Interpretable and Robust AI in EEG Systems: A Survey

The close coupling of artificial intelligence (AI) and electroencephalography (EEG) has substantially advanced human-computer interaction (HCI) technologies in the AI era. Different from traditional EEG systems, the interpretability and robustness of AI-based EEG systems are becoming particularly crucial. The interpretability clarifies the inner working mechanisms of AI models and thus can gain the trust of users. The robustness reflects the AI's reliability against attacks and perturbations, which is essential for sensitive and fragile EEG signals. Thus the interpretability and robustness of AI in EEG systems have attracted increasing attention, and their research has achieved great progress recently. However, there is still no survey covering recent advances in this field. In this paper, we present the first comprehensive survey and summarize the interpretable and robust AI techniques for EEG systems. Specifically, we first propose a taxonomy of interpretability by characterizing it into three types: backpropagation, perturbation, and inherently interpretable methods. Then we classify the robustness mechanisms into four classes: noise and artifacts, human variability, data acquisition instability, and adversarial attacks. Finally, we identify several critical and unresolved challenges for interpretable and robust AI in EEG systems and further discuss their future directions

Vegetation greening offsets urbanization induced fast warming in Guangdong, Hong Kong, and Macao region (GHMR)

Previous studies show that the environment in the Guangdong, Hong Kong, and Macao region is under the double stress of global warming and urbanization. Here, we show that due to the increase of regional greenness, the effect of urbanization warming on surface air temperature (SAT) decreased with time and became statistically insignificant from 2004 to 2018, compared to 1979 onward; while the urbanization itself has significantly warmed land surface temperature (LST), with a warming rate of 0.14°C ± 0.04°C/10a at daytime and 0.02°C ± 0.02°C/10a at nighttime during 2004–2018, respectively. The anthropogenic heat was found to have a limited influence on SAT, but more significant and tangible effects on LST. It is essential to improve the control of additional warming effects caused by urbanization

Preparation and Characterization of Baicalein-Loaded Nanoliposomes for Antitumor Therapy

Baicalein (BAI) is a major constituent of Scutellaria baicalensis Georgi. Previous studies showed that BAI had obvious effects on U14 cervical tumor-bearing mice model and HeLa cells. However, the use of BAI is inconvenient and troublesome, due to its low oral bioavailability. The aim of this study was to develop baicalein-loaded nanoliposomes (BAI-LP) to improve its bioavailability. In this study, BAI-LP was prepared by thin film hydration method. The average size, polydispersity index (PDI), zeta potential and encapsulation efficiency (EE) of the BAI-LP were 194.6±2.08 nm, 0.17±0.025, -30.73±0.41 mV, and 44.3±2.98%, respectively. Drug storage stability study showed no significant changes in these values after 4 weeks of storing at 4°C. Additionally, Sulforhodamine B (SRB) experimental results indicated that the BAI-LP could achieve better anti-tumor effects than free BAI. The results of the experiment demonstrated that BAI-LP had a better antitumor effect with a higher inhibition rate of 66.34±15.33% than free BAI with a inhibition rate of 41.89±10.50% by using U14 cervical tumor-bearing mice model. In conclusion, the study suggested that BAI-LP would serve as a potent delivery vehicle for BAI in future cancer therapy

An Integration of Genome-Wide Association Study and Gene Expression Profiling to Prioritize the Discovery of Novel Susceptibility Loci for Osteoporosis-Related Traits

Osteoporosis is a complex disorder and commonly leads to fractures in elderly persons. Genome-wide association studies (GWAS) have become an unbiased approach to identify variations in the genome that potentially affect health. However, the genetic variants identified so far only explain a small proportion of the heritability for complex traits. Due to the modest genetic effect size and inadequate power, true association signals may not be revealed based on a stringent genome-wide significance threshold. Here, we take advantage of SNP and transcript arrays and integrate GWAS and expression signature profiling relevant to the skeletal system in cellular and animal models to prioritize the discovery of novel candidate genes for osteoporosis-related traits, including bone mineral density (BMD) at the lumbar spine (LS) and femoral neck (FN), as well as geometric indices of the hip (femoral neck-shaft angle, NSA; femoral neck length, NL; and narrow-neck width, NW). A two-stage meta-analysis of GWAS from 7,633 Caucasian women and 3,657 men, revealed three novel loci associated with osteoporosis-related traits, including chromosome 1p13.2 (RAP1A, p = 3.6×10−8), 2q11.2 (TBC1D8), and 18q11.2 (OSBPL1A), and confirmed a previously reported region near TNFRSF11B/OPG gene. We also prioritized 16 suggestive genome-wide significant candidate genes based on their potential involvement in skeletal metabolism. Among them, 3 candidate genes were associated with BMD in women. Notably, 2 out of these 3 genes (GPR177, p = 2.6×10−13; SOX6, p = 6.4×10−10) associated with BMD in women have been successfully replicated in a large-scale meta-analysis of BMD, but none of the non-prioritized candidates (associated with BMD) did. Our results support the concept of our prioritization strategy. In the absence of direct biological support for identified genes, we highlighted the efficiency of subsequent functional characterization using publicly available expression profiling relevant to the skeletal system in cellular or whole animal models to prioritize candidate genes for further functional validation