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Abstract The objective of this study is to promptly and accurately allocate resources, scientifically guide grain distribution, and enhance the precision of crop yield prediction (CYP), particularly for corn, along with ensuring application stability. The digital camera is selected to capture the digital image of a 60 m × 10 m experimental cornfield. Subsequently, the obtained data on corn yield and statistical growth serve as inputs for the multi-source information fusion (MSIF). The study proposes an MSIF-based CYP Random Forest model by amalgamating the fluctuating corn yield dataset. In relation to the spatial variability of the experimental cornfield, the fitting degree and prediction ability of the proposed MSIF-based CYP Random Forest are analyzed, with statistics collected from 1-hectare, 10-hectare, 20-hectare, 30-hectare, and 50-hectare experimental cornfields. Results indicate that the proposed MSIF-based CYP Random Forest model outperforms control models such as support vector machine (SVM) and Long Short-Term Memory (LSTM), achieving the highest prediction accuracy of 89.30%, surpassing SVM and LSTM by approximately 13.44%. Meanwhile, as the experimental field size increases, the proposed model demonstrates higher prediction accuracy, reaching a maximum of 98.71%. This study is anticipated to offer early warnings of potential factors affecting crop yields and to further advocate for the adoption of MSIF-based CYP. These findings hold significant research implications for personnel involved in Agricultural and Forestry Economic Management within the context of developing agricultural economy.

Abstract Purpose The research endeavors to explore the implications of CD47 in cancer immunotherapy effectiveness. Specifically, there is a gap in comprehending the influence of CD47 on the tumor immune microenvironment, particularly in relation to CD8 + T cells. Our study aims to elucidate the prognostic and immunological relevance of CD47 to enhance insights into its prospective utilities in immunotherapeutic interventions. Methods Differential gene expression analysis, prognosis assessment, immunological infiltration evaluation, pathway enrichment analysis, and correlation investigation were performed utilizing a combination of R packages, computational algorithms, diverse datasets, and patient cohorts. Validation of the concept was achieved through the utilization of single-cell sequencing technology. Results CD47 demonstrated ubiquitous expression across various cancer types and was notably associated with unfavorable prognostic outcomes in pan-cancer assessments. Immunological investigations unveiled a robust correlation between CD47 expression and T-cell infiltration rather than T-cell exclusion across multiple cancer types. Specifically, the CD47-high group exhibited a poorer prognosis for the cytotoxic CD8 + T cell Top group compared to the CD47-low group, suggesting a potential impairment of CD8 + T cell functionality by CD47. The exploration of mechanism identified enrichment of CD47-associated differentially expressed genes in the CD8 + T cell exhausted pathway in multiple cancer contexts. Further analyses focusing on the CD8 TCR Downstream Pathway and gene correlation patterns underscored the significant involvement of TNFRSF9 in mediating these effects. Conclusion A robust association exists between CD47 and the exhaustion of CD8 + T cells, potentially enabling immune evasion by cancer cells and thereby contributing to adverse prognostic outcomes. Consequently, genes such as CD47 and those linked to T-cell exhaustion, notably TNFRSF9, present as promising dual antigenic targets, providing critical insights into the field of immunotherapy.

The interaction between organic phosphorus (OP) and iron oxide significantly influences the phosphorus cycle in the natural environment. In shallow lakes, intense oxidation-reduction fluctuations constantly alter the existing form of iron oxides, but little is known about their impact on the adsorption and fractionation of OP molecules. In this study, electrospray ionization coupled with Fourier transform ion cyclotron resonance mass spectrometry (ESI-FT-ICR MS) was used to investigate the fractionation of OP from alkali-extracted sediment induced by crystalline goethite and amorphous ferrihydrite adsorption at a molecular scale. The results showed that ferrihydrite and goethite both exhibited high OP adsorption, and the adsorption amount decreased as the pH increased. The adsorption kinetics matched the pseudo-second-order equation. The ESI-FT-ICR MS analysis showed that 91 P-containing formulas were detected in the alkaline-extracted sediment solution. Ferrihydrite and goethite adsorbed 51 and 24 P-containing formulas, respectively, with adsorption rates of 56.0 % and 26.4 %. Ferrihydrite could adsorb more OP compounds than goethite, but no obvious molecular species selectivity was observed during the adsorption. The P-containing compounds, including unsaturated hydrocarbons-, lignin/carboxyl-rich alicyclic molecule (CRAM)-, tannin-, and carbohydrate-like molecular compounds, were more suitable for iron oxide adsorption. The double bond equivalence (DBE) is a valuable parameter that indicates OP fractionation during adsorption, and P-containing compounds with lower DBE values such as lipid- and protein-like molecular were prone to remain in the solution after adsorption. These research results provide insights into the biogeochemical cycling process of P in the natural environment.

With the intensification of global climate change and the increasing complexity of agricultural environments, the improvement of rice stress tolerance is an important focus of current breeding research. This review summarizes the current knowledge on the impact of various abiotic stresses on rice and the associated epigenetic responses (DNA methylation). Abiotic stress factors, including high temperature, drought, cold, heavy metal pollution, and high salinity, have a negative impact on crop productivity. Epigenetic changes are key regulatory factors in plant stress responses, and DNA methylation is one of the earliest discovered and thoroughly studied mechanisms in these epigenetic regulatory mechanisms. The normal growth of rice is highly dependent on the environment, and changes in the environment can lead to rice sterility and severe yield loss. Changes in the regulation of the DNA methylation pathway are involved in rice’s response to stress. Various DNA methylation-regulating protein complexes that function during rice development have been identified. Significant changes in DNA methylation occur in numerous stress-responsive genes, particularly those in the abscisic acid signaling pathway. These findings underscore the complex mechanisms of the abiotic stress response in rice. We propose the effective improvement of tolerance traits by regulating the epigenetic status of rice and emphasize the role of DNA methylation in abiotic stress tolerance, thereby addressing global climate change and ensuring food security.

Abstract Aspirin and its active metabolite salicylate have emerged as promising agents for the chemoprevention of colorectal cancer (CRC). Moreover, aspirin suppresses the progression of established CRCs. However, the underlying molecular mechanisms are not completely understood. Here we found that salicylate induces the expression of the miR-34a and miR-34b/c genes, which encode tumor suppressive microRNAs, in a p53-independent manner. Salicylate activated AMPK, thereby activating NRF2, which directly induced miR-34a/b/c expression via ARE motifs. In addition, salicylate suppressed c-MYC, a known repressor of NRF2-mediated transactivation, via activating AMPK. The suppression of c-MYC by salicylate was necessary for NRF2-mediated activation of miR-34a/b/c. Inactivation of miR-34a/b/c largely abrogated the inhibitory effects of salicylate on migration, invasion and metastasis formation by CRC cells. In the future, aspirin and its derivates may be used therapeutically to activate miR-34a and miR-34b/c in tumors that have lost p53.

Members of the microRNA-34/miR-34 family are induced by the p53 tumor suppressor and themselves possess tumor suppressive properties, as they inhibit the translation of mRNAs that encode proteins involved in processes, such as proliferation, migration, invasion, and metastasis. Here we performed a comprehensive integrative meta-analysis of multiple computational and experimental miR-34 related datasets and developed tools to identify and characterize novel miR-34 targets. A miR-34 target probability score was generated for every mRNA to estimate the likelihood of representing a miR-34 target. Experimentally validated miR-34 targets were strongly enriched among mRNAs with the highest scores providing a proof of principle for our analysis. We integrated the results from the meta-analysis in a user-friendly METAmiR34TARGET website (www.metamir34target.com/) that allows to graphically represent the meta-analysis results for every mRNA. Moreover, the website harbors a screen function, which allows to select multiple miR-34-related criteria/analyses and cut-off values to facilitate the stringent and comprehensive prediction of relevant miR-34 targets in expression data obtained from cell lines and tumors/tissues. Furthermore, information on more than 200 miR-34 target mRNAs, that have been experimentally validated so far, has been integrated in the web-tool. The website and datasets provided here should facilitate further investigation into the mechanisms of tumor suppression by the p53/miR-34 connection and identification of potential cancer drug targets.

In order to explore the species composition, spatial distribution and relationship between the phytoplankton community and environmental factors in Lake Longhu, the phytoplankton community structures and environmental factors were investigated in July 2020. Clustering analysis (CA) and analysis of similarities (ANOSIM) were used to identify differences in phytoplankton community composition. Generalized additive model (GAM) and variance partitioning analysis (VPA) were further analyzed the contribution of spatial distribution and environmental factors in phytoplankton community composition. The critical environmental factors influencing phytoplankton community were identified using redundancy analysis (RDA). The results showed that a total of 68 species of phytoplankton were found in 7 phyla in Lake Longhu. Phytoplankton density ranged from 4.43 × 105 to 2.89 × 106 ind./L, with the average density of 2.56 × 106 ind./L; the biomass ranged from 0.58–71.28 mg/L, with the average biomass of 29.38 mg/L. Chlorophyta, Bacillariophyta and Cyanophyta contributed more to the total density, while Chlorophyta and Cryptophyta contributed more to the total biomass. The CA and ANOSIM analysis indicated that there were obvious differences in the spatial distribution of phytoplankton communities. The GAM and VPA analysis demonstrated that the phytoplankton community had obvious distance attenuation effect, and environmental factors had spatial autocorrelation phenomenon, which significantly affected the phytoplankton community construction. There were significant distance attenuation effects and spatial autocorrelation of environmental factors that together drove the composition and distribution of phytoplankton community structure. In addition, pH, water temperature, nitrate nitrogen, nitrite nitrogen and chemical oxygen demand were the main environmental factors affecting the composition of phytoplankton species in Lake Longhu.

IntroductionLumbar puncture is an important medical procedure for various diagnostics and therapies, but it can be hazardous due to individual variances in subcutaneous soft tissue, especially in the elderly and obese. Our research describes a novel robot-assisted puncture system that automatically controls and maintains the probe at the target tissue layer through a process of tissue recognition.MethodsThe system comprises a robotic system and a master computer. The robotic system is constructed based on a probe consisting of a pair of concentric electrodes. From the probe, impedance spectroscopy measures bio-impedance signals and transforms them into spectra that are communicated to the master computer. The master computer uses a Bayesian neural network to classify the bio-impedance spectra as corresponding to different soft tissues. By feeding the bio-impedance spectra of unknown tissues into the Bayesian neural network, we can determine their categories. Based on the recognition results, the master computer controls the motion of the robotic system.ResultsThe proposed system is demonstrated on a realistic phantom made of ex vivo tissues to simulate the spinal environment. The findings indicate that the technology has the potential to increase the precision and security of lumbar punctures and associated procedures.DiscussionIn addition to lumbar puncture, the robotic system is suitable for related puncture operations such as discography, radiofrequency ablation, facet joint injection, and epidural steroid injection, as long as the required tissue recognition features are available. These operations can only be carried out once the puncture needle and additional instruments reach the target tissue layer, despite their ensuing processes being distinct.

Background The predictive efficacy of current biomarker of immune checkpoint inhibitors (ICIs) is not sufficient. This study investigated the causality between radiomic biomarkers and immunotherapy response status in patients with stage IB–IV non-small cell lung cancer (NSCLC), including its biological context for ICIs treatment response prediction.Methods CT images from 319 patients with pretreatment NSCLC receiving immunotherapy between January 2015 and November 2021 were retrospectively collected and composed a discovery (n=214), independent validation (n=54), and external test cohort (n=51). A set of 851 features was extracted from tumorous and peritumoral volumes of interest (VOIs). The reference standard is the durable clinical benefit (DCB, sustained disease control for more than 6 months assessed via radiological evaluation). The predictive value of combined radiomic signature (CRS) for pathological response was subsequently assessed in another cohort of 98 patients with resectable NSCLC receiving ICIs preoperatively. The association between radiomic features and tumor immune landscape on the online data set (n=60) was also examined. A model combining clinical predictor and radiomic signatures was constructed to improve performance further.Results CRS discriminated DCB and non-DCB patients well in the training and validation cohorts with an area under the curve (AUC) of 0.82, 95% CI: 0.75 to 0.88, and 0.75, 95% CI: 0.64 to 0.87, respectively. In this study, the predictive value of CRS was better than programmed cell death ligand-1 (PD-L1) expression (AUC of PD-L1 subset: 0.59, 95% CI: 0.50 to 0.69) or clinical model (AUC: 0.66, 95% CI: 0.51 to 0.81). After combining the clinical signature with CRS, the predictive performance improved further with an AUC of 0.837, 0.790 and 0.781 in training, validation and D2 cohorts, respectively. When predicting pathological response, CRS divided patients into a major pathological response (MPR) and non-MPR group (AUC: 0.76, 95% CI: 0.67 to 0.81). Moreover, CRS showed a promising stratification ability on overall survival (HR: 0.49, 95% CI: 0.27 to 0.89; p=0.020) and progression-free survival (HR: 0.43, 95% CI: 0.26 to 0.74; p=0.002).Conclusion By analyzing both tumorous and peritumoral regions of CT images in a radiomic strategy, we developed a non-invasive biomarker for distinguishing responders of ICIs therapy and stratifying their survival outcome efficiently, which may support the clinical decisions on the use of ICIs in advanced as well as patients with resectable NSCLC.

Accurate detection and counting of live pigs are integral to scientific breeding and production in intelligent agriculture. However, existing pig counting methods are challenged by heavy occlusion and varying illumination conditions. To overcome these challenges, we proposed IO-YOLOv5 (Illumination-Occlusion YOLOv5), an improved network that expands on the YOLOv5 framework with three key contributions. Firstly, we introduced the Simple Attention Receptive Field Block (SARFB) module to expand the receptive field and give greater weight to important features at different levels. The Ghost Spatial Pyramid Pooling Fast Cross Stage Partial Connections (GSPPFC) module was also introduced to enhance model feature reuse and information flow. Secondly, we optimized the loss function by using Varifocal Loss to improve the model’s learning ability on high-quality and challenging samples. Thirdly, we proposed a public dataset consisting of 1270 images and 15,672 pig labels. Experiments demonstrated that IO-YOLOv5 achieved a mean average precision (mAP) of 90.8% and a precision of 86.4%, surpassing the baseline model by 2.2% and 3.7% respectively. By using a model ensemble and test time augmentation, we further improved the mAP to 92.6%, which is a 4% improvement over the baseline model. Extensive experiments showed that IO-YOLOv5 exhibits excellent performance in pig recognition, particularly under heavy occlusion and various illuminations. These results provide a strong foundation for pig recognition in complex breeding environments.

Background and objective MiR-148b-3p is an important microRNA that has been reported to be significantly related to various types of cancer, but its role in lung adenocarcinoma remains elusive. The purpose of this study is to detect the expression level of miR-148b-3p in lung adenocarcinoma specimens, and to analyze its correlation with the clinicopathological features as well as the prognosis of patients with lung adenocarcinoma. Methods A total of 123 tumor specimens from lung adenocarcinoma patients who underwent surgical resection in our department from January 2011 to December 2012 were collected. The expression of miR-148b-3p was detected by quantitative real-time PCR (qRT-PCR), and its correlation with clinicopathological features of patients with lung adenocarcinoma was analyzed. Multivariate Cox proportional hazard models were used to analyze independent predictors of overall survival in patients with lung adenocarcinoma. The overall survival (OS) of patients in miR-148b-3p high expression group and miR-148b-3p low expression group were estimated by means of the Kaplan-Meier method and were compared using the Log-rank test method. Results Of the 123 patients with lung adenocarcinoma, 71 were in miR-148b-3p high expression group and 52 in low expression group. MiR-148b-3p was significantly associated with tumor grade (P=0.001) and tumor size (P=0.007), but not with age, gender, smoking history, history of alcohol, tumor thrombus, pleural invasion, node status or metastasis status. Multivariate Cox proportional hazard model analysis showed that tumor size (P=0.032), node status (P=0.005) and miR-148b-3p expression level (P=0.047) were significant independent predictors of overall survival of patients with lung adenocarcinoma. Kaplan-Meier survival analysis showed that the overall survival of patients with high expression of miR-148b-3p was significantly better than that of patients with low expression (P=0.010). Conclusion MiR-148b-3p was significantly associated with tumor grade and tumor size in lung adenocarcinoma, and served as an independent predictor of overall survival of patients with lung adenocarcinoma. The overall survival of patients with high expression level of miR-148b-3p was significantly better than that of patients with low expression.