Differentiation of Eight Commercial Mushrooms by Electronic Nose and Gas Chromatography-Mass Spectrometry

Volatile profiles of eight mushrooms were characterized by gas chromatography-mass spectrometry and electronic nose analysis. Volatile compounds including 11 alcohols, 11 ketones, 15 aldehydes, 3 sulfur compounds and alkenes, 8 terpenes, 7 acid and esters, 5 heterocyclic compounds, 20 aromatic compounds, and 4 other compounds were identified. The overall aroma properties of the mushrooms were analyzed by the electronic nose. Results indicated that the e-nose sensors have the ability to accurately respond to different mushrooms with similar fingerprint chromatograms. The relationship between the GC-MS data and e-nose responses of different mushrooms was modeled by principal component analysis and partial least squares regression. This combination for the volatile analysis with chemometric methods can be applied to distinguish different mushrooms successfully. Furthermore, it is concluded that the volatile composition of commercial mushrooms could benefit a finger spectrum by e-nose to identify the species of edible fungi

Mechanisms for Electron Uptake by \u3cem\u3eMethanosarcina acetivorans\u3c/em\u3e during Direct Interspecies Electron Transfer

Direct interspecies electron transfer (DIET) between bacteria and methanogenic archaea appears to be an important syntrophy in both natural and engineered methanogenic environments. However, the electrical connections on the outer surface of methanogens and the subsequent processing of electrons for carbon dioxide reduction to methane are poorly understood. Here, we report that the genetically tractable methanogen Methanosarcina acetivorans can grow via DIET in coculture with Geobacter metallireducens serving as the electron-donating partner. Comparison of gene expression patterns in M. acetivorans grown in coculture versus pure-culture growth on acetate revealed that transcripts for the outer-surface multiheme c-type cytochrome MmcA were higher during DIET-based growth. Deletion of mmcA inhibited DIET. The high aromatic amino acid content of M. acetivorans archaellins suggests that they might assemble into electrically conductive archaella. A mutant that could not express archaella was deficient in DIET. However, this mutant grew in DIET-based coculture as well as the archaellum-expressing parental strain in the presence of granular activated carbon, which was previously shown to serve as a substitute for electrically conductive pili as a conduit for long-range interspecies electron transfer in other DIET-based cocultures. Transcriptomic data suggesting that the membrane-bound Rnf, Fpo, and HdrED complexes also play a role in DIET were incorporated into a charge-balanced model illustrating how electrons entering the cell through MmcA can yield energy to support growth from carbon dioxide reduction. The results are the first genetics-based functional demonstration of likely outersurface electrical contacts for DIET in a methanogen

Mechanisms for Electron Uptake by \u3cem\u3eMethanosarcina acetivorans\u3c/em\u3e during Direct Interspecies Electron Transfer

Direct interspecies electron transfer (DIET) between bacteria and methanogenic archaea appears to be an important syntrophy in both natural and engineered methanogenic environments. However, the electrical connections on the outer surface of methanogens and the subsequent processing of electrons for carbon dioxide reduction to methane are poorly understood. Here, we report that the genetically tractable methanogen Methanosarcina acetivorans can grow via DIET in coculture with Geobacter metallireducens serving as the electron-donating partner. Comparison of gene expression patterns in M. acetivorans grown in coculture versus pure-culture growth on acetate revealed that transcripts for the outer-surface multiheme c-type cytochrome MmcA were higher during DIET-based growth. Deletion of mmcA inhibited DIET. The high aromatic amino acid content of M. acetivorans archaellins suggests that they might assemble into electrically conductive archaella. A mutant that could not express archaella was deficient in DIET. However, this mutant grew in DIET-based coculture as well as the archaellum-expressing parental strain in the presence of granular activated carbon, which was previously shown to serve as a substitute for electrically conductive pili as a conduit for long-range interspecies electron transfer in other DIET-based cocultures. Transcriptomic data suggesting that the membrane-bound Rnf, Fpo, and HdrED complexes also play a role in DIET were incorporated into a charge-balanced model illustrating how electrons entering the cell through MmcA can yield energy to support growth from carbon dioxide reduction. The results are the first genetics-based functional demonstration of likely outer-surface electrical contacts for DIET in a methanogen

AutoPCF: Efficient Product Carbon Footprint Accounting with Large Language Models

The product carbon footprint (PCF) is crucial for decarbonizing the supply chain, as it measures the direct and indirect greenhouse gas emissions caused by all activities during the product's life cycle. However, PCF accounting often requires expert knowledge and significant time to construct life cycle models. In this study, we test and compare the emergent ability of five large language models (LLMs) in modeling the 'cradle-to-gate' life cycles of products and generating the inventory data of inputs and outputs, revealing their limitations as a generalized PCF knowledge database. By utilizing LLMs, we propose an automatic AI-driven PCF accounting framework, called AutoPCF, which also applies deep learning algorithms to automatically match calculation parameters, and ultimately calculate the PCF. The results of estimating the carbon footprint for three case products using the AutoPCF framework demonstrate its potential in achieving automatic modeling and estimation of PCF with a large reduction in modeling time from days to minutes

Marketing Budget Allocation with Offline Constrained Deep Reinforcement Learning

We study the budget allocation problem in online marketing campaigns that utilize previously collected offline data. We first discuss the long-term effect of optimizing marketing budget allocation decisions in the offline setting. To overcome the challenge, we propose a novel game-theoretic offline value-based reinforcement learning method using mixed policies. The proposed method reduces the need to store infinitely many policies in previous methods to only constantly many policies, which achieves nearly optimal policy efficiency, making it practical and favorable for industrial usage. We further show that this method is guaranteed to converge to the optimal policy, which cannot be achieved by previous value-based reinforcement learning methods for marketing budget allocation. Our experiments on a large-scale marketing campaign with tens-of-millions users and more than one billion budget verify the theoretical results and show that the proposed method outperforms various baseline methods. The proposed method has been successfully deployed to serve all the traffic of this marketing campaign.Comment: WSDM 23, Best Paper Candidat

Enhancing Recommender Systems with Large Language Model Reasoning Graphs

Recommendation systems aim to provide users with relevant suggestions, but often lack interpretability and fail to capture higher-level semantic relationships between user behaviors and profiles. In this paper, we propose a novel approach that leverages large language models (LLMs) to construct personalized reasoning graphs. These graphs link a user's profile and behavioral sequences through causal and logical inferences, representing the user's interests in an interpretable way. Our approach, LLM reasoning graphs (LLMRG), has four components: chained graph reasoning, divergent extension, self-verification and scoring, and knowledge base self-improvement. The resulting reasoning graph is encoded using graph neural networks, which serves as additional input to improve conventional recommender systems, without requiring extra user or item information. Our approach demonstrates how LLMs can enable more logical and interpretable recommender systems through personalized reasoning graphs. LLMRG allows recommendations to benefit from both engineered recommendation systems and LLM-derived reasoning graphs. We demonstrate the effectiveness of LLMRG on benchmarks and real-world scenarios in enhancing base recommendation models.Comment: 12 pages, 6 figure

Identification of molecular subtypes, risk signature, and immune landscape mediated by necroptosis-related genes in non-small cell lung cancer

BackgroundNon-small cell lung cancer (NSCLC) is a highly heterogeneous malignancy with an extremely high mortality rate. Necroptosis is a programmed cell death mode mediated by three major mediators, RIPK1, RIPK3, and MLKL, and has been shown to play a role in various cancers. To date, the effect of necroptosis on NSCLC remains unclear.MethodsIn The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, we downloaded transcriptomes of lung adenocarcinoma (LUAD) patients and their corresponding clinicopathological parameters. We performed multi-omics analysis using consensus clustering based on the expression levels of 40 necroptosis-related genes. We constructed prognostic risk models and used the receiver operating characteristic (ROC) curves, nomograms, and survival analysis to evaluate prognostic models.ResultsWith the use of consensus clustering analysis, two distinct subtypes of necroptosis were identified based on different mRNA expression levels, and cluster B was found to have a better survival advantage. Correlation results showed that necroptosis was significantly linked with clinical features, overall survival (OS) rate, and immune infiltration. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analysis confirmed that these differential genes were valuable in various cellular and biological functions and were significantly enriched in various pathways such as the P53 signaling pathway and cell cycle. We further identified three genomic subtypes and found that gene cluster B patients had better prognostic value. Multivariate Cox analysis identified the 14 best prognostic genes for constructing prognostic risk models. The high-risk group was found to have a poor prognosis. The construction of nomograms and ROC curves showed stable validity in prognostic prediction. There were also significant differences in tumor immune microenvironment, tumor mutational burden (TMB), and drug sensitivity between the two risk groups. The results demonstrate that the 14 genes constructed in this prognostic risk model were used as tumor prognostic biomarkers to guide immunotherapy and chemotherapy. Finally, we used qRT-PCR to validate the genes involved in the signature.ConclusionThis study promotes our new understanding of necroptosis in the tumor microenvironment of NSCLC, mines prognostic biomarkers, and provides a potential value for guiding immunotherapy and chemotherapy

Evaluate how steaming and sulfur fumigation change the microstructure, physicochemical properties and in vitro digestibility of Gastrodia elata Bl. starch

The sulfur dioxide gas (SO2) generated by sulfur burning can improve the appearance quality of food and enhance the storage time. However, excessive sulfur dioxide will pollute the environment and cause deterioration of food quality, and even the high residual levels can increase the risk of cancer. As Gastrodia elata Blume is prone to corruption during processing, sulfur fumigation is often used for preservation. In this study, spectral analysis and Texture Profile Analysis (TPA) were used to investigate the effects of traditional sulfur fumigation processing on the morphology quality, edible quality and structural characteristics of G. elata. The results showed that compared with direct drying, the pH decreased by 0.399 of the sulfur fumigated after steamed treatment G. elata, and the morphology quality, pasting ability and gel edible quality of the starch were significantly improved. In addition, it was suggested that sulfur fumigation after steaming could promote the release of molecular chains from starch granules and thus enhance the cross-linking between molecules, which explained the reason for the improve of starch edible quality. This study can provide technical and theoretical support for improving the quality of starch rich foods, replacing sulfur fumigation and reducing potential environmental hazards