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Abstract Objective Colorectal cancer (CRC) is a form of malignancy that exhibits a comparatively elevated occurrence and fatality rate. Given the relatively slower progress in diagnostic and therapeutic approaches for CRC, there is a need to investigate more accurate and efficient biomarkers. Methods Core regulatory genes were screened using the TCGA database, and the expression of neurexophilin 4 (NXPH4) and its prognostic implications were validated using tissue microarray staining. The assessment of NXPH4 functions involved a range of experiments, including cellular, organoid, and murine models. Furthermore, a regulatory network between m5C, NXPH4, and HIF1A was established through several in vitro experiments. Results The overexpression of NXPH4 is associated with unfavorable prognoses in patients with CRC and hepatocellular carcinoma. Additionally, it facilitates the progression of malignant tumors both in laboratory settings and in living organisms of colorectal carcinoma. Our research also reveals that NXPH4 mRNA can avoid degradation through RNautophagy, relying on an m5C-dependent mechanism. Moreover, NXPH4 amplifies the HIF signaling pathway and stabilizes HIF1A by competitively binding to PHD4. Conclusions NXPH4, regulated by m5C, promotes malignant tumor progression and regulates the HIF pathway. Consequently, targeting NXPH4 through molecular therapies could potentially serve as an efficacious therapeutic strategy for the management of CRC exhibiting elevated NXPH4 expression. Graphical abstract

BackgroundGastric cancer (GC) is one of the most common malignant tumors and is associated with poor prognosis. To improve the prognosis of GC patients, an effective immune-related prognostic biomarker is urgent. Here, we aim to explore the correlation between the expression of procalcitonin gamma subfamily A, 10 (PCDHGA10) and clinicopathological characteristics, especially its relation with tumor-infiltrating immune cells (TILs) in GC.MethodsThe differential mRNA expression of PCDHGA10 between GC tissues and normal gastric mucosa and prognostic potential were assessed from The Cancer Genome Atlas (TCGA). Then, based on tissue microarrays (TMAs) with multiplex immunohistochemistry (mIHC) from GC patients, we statistically assess the correlation between PCDHGA10 protein expression and the clinical profiles and prognosis of the patients. Additionally, with IHC and mIHC, we applied the machine-learning algorithms to evaluate the localization and expression levels of TILs and immune checkpoints in the tumor microenvironment. We analyzed the relationship between PCDHGA10 protein expression and TILs and immune checkpoints.ResultsThrough the database and TMA analysis, the expression of PCDHGA10 was significantly higher in GC tissues compared with normal tissues. High PCDHGA10 expression independently predicted poor prognosis in GC. Additionally, elevated PCDHGA10 expression was positively associated with the number of CD8+ T cells, CD68+ macrophages, Foxp3+ T cells, and CD4+ T cells in GC tissues and the stromal region. Besides, the expression of PCDHGA10 was positively correlated with immune checkpoints, including CTLA-4, LAG3, and PD-L1.ConclusionsPCDHGA10 might be a potential prognostic marker and an immunological therapeutic target for GC.

This paper proposes a novel polar coding scheme tailored for indoor visible light communication (VLC) systems. Simulation results demonstrate a significant reduction in bit error rate (BER) compared to uncoded transmission, with a coding gain of at least 5 dB. Furthermore, the reliable communication area of the VLC system is substantially extended. Building on this foundation, this study explores the joint design of polar codes and physical-layer network coding (PNC) for VLC systems. Simulation results illustrate that the BER of our scheme closely approaches that of the conventional VLC relay scheme. Moreover, our approach doubles the throughput, cuts equipment expenses in half, and boosts effective bit rates per unit time-slot twofold. This proposed design noticeably advances the performance of VLC systems and is particularly well-suited for scenarios with low-latency demands.

Abstract Biochar has the potential to provide a multitude of benefits when used in soil remediation and increasing soil organic matter enrichment. Nevertheless, the intricated, hydrophobic pores and groups weaken its water-holding capacity in dry, sandy soils in arid lands. In order to combat this issue, starch-carbon-based material (SB), sodium alginate-carbon-based material (SAB), and chitosan-carbon-based material (CB) have been successfully synthesized through the graft-polymerization of biochar (BC). A series of soil column simulations were used to scrutinize the microstructure of the carbon-based material and explore its water absorption properties and its effects on sandy soil water infiltration, water retention, and aggregation. The results indicated that SB, SAB, and CB achieved water maximum absorption rates of 155, 188, and 172 g g−1, respectively. Considering their impact on sandy soils, SB, SAB, and CB lengthened infiltration times by 1920, 3330, and 3880 min, respectively, whilst enhancing the water retention capabilities of the soil by 18%, 25%, and 23% in comparison to solely adding BC. The utilization of these innovative materials notably encouraged the formation of sandy soil aggregates ranging from 2.0 to 0.25 mm, endowing the aggregates with enhanced structural stability. Findings from potting experiments suggested that all three carbon-based materials were conducive to the growth of soybean seeds. Thus, it is evident that the carbon-based materials have been fabricated with success, and they have great potential not only to significantly augment the water retention capacities and structural robustness of sandy soils in arid areas, but also to bolster the development of soil aggregates and crop growth. These materials possess significant application potential for enhancing the quality of sandy soils in arid and semi-arid regions. Graphical Abstract

The Fe3+-loaded NaBiO3 was prepared for photocatalytic removal of tetracycline hydrochloride (TCH) from swine wastewater. Fe3+ greatly reduced the recombination efficiency of the photoinduced electron–hole pairs of NaBiO3. Moreover, the TCH photocatalytic degradation rate constant of Fe3+-loaded NaBiO3 was two-times higher than that of Vis+NaBiO3 after 100 min of reaction. Fe-NaBiO3 combined with PS (100 mg L−1) favored TCH (10 mg L−1) degradation rate (98.8%) at neutral pH. Moreover, the oxidation system was not inhibited by the fulvic acid, and showed a good photocatalytic stability. The superoxide radicals were the main active species involved in the catalytic degradation.

Abstract Unlike traditional video recommendations, micro-video inherits the characteristics of social platforms, such as social relation. A large amount of micro-videos showing explosive growth is badly affecting the user’s choice. In this paper, we propose a multi-source multi-net micro-video recommendation model that recommends micro-videos fitting users’ best interests. Different from existing works, as micro-video inherits the characteristics of social platforms, we simultaneously incorporate multi-source content data of items and multi-networks of users to learn user and item representations for recommendation. This information can be complementary to each other in a way that multi-modality data can bridge the semantic gap among items, while multi-type user networks, such as following and reposting, are able to propagate the preferences among users. Furthermore, to discover the hidden categories of micro-videos that properly match users’ interests, we interactively learn the user–item representations and perform the hidden item category clustering. The resulted categorical representations are interacted with user representations to model user preferences at different levels of hierarchies. Finally, multi-source content item data, multi-type user networks and hidden item categories are jointly modelled in a unified recommender, and the parameters of the model are collaboratively learned to boost the recommendation performance. Experiments on a real dataset demonstrate the effectiveness of the proposed model and its advantage over the state-of-the-art baselines.

An activity recognition system essentially processes raw sensor data and maps them into latent activity classes. Most of the previous systems are built with supervised learning techniques and pre-defined data sources, and result in static models. However, in realistic and dynamic environments, original data sources may fail and new data sources become available, a robust activity recognition system should be able to perform evolution automatically with dynamic sensor availability in dynamic environments. In this paper, we propose methods that automatically incorporate dynamically available data sources to adapt and refine the recognition system at run-time. The system is built upon ensemble classifiers which can automatically choose the features with the most discriminative power. Extensive experimental results with publicly available datasets demonstrate the effectiveness of our methods.