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Purpose: Microbial dysbiosis is considered as a hallmark of colorectal cancer (CRC). Trimethylamine-N-oxide (TMAO) as a gut microbiota-dependent metabolite has recently been implicated in CRC development. Nevertheless, evidence relating TMAO to intestinal carcinogenesis remains largely unexplored. Herein, we aimed to examine the crucial role of TMAO in CRC progression. Methods: Apcmin/+ mice were treated with TMAO or sterile PBS for 14 weeks. Intestinal tissues were isolated to evaluate the effects of TMAO on the malignant transformation of intestinal adenoma. The gut microbiota of mouse feces was detected by 16S rRNA sequencing analysis. HCT-116 cells were used to provide further evidence of TMAO on the progression of CRC. Results: TMAO administration increased tumor cell and stem cell proliferation, and decreased apoptosis, accompanied by DNA damage and gut barrier impairment. Gut microbiota analysis revealed that TMAO induced changes in the intestinal microbial community structure, manifested as reduced beneficial bacteria. Mechanistically, TMAO bound to farnesoid X receptor (FXR), thereby inhibiting the FXR-fibroblast growth factor 15 (FGF15) axis and activating the Wnt/β-catenin signaling pathway, whereas the FXR agonist GW4064 could blunt TMAO-induced Wnt/β-catenin pathway activation. Conclusion: The microbial metabolite TMAO can enhance intestinal carcinogenesis by inhibiting the FXR-FGF15 pathway. [ABSTRACT FROM AUTHOR]

The way people imagine greatly affects performance of brain-computer interface (BCI) based on motion imagery (MI). Action sequence is a basic unit of imitation, learning, and memory for motor behavior. Whether it influences the MI-BCI is unknown, and how to manifest this influence is difficult since the MI is a spontaneous brain activity. To investigate the influence of the action sequence, this study proposes a novel paradigm named action sequences observing and delayed matching task to use images and videos to guide people to observe, match and reinforce the memory of sequence. Seven subjects' ERPs and MI performance are analyzed under four different levels of complexities or orders of the sequence. Results demonstrated that the action sequence in terms of complexity and sequence order significantly affects the MI. The complex action in positive order obtains stronger ERD/ERS and more pronounced MI feature distributions, and yields an MI classification accuracy that is 12.3% higher than complex action in negative order (p < 0.05). In addition, the ERP amplitudes derived from the supplementary motor area show a positive correlation to the MI. This study demonstrates a new perspective of improving imagery in the MI-BCI by considering the complexity and order of the action sequences, and provides a novel index for manifesting the MI performance by ERP. [ABSTRACT FROM AUTHOR]

Land use changes have profoundly influenced global environmental dynamics. The Yellow River (YR), as the world's fifth-longest river, significantly contributes to regional social and economic growth due to its extensive drainage area, making it a key global player. To ensure ecological stability and coordinate land use demand, modeling the future land allocation patterns of the Yellow River Basin (YRB) will assist in striking a balance between land use functions and the optimization of its spatial design, particularly in water and sand management. In this research, we used a multi-objective genetic algorithm (MOGA) with the PLUS model to simulate several different futures for the YRB's land use between 1990 and 2020 and predict its spatial pattern in 2030. An analysis of the spatiotemporal evolution of land use changes in the YRB indicated that construction land expansion is the primary driver of landscape pattern and structure changes and ecological degradation, with climate change also contributing to the expansion of the watershed area. On the other hand, the multi-scenario simulation, constrained by specific targets, revealed that economic development was mainly reflected in land expansion for construction. At the same time, grassland and woodland were essential pillars to support the region's ecological health, and increasing the development of unused land emerged as a potential pathway towards sustainable development in the region. This study could be used as a template for the long-term growth of other large river basins by elucidating the impacts of human activities on land use and rationalizing land resource allocation under various policy constraints. [ABSTRACT FROM AUTHOR]

Graphene quantum dots (GQDs), characterized by nanostructures with diameters below 10 nm and strong hydrophilicity, exhibit advantageous migration behavior in groundwater environments. The impacts of ionic strength, cation type and pH on the transport of GQDs in porous media diverge from those observed for other nanosized particles. We investigated the cooperative effects of ionic strength (IS, 1 mM NaCl and 100 mM NaCl, 1 mM CaCl2 and 10 mM CaCl2), cation types (Na+, Ca2+), and pH (4, 7, 9). Through saturated column experiments and numerical modeling, we observed increased GQDs deposition with rising IS, notably enhanced by the presence of divalent cations due to bridging effects and self-aggregation. In the presence of monovalent cations, acidic environment favored the deposition of GQDs, whereas in the presence of divalent cations, an acidic environment with 1 mM CaCl2 was the most favorable for the deposition of GQDs, and shifted to an alkaline condition at 10 mM CaCl2, which may be due to the deprotonation process and the enhancement of self-aggregation. This research sheds light on critical factors governing the environmental behavior of ultra-small nanoparticles and thereby aids in the assessment of their ecological impact. [ABSTRACT FROM AUTHOR]

Weakly Supervised Semantic Segmentation is a crucial task in computer vision. However, existing methods that utilize Class Activation Maps (CAMs) with classification tasks can only identify a small part of the region. To address this limitation, we propose a novel Attention Activation Remodulation (AAR) scheme that leverages traditional CAMs and the remodulation branch to obtain weighted CAMs for recalibrated supervision. The AAR scheme re-arranges important features' distribution from the channel and space perspectives, which regulates segmentation-oriented activation responses. In addition, we propose a Feature Pixel Extraction Module (FPEM) that utilizes contextual information to improve pixel prediction. Furthermore, the proposed scheme can be combined with other methods to improve overall performance. Extensive experiments on the PASCAL VOC 2012 dataset demonstrate the effectiveness of the AAR mechanism and FPEM module. [ABSTRACT FROM AUTHOR]

Deep learning has great potential on decoding EEG in brain-computer interface. While common deep learning algorithms cannot directly train models with data from multiple individuals because of the inter-individual differences in EEG. Collecting enough data for each subject to satisfy the training of deep learning would result in an increase in training cost. This study proposes a novel transfer learning, EEGNet-based multi-source domain filter for transfer learning (EEGNet-MDFTL), to reduce the amount of training data and improve the performance of BCI. The EEGNet-MDFTL uses bagging ensemble learning to learn domain-invariant features from the multi-source domain and utilizes model loss value to filter the multi-source domain. Compared with baseline methods, the accuracy of the EEGNet-MDFTL reaches 91.96%, higher than two state-of-the-art methods, which demonstrates source domain filter can select similar source domains to improve the accuracy of the model, and remains a high level even when the data amount is reduced to 1/8, proving that ensemble learning learns enough domain invariant features from the multi-source domain to make the model insensitive to data amount. The proposed EEGNet-MDFTL is effective in improving the decoding performance with a small amount of data, which is helpful to save the BCI training cost. [ABSTRACT FROM AUTHOR]

This paper addresses the co-design of anti-windup compensator and a novel saturation-based dynamic event-triggered condition for asymmetric saturated system. Asymmetric saturation frequently appears in practical systems, which may seriously degrade the system performance. For the systems with undetectable state, dynamic output feedback controller is employed in this paper. A saturation-based dynamic event-triggered mechanism related to the upper and lower bounds of asymmetric saturation is proposed in this paper, which has better performance in reducing the event-triggered number than static event-triggered condition. In addition, the minimum triggering time interval is calculated to avoid Zeno behavior. An optimization problem is formulated to maximize estimated stable region for the closed-loop system. Finally, the proposed results are illustrated by a numerical example. [ABSTRACT FROM AUTHOR]

Stabilizing active PtNi alloy catalyst toward oxygen reduction reaction is essential for fuel cell. Doping of specific metals is an empirical strategy, however, the atomistic insight into how dopant boosts the stability of PtNi catalyst still remains elusive. Here, with typical examples of Mo and Au dopants, we identify the distinct roles of Mo and Au in stabilizing PtNi nanowires catalysts. Specifically, due to the stronger interaction between atomic orbital for Ni-Mo and Pt-Au, the Mo dopant mainly suppresses the outward diffusion of Ni atoms while the Au dopant contributes to the stabilization of surface Pt overlayer. Inspired by this atomistic understanding, we rationally construct the PtNiMoAu nanowires by integrating the different functions of Mo and Au into one entity. Such catalyst assembled in fuel cell cathode thus presents both remarkable activity and durability, even surpassing the United States Department of Energy technical targets for 2025. Doping strategies have been shown to stabilize the active platinum-nickel (PtNi) catalyst in fuel cells, however, the atomistic mechanism is less known. Here, the authors identify the roles of Mo and Au dopants in improving the durability of a PtNi nanowire catalyst for fuel cells. [ABSTRACT FROM AUTHOR]

With the advancement of society, ensuring the safety of personnel involved in municipal construction projects, particularly in the context of pandemic control measures, has become a matter of utmost importance. This paper introduces a security measure for municipal engineering, combining deep learning with object detection technology. It proposes a lightweight artificial intelligence (AI) detection method capable of simultaneously identifying individuals wearing masks and safety helmets. The method primarily incorporates the ShuffleNetv2 feature extraction mechanism within the framework of the YOLOv5s network to reduce computational overhead. Additionally, it employs the ECA attention mechanism and optimized loss functions to generate feature maps with more comprehensive information, thereby enhancing the precision of target detection. Experimental results indicate that this algorithm improves the mean average precision (mAP) value by 4.3%. Furthermore, it reduces parameter and computational loads by 54.8% and 53.8%, respectively, effectively striking a balance between lightweight operation and precision. This study serves as a valuable reference for research pertaining to lightweight target detection in the realm of municipal construction safety measures. [ABSTRACT FROM AUTHOR]

Objectives: To research the relationship between quantitative electroencephalogram (qEEG) and impaired cognitive function patients who have obstructive sleep apnea (OSA) but no dementia. Methods: Subjects who complained of snoring between March 2020 and April 2021 in the Sleep Medicine Center of Weihai Municipal Hospital were included. All subjects underwent overnight in-laboratory polysomnography (PSG) and were assessed using a neuropsychological scale. Standard fast fourier transform (FFT) was used to obtain the electroencephalogram (EEG) power spectral density curve, and to calculate the delta, theta, alpha, and beta relative power and the ratio between slow and fast frequencies. Binary logistic regression was used to assess the risk factors for cognitive impairment in patients who had OSA but no dementia. Correlation analysis was performed to determine the relationship between qEEG and cognitive impairment. Results: A total of 175 participants without dementia who met the inclusion criteria were included in this study. There were 137 patients with OSA, including 76 with mild cognitive impairment (OSA + MCI), 61 without mild cognitive impairment (OSA-MCI), and 38 participants without OSA (non-OSA). The relative theta power in the frontal lobe in stage 2 of non-rapid eye movement sleep (NREM 2) in OSA + MCI was higher than that in OSA-MCI (P = 0.038) and non-OSA (P = 0.018). Pearson correlation analysis showed that the relative theta power in the frontal lobe in NREM 2 was negatively correlated with Mini-Mental State Examination (MMSE) scores, Montreal Cognitive Assessment (MoCA) Beijing version scores, and MoCA subdomains scores (visual executive function, naming, attention, language, abstraction, delayed recall and orientation) outside language. Conclusions: In patients who had OSA but no dementia, the EEG slower frequency power increased. The relative theta power in the frontal lobe in NREM 2 was associated with MCI of patients with OSA. These results suggest that the slowing of theta activity may be one of the neurophysiological changes in the early stage of cognitive impairment in patients with OSA. [ABSTRACT FROM AUTHOR]

Improving information transfer rate is a key to prompt the speed of outputting instructions of the event-related potential-based brain–computer interface. Our previous study designed a dual-stimuli interface that simultaneously presents two types of different stimuli to improve the speed. While, adding more stimuli into this interface makes subject easily affected by "flanker effect" that decreases the accuracy of recognizing intention. To achieve high recognition accuracy with many stimuli, this study proposes a dual stimuli interface based on whole flash and local move (DS-WL) and two rules of stimulus arrangement to induce the brain signals. Twenty subjects participated in the experiment, and their signals are recognized by a back propagation neural network classifier. The local move induces larger and later signals of targets to help discriminate the two kinds of stimuli; the rules reduce the N200 and P300 amplitudes of non-target, which improves accuracy. This study demonstrates that the DS-WL is a useful way to shorten the instruction output cycle and speed up the instructions outputting by local move and rules. [ABSTRACT FROM AUTHOR]

Despite Siamese-based trackers have achieved great success in recent years, researchers have focused more on the accuracy of trackers than their complexity, which leads to their inapplicability in some scenarios, and the real-time speed can be greatly limited. In this work, we propose a lightweight network method called SiamLight for object tracking. MobileNet-V3 is selected as the backbone network. The PG-corr module is added as the feature fusion module, a strategy that decomposes the template feature into spatial and channel kernels, reducing the matching regions and suppressing the effect of similar interference. In addition, we also add the CSM module, which carries out attention to the channel and spatial simultaneously. CSM module not only reduces the number of parameters but also ensures that it can be integrated into existing network architectures as a plug-and-play module. Finally, multiple separable convolution blocks are added to the classification and regression branches to meet our lightweight parameters and Flops requirements. The experiments on LaSOT, VOT2018, VOT2019, OTB100, and UAV123 benchmarks show that the method has fewer Flops and parameters than state-of-the-art trackers. [ABSTRACT FROM AUTHOR]

Coexistence of heavy metals in aquatic environments exert complex effects on amphibians. Here, the adverse effects of Pb (0.14 μM) combined with Cu at concentrations of 0, 0.25, and 1.0 μM were investigated in Bufo gargarizans tadpoles. Tadpoles were chronically exposed from Gosner stage (Gs) 26 to Gs 38, and morphology of tadpoles as well as intestinal histology and bacterial community were assessed. Our results indicated that Pb+Cu1.0 exposure induced significant retardation of somatic mass, total length, intestine mass, and intestine length as well as intestinal histological alterations. Pb+Cu0.25 and Pb+Cu1.0 exposure were associated with the loss of gut bacterial diversity. Proteobacteria and Bacteroidetes were two dominant phyla in tadpoles independently of heavy metal exposure, but the abundance of Proteobacteria increased significantly in Pb+Cu1.0 group and Bacteroidetes decreased significantly in all treatment groups. Furthermore, functional prediction indicated that metabolic disorders were associated with Pb+Cu0.25 and Pb+Cu1.0 exposure. Overall, relative limited shifts in intestinal bacterial diversity, composition, and functionality caused by Pb+Cu0 exposure, while coexistence of Pb and Cu induced gut dysbiosis and might further cause disturbance of metabolic homeostasis. The findings of this study provide insights into the effects of Pb and Cu coexistence on the health of amphibians. [ABSTRACT FROM AUTHOR]

The enantioselective pyridinylation is important for providing chiral compounds bearing heterocycles of pharmaceutical interests. 4-CN-pyrinde is extensively applied in the radical pyridinylation reaction, however, its' enantioselective application is highly challenging. To achieve this goal, we propose an electrochemical catalytic activation of 4-CN-pyridine with a chiral transition metal complex instead of direct cathodic reduction. The chiral catalyst acts as the electron mediator and the transition metal catalysis in turn. The radical species from 4-CN-pyridine is captured via radical rebound by chiral catalyst, and undergoes enantioselective pyridinylation reaction. Here, we show the first method for catalytic asymmetric allylic 4-pyridinylation reactions using 4-CN-pyridine under electrochemical conditions. Controlling the enantioselectivity of radical reactions is a persistent challenge in organic synthesis. Here, the authors report a method to form asymmetric pyridine derivatives via the combination of chiral palladium catalysis and electrochemistry. [ABSTRACT FROM AUTHOR]

Eukaryotic genomes are densely packaged into hierarchical three-dimensional (3D) structures that contain information about gene regulation and many other biological processes. With the development of imaging and sequencing-based technologies, 3D genome studies have revealed that the high-order chromatin structure is composed of hierarchical levels, including chromosome territories, A/B compartments, topologically associated domains, and chromatin loops. However, how this chromatin architecture is formed and maintained is not completely clear. In this review, we introduce experimental methods to investigate the 3D genome, review major architectural proteins that regulate 3D chromatin organization in mammalian cells, such as CTCF (CCCTC-binding factor), cohesin, lamins, and transcription factors, and discuss relevant mechanisms such as phase separation. [ABSTRACT FROM AUTHOR]

This study aims to identify lower risk exterior shelter areas and building air intake locations by modeling particulate matter (PM) dispersion in a three-dimensional street canyon from a point source and comprises a wind tunnel experiment and a computational fluid dynamics (CFD) simulation. The model street canyon was a two-by-two building array of constant height with aspect ratios of 0.35, 0.70, and 1.05. A PM emission source external to the street canyon was simulated with water droplets from an ultrasonic humidifier in the wind tunnel. The CFD simulation, using the standard turbulence k-ϵ turbulence model and the Discrete Phase Model, showed that, inside the street canyon, the sidewalks had the lowest PM concentration at the breathing level of 1.5 m above the ground. Regardless of wind direction and PM source location, the roofs had the lowest PM concentration among all building surfaces with access to the internal intersection. Therefore, if an accidental point release of PM outside the street canyons occurs, pedestrians that could not enter buildings should stay on the sidewalks. For building construction and renovation, new ventilation air intakes should be installed on the roofs, and the intakes that are already installed on other building façade surfaces should be advised to be closed during such episode. [ABSTRACT FROM AUTHOR]