrated spatiotemporal topology of dynamic FC as shown in reduced temporal global efficiency and increased temporal local efficiency at fatigue state. Specifically, we found localized changes of temporal closeness centrality mainly resided in the frontal and parietal areas. Finally, the changes of temporal network measures were associated with those of behavioral metrics. Our findings provide new insights into dynamic characteristics of functional connectivity during driving fatigue and demonstrate the potential for using temporal network metrics as reliable biomarkers for driving fatigue detection.

Growth and contaminant removal effect of several plants in constructed wetlands.

The aim of the present study is to probe the relation between plant growth and its decontamination effect in constructed wetlands. Four species were studied in the small-scale mono-cultured constructed wetlands, which were fed with domestic wastewater. Plant growth indexes were correlated with contaminant removal performance of the constructed wetlands. Wetlands planted with Cyperus flabelliformis Rottb. showed the highest growth indexes such as shoot growth, biomass, root activity, root biomass increment, and the highest contaminant removal rates, whereas wetlands planted with Vetiveria zizanioides L. Nash had the lowest growth indexes and the lowest removal rates. Above-ground biomass and total biomass were significantly correlated with ammonia nitrogen removal, and below-ground biomass with soluble reactive phosphorus removal. Photosynthetic rate had higher correlation with nitrogen removal in these species. Root activity and root biomass increment was more correlated with 5 d biochemical oxygen demand removal. Chemical oxygen demand removal had lower correlations with plant growth indexes. All four species had higher removal rates in summer and autumn. The results suggest that the effect of plant growth on contaminant removal in constructed wetlands were different specifically in plants and contaminants.

Occurrence, profiles, and ecological risks of polybrominated diphenyl ethers in mangrove sediments of Shantou, China.

Surface sediments were collected from three mangrove wetlands (Yifeng Xi, Shuanghan, and Su'ai Wan) in Shantou coastal zone of South China to investigate spatial distributions of polybrominated diphenyl ethers (PBDEs). The results demonstrate that PBDEs were detected in all the samples, indicating their widespread occurrence in coastal sediments of the studied area. Σ9PBDEs (defined as the sum of nine targeted PBDE congeners except BDE-209) and BDE-209 are in the range of 2.3 to 11.5 and 16.7 to 58.2 ng/g, respectively. BDE-209 is the dominant PBDE congener in all sediment samples. The sediment concentrations of ∑9PBDEs and BDE-209 among the three wetlands decrease in the order of Su'ai Wan > Shuanghan > Yifeng Xi. The concentrations of ∑9PBDEs are higher in mangrove sediments than in mudflats, but no obvious regularity can be found on the correlation between mangrove species and PBDE levels in sediments. The contents of total organic carbon are moderately correlated with BDE-209 concentrations in sediments but not with ∑9PBDE concentrations. The samples collected from different locations show slightly different composition profiles except BDE-209, with BDE-100 and BDE-47 being the pre-dominated congeners. The mudflats exhibit higher abundances of tri- to hexa-substituted congeners than the mangrove sediments. Ecological risk assessment demonstrates that the surface sediments from Shantou may pose a potential ecological risk of exposure to sediment-dwelling organisms.

Highly hard yet toughened bcc-W coating by doping unexpectedly low B content.

Either hardness or toughness has been the core interest in scientific exploration and technological pursuit for a long time. However, it is still a big challenge to enhance the hardness and toughness at the same time, since the improvement of one side is always at the expense of the other one. Here, we have succeeded in dealing with this pair of conflict based on tungsten (W) coating by doping boron (B) via magnetron co-sputtering. The results reveal that the introduction of low concentrations of B (6.3 at. %), in the doping regime, leads to the formation of W(B) supersaturated solid solution with refined grains. Meanwhile, the doping-induced higher compressive stress, higher H/E* and denser microstructure result in a surprising combination of improved hardness (2 × larger than pure W) and superior toughness (higher crack formation threshold compared to pure W). We believe this is an innovative sight to design new generation of transition-metal-based multifunctional coatings. Besides, our results are applicable for industrial application because it can be realized by simple manufacturing approaches, e.g. magnetron sputtering technology.

Historical trends and ecological risks of polybrominated diphenyl ethers (PBDEs) and alternative halogenated flame retardants (AHFRs) in a mangrove in South China.

While the production of polybrominated diphenyl ethers (PBDEs) was gradually phased out in the last decade, they may still pose hidden danger to the environment due to their toxicity and persistence. On the other hand, alternative halogenated flame retardants (AHFRs) have been increasingly used as substitutes for PBDEs and may further worsen environmental health. To determine the environmental impact of PBDEs and AHFRs, we examined the historical trends and ecological risks of PBDEs and AHFRs in a typical industrialized city in South China by measuring their concentrations in mangrove sediment. Results showed that the concentrations of PBDEs increased abruptly from 1997 to 2009 due to the use of commercial deca-BDE mixture, but were stabilized in recent years. The concentrations of AHFRs, mainly contributed by decabromodiphenyl ethane (DBDPE), kept increasing from 1997 onwards. Based on the temporal trends, DBDPE is predicted to be predominant over BDE 209 in future. Despite the observed similar concentration between BDE 209 and DBDPE, the former posed a high ecological risk, while the ecological risk of the latter was negligible. Therefore, more attention is required to manage the contamination of BDE 209 in the environment.