First-principles study of the discharge electrochemical and catalytic performance of the sulfur cathode host Fe0.875M0.125S2 (M = Ti, V).

Lithium-sulfur batteries (LSBs) are one of the most promising energy storage devices with high energy density. However, their application and commercialization are hampered by the slow Li-S redox chemistry. Fe0.875M0.125S2 (M = Ti, V), as the sulfur cathode host, enhances the Li-S redox chemistry. FeS2 with Pa3̄ is transformed into Li2FeS2 with P3̄m1 after discharge. The structure changes and physicochemical properties during Fe0.875M0.125S2 discharge process are further investigated to screen out the sulfur cathode host materials with the best comprehensive properties. The discharge structure of Fe0.875M0.125S2 is verified by the thermodynamic stability of Li-deficient phases, voltage and capacity based on Monte Carlo methods. Fe0.875M0.125S2 with Pa3̄ is transformed into Li2Fe0.875M0.125S2 with P3̄m1 after discharge. Using the first-principles calculations, the physicochemical properties of Li2Fe0.875M0.125S2 are systematically investigated, including the formation energy, voltage, theoretical capacity, electrical conductivity, Li+ diffusion, catalytic performance and Li2S oxidation decomposition. The average redox voltage of Li2Fe0.875V0.125S2 is higher than that of Li2Fe0.875Ti0.125S2. Li2Fe0.875M0.125S2 shows metallic properties. Li2Fe0.875V0.125S2 is more beneficial to the reduction reaction of Li2S2 and Li2S oxidation decomposition. Fe0.875V0.125S2 has more potential as the sulfur cathode host than Fe0.875Ti0.125S2 in LSBs. A new strategy for the selection of the sulfur cathode host material for LSBs is provided by this work.

Structural Study of SARS-CoV-2 Antibodies Identifies a Broad-Spectrum Antibody That Neutralizes the Omicron Variant by Disassembling the Spike Trimer.

The continuous emergence of novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants poses new challenges in the fight against the coronavirus disease 2019 (COVID-19) pandemic. The newly emerging Omicron strain caused serious immune escape and raised unprecedented concern all over the world. The development of an antibody targeting a conserved and universal epitope is urgently needed. A subset of neutralizing antibodies (NAbs) against COVID-19 from convalescent patients were isolated in our previous study. In this study, we investigated the accommodation of these NAbs to SARS-CoV-2 variants of concern (VOCs), revealing that IgG 553-49 neutralizes pseudovirus of the SARS-CoV-2 Omicron variant. In addition, we determined the cryo-electron microscopy (cryo-EM) structure of the SARS-CoV-2 spike (S) protein complexed with three monoclonal antibodies targeting different epitopes, including 553-49, 553-15, and 553-60. Notably, 553-49 targets a novel conserved epitope and neutralizes the virus by disassembling S trimers. IgG 553-15, an antibody that neutralizes all of the VOCs except Omicron, cross-links two S trimers to form a trimer dimer, demonstrating that 553-15 neutralizes the virus by steric hindrance and virion aggregation. These findings suggest the potential to develop 553-49 and other antibodies targeting this highly conserved epitope as promising therapeutic reagents for COVID-19. IMPORTANCE The emergence of the Omicron strain of SARS-CoV-2 caused higher immune escape, raising unprecedented concerns about the effectiveness of antibody therapies and vaccines. In this study, we identified a SARS-CoV-2 neutralizing antibody, 553-49, which neutralizes all variants by targeting a completely conserved novel epitope. In addition, we revealed that IgG 553-15 neutralizes SARS-CoV-2 by cross-linking virions and that 553-60 functions by blocking receptor binding. Comparison of different receptor binding domain (RBD) epitopes revealed that the 553-49 epitope is hidden in the S trimer and keeps a high degree of conservation during SARS-CoV-2 evolution, making 553-49 a promising therapeutic reagent against the emerging Omicron and future variants of SARS-CoV-2.

Soil phosphorus availability alters the correlations between root phosphorus-uptake rates and net photosynthesis of dominant C3 and C4 species in a typical temperate grassland of Northern China.

Phosphorus (P) fertilization can alleviate a soil P deficiency in grassland ecosystems. Understanding plant functional traits that enhance P uptake can improve grassland management. We measured impacts of P addition on soil chemical and microbial properties, net photosynthetic rate (Pn ) and nonstructural carbohydrate concentrations ([NSC]), and root P-uptake rate (PUR), morphology, anatomy, and exudation of two dominant grass species: Leymus chinensis (C3 ) and Cleistogenes squarrosa (C4 ). For L. chinensis, PUR and Pn showed a nonlinear correlation. Growing more adventitious roots compensated for the decrease in P transport per unit root length, so that it maintained a high PUR. For C. squarrosa, PUR and Pn presented a linear correlation. Increased Pn was associated with modifications in root morphology, which further enhanced its PUR and a greater surplus of photosynthate and significantly stimulated root exudation (proxied by leaf [Mn]), which had a greater impact on rhizosheath micro-environment and microbial PLFAs. Our results present correlations between the PUR and the Pn of L. chinensis and C. squarrosa and reveal that NSC appeared to drive the modifications of root morphology and exudation; they provide more objective basis for more efficient P-input in grasslands to address the urgent problem of P deficiency.

Molecular mechanism of benzo [a] pyrene regulating lipid metabolism via aryl hydrocarbon receptor.

BACKGROUND: Benzo [a] pyrene (BaP), a potent carcinogen, has been proved that it has toxicological effects via activation the aryl hydrocarbon receptor (AhR) pathway. AhR can participate in regulating lipogenesis and lipolysis. This topic will verify whether BaP regulates lipid metabolism via AhR. METHODS: (1) C57BL/6 mice were gavaged with BaP for 12 weeks to detect serum lipids, glucose tolerance, and insulin resistance. Morphological changes in white adipose tissue (WAT) were detected by Hematoxylin and Eosin staining. The mRNA expression levels of adipogenesis-related factors included recombinant human CCAAT/enhancer binding protein alpha (C/EBPα), peroxisome proliferator-activated receptor gamma (PPARγ), and fatty acid binding protein 4 (FABP4) and inflammatory factors included nuclear factor kappa-B (NF-κB), monocyte chemotactic protein-1 (MCP-1), and tumor necrosis factor alpha (TNF-α) were detected using PCR. (2) Neutral lipid content changes in differentiated 3 T3-L1 adipocytes treated with BaP with and w/o AhR inhibitor were detected by Oil red staining. The protein expression levels of adipogenesis- and decomposition-related factors included PPARγ coactivator-1 alpha (PGC-1α), and peroxisome proliferation-activated receptor alpha (PPARα) were detected using western blotting. The mRNA expression levels of inflammatory factors were detected using PCR. RESULTS: (1) BaP inhibited body weight gain, decreased lipid content, increased lipid levels, and decreased glucose tolerance and insulin tolerance in mice; (2) BaP reduced the expressions of C/EBPα, PPARγ, FABP4, PGC-1α, and PPARα and increased the expressions of NF-κB, MCP-1, and TNF-α by activating AhR. CONCLUSION: BaP inhibit fat synthesis and oxidation while inducing inflammation by activating AhR, leading to WAT dysfunction and causing metabolic complications.

[Research on automatic removal of ocular artifacts from single channel electroencephalogram signals based on wavelet transform and ensemble empirical mode decomposition].

The brain-computer interface (BCI) systems used in practical applications require as few electroencephalogram (EEG) acquisition channels as possible. However, when it is reduced to one channel, it is difficult to remove the electrooculogram (EOG) artifacts. Therefore, this paper proposed an EOG artifact removal algorithm based on wavelet transform and ensemble empirical mode decomposition. Firstly, the single channel EEG signal is subjected to wavelet transform, and the wavelet components which involve EOG artifact are decomposed by ensemble empirical mode decomposition. Then the predefined autocorrelation coefficient threshold is used to automatically select and remove the intrinsic modal functions which mainly composed of EOG components. And finally the 'clean' EEG signal is reconstructed. The comparative experiments on the simulation data and the real data show that the algorithm proposed in this paper solves the problem of automatic removal of EOG artifacts in single-channel EEG signals. It can effectively remove the EOG artifacts when causes less EEG distortion and has less algorithm complexity at the same time. It helps to promote the BCI technology out of the laboratory and toward commercial application.

[A research for single trial detection of error related negativity].

Error related negativity (ERN) is generated in frontal and central cortical regions when individuals perceive errors. Because ERN has low signal-to-noise ratio and large individual difference, it is difficult for single trial ERN recognition. In current study, the optimized electroencephalograph (EEG) channels were selected based on the brain topography of ERN activity and ERN offline recognition rate, and the optimized EEG time segments were selected based on the ERN offline recognition rate, then the low frequency time domain and high frequency time-frequency domain features were analyzed based on wavelet transform, after which the ERN single detection algorithm was proposed based on the above procedures. Finally, we achieved average recognition rate of 72.0% ± 9.6% in 10 subjects by using the sample points feature in 0~3.9 Hz and the power and variance features in 3.9~15.6 Hz from the EEG segments of 200~600 ms on the selected 6 channels. Our work has the potential to help the error command real-time correction technique in the application of online brain-computer interface system.

Oleanane triterpene saponins with cardioprotective activity from Clinopodium polycephalum.

Two new triterpene saponins, clinopodiside VI (1) and saikosaponin c (2), along with six known saikosaponins (3-8), were isolated from the plant of Clinopodium polycephalum. Compounds 1-3 showed moderate inhibition against H9c2 cell damage induced by H2O2.

[Monitoring depth of anesthesia and effect analysis in primary visual cortex of rats based on complexity of local field potential].

In the present study carried out in our laboratory, we recorded local field potential (LFP) signals in primary visual cortex (V1 area) of rats during the anesthesia process in the electrophysiological experiments of invasive microelectrode array implant, and obtained time evolutions of complexity measure Lempel-ziv complexity (LZC) by nonlinear dynamic analysis method. Combined with judgment criterion of tail flick latency to thermal stimulus and heart rate, the visual stimulation experiments are carried out to verify the reliability of anesthetized states by complexity analysis. The experimental results demonstrated that the time varying complexity measures LZC of LFP signals of different channels were similar to each other in the anesthesia process. In the same anesthesia state, the difference of complexity measure LZC between neuronal responses before and after visual stimulation was not significant. However, the complexity LZC in different anesthesia depths had statistical significances. Furthermore, complexity threshold value represented the depth of anesthesia was determined using optimization method. The reliability and accuracy of monitoring the depth of anesthesia using complexity measure LZC of LFP were all high. It provided an effective method of realtime monitoring depth of anesthesia for craniotomy patients in clinical operation.

MGFKD: A semi-supervised multi-source domain adaptation algorithm for cross-subject EEG emotion recognition.

Currently, most models rarely consider the negative transfer problem in the research field of cross-subject EEG emotion recognition. To solve this problem, this paper proposes a semi-supervised domain adaptive algorithm based on few labeled samples of target subject, which called multi-domain geodesic flow kernel dynamic distribution alignment (MGFKD). It consists of three modules: 1) GFK common feature extractor: projects the feature distribution of source and target subjects to the Grassmann manifold space, and obtains the latent common features of the two feature distributions through GFK method. 2) Source domain selector: obtains pseudo-labels of the target subject through weak classifier, finds "golden source subjects" by using few known labels of target subjects. 3) Label corrector: uses a dynamic distribution balance strategy to correct the pseudo-labels of the target subject. We conducted comparison experiments on the SEED and SEED-IV datasets, and the results show that MGFKD outperforms unsupervised and semi-supervised domain adaptation algorithms, achieving an average accuracy of 87.51±7.68% and 68.79±8.25% on the SEED and SEED-IV datasets with only one labeled sample per video for target subject. Especially when the number of source domains is set as 6 and the number of known labels is set as 5, the accuracy increase to 90.20±7.57% and 69.99±7.38%, respectively. The above results prove that our proposed algorithm can efficiently and quickly improve the cross-subject EEG emotion classification performance. Since it only need a small number of labeled samples of new subjects, making it has strong application value in future EEG-based emotion recognition applications.

Multilayer network-based channel selection for motor imagery brain-computer interface.

Objective. The number of electrode channels in a motor imagery-based brain-computer interface (MI-BCI) system influences not only its decoding performance, but also its convenience for use in applications. Although many channel selection methods have been proposed in the literature, they are usually based on the univariate features of a single channel. This leads to a loss of the interaction between channels and the exchange of information between networks operating at different frequency bands.Approach. We integrate brain networks containing four frequency bands into a multilayer network framework and propose a multilayer network-based channel selection (MNCS) method for MI-BCI systems. A graph learning-based method is used to estimate the multilayer network from electroencephalogram (EEG) data that are filtered by multiple frequency bands. The multilayer participation coefficient of the multilayer network is then computed to select EEG channels that do not contain redundant information. Furthermore, the common spatial pattern (CSP) method is used to extract effective features. Finally, a support vector machine classifier with a linear kernel is trained to accurately identify MI tasks.Main results. We used three publicly available datasets from the BCI Competition containing data on 12 healthy subjects and one dataset containing data on 15 stroke patients to validate the effectiveness of our proposed method. The results showed that the proposed MNCS method outperforms all channels (85.8% vs. 93.1%, 84.4% vs. 89.0%, 71.7% vs. 79.4%, and 72.7% vs. 84.0%). Moreover, it achieved significantly higher decoding accuracies on MI-BCI systems than state-of-the-art methods (pairedt-tests,p< 0.05).Significance. The experimental results showed that the proposed MNCS method can select appropriate channels to improve the decoding performance as well as the convenience of the application of MI-BCI systems.

An EEG Study on [Formula: see text] Phase-Amplitude Coupling-Based Functional Brain Network in Epilepsy Patients.

Epilepsy, a chronic neuropsychiatric brain disorder characterized with recurrent seizures, is closely associated with abnormal neural communications within the brain. Despite that the phase-amplitude coupling (PAC) has been suggested to offer a new way to observe neural interactions during epilepsy, however, few studies pay attention to alterations of the epileptic functional brain network based on PAC, especially on the [Formula: see text] PAC. Therefore, we use scalp electroencephalography (EEG) data of epileptic patients and the [Formula: see text] PAC modulation index (MI) to construct functional brain networks to examine variations of neural interactions during different epileptic phases. Statistically, the findings show that between-channel MI values in the post-ictal period significantly increase compared to that in the pre-ictal period, and the between-channel MI value has a close association with the information of phase and amplitude provided by the channels. Importantly, in both the phase-amplitude and amplitude-phase functional brain networks, the average node degree is remarkably higher in the post-ictal period than that in the pre-ictal period, whereas the characteristic path length in the ictal and post-ictal periods is significantly lower than that in the pre-ictal period. Besides, the average betweenness centrality in the post-ictal period is remarkably higher than that in the ictal period. Interestingly, the positive correlations between within-channel MI values and between-channel MI values can be observed during the pre-ictal, ictal and post-ictal periods. These findings suggest that the [Formula: see text] PAC-based functional brain network may provide a novel perspective to understanding alterations of neural interactions during the epileptic evolution, and may contribute to effectively controlling the spread of epileptic seizures.

Correlations between root phosphorus acquisition and foliar phosphorus allocation reveal how grazing promotes plant phosphorus utilization.

Overgrazing and phosphorus (P) deficiency are two major factors limiting the sustainable development of grassland ecosystems. Exploring plant P utilization and acquisition strategies under grazing can provide a solid basis for determining a reasonable grazing intensity. Both foliar P allocation and root P acquisition are crucial mechanisms for plants to adapt to environmental P availability; however, their changing characteristics and correlation under grazing remain unknown. Here, we investigated foliar P fractions, root P-acquisition traits and gene expression, as well as rhizosphere and bulk soil properties of two dominant plant species, Leymus chinensis (a rhizomatous grass) and Stipa grandis (a bunchgrass), in a field grazing intensity gradient site in Inner Mongolia. Grazing induced different degrees of compensatory growth in the two dominant plant species, increased rhizosphere P availability, and alleviated plant P limitation. Under grazing, the foliar metabolite P of L. chinensis increased, whereas the nucleic acid P of S. grandis increased. Increased P fractions in L. chinensis were positively correlated with increased root exudates and rapid inorganic P absorption. For S. grandis, increased foliar P fractions were positively correlated with more fine roots, more root exudates, and up-regulated expression of genes involved in defense and P metabolism. Overall, efficient root P mobilization and uptake traits, as well as increases in leaf metabolic activity-related P fractions, supported plant compensatory growth under grazing, a process that differed between tiller types. The highest plant productivity and leaf metabolic activity-related P concentrations under medium grazing intensity clarify the underlying basis for sustainable livestock production.

Identification of disease-specific genes related to immune infiltration in nonalcoholic steatohepatitis using machine learning algorithms.

To identify disease signature genes associated with immune infiltration in nonalcoholic steatohepatitis (NASH), we downloaded 2 publicly available gene expression profiles, GSE164760 and GSE37031, from the gene expression omnibus database. These profiles represent human NASH and control samples and were used for differential genes (DEGs) expression screening. Two machine learning methods, the Least Absolute Shrinkage and Selection Operator regression model and Support Vector Machine Recursive Feature Elimination, were used to identify candidate disease signature genes. The CIBERSORT deconvolution algorithm was employed to analyze the infiltration of 22 immune cell types in NASH. Additionally, we constructed a NASH cell model using HepG2 cells treated with oleic acid and free fatty acids. The construction of the cell model was verified using oil red O staining, and Western blotting was used to detect the protein expression of the disease signature genes in both control and model groups. As a result, a total of 262 DEGs were identified. These DEGs were primarily associated with metal ion transmembrane transporter activity, sodium ion transmembrane transporter protein activity, calcium ion, and neuroactive ligand-receptor interactions. FOS, IGFBP2, dual-specificity phosphatase 1 (DUSP1), and IKZF3 were identified as disease signature genes of NASH by the least absolute shrinkage and selection operator and Support Vector Machine Recursive Feature Elimination algorithms for DEGs analysis. The receiver operating characteristic curves showed that FOS, IGFBP2, DUSP1, and IKZF3 had good diagnostic value (area under receiver operating characteristic curve > 0.8). These findings were validated in the GSE89632 dataset and through cellular assays. Immunocyte infiltration analysis revealed that NASH was associated with CD8 T cells, CD4 T cells, follicular helper T cells, resting NK cells, eosinophils, regulatory T cells, and γδ T cells. The FOS, IGFBP2, DUSP1, and IKZF3 genes were specifically associated with follicular helper T cells. Lipid droplet aggregation significantly increased in HepG2 cells treated with oleic acid and free fatty acids, indicating successful construction of the cell model. In this model, the expression of FOS, IGFBP2, and DUSP1 was significantly decreased, while that of IKZF3 was significantly elevated (P < .01, P < .001) compared with the control group. Therefore, FOS, IGFBP2, DUSP1, and IKZF3 can be considered as disease signature genes associated with immune infiltration in NASH.

Dynamic molecular architecture and substrate recruitment of cullin3-RING E3 ligase CRL3KBTBD2.

Phosphatidylinositol 3-kinase α, a heterodimer of catalytic p110α and one of five regulatory subunits, mediates insulin- and insulin like growth factor-signaling and, frequently, oncogenesis. Cellular levels of the regulatory p85α subunit are tightly controlled by regulated proteasomal degradation. In adipose tissue and growth plates, failure of K48-linked p85α ubiquitination causes diabetes, lipodystrophy and dwarfism in mice, as in humans with SHORT syndrome. Here we elucidated the structures of the key ubiquitin ligase complexes regulating p85α availability. Specificity is provided by the substrate receptor KBTBD2, which recruits p85α to the cullin3-RING E3 ubiquitin ligase (CRL3). CRL3KBTBD2 forms multimers, which disassemble into dimers upon substrate binding (CRL3KBTBD2-p85α) and/or neddylation by the activator NEDD8 (CRL3KBTBD2~N8), leading to p85α ubiquitination and degradation. Deactivation involves dissociation of NEDD8 mediated by the COP9 signalosome and displacement of KBTBD2 by the inhibitor CAND1. The hereby identified structural basis of p85α regulation opens the way to better understanding disturbances of glucose regulation, growth and cancer.

Case Study: Extension of MSW landfill with reinforced earth berm.

This paper reports a case study of an extension design of an existing municipal solid waste (MSW) earth berm in Guangzhou, China. The berm has limited space for extension and was constructed during a rainy season. Due to the space constraint, the extended berm was designed with a steep slope 1.1 horizontal to 1.0 vertical (1.1H:1.0 V). Due to the rainy season, the new extended earth berm was built with a mix of gravel and sandy clay (i.e. Vgravel/Vclay = 50:50). To make the new berm design satisfies the stability requirement, it has been reinforced with geotextile layers within the berm and piles at the toe of the berm. A 2D finite-element analysis has been conducted to demonstrate the rationality of the design. The performance of the new berm was monitored using field sensors, which include the vertical and lateral displacements of the berm, vertical earth pressure of the berm, and tensile strain of the geotextile layers during and after the berm construction. Numerical and field tests results show that the failure surface appears along the interface between the old berm and the horizonal displacements are mainly observed in the new berm. The results of this study proven that a steep slope berm with geotextiles reinforcement can resistant the load from extended MSW landfill, which can provide a guidance for the design and construction of the extension of MSW landfill.

Recognition of single upper limb motor imagery tasks from EEG using multi-branch fusion convolutional neural network.

Motor imagery-based brain-computer interfaces (MI-BCI) have important application values in the field of neurorehabilitation and robot control. At present, MI-BCI mostly use bilateral upper limb motor tasks, but there are relatively few studies on single upper limb MI tasks. In this work, we conducted studies on the recognition of motor imagery EEG signals of the right upper limb and proposed a multi-branch fusion convolutional neural network (MF-CNN) for learning the features of the raw EEG signals as well as the two-dimensional time-frequency maps at the same time. The dataset used in this study contained three types of motor imagery tasks: extending the arm, rotating the wrist, and grasping the object, 25 subjects were included. In the binary classification experiment between the grasping object and the arm-extending tasks, MF-CNN achieved an average classification accuracy of 78.52% and kappa value of 0.57. When all three tasks were used for classification, the accuracy and kappa value were 57.06% and 0.36, respectively. The comparison results showed that the classification performance of MF-CNN is higher than that of single CNN branch algorithms in both binary-class and three-class classification. In conclusion, MF-CNN makes full use of the time-domain and frequency-domain features of EEG, can improve the decoding accuracy of single limb motor imagery tasks, and it contributes to the application of MI-BCI in motor function rehabilitation training after stroke.

Role and mechanism of benzo[a]pyrene in the transformation of chronic obstructive pulmonary disease into lung adenocarcinoma.

OBJECTIVE: This experiment is explores the genes that play a key role, their expression changes and the biological processes in the transformation of chronic obstructive pulmonary disease (COPD) into lung adenocarcinoma (LAC). Meanwhile, identify the effects of Benzo[a]pyrene (BaP) in the conversion of COPD into LAC. METHODS: 1. Differential expression genes of COPD and LAC were screened and analyzed by high-throughput microarray data between the two diseases and their respective control groups. 2. The screened genes were used for routine bioinformatics analysis such as functional analysis, expression verification, protein interaction analysis and functional enrichment. 3. Cigarette smoke extract (CSE) combined with lipopolysaccharide (LPS) was used to establish an in vitro COPD model. 4. MTT assay was used to detect the influence of B(a)P in effect on A549 cell proliferation. CCK-8, Transwell invasion test and scratch test were used to detect the cell proliferation, invasion and migration ability, while qPCR and Western Blot tests were used to observe the cell proliferation, apoptosis and changes in related indicators such as EMT. 5. Experimental method of separately adding agonists (tBHQ) and inhibitors (DIC) of NQO1 was used to confirm the effect of NQO1 on A549 cell proliferation, apoptosis, migration and invasion. 6. To further clarify whether BaP exerted effect on cell proliferation, apoptosis, migration and invasion through NQO1, we knocked down NQO1 gene and then infecting cells with BaP. RESULTS: 1. We screened genes of COPD and LAC using datasets from GSE151052, GSE118370, and GSE140797. After screening, the genes upregulated in COPD and downregulated in LAC were RTKN2, SLC6A4, and HBB, the gene downregulated in COPD and upregulated in LAC was NQO1, the genes downregulated in both COPD and LAC were FPR1, LYVE1 and PKHD1L1. 2. The main signaling pathways in which the target genes were enriched are cell cycle, EMT, PI3K/AKT, and apoptosis. In the data included GEPIA, PKHD1L1, FPR1, LYVE1, RTKN2, HBB, and SLC6A4 were significantly downregulated and NQO1 was upregulated in LAC relative to controls. In addition, there were 46 interaction proteins in the target genes, and the functions they enriched included hydrogen peroxide catabolism, etc. 3. When A549 cell was stimulated with 100 ng/mL LPS+ 10% CSE, the COX-2 expression indicated that COPD model in vitro was successfully established. 4. The optimal dose and action time were screened which were 1 µM and 24 h. Compared to the control group, COPD and BaP group increased cell proliferation and invasion capabilities. On the basis of COPD, adding BaP could further increase the proliferation and migration capabilities. Interestingly, the levels of NQO1 decreased in COPD models, while increased by BaP. 5. tBHQ can increase the proliferation and migration capacity of A549 cells, which is inhibited by the addition of DIC. 6. The enhanced proliferation, migration and invasion of A549 cells by BaP were attenuated after knockdown of NQO1. CONCLUSION: Our study reveals that PKHD1L1, FPR1, LYVE1, RTKN2, HBB, SLC6A4 and NQO1 may play an important role in the conversion of COPD to LAC. High NQO1 expression may increase the proliferation and migration ability of A549 cells, and BaP may promote the EMT state by increasing the expression of NQO1, thereby making the COPD model in vitro expose the tumor characteristics.

IGFBP­rP1 affects the proliferation, apoptosis and macrophage polarization of endometrial cancer cells by regulating the PI3K/AKT pathway.

Insulin-like growth factor binding protein-related protein 1 (IGFBP-rP1) is a potential tumor suppressor gene in a variety of cancers including colorectal cancer and breast cancer. However, its role and the potential mechanism in endometrial carcinoma (EC) are still unclear. The purpose of this study was to explore the effect of IGFBP-rP1 on EC cell proliferation and apoptosis and its underlying mechanism. Western blot analysis and reverse transcription-quantitative PCR were used to assess protein and gene expression levels of IGFBP-rP1 in EC cells. Overexpression of IGFBP-rP1 and/or AKT serine/threonine kinase was used to analyze their effects on cell proliferation and apoptosis of the EC cells. Co-immunoprecipitation and glutathione S transferase pull-down assays were used to analyze the interaction between IGFBP-rP1 and AKT. The expression of IGFBP-rP1 in EC cells was downregulated. Overexpression of IGFBP-rP1 inhibited the proliferation and induced apoptosis of EC cells, which were abolished by the overexpression of AKT. In addition, IGFBP-rP1 directly interacted with AKT to inhibit PI3K/AKT signaling. Additionally, M0 macrophages were induced by EC cells to differentiate into M2 macrophages, which was reversed by IGFBP-rP1. Overexpression of AKT in EC cells abolished the inhibitory effect of IGFBP-rP1 on M2 polarization. IGFBP-rP1 as an oncogenic factor inhibits M2 polarization of TAMs through PI3K/AKT signaling pathway and may be a potentially valuable target for EC therapy.

A model description of beta oscillations in the external globus pallidus.

The external globus pallidus (GPe), a subcortical nucleus located in the indirect pathway of the basal ganglia, is widely considered to have tight associations with abnormal beta oscillations (13-30 Hz) observed in Parkinson's disease (PD). Despite that many mechanisms have been put forward to explain the emergence of these beta oscillations, however, it is still unclear the functional contributions of the GPe, especially, whether the GPe itself can generate beta oscillations. To investigate the role played by the GPe in producing beta oscillations, we employ a well described firing rate model of the GPe neural population. Through extensive simulations, we find that the transmission delay within the GPe-GPe pathway contributes significantly to inducing beta oscillations, and the impacts of the time constant and connection strength of the GPe-GPe pathway on generating beta oscillations are non-negligible. Moreover, the GPe firing patterns can be significantly modulated by the time constant and connection strength of the GPe-GPe pathway, as well as the transmission delay within the GPe-GPe pathway. Interestingly, both increasing and decreasing the transmission delay can push the GPe firing pattern from beta oscillations to other firing patterns, including oscillation and non-oscillation firing patterns. These findings suggest that if the transmission delays within the GPe are at least 9.8 ms, beta oscillations can be produced originally in the GPe neural population, which also may be the origin of PD-related beta oscillations and should be regarded as a promising target for treatments for PD.

Improving AR-SSVEP Recognition Accuracy Under High Ambient Brightness Through Iterative Learning.

Augmented reality-based brain-computer interface (AR-BCI) system is one of the important ways to promote BCI technology outside of the laboratory due to its portability and mobility, but its performance in real-world scenarios has not been fully studied. In the current study, we first investigated the effect of ambient brightness on AR-BCI performance. 5 different light intensities were set as experimental conditions to simulate typical brightness in real scenes, while the same steady-state visual evoked potentials (SSVEP) stimulus was displayed in the AR glass. The data analysis results showed that SSVEP can be evoked under all 5 light intensities, but the response intensity became weaker when the brightness increased. The recognition accuracies of AR-SSVEP were negatively correlated to light intensity, the highest accuracies were 89.35% with FBCCA and 83.33% with CCA under 0 lux light intensity, while they decreased to 62.53% and 49.24% under 1200 lux. To solve the accuracy loss problem in high ambient brightness, we further designed a SSVEP recognition algorithm with iterative learning capability, named ensemble online adaptive CCA (eOACCA). The main strategy is to provide initial filters for high-intensity data by iteratively learning low-light-intensity AR-SSVEP data. The experimental results showed that the eOACCA algorithm had significant advantages under higher light intensities ( 600 lux). Compared with FBCCA, the accuracy of eOACCA under 1200 lux was increased by 13.91%. In conclusion, the current study contributed to the in-depth understanding of the performance variations of AR-BCI under different lighting conditions, and was helpful in promoting the AR-BCI application in complex lighting environments.