Preoperative Prediction of Axillary Lymph Node Metastasis in Patients With Breast Cancer Through Multimodal Deep Learning Based on Ultrasound and Magnetic Resonance Imaging Images.

RATIONALE AND OBJECTIVES: Deep learning can enhance the performance of multimodal image analysis, which is known for its noninvasive attributes and complementary efficacy, in predicting axillary lymph node (ALN) metastasis. Therefore, we established a multimodal deep learning model incorporating ultrasound (US) and magnetic resonance imaging (MRI) images to predict ALN metastasis in patients with breast cancer. MATERIALS AND METHODS: A retrospective cohort of patients with histologically confirmed breast cancer from two hospitals composed of the primary cohort (n = 465) and the external validation cohort (n = 123). All patients had undergone both preoperative US and MRI scans. After data preprocessing, three convolutional neural network models were used to analyze the US and MRI images, respectively. After integrating the US and MRI deep learning prediction results (DLUS and DLMRI, respectively), a multimodal deep learning (DLMRI+US+Clinical parameter) model was constructed. The predictive ability of the proposed model was compared to that of the DLUS, DLMRI, combined bimodal (DLMRI+US), and clinical parameter models. Evaluation was performed using the area under the receiver operating characteristic curves (AUCs) and decision curves. RESULTS: A total of 588 patients with breast cancer participated in this study. The DLMRI+US+Clinical parameter model outperformed the alternative models, achieving the highest AUCs of 0.819 (95% confidence interval [CI] 0.734-0.903) and 0.809 (95% CI 0.723-0.895) on the internal and external validation sets, respectively. The decision curve analysis confirmed its clinical usefulness. CONCLUSION: The DLMRI+US＋Clinical parameter model demonstrates the feasibility and reliability of its performance for ALN metastasis prediction in patients with breast cancer.

Deep learning model based on contrast-enhanced ultrasound for predicting vessels encapsulating tumor clusters in hepatocellular carcinoma.

OBJECTIVES: To establish and validate a non-invasive deep learning (DL) model based on contrast-enhanced ultrasound (CEUS) to predict vessels encapsulating tumor clusters (VETC) patterns in hepatocellular carcinoma (HCC). MATERIALS AND METHODS: This retrospective study included consecutive HCC patients with preoperative CEUS images and available tissue specimens. Patients were randomly allocated into the training and test cohorts. CEUS images were analyzed using the ResNet-18 convolutional neural network for the development and validation of the VETC predictive model. The predictive value for postoperative early recurrence (ER) of the proposed model was further evaluated. RESULTS: A total of 242 patients were enrolled finally, including 195 in the training cohort (54.6 ± 11.2 years, 178 males) and 47 in the test cohort (55.1 ± 10.6 years, 40 males). The DL model (DL signature) achieved favorable performance in both the training cohort (area under the receiver operating characteristics curve [AUC]: 0.92, 95% confidence interval [CI]: 0.88-0.96) and test cohort (AUC: 0.90, 95% CI: 0.82-0.99). The stratified analysis demonstrated good discrimination of DL signature regardless of tumor size. Moreover, the DL signature was found independently correlated with postoperative ER (hazard ratio [HR]: 1.99, 95% CI: 1.29-3.06, p = 0.002). C-indexes of 0.70 and 0.73 were achieved when the DL signature was used to predict ER independently and combined with clinical features. CONCLUSION: The proposed DL signature provides a non-invasive and practical method for VETC-HCC prediction, and contributes to the identification of patients with high risk of postoperative ER. CLINICAL RELEVANCE STATEMENT: This DL model based on contrast-enhanced US displayed an important role in non-invasive diagnosis and prognostication for patients with VETC-HCC, which was helpful in individualized management. KEY POINTS: Preoperative biopsy to determine VETC status in HCC patients is limited. The contrast-enhanced DL model provides a non-invasive tool for the prediction of VETC-HCC. The proposed deep-learning signature assisted in identifying patients with a high risk of postoperative ER.

Hot water mobilizes the metabolism of energy, soluble sugar, cell wall, and phenolics to cope with chilling injury in postharvest snap beans.

BACKGROUND: Snap beans (Phaseoulus vulgaris L.) are very sensitive to low temperature during postharvest storage. Pitting, rusting, and water-soaked patches are typical chilling injury (CI) symptoms of snap beans. The appearance of these symptoms reduces the storage quality of snap beans. The energy, soluble carbohydrates, cell wall, and phenolic metabolisms of refrigerated snap beans and their relationship to CI treated with 35 °C hot water (HW) were investigated. RESULTS: HW treatment reduced CI index and electrolyte leakage and increased the contents of soluble solids, titratable acidity, and chlorophyll. HW treatment maintained higher activities of proton ATPase, calcium ATPase, and cytochrome c oxidase, which resulted in the accumulation of more adenosine triphosphate, adenosine diphosphate, and energy charge. The accumulation of soluble sugar induced by HW treatment was correlated with the stimulation of sucrose phosphate synthase and sucrose synthase. The prevention effect of HW treatment on the degradation of cell wall components was related to the inhibition of pectin methylesterase and cellulase. HW-induced phenol accumulation is associated with an increase in shikimate dehydrogenase, phenylalanine ammonia lyase, cinnamate-4-hydroxylase, and 4-coumarine-coenzyme A ligase, as well as a decrease in polyphenol oxidase. CONCLUSION: The alleviating effect of HW on CI is due to its regulation of energy, soluble sugar, cell wall, and phenolic metabolism. Therefore, HW treatment may be an effective means to reduce CI of snap beans. © 2024 Society of Chemical Industry.

Natural Attenuation of Groundwater Uranium in Post-Neutral-Mining Sites Evidenced from Multiple Isotopes and Dissolved Organic Matter.

Although natural attenuation is an economic remediation strategy for uranium (U) contamination, the role of organic molecules in driving U natural attenuation in postmining aquifers is not well-understood. Groundwaters were sampled to investigate the chemical, isotopic, and dissolved organic matter (DOM) compositions and their relationships to U natural attenuation from production wells and postmining wells in a typical U deposit (the Qianjiadian U deposit) mined by neutral in situ leaching. Results showed that Fe(II) concentrations and δ34SSO4 and δ18OSO4 values increased, but U concentrations decreased significantly from production wells to postmining wells, indicating that Fe(III) reduction and sulfate reduction were the predominant processes contributing to U natural attenuation. Microbial humic-like and protein-like components mediated the reduction of Fe(III) and sulfate, respectively. Organic molecules with H/C > 1.5 were conducive to microbe-mediated reduction of Fe(III) and sulfate and facilitated the natural attenuation of dissolved U. The average U attenuation rate was -1.07 mg/L/yr, with which the U-contaminated groundwater would be naturally attenuated in approximately 11.2 years. The study highlights the specific organic molecules regulating the natural attenuation of groundwater U via the reduction of Fe(III) and sulfate.

Stepwise Reduction of Redox Noninnocent Nitrosobenzene to Aniline via a Rare Phenylhydroxylamino Intermediate on a Thiolate-Bridged Dicobalt Scaffold.

Nitrosobenzene (PhNO) and phenylhydroxylamine (PhNHOH) are of paramount importance because of their involvement as crucial intermediates in the biological metabolism and catalytic transformation of nitrobenzene (PhNO2) to aniline (PhNH2). However, a complete reductive transformation cycle of PhNO to PhNH2 via the PhNHOH intermediate has not been reported yet. In this context, we design and construct a new thiolate-bridged dicobalt scaffold that can accomplish coordination activation and reductive transformation of PhNO. Notably, an unprecedented reversible ligand-based redox sequence PhNO0 ↔ PhNO•- ↔ PhNO2- can be achieved on this well-defined {CoIII(µ-SPh)2CoIII} functional platform. Further detailed reactivity investigations demonstrate that the PhNO0 and PhNO•- complexes cannot react with the usual hydrogen and hydride donors to afford the corresponding phenylhydroxylamino (PhNHO-) species. However, the double reduced PhNO2- complex can readily undergo N-protonation with an uncommon weak proton donor PhSH to selectively yield a stable dicobalt PhNHO- bridged complex with a high pKa value of 13-16. Cyclic voltammetry shows that there are two successive reduction events at E1/2 = -0.075 V and Ep = -1.08 V for the PhNO0 complex, which allows us to determine both bond dissociation energy (BDEN-H) of 59-63 kcal·mol-1 and thermodynamic hydricity (ΔGH-) of 71-75 kcal·mol-1 of the PhNHO- complex. Both values indicate that the PhNO•- complex is not a potent hydrogen abstractor and the PhNO0 complex is not an efficient hydride acceptor. In the presence of BH3 as a combination of protons and electrons, facile N-O bond cleavage of the coordinated PhNHO- group can be realized to generate PhNH2 and a dicobalt hydroxide-bridged complex. Overall, we present the first stepwise reductive sequence, PhNO0 ↔ PhNO•- ↔ PhNO2- ↔ PhNHO- → PhNH2.

A comprehensive drought index based on spatial principal component analysis and its application in northern China.

In the background of the greenhouse effect, drought events occurred more frequently. How to monitor drought events scientifically and efficiently is very urgent at present. In this study, we employed the Vegetation Water Supply Index (VSWI), Temperature Vegetation Drought Index (TVDI), and Crop Water Stress Index (CWSI) as individual variables to construct a composite drought index (CDI) using spatial principal component analysis (SPCA). The validity of CDI was assessed using gross primary productivity (GPP), soil moisture (SM), Standardized Precipitation Evapotranspiration Index (SPEI), and Vegetation Condition Index (VCI). CDI was subsequently used for drought monitoring in northern China from 2011 to 2020. The results showed that (1) at a 99% confidence level, the Pearson correlation coefficients between CDI and GPP was 0.72, while the value between CDI and SM was 0.69, which indicated the relationship between SM, GPP, and CDI was significant. (2) We compared CDI with other variables such as Standardized Precipitation Evapotranspiration Index (SPEI) and Crop Drought Index (CDI) and found that the monitoring result of CDI was more sensitive, which indicated that the proposed CDI had a better effect in local drought monitoring. (3) The results of CDI showed that the drought status in the northern region during 2011-2020 lasted from March to October, and the high severe drought period generally occurs in March-May and September-October, with low severe drought in June-August.

1-Methylcyclopropene Alleviates Postharvest Chilling Injury of Snap Beans by Enhancing Antioxidant Defense System.

Research background: Chilling injury is a major disorder affecting the quality of tropical and subtropical vegetables during low temperature storage. Snap bean (Phaseolus vulgaris L.) is sensitive to chilling injury. The main purpose of the present study is to investigate the alleviating effects of 1-methylcyclopropene (1-MCP) on chilling injury of snap bean. In addition, the related mechanisms were also detected from the perspective of the changes of antioxidant defense system. Experimental approach: Snap beans were exposed to different volume fractions of 1-MCP. After 24 h of treatment, snap beans were stored at 4 °C for up to 14 days. Chilling injury index, electrolyte leakage, titratable acidity and total soluble solids were determined. Contents of chlorophyll, ascorbic acid and malondialdehyde were assessed. The total antioxidant capacity, Fe(II) ion chelating capacity, scavenging capacities on free radicals and activities of antioxidant enzymes were detected. Total phenol content and activities of related metabolic enzymes were also determined. Results and conclusions: 1-MCP treatment reduced chilling injury index, electrolyte leakage rate and malondialdehyde content of snap beans. The amounts of total soluble solids, titratable acid, ascorbic acid and total chlorophyll in 1-MCP-treated snap beans were significantly higher than those of control. The snap beans treated with 1-MCP showed stronger total antioxidant capacity and metal chelating activity. The 1-MCP treatment enhanced scavenging effects of snap beans on superoxide, hydroxyl and 1,1-diphenyl-2-trinitrophenylhydrazine radicals. The activities of peroxidase, ascorbate peroxidase, superoxide dismutase and catalase in 1-MCP-treated group were higher than of control. The treatment also enhanced the accumulation of phenolic compounds in snap beans by regulating the activities of phenol-metabolizing enzymes such as shikimate dehydrogenase, phenylalanine ammonia lyase enzyme, cinnamic acid 4-hydroxylase and polyphenol oxidase. In conclusion, with the mechanism that involves the activation of enzymatic and non-enzymatic antioxidant systems, 1-MCP has the ability to avoid chilling injury of snap bean. Novelty and scientific contribution: This study gives insights into whether 1-MCP can regulate postharvest cold resistance in vegetables by enhancing the enzymatic antioxidant system and inducing the accumulation of non-enzymatic antioxidants. Considering the results, 1-MCP treatment could be an effective method to alleviate postharvest chilling injury of snap beans during low temperature storage.

Deep learning models of ultrasonography significantly improved the differential diagnosis performance for superficial soft-tissue masses: a retrospective multicenter study.

BACKGROUND: Most of superficial soft-tissue masses are benign tumors, and very few are malignant tumors. However, persistent growth, of both benign and malignant tumors, can be painful and even life-threatening. It is necessary to improve the differential diagnosis performance for superficial soft-tissue masses by using deep learning models. This study aimed to propose a new ultrasonic deep learning model (DLM) system for the differential diagnosis of superficial soft-tissue masses. METHODS: Between January 2015 and December 2022, data for 1615 patients with superficial soft-tissue masses were retrospectively collected. Two experienced radiologists (radiologists 1 and 2 with 8 and 30 years' experience, respectively) analyzed the ultrasound images of each superficial soft-tissue mass and made a diagnosis of malignant mass or one of the five most common benign masses. After referring to the DLM results, they re-evaluated the diagnoses. The diagnostic performance and concerns of the radiologists were analyzed before and after referring to the results of the DLM results. RESULTS: In the validation cohort, DLM-1 was trained to distinguish between benign and malignant masses, with an AUC of 0.992 (95% CI: 0.980, 1.0) and an ACC of 0.987 (95% CI: 0.968, 1.0). DLM-2 was trained to classify the five most common benign masses (lipomyoma, hemangioma, neurinoma, epidermal cyst, and calcifying epithelioma) with AUCs of 0.986, 0.993, 0.944, 0.973, and 0.903, respectively. In addition, under the condition of the DLM-assisted diagnosis, the radiologists greatly improved their accuracy of differential diagnosis between benign and malignant tumors. CONCLUSIONS: The proposed DLM system has high clinical application value in the differential diagnosis of superficial soft-tissue masses.

Application and prospects of AI-based radiomics in ultrasound diagnosis.

Artificial intelligence (AI)-based radiomics has attracted considerable research attention in the field of medical imaging, including ultrasound diagnosis. Ultrasound imaging has unique advantages such as high temporal resolution, low cost, and no radiation exposure. This renders it a preferred imaging modality for several clinical scenarios. This review includes a detailed introduction to imaging modalities, including Brightness-mode ultrasound, color Doppler flow imaging, ultrasound elastography, contrast-enhanced ultrasound, and multi-modal fusion analysis. It provides an overview of the current status and prospects of AI-based radiomics in ultrasound diagnosis, highlighting the application of AI-based radiomics to static ultrasound images, dynamic ultrasound videos, and multi-modal ultrasound fusion analysis.

High-Accuracy Maize Disease Detection Based on Attention Generative Adversarial Network and Few-Shot Learning.

This study addresses the problem of maize disease detection in agricultural production, proposing a high-accuracy detection method based on Attention Generative Adversarial Network (Attention-GAN) and few-shot learning. The method introduces an attention mechanism, enabling the model to focus more on the significant parts of the image, thereby enhancing model performance. Concurrently, data augmentation is performed through Generative Adversarial Network (GAN) to generate more training samples, overcoming the difficulties of few-shot learning. Experimental results demonstrate that this method surpasses other baseline models in accuracy, recall, and mean average precision (mAP), achieving 0.97, 0.92, and 0.95, respectively. These results validate the high accuracy and stability of the method in handling maize disease detection tasks. This research provides a new approach to solving the problem of few samples in practical applications and offers valuable references for subsequent research, contributing to the advancement of agricultural informatization and intelligence.

A new drought index and its application based on geographically weighted regression (GWR) model and multi-source remote sensing data.

Drought is the most widespread natural disaster in the world. How to monitor regional drought scientifically and accurately has become a hot topic for many scholars. In this paper, Geographically Integrated Dryness Index (GIDI) was integrated from an assortment source including Precipitation Condition Index (PCI), Temperature Condition Index (TCI), Soil Moisture Condition Index (SMCI), Vegetation Condition Index (VCI), and Standardized Precipitation Evapotranspiration Index (SPEI) (as the dependent variable) based on geographically weighted regression method. Besides, the comprehensive drought situation and changing trends in China from 2001 to 2019 were also examined. The results showed that (1) GIDI has excellent performance in monitoring medium- and long-term droughts and the monitoring results shows 2003, 2016, and 2019 were relatively wet years, while 2007, 2009, and 2011 were major drought years, and spring and March were the most frequent droughts season and month, respectively, and (2) except for the middle and upper reaches of the Yellow River and Northeastern China, which have a tendency to become wet, other places have a tendency to fluctuating dry. This study took advantage of simple and efficient methods to integrate existing indices to obtain a new index for monitoring a wider range of droughts, taking into account the physical mechanism of drought formation and the time scale of drought development, so it can scientifically evaluate the spatial and temporal distribution characteristics of drought and changes.

Research on Trajectory-Tracking Control System of Tracked Wall-Climbing Robots.

Different from the vehicles and robots that move on the ground, complex and nonlinear track-wall interactions bring considerable difficulties to the accurate control of tracked wall-climbing robots due to the effect of gravity and adsorption. In this article, the authors propose a trajectory-tracking control system for tracked wall-climbing robots based on the fuzzy logic computed-torque control (FLCT) method. A key element in the proposed control strategy is to consider the adsorption force and gravity compensation based on the dynamic model. Validated via numerical simulations and experiments, the results show that the proposed controller can track the reference trajectory quickly, accurately and stably.

Reconstruction and application of the temperature-vegetation-precipitation drought index in mainland China based on remote sensing datasets and a spatial distance model.

In recent years, remote sensing drought monitoring indices have been gradually developed and have been widely used for global or regional drought monitoring due to their strong drought-monitoring capabilities and easy implementation advantages. However, some defects of remote sensing drought indices stand to be improved due to certain errors in the inversion of surface characteristics by remote sensing datasets. The temperature-vegetation-precipitation drought index (TVPDI) was taken as the research object, and the leaf area index (LAI), the difference between the land surface temperature (LST) and monthly average temperature, and Global Precipitation Measurement (GPM) precipitation data were selected instead of the normalized difference vegetation index (NDVI), LST and tropical rainfall measuring mission (TRMM) data to improve TVPDI. The improved remote sensing drought index was named the improved temperature-vegetation-precipitation drought index (iTVPDI). The drought-monitoring accuracy of iTVPDI was verified by the gross primary productivity (GPP), soil moisture, and crop yield per unit. The drought-monitoring ability of iTVPDI was verified with traditional drought indices, including the standardized precipitation index (SPI), standardized precipitation evapotranspiration index (SPEI), Palmer drought severity index (PDSI), temperature-vegetation drought index (TVDI), drought severity index (DSI) and crop water stress index (CWSI). The drought-monitoring accuracy of iTVPDI was verified by selecting sample areas. iTVPDI was applied to monitor drought in mainland China over the 2001-2020 period and obtained four main results. First, the correlation analyses of iTVPDI and TVPDI with GPP, crop yield per unit area, and soil moisture showed that iTVPDI had a stronger monitoring ability in Northeast, North, and Southwest China; the R2 value obtained with soil moisture was 0.62 (p < 0.05), and this value was higher than that of TVPDI. Then, the correlation analyses of iTVPDI and TVPDI with SPI, SPEI, PDSI, CWSI, DSI and TVDI showed that the correlation coefficients of iTVPDI and TVPDI with these six indicators were basically consistent, which indicated that the drought-monitoring capability of iTVPDI was consistent with that of TVPDI. In local areas such as the Qinghai-Tibet Plateau in China, the monitoring ability of iTVPDI was stronger than that of TVPDI. Third, through the sample area analysis, iTVPDI was found to moderate the NDVI-characterized vegetation factors in TVPDI in low-vegetation-cover areas affected by soil disturbances and in high-vegetation-cover areas affected by oversaturation. Finally, the results obtained from the application of iTVPDI in mainland China showed that during the warm-dry to warm-wet climate transition between 2001 and 2021, in 2010 and 2018, and in other special drought years, iTVPDI had the best response.

Nickel nanoparticle decorated N-doped carbon nanofibers for light weight and high-efficiency microwave absorption.

Microwave absorbers with light weight and excellent microwave absorption performance are urgently needed in the microwave absorption field, which is still a challenge. Herein, N-doped carbon nanofibers decorated with nickel nanoparticles (Ni@CNFs) were synthesized by a facile electrospinning method combined with a two-step heat treatment, in which Ni nanoparticles are uniformly dispersed in carbon nanofibers. Benefitting from the special nanoarchitecture (including Ni nanoparticles encapsulated with graphitic carbon layers and carbon nanotube protrusions anchored on CNFs), three-dimensional conductive networks, the synergistic effect between suitable impedance matching and satisfactory electromagnetic (EM) attenuation ability, a superb comprehensive microwave absorption (MA) property is achieved for the optimal Ni@CNF sample with a rather low filler loading of 5 wt%. The optimal reflection loss (RL) reaches -66.3 dB at a small thickness of 3.1 mm and the maximum effective absorption bandwidth (EAB, RL < -10.0 dB) as wide as 4.56 GHz is obtained at 2.0 mm. This study demonstrates that the carefully designed Ni@CNF composites are superior to many previously reported magnetic carbon-based hybrid absorbers and can be applied as promising candidates for light weight and high-efficiency EM wave absorbers.

Hierarchical Sliding-Mode Surface-Based Adaptive Actor-Critic Optimal Control for Switched Nonlinear Systems With Unknown Perturbation.

This article studies the hierarchical sliding-mode surface (HSMS)-based adaptive optimal control problem for a class of switched continuous-time (CT) nonlinear systems with unknown perturbation under an actor-critic (AC) neural networks (NNs) architecture. First, a novel perturbation observer with a nested parameter adaptive law is designed to estimate the unknown perturbation. Then, by constructing an especial cost function related to HSMS, the original control issue is further converted into the problem of finding a series of optimal control policies. The solution to the HJB equation is identified by the HSMS-based AC NNs, where the actor and critic updating laws are developed to implement the reinforcement learning (RL) strategy simultaneously. The critic update law is designed via the gradient descent approach and the principle of standardization, such that the persistence of excitation (PE) condition is no longer needed. Based on the Lyapunov stability theory, all the signals of the closed-loop switched nonlinear systems are strictly proved to be bounded in the sense of uniformly ultimate boundedness (UUB). Finally, the simulation results are presented to verify the validity of the proposed adaptive optimal control scheme.

Drought monitoring in arid and semi-arid region based on multi-satellite datasets in northwest, China.

Drought is a complex natural disaster affected by multiple climate factors and underlying surface. In recent years, drought monitoring indices of remote sensing have been widely applied to monitor drought in a certain region or global. However, some remote sensing drought monitoring indices do not consider the drought-causing factors enough to reflect the comprehensive drought situation of a region fully. In this paper, a new remote sensing drought monitoring index, called Remote Sensing Drought Evaluation Index (RSDEI), was constructed by combining Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Precipitation Condition Index (PCI), and Soil Moisture Condition Index (SMCI) using the spatial principal component analysis (SPCA) method. The reasonableness of RSDEI was test and verified using Net Primary Productivity (NPP), Standardized Precipitation Evapotranspiration Index (SPEI), and unit area crop yield. The RSDEI was also applied to the drought condition monitoring of the northwest arid and semi-arid region from 2001 to 2019.The result demonstrated that the results showed that the RSDEI had a high correlation coefficient with SPEI-12 (R=0.85, p<0.01). It is concluded that the correlation coefficient between RSDEI and NPP is 0.74 at 95% confidence level, which indicates that RSDEI and NPP have a strong correlation. Then, the correlation between RSDEI and crop yield per unit area is 0.89. The results of RSDEI showed that the drought in northwest China started in May and lasted in September from 2001 to 2019. The lowest value of RSDEI appeared in May, which inflected the significant difference of drought level in different month in northwest China. The result of CV (coefficient of variation) showed that the drought variation in the study area had a stable low fluctuation condition as a whole, in the northwest and northeast of study area, which indicated that the changes of drought were different in the past 19 years. The Hurst exponent analysis showed that the area with the positive evolution of Hurst index (0.5

Phosphorylated biomass-derived porous carbon material for efficient removal of U(VI) in wastewater.

A simple strategy to prepare cost-effective adsorbent materials for the removal of U(VI) in radioactive wastewater is of great significance to environmental protection. Here, activated orange peel was used as a precursor for the synthesis of biomass charcoal, and then a phosphorylated honeycomb-like porous carbon (HLPC-PO4) material was prepared through simple phosphorylation modification. FT-IR and XPS showed that P-O-C, P-C, and PË­O bonds appeared in HLPC-PO4, indicating that the phosphorylation process is mainly the reaction of C-O bonds on the surface of the material with -PO4. The results of the batch experiments showed that the uptake equilibrium of HLPC-PO4 to U(VI) occurred within 20 min, and the kinetic simulation showed that the process was monolayer chemical adsorption. Interestingly, the maximum U(VI) uptake capacity of HLPC-PO4 at T = 298.15 K and pH = 6.0 was 552.6 mg/g, which was more than 3 times that of HLPC. In addition, HLPC-PO4 showed an adsorption selectivity of 70.1% for U(VI). After 5 cycles, HLPC-PO4 maintained its original adsorption capacity of 90.5%. The adsorption mechanism can be explained as the complexation of U(VI) with P-O and PË­O on the surface of the adsorbent, confirming the strong bonding ability of -PO4 to U(VI).

Stress-Induced Effects in Resting EEG Spectra Predict the Performance of SSVEP-Based BCI.

Most research in Brain-Computer-Interfaces (BCI) focuses on technologies to improve accuracy and speed. Little has been done on the effects of subject variability, both across individuals and within the same individual, on BCI performance. For example, stress, arousal, motivation, and fatigue can all affect the electroencephalogram (EEG) signals used by a BCI, which in turn impacts performance. Overcoming the impact of such user variability on BCI performance is an impending and inevitable challenge for routine applications of BCIs in the real world. To systematically explore the factors affecting BCI performance, this study embeds a Steady-State Visually Evoked Potential (SSVEP) based BCI into a "game with a purpose" (GWAP) to obtain data over significant lengths of time, under both high- and low-stress conditions. Ten healthy volunteers played a GWAP that resembles popular match-three games, such as Jewel Quest, Zoo Boom, or Candy Crush. We recorded the target search time, target search accuracy, and EEG signals during gameplay to investigate the impacts of stress on EEG signals and BCI performance. We used Canonical Correlation Analysis (CCA) to determine whether the subject had found and attended to the correct target. The experimental results show that SSVEP target-classification accuracy is reduced by stress. We also found a negative correlation between EEG spectra and the SNR of EEG in the frontal and occipital regions during gameplay, with a larger negative correlation for the high-stress conditions. Furthermore, CCA also showed that when the EEG alpha and theta power increased, the search accuracy decreased, and the spectral amplitude drop was more evident under the high-stress situation. These results provide new, valuable insights into research on how to improve the robustness of BCIs in real-world applications.