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To study the regulatory role of marine bacterial motility on biofilm formation and settlement of marine bivalves, this study used the economically important marine mussel Mytilus coruscus as the research target, and investigated the wild-type strain and ΔcheW strain of Pseudoalteromonas marina. These bacteria have different motility; thus, the motility analysis of these strains was carried out at various time points, and the differences in the biofilm thickness and composition and bacterial density were also analyzed at different time points. Finally, their effects on dynamic biofilm succession and settlement of M. coruscus were evaluated. This study found that the motility of the wild-type strain and the ΔcheW strain at 6 h, 12 h, 24 h, 48 h, 72 h, and 96 h was significantly different (P < 0.05). It was found that both bacterial circle radii increased with time and reached their maximum at 96 h. Overall, the wild-type strain formed a larger bacterial circle than the ΔcheW strain. The two strains' bacterial density and biofilm thickness increased with time and reached their maximum at 48 h, begging to spread after 72 h. At all time points, the mussel settlement rates on the wild-type strain biofilm were higher than on the ΔcheW biofilm. Initially, the mussel settlement rates increased, but after 72 h a decreasing tendency was observed, reaching the highest value at 48 h. These settlement results were consistent with the changes in the biofilm composition under different time points. Therefore, this study concluded that bacterial motility affects biofilm formation, mainly biofilms thickness, bacterial density, and extracellular compounds during the dynamic succession of biofilms, thereby regulating the settlement of the mussel M. coruscus. This study provides novel insights into the interaction between bacterial motility, biofilm formation, and the settlement of M. coruscus, which may help optimize the production and breeding of economically important marine animals.

Nucleosides and their analogs are pharmacologically important molecules. Here, the authors report an additive-controlled stereodivergent iodocyclization method for the selective synthesis of α- or β-nucleosides.

PurposeColon adenocarcinoma (COAD) is the most common type of colorectal cancer (CRC) and is associated with poor prognosis. Emerging evidence has demonstrated that glycosylation by long noncoding RNAs (lncRNAs) was associated with COAD progression. To date, however, the prognostic values of glycosyltransferase (GT)-related lncRNAs in COAD are still largely unknown.MethodsWe obtained the expression matrix of mRNAs and lncRNAs in COAD from The Cancer Genome Atlas (TCGA) database. Then, the univariate Cox regression analysis was conducted to identify 33 prognostic GT-related lncRNAs. Subsequently, LASSO and multivariate Cox regression analysis were performed, and 7 of 33 GT-related lncRNAs were selected to conduct a risk model. Gene set enrichment analysis (GSEA) was used to analyze gene signaling pathway enrichment of the risk model. ImmuCellAI, an online tool for estimating the abundance of immune cells, and correlation analysis were used to explore the tumor-infiltrating immune cells in COAD. Finally, the expression levels of seven lncRNAs were detected in colorectal cancer cell lines by reverse transcription-quantitative polymerase chain reaction (RT-qPCR).ResultsA total of 1,140 GT-related lncRNAs were identified, and 7 COAD-specific GT-related lncRNAs (LINC02381, MIR210HG, AC009237.14, AC105219.1, ZEB1-AS1, AC002310.1, and AC020558.2) were selected to conduct a risk model. Patients were divided into high- and low-risk groups based on the median of risk score. The prognosis of the high-risk group was worse than that of the low-risk group, indicating the good reliability and specificity of our risk model. Additionally, a nomogram based on the risk score and clinical traits was built to help clinical decisions. GSEA showed that the risk model was significantly enriched in metabolism-related pathways. Immune infiltration analysis revealed that five types of immune cells were significantly different between groups, and two types of immune cells were negatively correlated with the risk score. Besides, we found that the expression levels of these seven lncRNAs in tumor cells were significantly higher than those in normal cells, which verified the feasibility of the risk model.ConclusionThe efficient risk model based on seven GT-related lncRNAs has prognostic potential for COAD, which may be novel biomarkers and therapeutic targets for COAD patients.

This article presents a new prediction model, the ordinary differential equations–memory kernel function (ODE–MKF), constructed from multiple backtracking initial values (MBIV). The model is similar to a simplified numerical model after spatial dimension reduction and has both nonlinear characteristics and the low-cost advantage of a time series model. The ODE–MKF focuses on utilizing more temporal information and includes machine learning to solve complex mathematical inverse problems to establish a predictive model. This study first validates the feasibility of the ODE–MKF via experiments using the Lorenz system. The results demonstrate that the ODE–MKF prediction model could describe the nonlinear characteristics of complex systems and exhibited ideal predictive robustness. The prediction of the El Niño-Southern Oscillation (ENSO) index further demonstrates its effectiveness, as it achieved 24-month lead predictions and effectively improved nonlinear problems. Furthermore, the reliability of the model was also tested, and approximately 18 months of prediction were achieved, which was verified with the Clouds and the Earth’s Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) radiation fluxes. The short-term memory index Southern Oscillation (SO) was further used to examine the applicability of ODE–MKF. A six-month lead prediction of the SO trend was achieved, indicating that the predictability of complex systems is related to their inherent memory scales.

BackgroundCuproptosis, a newly identified form of programmed cell death, is thought to play a role in tumorigenesis. Long non-coding RNAs (lncRNAs) are reported to be associated with tumor progression and prognosis in colon adenocarcinoma (COAD). However, the role and prognostic value of cuproptosis-related lncRNAs in COAD remains unknown. This study is devoted to constructing and validating a cuproptosis-related lncRNA signature that can predict COAD patient outcomes using bioinformatics methods.MethodsThe COAD mRNA and lncRNA expression profiles and corresponding clinical data were downloaded from The Cancer Genome Atlas (TCGA) database and 2,567 cuproptosis-related lncRNAs were obtained. A 10 cuproptosis-related-lncRNA prognostic signature was then constructed using the least absolute shrinkage and selection operator (LASSO) algorithm and Cox regression model and patients were divided into high- and low-risk groups. Kaplan-Meier analysis, receiver operating characteristic (ROC) curve, and a nomogram were employed to evaluate the predictive power of the signature. The immune characteristics and drug sensitivity were also investigated based on the signature. Quantitative reverse transcription polymerase chain reaction (qRT-PCR) was performed to verify the risk model. In vitro experiments were conducted to validate the expression of the ten lncRNAs during cuproptosis.ResultsThe high-risk group was associated with shorter overall survival (OS) time in COAD patients (p

Colorectal cancer and gastric cancer are the most prevalent gastrointestinal malignancies worldwide, and early detection of these cancers is crucial to reduce their incidence and mortality. RNA methylation plays an important regulatory role in a variety of physiological activities, and it has drawn great attention in recent years. Methylated adenosine (A) modifications such as N6-methyladenosine (m6A), N1-methyladenosine (m1A), 2′-O-methyladenosine (Am), N6,2′-O-dimethyladenosine (m6Am), and N6,N6-dimethyladenosine (m62A) are typical epigenetic markers of RNA, and they are closely correlated to various diseases including cancer. Serum is a valuable source of biofluid for biomarker discovery, and determination of these adenosine modifications in human serum is desirable since they are emerging biomarkers for detection of diseases. In this work, a targeted quantitative analysis method using hydrophilic interaction liquid chromatography–tandem mass spectrometry (HILIC-MS/MS) was developed and utilized to analyze these methylated adenosine modifications in serum samples. The concentration differences between the healthy volunteers and cancer patients were evaluated by Mann–Whitney test, and receiver operator characteristic (ROC) curve analysis was performed to access the potential of these nucleosides as biomarkers. We demonstrated the presence of the m6Am in human serum for the first time, and we successfully quantified the concentrations of A, m6A, m1A, and m6Am in serum samples from 99 healthy controls, 51 colorectal cancer patients, and 27 gastric cancer patients. We found that the levels of m6A and m6Am in serum were both increased in colorectal cancer or gastric cancer patients, compared to that in healthy controls. These results indicate that m6A and m6Am in serum may act as potential biomarkers for early detection and prognosis of colorectal cancer and gastric cancer. In addition, the present work will stimulate investigations on the effects of adenosine methylation on the initiation and progression of colorectal cancer and gastric cancer.

The state of charge (SOC) is one of the crucial states for battery management. The Kalman filter (KF) family algorithms are promising for SOC estimation. Based on the KF theory, a sufficiently accurate system model is the precondition for a better performance of the algorithm. Thus, we manage to improve the algorithm by estimating the battery model error. In this paper, the sources that may cause model errors are analyzed. Then, in order to estimate the unknown error term, the bias term characterizing the model error is adjoined to the original state vector to form a new state vector, and the KF is utilized to estimate the new state vector. It is a joint estimation algorithm for both SOC and the model error. Subsequently, by decoupling this joint estimation algorithm, a battery model error observer has been built. Finally, to verify the robustness of the proposed method against the battery model error, different types of errors such as open circuit voltage drift and voltage sensor drift are injected. The results indicate that the improved SOC estimation algorithm has better robustness and accuracy against the model mismatch compared with the standard KF algorithm.

Hypoxia plays a key role in colorectal cancer (CRC) metastasis, but its underlying mechanism remains largely unknown. Dicer1, an RNase, has been considered as a tumor regulator in many tumors. However, whether Dicer1 affects CRC progression under hypoxia remains uncertain. In this study, we found that Dicer1 expression was induced by hypoxia in CRC cells and it mediates hypoxia-induced CRC cell progression. Furthermore, we found that the expression of tRF-20-MEJB5Y13, a small non-coding RNA derived from tRNA, was increased under hypoxic conditions, and its upregulation by Dicer1 resulted in hypoxia-induced CRC cell invasion and migration. These results advance the current understanding of the role of Dicer1 in regulating hypoxia signals and provide a new pathway for the development of therapeutic interventions for inhibiting cancer progression.