Knowledge-enhanced Agents for Interactive Text Games

Communication via natural language is a key aspect of machine intelligence, and it requires computational models to learn and reason about world concepts, with varying levels of supervision. Significant progress has been made on fully-supervised non-interactive tasks, such as question-answering and procedural text understanding. Yet, various sequential interactive tasks, as in text-based games, have revealed limitations of existing approaches in terms of coherence, contextual awareness, and their ability to learn effectively from the environment. In this paper, we propose a knowledge-injection framework for improved functional grounding of agents in text-based games. Specifically, we consider two forms of domain knowledge that we inject into learning-based agents: memory of previous correct actions and affordances of relevant objects in the environment. Our framework supports two representative model classes: reinforcement learning agents and language model agents. Furthermore, we devise multiple injection strategies for the above domain knowledge types and agent architectures, including injection via knowledge graphs and augmentation of the existing input encoding strategies. We experiment with four models on the 10 tasks in the ScienceWorld text-based game environment, to illustrate the impact of knowledge injection on various model configurations and challenging task settings. Our findings provide crucial insights into the interplay between task properties, model architectures, and domain knowledge for interactive contexts.Comment: Published at K-CAP '2

PEER: A Comprehensive and Multi-Task Benchmark for Protein Sequence Understanding

We are now witnessing significant progress of deep learning methods in a variety of tasks (or datasets) of proteins. However, there is a lack of a standard benchmark to evaluate the performance of different methods, which hinders the progress of deep learning in this field. In this paper, we propose such a benchmark called PEER, a comprehensive and multi-task benchmark for Protein sEquence undERstanding. PEER provides a set of diverse protein understanding tasks including protein function prediction, protein localization prediction, protein structure prediction, protein-protein interaction prediction, and protein-ligand interaction prediction. We evaluate different types of sequence-based methods for each task including traditional feature engineering approaches, different sequence encoding methods as well as large-scale pre-trained protein language models. In addition, we also investigate the performance of these methods under the multi-task learning setting. Experimental results show that large-scale pre-trained protein language models achieve the best performance for most individual tasks, and jointly training multiple tasks further boosts the performance. The datasets and source codes of this benchmark are all available at https://github.com/DeepGraphLearning/PEER_BenchmarkComment: Accepted by NeurIPS 2022 Dataset and Benchmark Track. arXiv v2: source code released; arXiv v1: release all benchmark result

Whole-Genome Sequencing Of Mesorhizobium huakuii 7653R Provides Molecular Insights into Host Specificity and Symbiosis Island Dynamics

Background Evidence based on genomic sequences is urgently needed to confirm the phylogenetic relationship between Mesorhizobium strain MAFF303099 and M. huakuii. To define underlying causes for the rather striking difference in host specificity between M. huakuii strain 7653R and MAFF303099, several probable determinants also require comparison at the genomic level. An improved understanding of mobile genetic elements that can be integrated into the main chromosomes of Mesorhizobium to form genomic islands would enrich our knowledge of how genome dynamics may contribute to Mesorhizobium evolution in general. Results In this study, we sequenced the complete genome of 7653R and compared it with five other Mesorhizobium genomes. Genomes of 7653R and MAFF303099 were found to share a large set of orthologs and, most importantly, a conserved chromosomal backbone and even larger perfectly conserved synteny blocks. We also identified candidate molecular differences responsible for the different host specificities of these two strains. Finally, we reconstructed an ancestral Mesorhizobium genomic island that has evolved into diverse forms in different Mesorhizobium species. Conclusions Our ortholog and synteny analyses firmly establish MAFF303099 as a strain of M. huakuii. Differences in nodulation factors and secretion systems T3SS, T4SS, and T6SS may be responsible for the unique host specificities of 7653R and MAFF303099 strains. The plasmids of 7653R may have arisen by excision of the original genomic island from the 7653R chromosome

Association between life’s essential 8 and periodontitis: a study based on NHANES 2009–2014

BackgroundThis research aims to investigate the relationship between Life’s Essentials 8 (LE8), the American Heart Association’s latest indicator, and periodontitis. The purpose is to provide guidance on preventative measures.MethodsData for our investigation were obtained from the National Health and Nutrition Examination Survey (NHANES) 2009–2014, with a total of 8,784 participants eligible. LE8 scores were compiled from 8 index scores (the score for each component of diet, physical activity, nicotine exposure, sleep duration, body mass index, blood lipids, blood glucose, and blood pressure). Periodontitis was classified by the Centers for Disease Control and Prevention and American Academy of Periodontology (CDC/AAP). The study utilized multivariable logistic analyses to investigate the potential correlation.ResultsAfter controlling for all covariates, LE8 was discovered to have a significant negative correlation with periodontitis prevalence [0.91 (0.88, 0.94)]. This trend continued to hold statistical significance even after converting LE8 into a categorical variable. Furthermore, a noteworthy adverse correlation was discovered across both genders, specifically males [0.35 (0.22, 0.55)] and females [0.39 (0.25, 0.60)], as well as for the majority of categorical classifications, namely ethnicity, age, education level, and marital status. However, only the age subgroups displayed some degree of significant difference from each other.ConclusionLife’s essential 8 was negatively associated with periodontitis, but more prospective trails are needed to confirm our findings

Label-free proteomic analysis reveals the hepatoprotective mechanism of gypenosides in liver injury rats

Chronic liver disease, a long-term condition resulting from various causes such as alcohol abuse, metabolic disorders, and viral hepatitis, is becoming a significant global health challenge. Gypenosides (GPs), derived from the traditional Chinese medicine Gynostemma pentaphyllum (Thunb.) Makino, exhibited hepatoprotective properties in recent years, yet the precise therapeutic mechanism remains unclear. In this study, label-free and parallel reaction monitoring (PRM) proteomics were used to elucidate the hepatoprotective mechanism of GPs in liver injury rats. Through label-free proteomics, we identified 2104 differentially expressed proteins (DEPs) associated with liver injury, along with 1974 DEPs related to the effects of GPs. Bioinformatics analysis revealed that GPs primarily restored metabolic processes involving valine, leucine, and isoleucine degradation, as well as propanoate and butanoate metabolism, and steroid hormone biosynthesis during liver injury. Subsequently, overlapping the two groups of DEPs identified 1508 proteins reversed following GPs treatment, with key targets further validated by PRM. Eight target proteins were identified for GPs treatment of liver injury, including Lgals3, Psat1, Phgdh, Cyp3a9, Cyp2c11, Cyp4a2, Glul, and Ces1d. These findings not only elucidated the hepatoprotective mechanism of GPs, but may also serve as potential therapeutic targets of chronic liver disease

Involvement of Fenton chemistry in rice straw degradation by the lignocellulolytic bacterium Pantoea ananatis Sd-1

Additional file 7: Table S2. Primers used for quantitative real time-PCR

Root Growth, Water and Nitrogen Use Efficiencies in Winter Wheat Under Different Irrigation and Nitrogen Regimes in North China Plain

Excessive nitrogen (N) application combined with water shortage has a negative effect on crop production, particularly wheat (Triticum aestivum L.) production in the North China Plain. This study examined root growth and water and nitrogen use efficiencies in wheat grown on loam soil in the North China Plain, from 2012 to 2014 using a fixed-position experiment initiated in 2010. The experiment followed a completely randomized split-plot design with four replications, taking irrigation [no irrigation (W0) versus irrigation at jointing plus flowering (W2)] as the main plot and N treatment (0, 180, 240, and 300 kg N ha-1) as the subplot. Compared with W0, W2 increased grain yield and root weight density (RWD) by up to 91.3 and 57.7% in 2012–2013, and 15.5 and 43.0% in 2013–2014, respectively, across all N application rates. Irrigation had no effect on grain water use efficiency (WUEY), but caused a decrease in biomass WUE at vegetative growth stage (WUEF) and at grain-filling stage (WUEM). Significant improvements in grain yield and biomass WUE during vegetative growth stage, and reductions in nitrogen-use efficiency (NUE) and RWD, were observed with increasing N application. Compared with non-N treatment, N treatment increased yield by up to 98.9 and 93.7% in 2012–2013 and 2013–2014, respectively, decreasing RWD by 12.0 and 16.9%. Correlation analysis further revealed that RWD was positively correlated with grain yield, evapotranspiration (ET) and NUE. NUE was also positively correlated with nitrogen uptake efficiency (UPE). Overall, the findings suggest that optimal N application improves NUE by increasing above–ground nitrogen uptake as a result of optimized RWD and a synchronous increase in WUE, thus increasing yield. Under the experimental conditions, an N application rate of 240 kg N ha-1 plus irrigation at jointing and flowering is recommended