Hollow silica is a special type of novel inorganic material with one or more cavities inside. In addition to the excellent properties as with its solid counterparts, hollow silica exhibits unique ...characteristics, such as low density, high specific surface and good adsorption performance. Researchers have developed many routes to prepare mono-dispersed hollow silica with regular morphology. However, most studies focused on hollow silica spheres, ignoring the structural superiority of other hollow structures. Template synthesis is highly prominent due to its flexibility and versatility. What's more, it is suitable for the preparation of hollow silica with various morphologies. In this article, the research progress of template synthesis was firstly provided. Then different morphologies of hollow silica were introduced, including hollow spheres, hollow tubes, hollow cubes, etc. To better demonstrate the advantages and potential value of hollow silica materials, their performance in diverse applications were discussed. Finally, some perspectives on the future research and development of hollow silica were put forward.
Display omitted
•We provided general principles and research progress of template synthesis.•Spherical and non-spherical hollow silica structures were introduced.•The performance of hollow silica in diverse applications were discussed.
Three-dimensional topological insulators are a phase of matter that hosts unique spin-polarized gapless surface states that are protected by time-reversal symmetry. They exhibit unconventional charge ...and spin transport properties1,2. Intense laser fields can drive ballistic charge dynamics in Dirac bands3,4 or they can coherently steer spin5 and valley pseudospin6. Similarly, high-harmonic generation (HHG) in solids provides insights into the dynamics of the electrons in topological insulators7–13. Despite several theoretical attempts to identify a topological signature in the high-harmonic spectrum14–16, a unique fingerprint has yet to be found experimentally. Here, we observe HHG that arises from topological surface states in the intrinsic topological insulator BiSbTeSe2. The components of the even-order harmonics that are polarized along the pump polarization stem from the spin current in helical surface states, whereas the perpendicular components originate from the out-of-plane spin polarization related to the hexagonal wrapping effect17. The dependence of HHG on surface doping in ambient air also suggests the presence of a Rashba-split two-dimensional electron gas, whose strength can be enhanced by an increase in the intensity of the mid-infrared pump.High-harmonic generation up to the seventh harmonic is observed from the intrinsic three-dimensional topological insulator BiSbTeSe2. The parallel components of the even-order harmonics arise directly from the topological surface states.
Facing fast-increasing electronic documents in the Digital Media Age, the need to extract textual features of online texts for better communication is growing. Sentiment classification might be the ...key method to catch emotions of online communication, and developing corpora with annotation of emotions is the first step to achieving sentiment classification. However, the labour-intensive and costly manual annotation has resulted in the lack of corpora for emotional words. Furthermore, single-label semantic corpora could hardly meet the requirement of modern analysis of complicated user’s emotions, but tagging emotional words with multiple labels is even more difficult than usual. Improvement of the methods of automatic emotion tagging with multiple emotion labels to construct new semantic corpora is urgently needed. Taking Twitter short texts as the case, this study proposes a new semi-automatic method to annotate Internet short texts with multiple labels and form a multi-labelled corpus for further algorithm training. Each sentence is tagged with both the emotional tendency and polarity, and each tweet, which generally contains several sentences, is tagged with the first two major emotional tendencies. The semi-automatic multi-labelled annotation is achieved through the process of selecting the base corpus and emotional tags, data preprocessing, automatic annotation through word matching and weight calculation, and manual correction in case of multiple emotional tendencies are found. The experiments on the Sentiment140 published Twitter corpus demonstrate the effectiveness of the proposed approach and show consistency between the results of semi-automatic annotation and manual annotation. By applying this method, this study summarises the annotation specification and constructs a multi-labelled emotion corpus with 6500 tweets for further algorithm training.
Abstract
The current understanding of lactate extends from its origins as a byproduct of glycolysis to its role in tumor metabolism, as identified by studies on the Warburg effect. The lactate ...shuttle hypothesis suggests that lactate plays an important role as a bridging signaling molecule that coordinates signaling among different cells, organs and tissues. Lactylation is a posttranslational modification initially reported by Professor Yingming Zhao’s research group in 2019. Subsequent studies confirmed that lactylation is a vital component of lactate function and is involved in tumor proliferation, neural excitation, inflammation and other biological processes. An indispensable substance for various physiological cellular functions, lactate plays a regulatory role in different aspects of energy metabolism and signal transduction. Therefore, a comprehensive review and summary of lactate is presented to clarify the role of lactate in disease and to provide a reference and direction for future research. This review offers a systematic overview of lactate homeostasis and its roles in physiological and pathological processes, as well as a comprehensive overview of the effects of lactylation in various diseases, particularly inflammation and cancer.
Detecting changes in land cover is a critical task in remote sensing image interpretation, with particular significance placed on accurately determining the boundaries of lakes. Lake boundaries are ...closely tied to land resources, and any alterations can have substantial implications for the surrounding environment and ecosystem. This paper introduces an innovative end-to-end model that combines U-Net and spatial transformation network (STN) to predict changes in lake boundaries and investigate the evolution of the Lake Urmia boundary. The proposed approach involves pre-processing annual panoramic remote sensing images of Lake Urmia, obtained from 1996 to 2014 through Google Earth Pro Version 7.3 software, using image segmentation and grayscale filling techniques. The results of the experiments demonstrate the model’s ability to accurately forecast the evolution of lake boundaries in remote sensing images. Additionally, the model exhibits a high degree of adaptability, effectively learning and adjusting to changing patterns over time. The study also evaluates the influence of varying time series lengths on prediction accuracy and reveals that longer time series provide a larger number of samples, resulting in more precise predictions. The maximum achieved accuracy reaches 89.3%. The findings and methodologies presented in this study offer valuable insights into the utilization of deep learning techniques for investigating and managing lake boundary changes, thereby contributing to the effective management and conservation of this significant ecosystem.
Time of flight (TOF) based light detection and ranging (LiDAR) is a technology for calculating distance between start/stop signals of time of flight. In lab-built LiDAR, two ranging systems for ...measuring flying time between start/stop signals include time-to-digital converter (TDC) that counts time between trigger signals and analog-to-digital converter (ADC) that processes the sampled start/stop pulses waveform for time estimation. We study the influence of waveform characteristics on range accuracy and precision of two kinds of ranging system. Comparing waveform based ranging (WR) with analog discrete return system based ranging (AR), a peak detection method (WR-PK) shows the best ranging performance because of less execution time, high ranging accuracy, and stable precision. Based on a novel statistic mathematical method maximal information coefficient (MIC), WR-PK precision has a high linear relationship with the received pulse width standard deviation. Thus keeping the received pulse width of measuring a constant distance as stable as possible can improve ranging precision.
Compelling evidences demonstrated that gut microbiota dysbiosis plays a critical role in the pathogenesis of inflammatory bowel diseases (IBD). Therapies for targeting the microbiota may provide ...alternative options for the treatment of IBD, such as probiotics. Here, we aimed to investigate the protective effect of a probiotic strain, Pediococcus pentosaceus (P. pentosaceus) CECT 8330, on dextran sulfate sodium (DSS)-induced colitis in mice.
C57BL/6 mice were administered phosphate-buffered saline (PBS) or P. pentosaceus CECT 8330 (5 × 10
CFU/day) once daily by gavage for 5 days prior to or 2 days after colitis induction by DSS. Weight, fecal conditions, colon length and histopathological changes were examined. ELISA and flow cytometry were applied to determine the cytokines and regulatory T cells (Treg) ratio. Western blot was used to examine the tight junction proteins (TJP) in colonic tissues. Fecal short-chain fatty acids (SCFAs) levels and microbiota composition were analyzed by targeted metabolomics and 16S rRNA gene sequencing, respectively. The Kyoto Encyclopedia of Genes and Genomes (KEGG) and Cluster of orthologous groups of proteins (COG) pathway analysis were used to predict the microbial functional profiles.
P. pentosaceus CECT 8330 treatment protected DSS-induced colitis in mice as evidenced by reducing the weight loss, disease activity index (DAI) score, histological damage, and colon length shortening. P. pentosaceus CECT 8330 decreased the serum levels of proinflammatory cytokines (TNF-α, IL-1β, and IL-6), and increased level of IL-10 in DSS treated mice. P. pentosaceus CECT 8330 upregulated the expression of ZO-1, Occludin and the ratio of Treg cells in colon tissue. P. pentosaceus CECT 8330 increased the fecal SCFAs level and relative abundances of several protective bacteria genera, including norank_f_Muribaculaceae, Lactobacillus, Bifidobacterium, and Dubosiella. Furthermore, the increased abundances of bacteria genera were positively correlated with IL-10 and SCFAs levels, and negatively associated with IL-6, IL-1β, and TNF-α, respectively. The KEGG and COG pathway analysis revealed that P. pentosaceus CECT 8330 could partially recover the metabolic pathways altered by DSS.
P. pentosaceus CECT 8330 administration protects the DSS-induced colitis and modulates the gut microbial composition and function, immunological profiles, and the gut barrier function. Therefore, P. pentosaceus CECT 8330 may serve as a promising probiotic to ameliorate intestinal inflammation.
Text classification has been highlighted as the key process to organize online texts for better communication in the Digital Media Age. Text classification establishes classification rules based on ...text features, so the accuracy of feature selection is the basis of text classification. Facing fast-increasing Chinese electronic documents in the digital environment, scholars have accumulated quite a few algorithms for the feature selection for the automatic classification of Chinese texts in recent years. However, discussion about how to adapt existing feature selection algorithms for various types of Chinese texts is still inadequate. To address this, this study proposes three improved feature selection algorithms and tests their performance on different types of Chinese texts. These include an enhanced CHI square with mutual information (MI) algorithm, which simultaneously introduces word frequency and term adjustment (CHMI); a term frequency–CHI square (TF–CHI) algorithm, which enhances weight calculation; and a term frequency–inverse document frequency (TF–IDF) algorithm enhanced with the extreme gradient boosting (XGBoost) algorithm, which improves the algorithm’s ability of word filtering (TF–XGBoost). This study randomly chooses 3000 texts from six different categories of the Sogou news corpus to obtain the confusion matrix and evaluate the performance of the new algorithms with precision and the F1-score. Experimental comparisons are conducted on support vector machine (SVM) and naive Bayes (NB) classifiers. The experimental results demonstrate that the feature selection algorithms proposed in this paper improve performance across various news corpora, although the best feature selection schemes for each type of corpus are different. Further studies of the application of the improved feature selection methods in other languages and the improvement in classifiers are suggested.
Change detection of natural lake boundaries is one of the important tasks in remote sensing image interpretation. In an ordinary fully connected network, or CNN, the signal of neurons in each layer ...can only be propagated to the upper layer, and the processing of samples is independent at each moment. However, for time-series data with transferability, the learned change information needs to be recorded and utilized. To solve the above problems, we propose a lake boundary change prediction model combining U-Net and LSTM. The ensemble of LSTMs helps to improve the overall accuracy and robustness of the model by capturing the spatial and temporal nuances in the data, resulting in more precise predictions. This study selected Lake Urmia as the research area and used the annual panoramic remote sensing images from 1996 to 2014 (Lat: 37°00′ N to 38°15′ N, Lon: 46°10′ E to 44°50′ E) obtained by Google Earth Professional Edition 7.3 software as the research data set. This model uses the U-Net network to extract multi-level change features and analyze the change trend of lake boundaries. The LSTM module is introduced after U-Net to optimize the predictive model using historical data storage and forgetting as well as current input data. This method enables the model to automatically fit the trend of time series data and mine the deep information of lake boundary changes. Through experimental verification, the model’s prediction accuracy for lake boundary changes after training can reach 89.43%. Comparative experiments with the existing U-Net-STN model show that the U-Net-LSTM model used in this study has higher prediction accuracy and lower mean square error.
In this work, the distribution of organic phosphorus (Po) species in sediment profiles of five shallow lakes was analyzed and its effect on the photo-release of dissolved phosphate (Pi) was ...investigated during sediment resuspension under simulated sunlight irradiation. The results show that Po was highly enriched in the surface sediment and gradually decreased as sediment depths increased: 33.10 ± 2.55–96.71 ± 7.60 mg/kg, 33.55 ± 2.34–142.86 ± 5.73 mg/kg, 57.50 ± 3.46–149.68 ± 7.67 mg/kg, 55.18 ± 4.67–168.73 ± 8.31 mg/kg, 98.75 ± 7.56–275.74 ± 10.70 mg/kg for Lake Hou, Lake Tuan, Lake Tangling, Lake Guozheng and Lake Miao, respectively. The photo-release amount of dissolved Pi in the resuspension composed of surface sediments was also higher than that of deep sediment during sediment resuspension under the simulated sunlight irradiation for 9 h. The potential reasons for these results are: (1) difference in morphology and composition of sediments at different depths: the mean particle size of sediment decreased first and then increased as sediment depths increased; (2) difference in composition of Po species with depths in the sediment profiles: more photolytic Po species existed in surface sediments confirmed by sequential extraction and 31P NMR analysis; and (3) more OH production in the resuspension composed of surface sediment under simulated sunlight irradiation, which directly influence the photo-release of dissolved Pi from photodegradation of organic phosphorus. All of these results indicate that the distribution of organic phosphorus species in the sediment profiles plays an important role in P cycle and its photodegradation during sediment resuspension may be one of the potential pathways for phosphate supplement in shallow lakes.
Display omitted
•Organic phosphorus enriched in the surface sediment and gradually decreased as sediment depths increased.•Dissolved phosphate was photo-released when resuspended sediments were exposed to simulated sunlight irradiation.•Sediment containing more organic phosphorus promoted the photo-release of dissolved phosphate.•Sediment particle size, Po species and OH production were important factors for the photo-release of phosphate.
PDF
