Space-CNN: a decision classification method based on EEG signals from different brain regions.

Decision-making plays a critical role in an individual's interpersonal interactions and cognitive processes. Due to the issue of strong subjectivity in the classification research of art design decisions, we utilize the relatively objective electroencephalogram (EEG) to explore design decision problems. However, different regions of the brain do not have the same influence on the design decision classification, so this paper proposes a spatial feature based convolutional neural network (space-CNN) to explore the problem of decision classification of EEG signals from different regions. We recruit 16 subjects to collect their EEG data while viewing four stimulation patterns. After noise reduction of the raw data by discrete wavelet transform (DWT), the EEG image is generated by combining it with the spatial features of the EEG signal, which is used as the input of CNN. Our experimental results show that the degree of influence of different brain regions on decision-making is parietal lobe > frontal lobe > occipital lobe > temporal lobe. In addition, the average accuracy of space-CNN reaches 86.13%, which is about 6% higher than similar studies.

Towards clinical applicability and computational efficiency in automatic cranial implant design: An overview of the AutoImplant 2021 cranial implant design challenge.

Cranial implants are commonly used for surgical repair of craniectomy-induced skull defects. These implants are usually generated offline and may require days to weeks to be available. An automated implant design process combined with onsite manufacturing facilities can guarantee immediate implant availability and avoid secondary intervention. To address this need, the AutoImplant II challenge was organized in conjunction with MICCAI 2021, catering for the unmet clinical and computational requirements of automatic cranial implant design. The first edition of AutoImplant (AutoImplant I, 2020) demonstrated the general capabilities and effectiveness of data-driven approaches, including deep learning, for a skull shape completion task on synthetic defects. The second AutoImplant challenge (i.e., AutoImplant II, 2021) built upon the first by adding real clinical craniectomy cases as well as additional synthetic imaging data. The AutoImplant II challenge consisted of three tracks. Tracks 1 and 3 used skull images with synthetic defects to evaluate the ability of submitted approaches to generate implants that recreate the original skull shape. Track 3 consisted of the data from the first challenge (i.e., 100 cases for training, and 110 for evaluation), and Track 1 provided 570 training and 100 validation cases aimed at evaluating skull shape completion algorithms at diverse defect patterns. Track 2 also made progress over the first challenge by providing 11 clinically defective skulls and evaluating the submitted implant designs on these clinical cases. The submitted designs were evaluated quantitatively against imaging data from post-craniectomy as well as by an experienced neurosurgeon. Submissions to these challenge tasks made substantial progress in addressing issues such as generalizability, computational efficiency, data augmentation, and implant refinement. This paper serves as a comprehensive summary and comparison of the submissions to the AutoImplant II challenge. Codes and models are available at https://github.com/Jianningli/Autoimplant_II.

Ecological risk and health risk analysis of soil potentially toxic elements from oil production plants in central China.

In this study, the enrichment factor (EF) and pollution load index (PLI) were used to evaluate the pollution of potential toxic elements (PTEs) in the soil near the oil production plants in central China, and the potential ecological risk (PER) and human health risk (HHR) assessment model were used to evaluate the PER and HHR caused by the soil PTEs in the study area. The mean EFs of all PTEs were greater than 1, PTEs have accumulated to varying degrees, especially Cr, Ni and Pb were the most serious. The average value of PLI was 2.62, indicating that the soil PTEs were seriously polluted. The average [Formula: see text] values of PTEs were Cr > Pb > Cd > Ni > As > Cu > Zn > Mn, of which Cr, Pb, Cd and Ni were at medium and above PER levels. Both adults and children in the study area suffered from varying degrees of non-carcinogenic and carcinogenic risks. The total hazard index (THI) values of children (7.31) and adults (1.03) were all > 1, and the total carcinogenic risk index (TCRI) of children (9.44E-04) and adults (5.75E-04) were also > 10-4. In particular, the hazardous quotient (HQ) of Cr and Pb for children under the oral intake route were 4.91 and 1.17, respectively, caused serious non-carcinogenic risk. And the carcinogenic risk index (CRI) values of the PTEs in adults and children under the three exposure routes were Cr > Ni > > As > Pb > > Cd. Among them, the CRI values of Cr and Ni in children and adults by oral intake were both greater than 10-4, showing a strong carcinogenic risk. The results will provide scientific basis for environmental protection and population health protection in this area.