A Multimodal Ecological Civilization Pattern Recommendation Method Based on Large Language Models and Knowledge Graph

The Ecological Civilization Pattern Recommendation System (ECPRS) aims to recommend suitable ecological civilization patterns for target regions, promoting sustainable development and reducing regional disparities. However, the current representative recommendation methods are not suitable for recommending ecological civilization patterns in a geographical context. There are two reasons for this. Firstly, regions have spatial heterogeneity, and the (ECPRS)needs to consider factors like climate, topography, vegetation, etc., to recommend civilization patterns adapted to specific ecological environments, ensuring the feasibility and practicality of the recommendations. Secondly, the abstract features of the ecological civilization patterns in the real world have not been fully utilized., resulting in poor richness in their embedding representations and consequently, lower performance of the recommendation system. Considering these limitations, we propose the ECPR-MML method. Initially, based on the novel method UGPIG, we construct a knowledge graph to extract regional representations incorporating spatial heterogeneity features. Following that, inspired by the significant progress made by Large Language Models (LLMs) in the field of Natural Language Processing (NLP), we employ Large LLMs to generate multimodal features for ecological civilization patterns in the form of text and images. We extract and integrate these multimodal features to obtain semantically rich representations of ecological civilization. Through extensive experiments, we validate the performance of our ECPR-MML model. Our results show that F1@5 is 2.11% higher compared to state-of-the-art models, 2.02% higher than NGCF, and 1.16% higher than UGPIG. Furthermore, multimodal data can indeed enhance recommendation performance. However, the data generated by LLM is not as effective as real data to a certain extent

Dominant factor affecting Pb speciation and the leaching risk among land- use types around Pb-Zn mine

Soil lead (Pb) pollution around the mining area has severely threaten human health. However, Pb leaching risk in soils with different land uses and which is the proper land use are still unknown. In this work, Pb speciation characteristics and the dominant soil factors affecting Pb speciation in three land uses (farmland, woodland, and grassland) surrounding the Pb-Zn mine in Feng Country, Shaanxi province were investigated. Moreover, the Pb leaching risk and associated determining factors were evaluated by the combination of leached Pb concentration and structural equation model (SEM). The results showed that farmland presented the highest total Pb content (410.1 mg kg(-1)) among three land use types. The reducible fraction of Pb (Fe-Mn oxides bound) was the major speciation ( > 50%) in all tested soils of three land-use types. Soil total phosphorus (TP), water content (WC), and pH play major role in regulating Pb speciation. Though soil biological properties, like microbial communities, catalase, and microbial biomass nitrogen (MBN) exhibited distinct responses to three different land uses, they showed minor influence on Pb speciation. More interestingly, SEM analysis indicated that Pb leaching risk was directly linked with bacteria abundance, total Pb content, clay content, and C/N. Grassland presented the higher predicted Pb leaching concentration (85.03 mg kg(-1)), compared with that in woodland, suggesting that grassland was the worst land-use type to buffer the Pb toxicity. Woodland could be recommended as the proper native land use to alleviate environmental risk. Overall, our results demonstrated the dominant factor to regulate Pb speciation and pointed out the proper land-use in relieving Pb leaching risk around Pb-Zn mine. These finding provides the new strategies to the remediation and management of metal-contaminated soil

Reforestation regulated soil bacterial community structure along vertical profiles in the Loess Plateau

IntroductionReforestation is a widely used strategy for ecological restoration in areas facing ecological degradation. Soil bacteria regulate many functional processes in terrestrial ecosystems; however, how they respond to reforestation processes in surface and deep soils remains unclear.MethodsArtificial Robinia pseudoacacia plantation with different stand ages (8, 22, and 32 years) in a typical fallow forest on the Loess Plateau was selected to explore the differential response of soil bacterial community to reforestation in different soil depths (surface 0–200 cm, middle 200–500 cm, and deep 500-100 cm). Soil bacterial diversity, community composition and the co-occurrence patterns, as well as the functions were analyzed.Results and discussionThe results showed that alpha diversity and the presence of biomarkers (keynote species) decreased with the increasing soil depth, with a sharp reduction in family-level biomarker numbers in 500–1,000 cm depth, while reforestation had a positive impact on bacterial alpha diversity and biomarkers. Reforestation induced a more loosely connected bacterial community, as evidenced by an increase of 9.38, 22.87, and 37.26% in the average path length of the co-occurrence network in all three soil layers, compared to farmland. In addition, reforestation reduced the hierarchy and complexity but increased the modularity of the co-occurrence network in top and deep soil layers. Reforestation also led to enrichment in the relative abundance of functional pathways in all soil layers. This study sheds light on the strategies employed by deep soil bacteria in response to reforestation and underscores the significant potential of deep soil bacteria in terrestrial ecosystems, particularly in the context of human-induced environmental changes

Prediction of adenocarcinoma and squamous carcinoma based on CT perfusion parameters of brain metastases from lung cancer: a pilot study

ObjectivesPredicting pathological types in patients with adenocarcinoma and squamous carcinoma using CT perfusion imaging parameters based on brain metastasis lesions from lung cancer.MethodsWe retrospectively studied adenocarcinoma and squamous carcinoma patients with brain metastases who received treatment and had been pathologically tested in our hospital from 2019 to 2021. CT perfusion images of the brain were used to segment enhancing tumors and peritumoral edema and to extract CT perfusion parameters. The most relevant perfusion parameters were identified to classify the pathological types. Of the 45 patients in the study cohort (mean age 65.64 ± 10.08 years; M:F = 24:21), 16 were found to have squamous cell carcinoma. Twenty patients were with brain metastases only, and 25 patients were found to have multiple organ metastases in addition to brain metastases. After admission, all patients were subjected to the CT perfusion imaging examination. Differences in CT perfusion parameters between adenocarcinoma and squamous carcinoma were analyzed. The receiver operating characteristic (ROC) curves were used to predict the types of pathology of the patients.ResultsAmong the perfusion parameters, cerebral blood flow (CBF) and mean transit time (MTT) were significantly different between the two lung cancers (adenocarcinoma vs. squamous cell carcinoma: p < 0.001, p = 0.012.). Gender and tumor location were identified as the clinical predictive factors. For the classification of adenocarcinoma and squamous carcinoma, the model combined with CBF and clinical predictive factors showed better performance [area under the curve (AUC): 0.918, 95% confidence interval (CI): 0.797–0.979). The multiple organ metastasis model showed better performance than the brain metastasis alone model in subgroup analyses (AUC: 0.958, 95% CI: 0.794–0.999).ConclusionCT perfusion parameter analysis of brain metastases in patients with primary lung cancer could be used to classify adenocarcinoma and squamous carcinoma

Establishment and evaluation of a predictive model for length of hospital stay after total knee arthroplasty: A single-center retrospective study in China

BackgroundTotal knee arthroplasty (TKA) is the ultimate option for end-stage osteoarthritis, and the demand of this procedure are increasing every year. The length of hospital stay (LOS) greatly affects the overall cost of joint arthroplasty. The purpose of this study was to develop and validate a predictive model using perioperative data to estimate the risk of prolonged LOS in patients undergoing TKA.MethodsData for 694 patients after TKA collected retrospectively in our department were analyzed by logistic regression models. Multi-variable logistic regression modeling with forward stepwise elimination was used to determine reduced parameters and establish a prediction model. The discrimination efficacy, calibration efficacy, and clinical utility of the prediction model were evaluated.ResultsEight independent predictors were identified: non-medical insurance payment, Charlson Comorbidity Index (CCI) ≥ 3, body mass index (BMI) > 25.2, surgery on Monday, age > 67.5, postoperative complications, blood transfusion, and operation time > 120.5 min had a higher probability of hospitalization for ≥6 days. The model had good discrimination [area under the curve (AUC), 0.802 95% CI, 0.754–0.850]] and good calibration (p = 0.929). A decision curve analysis proved that the nomogram was clinically effective.ConclusionThis study identified risk factors for prolonged hospital stay in patients after TKA. It is important to recognize all the factors that affect hospital LOS to try to maximize the use of medical resources, optimize hospital LOS and ultimately optimize the care of our patients

Evidence based on Mendelian randomization and colocalization analysis strengthens causal relationships between structural changes in specific brain regions and risk of amyotrophic lateral sclerosis

BackgroundAmyotrophic lateral sclerosis (ALS) is a neurodegenerative disease characterized by the degeneration of motor neurons in the brain and spinal cord with a poor prognosis. Previous studies have observed cognitive decline and changes in brain morphometry in ALS patients. However, it remains unclear whether the brain structural alterations contribute to the risk of ALS. In this study, we conducted a bidirectional two-sample Mendelian randomization (MR) and colocalization analysis to investigate this causal relationship.MethodsSummary data of genome-wide association study were obtained for ALS and the brain structures, including surface area (SA), thickness and volume of subcortical structures. Inverse-variance weighted (IVW) method was used as the main estimate approach. Sensitivity analysis was conducted detect heterogeneity and pleiotropy. Colocalization analysis was performed to calculate the posterior probability of causal variation and identify the common genes.ResultsIn the forward MR analysis, we found positive associations between the SA in four cortical regions (lingual, parahippocampal, pericalcarine, and middle temporal) and the risk of ALS. Additionally, decreased thickness in nine cortical regions (caudal anterior cingulate, frontal pole, fusiform, inferior temporal, lateral occipital, lateral orbitofrontal, pars orbitalis, pars triangularis, and pericalcarine) was significantly associated with a higher risk of ALS. In the reverse MR analysis, genetically predicted ALS was associated with reduced thickness in the bankssts and increased thickness in the caudal middle frontal, inferior parietal, medial orbitofrontal, and superior temporal regions. Colocalization analysis revealed the presence of shared causal variants between the two traits.ConclusionOur results suggest that altered brain morphometry in individuals with high ALS risk may be genetically mediated. The causal associations of widespread multifocal extra-motor atrophy in frontal and temporal lobes with ALS risk support the notion of a continuum between ALS and frontotemporal dementia. These findings enhance our understanding of the cortical structural patterns in ALS and shed light on potentially viable therapeutic targets

Efficient internalization of TAT peptide in zwitterionic DOPC phospholipid membrane revealed by neutron diffraction

The aim of this study is to investigate the interactions between TAT peptides and a neutral DOPC bilayer by using neutron lamellar diffraction. The distribution of TAT peptides and the perturbation of water distribution across the DOPC bilayer were revealed. When compared to our previous study on an anionic DOPC/DOPS bilayer (X. Chen et al., Biochim Biophys Acta. 2013. 1828 (8), 1982\u20131988), a much deeper insertion of TAT peptides was found in the hydrophobic core of DOPC bilayer at a depth of 6.0 \uc5 from the center of the bilayer, a position close to the double bond of fatty acyl chain. We conclude that the electrostatic attractions between the positively charged TAT peptides and the negatively charged headgroups of phospholipid are not essential for the direct translocation. Furthermore, the interactions of TAT peptides with the DOPC bilayer were found to vary in a concentration-dependent manner. A limited number of peptides first associate with the phosphate moieties on the lipid headgroups by using the guanidinium ions pairing. Then the energetically favorable water defect structures are adopted to maintain the arginine residues hydrated by drawing water molecules and lipid headgroups into the bilayer core. Such bilayer deformations consequently lead to the deep intercalation of TAT peptides into the bilayer core. Once a threshold concentration of TAT peptide in the bilayer is reached, a significant rearrangement of bilayer will happen and steady-state water pores will form.Peer reviewed: YesNRC publication: Ye

Genome-Wide Identification and Characterization of BrrTCP Transcription Factors in Brassica rapa ssp. rapa

The teosinte branched1/cycloidea/proliferating cell factor (TCP) gene family is a plant-specific transcription factor that participates in the control of plant development by regulating cell proliferation. However, no report is currently available about this gene family in turnips (Brassica rapa ssp. rapa). In this study, a genome-wide analysis of TCP genes was performed in turnips. Thirty-nine TCP genes in turnip genome were identified and distributed on 10 chromosomes. Phylogenetic analysis clearly showed that the family was classified as two clades: class I and class II. Gene structure and conserved motif analysis showed that the same clade genes have similar gene structures and conserved motifs. The expression profiles of 39 TCP genes were determined through quantitative real-time PCR. Most CIN-type BrrTCP genes were highly expressed in leaf. The members of CYC/TB1 subclade are highly expressed in flower bud and weakly expressed in root. By contrast, class I clade showed more widespread but less tissue-specific expression patterns. Yeast two-hybrid data show that BrrTCP proteins preferentially formed heterodimers. The function of BrrTCP2 was confirmed through ectopic expression of BrrTCP2 in wild-type and loss-of-function ortholog mutant of Arabidopsis. Overexpression of BrrTCP2 in wild-type Arabidopsis resulted in the diminished leaf size. Overexpression of BrrTCP2 in triple mutants of tcp2/4/10 restored the leaf phenotype of tcp2/4/10 to the phenotype of wild type. The comprehensive analysis of turnip TCP gene family provided the foundation to further study the roles of TCP genes in turnips