Search

Short chain fatty acids (SCFAs) are produced by gut microbiota as fermentation products of digestion-resistant oligosaccharides and fibers. Their primary roles are functioning as major energy sources for colon cells and assisting in gut homeostasis by immunomodulation. Recent evidence suggests that they affect various organs both at cellular and molecular levels, and regulate functions in distance sites including gene expression, cell proliferation, cell differentiation, apoptosis and inflammation. In this study, we examined whether SCFAs are present in the mouse eye and whether SCFAs affect inflammatory responses of the eye and retinal astrocytes (RACs). We observed that intra-peritoneal injected SCFAs were detected in the eye and reduced intraocular inflammation induced by lipopolysaccharide (LPS). Moreover, SCFAs displayed two disparate effects on LPS-stimulated RACs - namely, cytokine and chemokine production was reduced, but the ability to activate T cells was enhanced. Our results support the existence of gut-eye cross talk and suggest that SCFAs can cross the blood-eye-barrier via the systemic circulation. If applied at high concentrations, SCFAs may reduce inflammation and impact cellular functions in the intraocular milieu.

In order to study the damage evolution law of coal and the influence of low temperature treatment on the mechanical and seepage properties of coal, the acoustic emission characteristics of coal samples during low temperature treatment and the mechanical and seepage properties of coal samples after low temperature treatment were analyzed through experimental tests. The results show that: when the normal temperature coal sample suddenly enters the low temperature environment, the AE signals are most dense and strong within 0-1 000 s, and gradually decrease and weaken in the middle and late period. Therefore, the damage mainly occurs in the first 1 000 s. With the decrease of the processing temperature, the number and intensity of AE signals of the coal sample increase significantly, and the distribution ratio of signal amplitude is roughly the same. It shows that the larger the temperature difference between coal and external environment, the higher the damage degree. The compressive strength, elastic modulus and Poisson’s ratio of coal samples treated at low temperature in the triaxial loading process are all reduced compared with those of normal temperature coal samples, and the decreasing amplitude is proportional to the low temperature treatment. The initial permeability of coal samples treated at 0 ℃, -20 ℃ and -40 ℃ increased by 8.15%, 23.46% and 74.87%, respectively, compared with normal temperature coal samples.

This study examines the interrelationships of Chinese university teachers' online teaching experiences, online teaching engagement, and online teaching outcomes in the context of the COVID‐19 pandemic. We conducted a survey of 7978 teachers from 235 universities in China from April 2020 to 2021 and explored the relationships among online teaching experiences, teaching engagement, and teaching outcomes in the context of college teachers. The key findings include: (1) Chinese college teachers' online teaching experiences positively predict online teaching engagement, (2) Chinese college teachers' online teaching engagement positively predicts online teaching outcomes, (3) Chinese college teachers' online teaching experiences positively predict online teaching outcomes, and (4) teachers' online teaching engagement partially mediates the relationship between online teaching experiences and online teaching outcomes. Practitioner Points: When implementing online teaching, it is essential not to overlook the online teaching experience and engagement of teachers.Teacher engagement in online teaching partially mediates the relationship between online teaching experience and outcomes.Online teaching still adheres to the general principles of effective pedagogy practiced by teachers. [ABSTRACT FROM AUTHOR]

A novel axially chiral all-hydrocarbon cyclo[7] (1,3-(4,6-dimethyl)benzene (CDMB-7) was designed and synthesized using atroposelective[2 + 5] cyclization through Suzuki–Miyaura coupling. CDMB-7 adopts an irregular bowl-like shape with C2 symmetry and exhibits two diastereoisomers in its crystallographic structure. The conformational isomers of CDMB-7 racemates remain stable at high temperatures (393 K). High-performance liquid chromatography (HPLC) confirmed that a single chiral isomer will spontaneously undergo racemization within 30 min at room temperature. This finding opens up possibilities for achieving adaptive chirality in all-hydrocarbon cyclo[7] m-benzene macrocycles. [ABSTRACT FROM AUTHOR]

Given their intricate nature and inherent ambiguity, sarcastic texts often mask deeper emotions, making it challenging to discern the genuine feelings behind the words. The proposal of the sarcasm detection task is to assist us with more accurately understanding the true intention of the speaker. Advanced methods, such as deep learning and neural networks, are widely used in the field of sarcasm detection. However, most research mainly focuses on sarcastic texts in English, as other languages lack corpora and annotated datasets. To address the challenge of low-resource languages in sarcasm detection tasks, a zero-shot cross-lingual transfer learning method is proposed in this paper. The proposed approach is based on prompt learning and aims to assist the model with understanding downstream tasks through prompts. Specifically, the model uses prompt templates to construct training data into cloze-style questions and then trains them using a pre-trained cross-lingual language model. Combining data augmentation and contrastive learning can further improve the capacity of the model for cross-lingual transfer learning. To evaluate the performance of the proposed model, we utilize a publicly accessible sarcasm dataset in English as training data in a zero-shot cross-lingual setting. When tested with Chinese as the target language for transfer, our model achieves F1-scores of 72.14% and 76.7% on two test datasets, outperforming the strong baselines by significant margins. [ABSTRACT FROM AUTHOR]

Industrial-scale geological storage of CO2 in saline aquifers may cause CO2 and brine leakage from abandoned wells into shallow fresh aquifers. This leakage problem involves the flow dynamics in both the wellbore and the storage reservoir. T2Well/ECO2N, a coupled wellbore-reservoir flow simulator, was used to analyze CO2 and brine leakage under different conditions with a hypothetical simulation model in water-CO2-brine systems. Parametric studies on CO2 and brine leakage, including the salinity, excess pore pressure (EPP) and initially dissolved CO2 mass fraction, are conducted to understand the mechanism of CO2 migration. The results show that brine leakage rates increase proportionally with EPP and inversely with the salinity when EPP varies from 0.5 to 1.5 MPa; however, there is no CO2 leakage into the shallow freshwater aquifer if EPP is less than 0.5 MPa. The dissolved CO2 mass fraction shows an important influence on the CO2 plume, as part of the dissolved CO2 becomes a free phase. Scenario simulation shows that the gas lifting effect will significantly increase the brine leakage rate into the shallow freshwater aquifer under the scenario of 3.89% dissolved CO2 mass fraction. The equivalent porous media (EPM) approach used to model the wellbore flow has been evaluated and results show that the EPM approach could either under- or over-estimate brine leakage rates under most scenarios. The discrepancies become more significant if a free CO2 phase evolves. Therefore, a model that can correctly describe the complex flow dynamics in the wellbore is necessary for investigating the leakage problems.

Leaf area index (LAI) is a crucial parameter for characterizing vegetation canopy structure and energy absorption capacity. The Moderate Resolution Imaging Spectroradiometer (MODIS) LAI has played a significant role in landmark studies due to its clear theoretical basis, extensive historical time series, extensive validation results, and open accessibility. However, MODIS LAI retrievals are calculated independently for each pixel and a specific day, resulting in high noise levels in the time series and limiting its applications in the regions of optical remote sensing. Reprocessing MODIS LAI predominantly relies on temporal information to achieve smoother LAI profiles with little use of spatial information and may easily ignore genuine LAI anomalies. To address these problems, we designed the spatiotemporal information compositing algorithm (STICA) for the reprocessing of MODIS LAI products. This method integrates information from multiple dimensions, including pixel quality information, spatiotemporal correlation, and the original retrieval, thereby enabling both "reprocessing" and "value-added data" with respect to the existing MODIS LAI products, leading to the development of the high-quality LAI (HiQ-LAI) dataset. Compared with ground measurements, HiQ-LAI shows better performance than the original MODIS product with a root-mean-square error (RMSE) or bias decrease from 0.87 or -0.17 to 0.78 or -0.06 , respectively. This is due to the improvement of HiQ-LAI with respect to capturing the seasonality in vegetation phenology and reducing abnormal time-series fluctuations. The time-series stability (TSS) index, which represents temporal stability, indicated that the area with smooth LAI time series expanded from 31.8 % (MODIS) to 78.8 % (HiQ) globally, and this improvement is more obvious in equatorial regions where optical remote sensing cannot usually achieve good performance. We found that HiQ-LAI demonstrates superior continuity and consistency compared with raw MODIS LAI from both spatial and temporal perspectives. We anticipate that the global HiQ-LAI time series, generated using the STICA procedure on the Google Earth Engine (GEE) platform, will substantially enhance support for diverse global LAI time-series applications. The 5 km 8 d HiQ-LAI datasets from 2000 to 2022 are available at 10.5281/zenodo.8296768 (Yan et al., 2023). [ABSTRACT FROM AUTHOR]

Vision sensors are becoming increasingly ubiquitous, and they continuously collect, store, communicate, and process vast amount of sensitive data that are vulnerable to being stolen and misused. Existing cryptosystems based on complex cipher algorithms generally require extensive computational resources, making them difficult to use in vision sensors that have limited processing capabilities. Here, we propose and experimentally demonstrate a novel in situ image cryptography scheme based on a neuromorphic vision sensor comprising all-optically controlled (AOC) memristors. Due to the unique light wavelength and irradiation history-dependent bidirectional persistent photoconductivity of AOC memristors, a visual image can be stored, encrypted, decrypted, denoised, and destroyed within a vision sensor. A decrypted image can be encoded in situ and then accurately recognized through a memristive neural network. Encrypted and destroyed images are capable of withstanding hacking attacks even with trained neural networks. Our cryptography scheme enables complete cryptographic operations entirely on a sensor and, therefore, effectively safeguards visual information. This work provides a simple yet efficient solution to the security challenges faced by vision sensors. [ABSTRACT FROM AUTHOR]