SBSM-Pro: Support Bio-sequence Machine for Proteins

Proteins play a pivotal role in biological systems. The use of machine learning algorithms for protein classification can assist and even guide biological experiments, offering crucial insights for biotechnological applications. We propose a support bio-sequence machine for proteins, a model specifically designed for biological sequence classification. This model starts with raw sequences and groups amino acids based on their physicochemical properties. It incorporates sequence alignment to measure the similarities between proteins and uses a novel MKL approach to integrate various types of information, utilizing support vector machines for classification prediction. The results indicate that our model demonstrates commendable performance across 10 datasets in terms of the identification of protein function and posttranslational modification. This research not only showcases state-of-the-art work in protein classification but also paves the way for new directions in this domain, representing a beneficial endeavour in the development of platforms tailored for biological sequence classification. SBSM-Pro is available for access at http://lab.malab.cn/soft/SBSM-Pro/.Comment: 38 pages, 9 figure

Uncovering the effects and molecular mechanism of Astragalus membranaceus (Fisch.) Bunge and its bioactive ingredients formononetin and calycosin against colon cancer: An integrated approach based on network pharmacology analysis coupled with experimental validation and molecular docking

Colon cancer is a highly malignant cancer with poor prognosis. Astragalus membranaceus (Fisch.) Bunge (Huang Qi in Chinese, HQ), a well-known Chinese herbal medicine and a popular food additive, possesses various biological functions and has been frequently used for clinical treatment of colon cancer. However, the underlying mechanism is not fully understood. Isoflavonoids, including formononetin (FMNT) and calycosin (CS), are the main bioactive ingredients isolated from HQ. Thus, this study aimed to explore the inhibitory effects and mechanism of HQ, FMNT and CS against colon cancer by using network pharmacology coupled with experimental validation and molecular docking. The network pharmacology analysis revealed that FMNT and CS exerted their anticarcinogenic actions against colon cancer by regulating multiple signaling molecules and pathways, including MAPK and PI3K-Akt signaling pathways. The experimental validation data showed that HQ, FMNT and CS significantly suppressed the viability and proliferation, and promoted the apoptosis in colon cancer Caco2 and HT-29 cells. HQ, FMNT and CS also markedly inhibited the migration of Caco2 and HT-29 cells, accompanied by a marked increase in E-cadherin expression, and a notable decrease in N-cadherin and Vimentin expression. In addition, HQ, FMNT and CS strikingly decreased the expression of ERK1/2 phosphorylation (p-ERK1/2) without marked change in total ERK1/2 expression. They also slightly downregulated the p-Akt expression without significant alteration in total Akt expression. Pearson correlation analysis showed a significant positive correlation between the inactivation of ERK1/2 signaling pathway and the HQ, FMNT and CS-induced suppression of colon cancer. The molecular docking results indicated that FMNT and CS had a strong binding affinity for the key molecules of ERK1/2 signaling pathway. Conclusively, HQ, FMNT and CS exerted good therapeutic effects against colon cancer by mainly inhibiting the ERK1/2 signaling pathway, suggesting that HQ, FMNT and CS could be useful supplements that may enhance chemotherapeutic outcomes and benefit colon cancer patients

Assessing Emergency Shelter Demand Using POI Data and Evacuation Simulation

Mapping the fine-scale spatial distribution of emergency shelter demand is crucial for shelter planning during disasters. To provide shelter for people within a reasonable evacuation distance under day and night disaster scenarios, we formed an approach for examining the distribution of day and night shelter demand at the plot-scale using point of interest (POI) data, and then analyzed the supply and demand status of shelters after an evacuation simulation built in Python programming language. Taking the downtown areas of Guangzhou, China as a case study, the results show that significant differences exist in the size and spatial distribution of shelter demand in daytime and nighttime, and the total demand is 7.929 million people, which is far larger than the resident population. The average evacuation time of all 16,883 routes is 12.6 min, and after the evacuation, 558 of 888 shelters exceed their capacity to varying degrees, accounting for 62.84% of the total, indicating that the shelters cannot completely receive the potential evacuees. The method proposed in this paper provides a direct quantitative basis for the number and size of new shelter resources being planned during urban renewal activities, and form a reference for land reuse and disaster prevention space organization in future urban planning

Convolutional Neural Networks-Based Object Detection Algorithm by Jointing Semantic Segmentation for Images

In recent years, increasing image data comes from various sensors, and object detection plays a vital role in image understanding. For object detection in complex scenes, more detailed information in the image should be obtained to improve the accuracy of detection task. In this paper, we propose an object detection algorithm by jointing semantic segmentation (SSOD) for images. First, we construct a feature extraction network that integrates the hourglass structure network with the attention mechanism layer to extract and fuse multi-scale features to generate high-level features with rich semantic information. Second, the semantic segmentation task is used as an auxiliary task to allow the algorithm to perform multi-task learning. Finally, multi-scale features are used to predict the location and category of the object. The experimental results show that our algorithm substantially enhances object detection performance and consistently outperforms other three comparison algorithms, and the detection speed can reach real-time, which can be used for real-time detection

Copper Selenide (CuSe) Monolith Fabricated by Facile Copper Foam Selenization for Efficient Photocatalytic Degradation of Methylene Blue

A critical challenge that impedes the application of photocatalytic techniques for organic dye degradation from polluted industrial effluents is that traditional powdery photocatalysts exposed limited photo-absorption sites and exhibited inefficient recyclability. To overcome these challenges, this study designed a one-step process to synthesize a monolithic copper selenide (CuSe)-based photocatalyst. The characterization results fully supported that the maintenance of the copper foam during the selenization process was the prerequisite for the monolithic photocatalyst to keep its structural integrity in photocatalytic reactions. The surface of the monolithic photocatalyst fully covered by active CuSe is crucial for the exposure of photocatalytically active sites and the efficient degradation of methylene blue (MB). It was found that the CuSe-based monolithic photocatalyst exhibited excellent MB degradation performances under harsh pH conditions and high MB concentrations. From these perspectives, it is reasonable to conclude that the CuSe-based monolithic photocatalyst as prepared is a promising alternative to traditional powdery photocatalysts for organic dye degradation and industrial effluent cleaning

Enhancement of Degradation and Dechlorination of Trichloroethylene via Supporting Palladium/Iron Bimetallic Nanoparticles onto Mesoporous Silica

This study is aimed to prevent the agglomeration of Pd/Fe bimetallic nanoparticles and thus improve the efficiency toward degradation and dechlorination of chlorinated organic contaminants. A mesoporous silica with a primary pore diameter of 8.3 nm and a specific surface area of 688 m2/g was prepared and used as the host of Pd/Fe nanoparticles. The Pd/Fe nanoparticles were deposited onto or into the mesoporous silica by reduction of ferrous ion and hexachloropalladate ion in aqueous phase. Batch degradation and dechlorination reactions of trichloroethylene were conducted with initial trichloroethylene concentration of 23.7 mg/L, iron loading of 203 or 1.91 × 103 mg/L and silica loading of 8.10 g/L at 25 °C. Concentration of trichloroethylene occurs on the supported Pd/Fe nanoparticles, with trichloroethylene degrading to 56% and 59% in 30 min on the supported Pd/Fe nanoparticles with weight percentage of palladium to iron at 0.075% and 0.10% respectively. The supported Pd/Fe nanoparticles exhibit better dechlorination activity. When the supported Pd/Fe nanoparticles with a weight percentage of palladium to iron of 0.10% were loaded much less than the bare counterpart, the yield of ethylene plus ethane in 10 h on them was comparable, i.e., 19% vs. 21%. This study offers a future approach to efficiently combine the reactivity of supported Pd/Fe nanoparticles and the adsorption ability of mesoporous silica

Energy absorption of gradient triply periodic minimal surface structure manufactured by stereolithography

Triply periodic minimal surface (TPMS) metamaterials possess exceptional properties not commonly found in natural materials. TPMS metamaterials are used in lightweight structures and impact energy absorption structures due to their surface geometry and mechanical properties. The quasi-static mechanic properties of resin-based homogeneous and gradient TPMS structures manufactured by stereolithography are investigated in this study. The results of both experimental and numerical simulations reveal that the gradient TPMS structures have superior energy absorption abilities compared to the homogeneous TPMS structures. Furthermore, the benefits of gradient TPMS structures can be further enhanced by changing the gradient variation interval of the relative density and cell thickness of TPMS. If the slope and intercept of the C value function of the TPMS structures remain constant, selecting a design where the gradient direction of the cell aligns with the direction of the load on the material can enhance the energy absorption capability of the TPMS structures