Genes for selenium dependent and independent formate dehydrogenase in the gut microbial communities of three lower, wood-feeding termites and a wood-feeding roach

The bacterial Wood-Ljungdahl pathway for CO_2-reductive acetogenesis is important for the nutritional mutualism occurring between wood-feeding insects and their hindgut microbiota. A key step in this pathway is the reduction of CO_2 to formate, catalysed by the enzyme formate dehydrogenase (FDH). Putative selenocysteine- (Sec) and cysteine- (Cys) containing paralogues of hydrogenase-linked FDH (FDH_H) have been identified in the termite gut acetogenic spirochete, Treponema primitia, but knowledge of their relevance in the termite gut environment remains limited. In this study, we designed degenerate PCR primers for FDH_H genes (fdhF) and assessed fdhF diversity in insect gut bacterial isolates and the gut microbial communities of termites and cockroaches. The insects examined herein represent three wood-feeding termite families, Termopsidae, Kalotermitidae and Rhinotermitidae (phylogenetically 'lower' termite taxa); the wood-feeding roach family Cryptocercidae (the sister taxon to termites); and the omnivorous roach family Blattidae. Sec and Cys FDH_H variants were identified in every wood-feeding insect but not the omnivorous roach. Of 68 novel alleles obtained from inventories, 66 affiliated phylogenetically with enzymes from T. primitia. These formed two subclades (37 and 29 phylotypes) almost completely comprised of Sec-containing and Cys-containing enzymes respectively. A gut cDNA inventory showed transcription of both variants in the termite Zootermopsis nevadensis (family Termopsidae). The gene patterns suggest that FDH_H enzymes are important for the CO_2-reductive metabolism of uncultured acetogenic treponemes and imply that the availability of selenium, a trace element, shaped microbial gene content in the last common ancestor of dictyopteran, wood-feeding insects, and continues to shape it to this day

Large D/H variations in bacterial lipids reflect central metabolic pathways

Large hydrogen-isotopic (D/H) fractionations between lipids and growth water have been observed in most organisms studied to date. These fractionations are generally attributed to isotope effects in the biosynthesis of lipids, and are frequently assumed to be approximately constant for the purpose of reconstructing climatic variables. Here, we report D/H fractionations between lipids and water in 4 cultured members of the phylum Proteobacteria, and show that they can vary by up to 500‰ in a single organism. The variation cannot be attributed to lipid biosynthesis as there is no significant change in these pathways between cultures, nor can it be attributed to changing substrate D/H ratios. More importantly, lipid/water D/H fractionations vary systematically with metabolism: chemoautotrophic growth (approximately −200 to −400‰), photoautotrophic growth (−150 to −250‰), heterotrophic growth on sugars (0 to −150‰), and heterotrophic growth on TCA-cycle precursors and intermediates (−50 to +200‰) all yield different fractionations. We hypothesize that the D/H ratios of lipids are controlled largely by those of NADPH used for biosynthesis, rather than by isotope effects within the lipid biosynthetic pathway itself. Our results suggest that different central metabolic pathways yield NADPH—and indirectly lipids—with characteristic isotopic compositions. If so, lipid δD values could become an important biogeochemical tool for linking lipids to energy metabolism, and would yield information that is highly complementary to that provided by ^(13)C about pathways of carbon fixation

The Academic Career and Contributions of Chengxun Yang

Professor Chengxun Yang has deepened study of the economics of socialism with Chinese characteristics. With “serving the people” as his goal, and “only the truth” as his sole reference point, he has extracted the essence of Lenin's thoughts on commodity production after a socialist revolution. He has proposed theories on developing socialist commodity economy in its early stages, and he has elaborated the mechanisms and forms of the “two leaps” in rural economy, which were originally proposed by Deng Xiaoping in 1990. Based on empirical studies, he has revealed profound contradictions in reforming state-owned enterprises. He has engaged in study of the economy of the Yellow River Basin. Prof. Yang also established a new discipline of the economics of science and technology under socialism with Chinese characteristics, within the framework of maintaining and advancing Marxist economics

Counterfactual Monotonic Knowledge Tracing for Assessing Students' Dynamic Mastery of Knowledge Concepts

As the core of the Knowledge Tracking (KT) task, assessing students' dynamic mastery of knowledge concepts is crucial for both offline teaching and online educational applications. Since students' mastery of knowledge concepts is often unlabeled, existing KT methods rely on the implicit paradigm of historical practice to mastery of knowledge concepts to students' responses to practices to address the challenge of unlabeled concept mastery. However, purely predicting student responses without imposing specific constraints on hidden concept mastery values does not guarantee the accuracy of these intermediate values as concept mastery values. To address this issue, we propose a principled approach called Counterfactual Monotonic Knowledge Tracing (CMKT), which builds on the implicit paradigm described above by using a counterfactual assumption to constrain the evolution of students' mastery of knowledge concepts.Comment: Accepted by CIKM 2023, 10 pages, 5 figures, 4 table

No Length Left Behind: Enhancing Knowledge Tracing for Modeling Sequences of Excessive or Insufficient Lengths

Knowledge tracing (KT) aims to predict students' responses to practices based on their historical question-answering behaviors. However, most current KT methods focus on improving overall AUC, leaving ample room for optimization in modeling sequences of excessive or insufficient lengths. As sequences get longer, computational costs will increase exponentially. Therefore, KT methods usually truncate sequences to an acceptable length, which makes it difficult for models on online service systems to capture complete historical practice behaviors of students with too long sequences. Conversely, modeling students with short practice sequences using most KT methods may result in overfitting due to limited observation samples. To address the above limitations, we propose a model called Sequence-Flexible Knowledge Tracing (SFKT).Comment: Accepted by CIKM 2023, 10 pages, 8 figures, 5 table

Cognition-Mode Aware Variational Representation Learning Framework for Knowledge Tracing

The Knowledge Tracing (KT) task plays a crucial role in personalized learning, and its purpose is to predict student responses based on their historical practice behavior sequence. However, the KT task suffers from data sparsity, which makes it challenging to learn robust representations for students with few practice records and increases the risk of model overfitting. Therefore, in this paper, we propose a Cognition-Mode Aware Variational Representation Learning Framework (CMVF) that can be directly applied to existing KT methods. Our framework uses a probabilistic model to generate a distribution for each student, accounting for uncertainty in those with limited practice records, and estimate the student's distribution via variational inference (VI). In addition, we also introduce a cognition-mode aware multinomial distribution as prior knowledge that constrains the posterior student distributions learning, so as to ensure that students with similar cognition modes have similar distributions, avoiding overwhelming personalization for students with few practice records. At last, extensive experimental results confirm that CMVF can effectively aid existing KT methods in learning more robust student representations. Our code is available at https://github.com/zmy-9/CMVF.Comment: Accepted by ICDM 2023, 10 pages, 5 figures, 4 table

Multi-Factors Aware Dual-Attentional Knowledge Tracing

With the increasing demands of personalized learning, knowledge tracing has become important which traces students' knowledge states based on their historical practices. Factor analysis methods mainly use two kinds of factors which are separately related to students and questions to model students' knowledge states. These methods use the total number of attempts of students to model students' learning progress and hardly highlight the impact of the most recent relevant practices. Besides, current factor analysis methods ignore rich information contained in questions. In this paper, we propose Multi-Factors Aware Dual-Attentional model (MF-DAKT) which enriches question representations and utilizes multiple factors to model students' learning progress based on a dual-attentional mechanism. More specifically, we propose a novel student-related factor which records the most recent attempts on relevant concepts of students to highlight the impact of recent exercises. To enrich questions representations, we use a pre-training method to incorporate two kinds of question information including questions' relation and difficulty level. We also add a regularization term about questions' difficulty level to restrict pre-trained question representations to fine-tuning during the process of predicting students' performance. Moreover, we apply a dual-attentional mechanism to differentiate contributions of factors and factor interactions to final prediction in different practice records. At last, we conduct experiments on several real-world datasets and results show that MF-DAKT can outperform existing knowledge tracing methods. We also conduct several studies to validate the effects of each component of MF-DAKT.Comment: Accepted by CIKM 2021, 10 pages, 10 figures, 6 table

Multi-source Education Knowledge Graph Construction and Fusion for College Curricula

The field of education has undergone a significant transformation due to the rapid advancements in Artificial Intelligence (AI). Among the various AI technologies, Knowledge Graphs (KGs) using Natural Language Processing (NLP) have emerged as powerful visualization tools for integrating multifaceted information. In the context of university education, the availability of numerous specialized courses and complicated learning resources often leads to inferior learning outcomes for students. In this paper, we propose an automated framework for knowledge extraction, visual KG construction, and graph fusion, tailored for the major of Electronic Information. Furthermore, we perform data analysis to investigate the correlation degree and relationship between courses, rank hot knowledge concepts, and explore the intersection of courses. Our objective is to enhance the learning efficiency of students and to explore new educational paradigms enabled by AI. The proposed framework is expected to enable students to better understand and appreciate the intricacies of their field of study by providing them with a comprehensive understanding of the relationships between the various concepts and courses.Comment: accepted by ICALT202

Evidence for Cascades of Perturbation and Adaptation in the Metabolic Genes of Higher Termite Gut Symbionts

Termites and their gut microbes engage in fascinating dietary mutualisms. Less is known about how these complex symbioses have evolved after first emerging in an insect ancestor over 120 million years ago. Here we examined a bacterial gene, formate dehydrogenase (fdhF), that is key to the mutualism in 8 species of “higher” termite (members of the Termitidae, the youngest and most biomass-abundant and species-rich termite family). Patterns of fdhF diversity in the gut communities of higher termites contrasted strongly with patterns in less-derived (more-primitive) insect relatives (wood-feeding “lower” termites and roaches). We observed phylogenetic evidence for (i) the sweeping loss of several clades of fdhF that may reflect extinctions of symbiotic protozoa and, importantly, bacteria dependent on them in the last common ancestor of all higher termites and (ii) a radiation of genes from the (possibly) single allele that survived. Sweeping gene loss also resulted in (iii) the elimination of an entire clade of genes encoding selenium (Se)-independent enzymes from higher termite gut communities, perhaps reflecting behavioral or morphological innovations in higher termites that relaxed preexisting environmental limitations of Se, a dietary trace element. Curiously, several higher termite gut communities may have subsequently reencountered Se limitation, reinventing genes for Se-independent proteins via convergent evolution. Lastly, the presence of a novel fdhF lineage within litter-feeding and subterranean higher (but not other) termites may indicate recent gene “invasion” events. These results imply that cascades of perturbation and adaptation by distinct evolutionary mechanisms have impacted the evolution of complex microbial communities in a highly successful lineage of insects