Protein solubility plays a crucial role in various biotechnological, industrial, and biomedical applications. With the reduction in sequencing and gene synthesis costs, the adoption of high-throughput experimental screening coupled with tailored bioinformatic prediction has witnessed a rapidly growing trend for the development of novel functional enzymes of interest (EOI). High protein solubility rates are essential in this process and accurate prediction of solubility is a challenging task. As deep learning technology continues to evolve, attention-based protein language models (PLMs) can extract intrinsic information from protein sequences to a greater extent. Leveraging these models along with the increasing availability of protein solubility data inferred from structural database like the Protein Data Bank holds great potential to enhance the prediction of protein solubility. In this study, we curated an Updated Escherichia coli protein Solubility DataSet (UESolDS) and employed a combination of multiple PLMs and classification layers to predict protein solubility. The resulting best-performing model, named Protein Language Model-based protein Solubility prediction model (PLM_Sol), demonstrated significant improvements over previous reported models, achieving a notable 6.4% increase in accuracy, 9.0% increase in F1_score, and 11.1% increase in Matthews correlation coefficient score on the independent test set. Moreover, additional evaluation utilizing our in-house synthesized protein resource as test data, encompassing diverse types of enzymes, also showcased the good performance of PLM_Sol. Overall, PLM_Sol exhibited consistent and promising performance across both independent test set and experimental set, thereby making it well suited for facilitating large-scale EOI studies. PLM_Sol is available as a standalone program and as an easy-to-use model at https://zenodo.org/doi/10.5281/zenodo.10675340.

Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) photon counting data are widely used in nearshore bathymetry. However, a large number of noisy photons unavoidably exist in the acquired photon data, and photon denoising is necessary to accurately extract the underwater signal photons. Based on the phenomenon that the number of photons increases at the target object, a methodology for detecting ICESat-2 bathymetry photons based on quasi full waveform is proposed. First, the ICESat-2 photon data are split at certain intervals to extract the sea surface and seafloor height photons within each interval. Second, a double-peaked Gaussian function is utilized to fit the photon heights in each interval to determine the sea surface and seafloor heights. Third, sea surface and seafloor height photons for each interval are connected along the track direction to generate sea surface and seafloor datums. Finally, the optimal buffer distance is calculated and the buffer is created to filter the photons, and then, the refraction correction is applied to the seafloor photons. ICESat-2 data from Vieques Island and Saint Croix Island are selected for the experiment and the results are compared with in situ bathymetry data. The results show that the mean absolute error (MAE) of the bathymetry results extracted by the proposed method ranged from 0.16 to 0.30 m and the RMSE ranged from 0.24 to 0.32 m in different areas. Compared with density-based spatial clustering of applications with noise (DBSCAN), ordering points to identify the clustering structure (OPTICS), and adaptive elevation difference thresholding algorithm (AEDTA), the proposed method accurately recognizes the signal photons for different densities of photon data and different complexities of seafloor topography, and the extracted signal photons are complete and continuous, showing excellent robustness. By selecting suitable split spacing along the track direction and histogram separation spacing, the proposed method in this study achieves excellent performance in bathymetry extraction.

Passive optical shallow water bathymetry algorithms often need some actual measured water depths to participate in model calculation. In recent years, some new bathymetry methods without using the actual measured depth have been developed, such as the dual band log linear model. However, many theoretical assumptions are used in the calculation of the diffuse attenuation coefficient of green band in the model, and the result may be inaccurate. In order to obtain more accurate diffuse attenuation coefficient, Hydrolight is used for water optics simulation, and 30 groups of simulation values of different water components and water depths are obtained. The experimental results of bathymetry model with simulated parameters show that the inversion accuracy of Dongdao Island and Oahu Island is satisfactory, the mean absolute error is less than 2 m, and the mean relative error is 20.9% and 15.9% respectively

Aiming at the deficiencies of traditional blog hotness evaluation methods, the paper presents a blog hotness evaluation model based on text opinion analysis (named BHEM-TOA). The model not only considers the number of reviews, comments and publication time of the blog topic, but also focuses on the comment opinion. BHEM-TOA emphasizes subjective opinions of reviewers about the blog topic. It utilizes the text opinion analysis method based on Chinese characters to extract opinioned comments, gets supportive and oppositive circumstances about the blog topic, then combines with the number of reviews, comments and publication time to realize blog hotness evaluation. To validate the performance of BHEM-TOA, the experiment constructs two data corpuses called TOAC and BHEC, and the experimental results demonstrate that BHEM-TOA could more precisely and comprehensively evaluate the hotness of the blog than traditional methods.