Prediction of oxygen distribution in the waste mass from an aeration well in bioreactor landfills.

The key to upgrade the efficiency of aerobic remediation of landfills is to determine the distribution characteristics of oxygen concentration during aerobic ventilation. This study discusses the distribution law of oxygen concentration with time and radial distance based on a single-well aeration test at an old landfill site. The transient analytical solution of the radial oxygen concentration distribution was deduced using the gas continuity equation and approximation of calculus and logarithmic functions. Oxygen concentration data from the field monitoring were compared with the results predicted by the analytical solution. The results indicated that the oxygen concentration initially increased and then decreased with prolonged aeration time. With an increase in radial distance, the oxygen concentration rapidly declined, followed by a gradual decrease. The influence radius of the aeration well increased slightly when the aeration pressure increased from 2 to 20 kPa. The field test data agreed with the analytical solution prediction results, preliminarily verifying the reliability of the oxygen concentration prediction model. Results from this study provide a basis of guidelines for the design, operation and maintenance management of a landfill aerobic restoration project.

Simulation and prediction of the effect of aeration, recirculation and degradation on landfill temperature in aerobic operation.

Much heat is released in aerobic landfills, which leads to temperature change. Quantitative prediction of temperature change with time and space is essential for the safe aerobic operation of landfill. In this article, based on the theory of porous media seepage mechanics and heat transfer, a seepage-temperature coupling model considering aeration, recirculation and degradation was established, which included internal energy change, heat conduction, convection and heat transfer. Moreover, combined with the long-time on-site monitoring temperature data from Wuhan Jinkou Landfill, the model's reliability was preliminarily verified. Sensitivity analysis was carried out for aeration intensity, aeration temperature, recirculation intensity and recirculation temperature. Among the four factors, recirculation intensity influences the peak temperature most with a decrease of 20.11%. Compared with Borglin's and Hao's models, it is found that waste should not be assumed as a cell for temperature prediction. By comparing the results of Non-linear Ascent Stage model, Linear Ascent Stage model and Absent Ascent Stage model, it showed that the temperature difference of the three models decreases with the increase of operation time. In addition, the time point of peak temperature, t0, affects the temperature distribution. The above results provide a reference for predicting the spatial and temporal distribution of temperature and regulations for long-term aerobic landfill operations.

V2IED: Dual-view learning framework for detecting events of interictal epileptiform discharges.

Interictal epileptiform discharges (IED) as large intermittent electrophysiological events are associated with various severe brain disorders. Automated IED detection has long been a challenging task, and mainstream methods largely focus on singling out IEDs from backgrounds from the perspective of waveform, leaving normal sharp transients/artifacts with similar waveforms almost unattended. An open issue still remains to accurately detect IED events that directly reflect the abnormalities in brain electrophysiological activities, minimizing the interference from irrelevant sharp transients with similar waveforms only. This study then proposes a dual-view learning framework (namely V2IED) to detect IED events from multi-channel EEG via aggregating features from the two phases: (1) Morphological Feature Learning: directly treating the EEG as a sequence with multiple channels, a 1D-CNN (Convolutional Neural Network) is applied to explicitly learning the deep morphological features; and (2) Spatial Feature Learning: viewing the EEG as a 3D tensor embedding channel topology, a CNN captures the spatial features at each sampling point followed by an LSTM (Long Short-Term Memories) to learn the evolution of these features. Experimental results from a public EEG dataset against the state-of-the-art counterparts indicate that: (1) compared with the existing optimal models, V2IED achieves a larger area under the receiver operating characteristic (ROC) curve in detecting IEDs from normal sharp transients with a 5.25% improvement in accuracy; (2) the introduction of spatial features improves performance by 2.4% in accuracy; and (3) V2IED also performs excellently in distinguishing IEDs from background signals especially benign variants.

EEG Temporal-Spatial Feature Learning for Automated Selection of Stimulus Parameters in Electroconvulsive Therapy.

The risk of adverse effects in Electroconvulsive Therapy (ECT), such as cognitive impairment, can be high if an excessive stimulus is applied to induce the necessary generalized seizure (GS); Conversely, inadequate stimulus results in failure. Recent efforts to automate this task can facilitate statistical analyses on individual parameters or qualitative predictions. However, this automation still significantly lags behind the requirements in clinical practices. This study addresses this issue by predicting the probability of GS induction under the joint restriction of a patient's EEG (electroencephalogram) and the stimulus parameters, sustained by a two-stage learning model (namely ECTnet): 1) Temporal-Spatial Feature Learning. Channel-wise convolution via multiple convolution kernels first learns the deep features of the EEG, followed by a "ConvLSTM" constructing the temporal-spatial features aided with the enforced convolution operations at the LSTM gates; 2) GS Prediction. The probability of seizure induction is predicted based on the EEG features fused with stimulus parameters, through which the optimal parameter setting(s) may be obtained by minimizing the stimulus charge while ensuring the probability above a threshold. Experiments have been conducted on EEG data from 96 subjects with mental disorders to examine the performance and design of ECTnet. These experiments indicate that ECTnet can effectively automate the selection of optimal stimulus parameters: 1) an AUC of 0.746, F1-score of 0.90, a precision of 89% and a recall of 93% in the prediction of seizure induction have been achieved, outperforming the state-of-the-art counterpart, and 2) inclusion of parameter features increases the F1-score by 0.054.

EEG Reconstruction with a Dual-scale CNN-LSTM Model for Deep Artifact Removal.

Artifact removal has been an open critical issue for decades in tasks centering on EEG analysis. Recent deep learning methods mark a leap forward from the conventional signal processing routines; however, those in general still suffer from insufficient capabilities 1) to capture potential temporal dependencies embedded in EEG and 2) to adapt to scenarios without a priori knowledge of artifacts. This study proposes an approach (namely DuoCL) to deep artifact removal with a dual-scale CNN (Convolutional Neural Network)-LSTM (Long Short-Term Memory) model, operating on the raw EEG in three phases: 1) Morphological Feature Extraction, a dual-branch CNN utilizes convolution kernels of two different scales to learn morphological features (individual sample); 2) Feature Reinforcement, the dual-scale features are then reinforced with temporal dependencies (inter-sample) captured by LSTM; and 3) EEG Reconstruction, the resulting feature vectors are finally aggregated to reconstruct the artifact-free EEG via a terminal fully connected layer. Extensive experiments have been performed to compare DuoCL to six state-of-the-art counterparts (e.g., 1D-ResCNN and NovelCNN). DuoCL can reconstruct more accurate waveforms and achieve the highest SNR & correlation ( CC) as well as the lowest error ( RRMSEt & RRMSEf). In particular, DuoCL holds potentials in providing a high-quality removal of unknown and hybrid artifacts.

Model prediction of herbicide residues in soybean oil: Relationship between physicochemical properties and processing factors.

The distribution and processing factors (PFs) of herbicides in cold-/hot-pressed soybean samples (n = 3) were studied on the laboratory scale. The hot-pressing process was found to have a significant effect on herbicide degradation in soybean samples. Specifically, for highly water-soluble pesticides with pKow > 2 in soybean oil, the PF values were generally > 1. Nonlinear curve fitting revealed that the PFs of herbicides in soybean oil were positively correlated with their octanol-water partition coefficients, but negatively correlated with their water solubility and melting points. A principal component analysis confirmed the dominant parameters among the herbicide PFs during soybean oil production. Using the physicochemical parameters of pesticides, the developed multiple linear regression model gave a fitting accuracy of ≥0.80 for predicting the theoretical PF values of pesticides in soybean oil products (0.39 < RMSE < 0.58). Thus, this model may be applicable for safety risk assessments and establishing maximum residue limits for pesticides in processed products.

Highly efficient and stable solar-powered desalination by tungsten carbide nanoarray film with sandwich wettability.

Solar-powered desalination is a promising way to resolve the worldwide water crisis for its low consumption and simple facility. Considering the fragility and aggregations of traditional materials, which may decrease efficiency, we herein introduce a robust tungsten carbide (WC) nanoarray film as a stable and efficient photothermal material, whose absorption is over 97.5% throughout almost the whole solar spectrum range (220-2200 nm) due to nanoarray structure and thus enhanced localized surface plasmon resonance. Besides, for the first time, we modified the film with sandwich wettability. It accelerates evaporation by reducing water's reflection of light, enlarging hydrophobic-hydrophilic boundaries, and depressing heat dissipation. Combining high absorption with unique wettability, the WC nanoarray film offers high solar-to-vapor efficiency of 90.8% and produces drinking water at the rate of (1.06 ±â€¯0.10) kg m-2 h-1 from man-made seawater and (0.98 ±â€¯0.18) kg m-2 h-1 from heavy metal sewage under one sun (AM 1.5) while 98% performance remains after 1 h × 100 times' reutilization.

Nitrogen-doped tungsten carbide nanoarray as an efficient bifunctional electrocatalyst for water splitting in acid.

Tungsten carbide is one of the most promising electrocatalysts for the hydrogen evolution reaction, although it exhibits sluggish kinetics due to a strong tungsten-hydrogen bond. In addition, tungsten carbide's catalytic activity toward the oxygen evolution reaction has yet to be reported. Here, we introduce a superaerophobic nitrogen-doped tungsten carbide nanoarray electrode exhibiting high stability and activity toward hydrogen evolution reaction as well as driving oxygen evolution efficiently in acid. Nitrogen-doping and nanoarray structure accelerate hydrogen gas release from the electrode, realizing a current density of -200 mA cm-2 at the potential of -190 mV vs. reversible hydrogen electrode, which manifest one of the best non-noble metal catalysts for hydrogen evolution reaction. Under acidic conditions (0.5 M sulfuric acid), water splitting catalyzed by nitrogen-doped tungsten carbide nanoarray starts from about 1.4 V, and outperforms most other water splitting catalysts.

[IL-33 enhances cytokine secretion and killing function of peripheral blood mononuclear cells in vitro].

OBJECTIVE: To investigate whether interleukin 33 (IL-33) can enhance cytokine secretion and killing activity of peripheral blood mononuclear cells (PBMCs) in vitro. METHODS: PBMCs were harvested from healthy volunteers and stimulated with different combination of cytokines (CD3 mAb/CD28 mAb/IL-2, CD3 mAb/CD28 mAb/IL-2/IL-12, CD3 mAb/CD28 mAb/IL-2/IL-12/IL-33) in vitro. The cells of each group were collected after 72 hours. Total RNA were extracted and assayed by real-time quantitative PCR (qRT-PCR) for the levels of interferon γ (IFN-γ) and granzyme B. The cytotoxic activity of the cells targeting A549 human lung adenocarcinoma cells was detected by CCK-8 assay. The levels of programmed death-1 (PD-1) and IFN-γ were determined by flow cytometry. RESULTS: There was no significant difference in cell morphology observed by microscope among the three groups. In the cells stimulated in the presence of IL-33, the levels of IFN-γ and granzyme B mRNAs were significantly elevated, cell killing ability was strengthened, and the level of IFN-γ increased significantly, PD-1 level decreased when compared with the other two groups. CONCLUSION: IL-33 might enhance the function of PBMCs and then promote adaptive anti-tumor immune response.

[The number of myeloid-derived suppressor cells in the peripheral blood and tumor tissues in patients with gastric cancer and its clinical significance].

OBJECTIVE: To investigate the number of myeloid-derived suppressor cells (MDSCs) in peripheral blood, tumor tissue and para-tumor normal tissues in patients with gastric cancer in an attempt to explore the relationship between MDSCs expression and clinicopathologic characteristics. METHODS: Peripheral blood was collected from 62 gastric cancer patients and 20 healthy volunteers (HC group). Gastric cancer tissues and adjacent normal tissues were obtained from 12 of the 62 gastric cancer patients. HLA-DR⁻ CD33⁺ CD11b⁺ MDSCs were analyzed by flow cytometry. Student's t-test, One-way ANOVA and Mann-Whitney U test were used to explore the correlation between MDSCs expression in peripheral blood and the depth of tumor invasion, degree of differentiation, TNM stage and lymph node metastasis. RESULTS: Compare with the HC group, the number of MDSCs in peripheral blood of newly-diagnosed gastric cancer patients was higher (P<0.01). The number of MDSCs in peripheral blood of gastric cancer patients was significantly associated with the depth of invasion, degree of differentiation, TNM stage and lymph node metastasis (P<0.05). The expression of MDSCs from gastric cancer tissues was obviously higher than that of the adjacent tissues in the same patient. The number of MDSCs in peripheral blood from recurrent/metastasis group was obviously higher than that from non-recurrent/metastasis group (P<0.05). In addition, compare with the HC group, the expression of negative costimulatory molecule T-cell immunoglobulin- and mucin-domain-containing molecule 3 (Tim-3) on HLA-DR⁻ CD33⁺ CD11b⁺ MDSCs in peripheral blood was higher in patients with gastric cancer. CONCLUSION: MDSCs expression in peripheral blood of gastric cancer patients was closely associated with tumor malignant degree and tumor recurrence/metastasis.