Search Results (23)

Limited research has been conducted on the potential and mechanisms of irrigating Suaeda salsa with wastewater and microalgae to improve saline–alkali land. This study used three irrigation treatments (freshwater, saline wastewater, and saline wastewater with microalgae) to irrigate S. salsa, and microalgae promoted the growth of S. salsa and increased soil nutrient content, increasing available nitrogen (4.85%), available phosphorus (44.51%), and organic carbon (24.05%) while alleviating salt stress through reduced soil salinity (13.52%) and electrical conductivity (21.62%). These changes promoted eutrophic bacteria while inhibiting oligotrophic bacteria. Bacterial community composition exhibited significant variations, primarily driven by soil pH, total nitrogen, and organic carbon content. Notably, rhizosphere bacteria showed enhanced functional capabilities, with increased abundance of salt stress resistance and nitrogen metabolism-related genes compared to original soil, particularly under saline irrigation conditions. Furthermore, microalgae addition enriched nitrogen metabolism-related gene abundance. These findings revealed the potential role of key bacteria in enhancing plant growth and the soil environment and highlighted the potential of applying S. salsa, wastewater, and microalgae for the synergistic improvement of saline–alkali land. Full article

Strong-flavor Baijiu, a widely popular distilled spirit in China, derives its characteristic aroma and quality largely from ethyl hexanoate, a key flavor compound. The concentration of ethyl hexanoate, influenced by its precursor hexanoic acid, is critical in defining the style and quality of this Baijiu variety. In this study, atmospheric and room temperature plasma (ARTP) mutagenesis technology was applied to strains isolated from Strong-flavor Daqu to enhance their acid and ester production capabilities. A hexanoic acid-producing strain, identified as Bacillus velezensis WY4 through morphological, physiological, biochemical, and molecular analyses, was used as the starting strain. Following 90 s of ARTP exposure, a mutant strain, WY4-3, was successfully developed, achieving a balance between high mutation diversity and moderate lethality. WY4-3 exhibited robust growth across a pH range of 4.2 to 5.0 and demonstrated high ethanol tolerance. After five days of fermentation, WY4-3 produced 0.36 g/L of total acid and 0.528 g/L of total ester, surpassing the wild-type strain. Enzymatic activity assays revealed significant enhancements in amylase (9.13%), saccharifying enzyme (101.72%), and esterification (573.71%) activities in WY4-3. Validation in multiple artificial esterification systems further confirmed the superior ester production capacity of this mutant strain. These findings enrich the microbial germplasm resources for Baijiu brewing and provide a solid foundation for strain selection and genetic improvement in Baijiu production processes. This study highlights the potential of ARTP mutagenesis in optimizing brewing microorganisms and improving the quality and consistency of Strong-flavor Baijiu. Full article

Objective: RAC1 aberrations in head and neck squamous cell carcinoma (HNSCC) remain clinically inactionable today. Methods: Here, we investigated the clinical significance and potential druggability of RAC1 genomic aberrations in HNSCC. Results: Notably, HPV(−)HNSCC patients bearing the unique HNSCC-prevalent RAC1-A159V hotspot mutation, P29S hotspot and G-box domain mutations, and RAC1 copy number increases all displayed dismal overall survival (TCGA-HNSCC). Here, we demonstrated that all five HNSCC patient-relevant RAC1 aberrations tested (A159V and P29S hotspot mutations, K116N, G15S, and N39S) could significantly drive HNSCC tumoroid growth and/invasion, with A159V, P29S, and K116N mutants being the most potent drivers. Interestingly, transcriptomics analyses revealed that RAC1 mutations and copy increase could both drive PI3K pathway activation, with the A159V mutant associated with the prominent intra-tumoral upregulation of phospho-RPS6(Ser235/236) in patient tumors. Importantly, proof-of-principle Rac targeting with EHop-016 resulted in remarkable antitumor activity in vivo against RAC1-A159V-mutated and RAC1-amplified HNSCC patient-derived xenografts (PDXs) and/engineered models. Lastly, melanoma and endometrial xenograft models bearing endogenous RAC1-amplification and RAC1-A159V mutation were also sensitive to EHop-016 targeting. Conclusions: In principle, RAC1 genomic aberrations in HNSCC can be potentially harnessed for precision drugging. Full article

Hyperspectral images are high-dimensional data containing rich spatial, spectral, and radiometric information, widely used in geological mapping, urban remote sensing, and other fields. However, due to the characteristics of hyperspectral remote sensing images—such as high redundancy, strong correlation, and large data volumes—the classification and recognition of these images present significant challenges. In this paper, we propose a band selection method (GE-AP) based on multi-feature extraction and the Affine Propagation Clustering (AP) algorithm for dimensionality reduction of hyperspectral images, aiming to improve classification accuracy and processing efficiency. In this method, texture features of the band images are extracted using the Gray-Level Co-occurrence Matrix (GLCM), and the Euclidean distance between bands is calculated. A similarity matrix is then constructed by integrating multi-feature information. The AP algorithm clusters the bands of the hyperspectral images to achieve effective band dimensionality reduction. Through simulation and comparison experiments evaluating the overall classification accuracy (OA) and Kappa coefficient, it was found that the GE-AP method achieves the highest OA and Kappa coefficient compared to three other methods, with maximum increases of 8.89% and 13.18%, respectively. This verifies that the proposed method outperforms traditional single-information methods in handling spatial and spectral redundancy between bands, demonstrating good adaptability and stability. Full article

Insider threats pose significant risks to organizational security, often going undetected due to their familiarity with the systems. Detection of insider threats faces challenges of imbalanced data distributions and difficulties in fine-grained detection. Specifically, anomalous users and anomalous behaviors take up a very small fraction of all insider behavior data, making precise detection of anomalous users challenging. Moreover, not all behaviors of anomalous users are anomalous, so it is difficult to detect their behaviors by standardizing with single rules or models. To address these challenges, this paper presents a novel approach for insider threat detection, leveraging machine learning techniques to conduct multi-granularity anomaly detection. We introduce the Multi-Granularity User Anomalous Behavior Detection (MG-UABD) system, which combines coarse-grained and fine-grained anomaly detection to improve the accuracy and effectiveness of detecting anomalous behaviors. The coarse-grained module screens all of the user activities to identify potential anomalies, while the fine-grained module focuses on specific anomalous users to refine the detection process. Besides, MG-UABD employs a combination of oversampling and undersampling techniques to address the imbalance in the datasets, ensuring robust model performance. Through extensive experimentation on the commonly used dataset CERT R4.2, we demonstrate that the MG-UABD system achieves superior detection rate and precision. Compared to the suboptimal model, the accuracy has increased by 3.1% and the detection rate has increased by 4.1%. Our findings suggest that a multi-granularity approach for anomaly detection, combined with tailored sampling strategies, is highly effective in addressing insider threats. Full article

Atherosclerosis (AS) is an inflammatory disease characterized by arterial inflammation. One important trigger for AS development is the excessive migration of vascular smooth muscle cells (VSMCs); however, the mechanism underlying this phenomenon remains unclear. Therefore, we investigated the role of DNA polymerase β (Pol β), a crucial enzyme involved in base excision repair, VSMC migration, and subsequent AS development. In this study, we revealed a significant increase in Pol β content within AS plaques in ApoE^−/−Pol β^+/+ mice. In vitro experiments demonstrated a significant decrease in hCASMC viability and migration ability upon Pol β knockdown, whereas the subsequent recovery of Pol β expression reversed this effect. Moreover, our investigations revealed that Pol β knockdown leads to the inhibition of the POSTN gene transcription by suppressing the YY1/TGF-β1 pathway, resulting in the decreased expression of the protein periostin during VSMC migration. Collectively, our findings provide insights into the role of Pol β in AS development, offering a novel approach for the clinical treatment of cardiovascular diseases. Full article

This study proposes a novel approach for detecting weak small infrared (IR) targets, called double-image and double-local contrast measurement (DDLCM), designed to overcome challenges of low contrast and complex backgrounds in images. In this approach, the original image is decomposed into odd and even images, and the gray difference contrast is determined using a dual-neighborhood sliding window structure, enhancing target saliency and contrast by increasing the distinction between the target and the local background. A central unit is then constructed to capture relationships between neighboring and non-neighboring units, aiding in clutter suppression and eliminating bright non-target interference. Lastly, the output value is derived by extracting the lowest contrast value of the weak small targets from the saliency map in each direction. Experimental results on two datasets demonstrate that the DDLCM algorithm significantly enhances real-time IR dim target detection, achieving an average performance improvement of 32.83%. The area under the ROC curve (AUC) decline is effectively controlled, with a maximum reduction limited to 3%. Certain algorithms demonstrate a notable AUC improvement of up to 43.96%. To advance infrared dim target detection research, we introduce the IFWS dataset for benchmarking and validating algorithm performance. Full article

Proteins, as crucial macromolecules performing diverse biological roles, are central to numerous biological processes. The ability to predict changes in protein thermal stability due to mutations is vital for both biomedical research and industrial applications. However, existing experimental methods are often costly and labor-intensive, while structure-based prediction methods demand significant computational resources. In this study, we introduce PON-Tm, a novel sequence-based method for predicting mutation-induced thermal stability variations in proteins. PON-Tm not only incorporates features predicted by a protein language model from protein sequences but also considers environmental factors such as pH and the thermostability of the wild-type protein. To evaluate the effectiveness of PON-Tm, we compared its performance to four well-established methods, and PON-Tm exhibited superior predictive capabilities. Furthermore, to facilitate easy access and utilization, we have developed a web server. Full article

The current work proposes a novel super-resolution convolutional transposed network (SRCTN) deep learning architecture for downscaling daily climatic variables. The algorithm was established based on a super-resolution convolutional neural network with transposed convolutions. This study designed synthetic experiments to downscale daily reference evapotranspiration (ET₀) data, which are a key indicator for climate change, from low resolutions (2°, 1°, and 0.5°) to a fine resolution (0.25°). The entire time period was divided into two major parts, i.e., training–validation (80%) and test periods (20%), and the training–validation period was further divided into training (80%) and validation (20%) parts. In the comparison of the downscaling performance between the SRCTN and Q-M models, the root-mean-squared error (RMSE) values indicated the accuracy of the models. For the SRCTN model, the RMSE values were reported for different scaling ratios: 0.239 for a ratio of 8, 0.077 for a ratio of 4, and 0.015 for a ratio of 2. In contrast, the RMSE values for the Q-M method were 0.334, 0.208, and 0.109 for scaling ratios of 8, 4, and 2, respectively. Notably, the RMSE values in the SRCTN model were consistently lower than those in the Q-M method across all scaling ratios, suggesting that the SRCTN model exhibited better downscaling performance in this evaluation. The results exhibited that the SRCTN method could reproduce the spatiotemporal distributions and extremes for the testing period very well. The trained SRCTN model in one study area performed remarkably well in a different area via transfer learning without re-training or calibration, and it outperformed the classic downscaling approach. The good performance of the SRCTN algorithm can be primarily attributed to the incorporation of transposed convolutions, which can be partially seen as trainable upsampling operations. Therefore, the proposed SRCTN method is a promising candidate tool for downscaling daily ET₀ and can potentially be employed to conduct downscaling operations for other variables. Full article

Water quality prediction, a well-established field with broad implications across various sectors, is thoroughly examined in this comprehensive review. Through an exhaustive analysis of over 170 studies conducted in the last five years, we focus on the application of machine learning for predicting water quality. The review begins by presenting the latest methodologies for acquiring water quality data. Categorizing machine learning-based predictions for water quality into two primary segments—indicator prediction and water quality index prediction—further distinguishes between single-indicator and multi-indicator predictions. A meticulous examination of each method’s technical details follows. This article explores current cutting-edge research trends in machine learning algorithms, providing a technical perspective on their application in water quality prediction. It investigates the utilization of algorithms in predicting water quality and concludes by highlighting significant challenges and future research directions. Emphasis is placed on key areas such as hydrodynamic water quality coupling, effective data processing and acquisition, and mitigating model uncertainty. The paper provides a detailed perspective on the present state of application and the principal characteristics of emerging technologies in water quality prediction. Full article

Three-phase four-wire power cables are a primary kind of power transmission method in low-voltage distribution networks. This paper addresses the problem that calibration currents are not easily electrified during the transporting of three-phase four-wire power cable measurements, and proposes a method for obtaining the magnetic field strength distribution in the tangential direction around the cable, finally enabling online self-calibration. The simulation and experimental results show that this method can self-calibrate the sensor arrays and reconstruct the phase current waveforms in three-phase four-wire power cables without calibration currents, and this method is not affected by disturbances such as wire diameter, current amplitudes, and high-frequency harmonics. This study reduces the time and equipment costs required to calibrate the sensing module compared to related studies using calibration currents. This research offers the possibility of fusing sensing modules directly with running primary equipment, and the development of hand-held measurement devices. Full article

The emergence of numerous variants of SARS-CoV-2 has presented challenges to the global efforts to control the COVID-19 pandemic. The major mutation is in the SARS-CoV-2 viral envelope spike protein that is responsible for virus attachment to the host, and is the main target for host antibodies. It is critically important to study the biological effects of the mutations to understand the mechanisms of how mutations alter viral functions. Here, we propose a protein co-conservation weighted network (PCCN) model only based on the protein sequence to characterize the mutation sites by topological features and to investigate the mutation effects on the spike protein from a network view. Frist, we found that the mutation sites on the spike protein had significantly larger centrality than the non-mutation sites. Second, the stability changes and binding free energy changes in the mutation sites were positively significantly correlated with their neighbors’ degree and the shortest path length separately. The results indicate that our PCCN model provides new insights into mutations on spike proteins and reflects the mutation effects on protein function alternations. Full article

Thermophilic proteins have important value in the fields of biopharmaceuticals and enzyme engineering. Most existing thermophilic protein prediction models are based on traditional machine learning algorithms and do not fully utilize protein sequence information. To solve this problem, a deep learning model based on self-attention and multiple-channel feature fusion was proposed to predict thermophilic proteins, called DeepTP. First, a large new dataset consisting of 20,842 proteins was constructed. Second, a convolutional neural network and bidirectional long short-term memory network were used to extract the hidden features in protein sequences. Different weights were then assigned to features through self-attention, and finally, biological features were integrated to build a prediction model. In a performance comparison with existing methods, DeepTP had better performance and scalability in an independent balanced test set and validation set, with AUC values of 0.944 and 0.801, respectively. In the unbalanced test set, DeepTP had an average precision (AP) of 0.536. The tool is freely available. Full article

Lycium ruthenicum is a therapeutic plant and its fruits (black goji) are commonly used as a traditional Chinese medicine. This review comprehensively discusses the recent research developments of black goji anthocyanins (BGAs), including chemical compositions, biosynthesis, color properties and health benefits. Among the 39 identified BGAs, most are 3,5-diglycoside derivatives of petunidin (>95%) with an individual anthocyanin [petunidin 3-O-rutinoside (trans-p-coumaroyl)-5-O-glucoside], accounting for 80% of the total BGAs. Due to their unique anthocyanin profile, BGAs possess various health benefits, including antioxidant activities, α-glucosidase inhibiting activity, alleviating insulin resistance, improving mitochondrial function, anti-inflammatory effects, etc., and therefore have the potential to treat a range of chronic diseases, such as type 2 diabetes mellitus, memory disorders, stroke, colitis, atherosclerosis, cardiovascular and cerebrovascular diseases. In addition, BGAs exhibit a pH-dependent “red-purple-blue” pattern of color change and thus could be used as natural colorants and to prepare smart food packaging materials. This review is valuable for broad applications of BGAs as promising natural colorants, functional foods and potential herbal medicines. Full article

This study explores the response characteristics of runoff to the variability of meteorological factors. A modified vector autoregressive (VAR) model is proposed by combining time-varying parameters (TVP) and stochastic volatility (SV). Markov chain Monte Carlo (MCMC) is used to estimate parameters. The TVP-SV-VAR model of daily runoff response to the variability of meteorological factors is established and applied to the daily runoff series from the Linjiacun hydrological station, Shaanxi Province, China. It is found that the posterior estimates of the stochastic volatility of the four variables fluctuate significantly with time, and the variance fluctuations of runoff and precipitation have strong synchronicity. The simultaneous impact of precipitation and evaporation on the pulse of runoff is close to 0. Runoff has a positive impulse response to precipitation, which decreases as the lag time increases, and a negative impulse response to temperature and evaporation with fluctuation. The response speed is precipitation > evaporation > temperature. The TVP-SV-VAR model avoids the hypothesis of homoscedasticity of variance and allows the variance to be randomly variable, which significantly improves the analysis performance. It provides theoretical support for the study of runoff response and water resource management under the conditions of climate change. Full article