Postdepositional Behavior of Molybdenum in Deep Sediments and Implications for Paleoredox Reconstruction

Molybdenum (Mo) is a trace element sensitive to oceanic redox conditions. The fidelity of sedimentary Mo as a paleoredox proxy of coeval seawater depends on the extent of Mo remobilization during postdepositional processes. Here we present the Mo content and isotope profiles for deep sediments from the Nankai Trough, Japan. The Mo signature suggests that these sediments have experienced extensive early diagenesis and hydrothermal alteration at depth. Iron (Fe)‐manganese (Mn) (oxyhydr)oxide alteration combined with Mo thiolation leads to a more than twenty‐fold enrichment of Mo within the sulfate reduction zone. Hydrothermal fluids and Mo adsorption onto Fe‐Mn (oxyhydr)oxides cause extremely negative Mo‐isotope values at the underthrust zone. These postdepositional Mo signals might be misinterpreted as expanded anoxia in the water column. Our findings highlight the importance of constraining postdepositional effects on Mo‐based proxies during paleoredox reconstruction

Approche bayesienne pour gérer les incertitudes dans l'identification à partir de mesures de champ

La méthode d'identification bayesienne présente l'avantage de pouvoir tenir compte de différentes sources d'incertitude présentes dans le problème et de quantifier l'incertitude avec laquelle les propriétés sont identifiées aussi bien en termes de variances que de corrélations. Son application à un problème d'identification de propriétés élastiques orthotropes à partir de mesures de champs de déplacements par Moiré interférométrique est présentée dans ce papier

Recent advances in hydrothermal carbonisation:from tailored carbon materials and biochemicals to applications and bioenergy

Introduced in the literature in 1913 by Bergius, who at the time was studying biomass coalification, hydrothermal carbonisation, as many other technologies based on renewables, was forgotten during the "industrial revolution". It was rediscovered back in 2005, on the one hand, to follow the trend set by Bergius of biomass to coal conversion for decentralised energy generation, and on the other hand as a novel green method to prepare advanced carbon materials and chemicals from biomass in water, at mild temperature, for energy storage and conversion and environmental protection. In this review, we will present an overview on the latest trends in hydrothermal carbonisation including biomass to bioenergy conversion, upgrading of hydrothermal carbons to fuels over heterogeneous catalysts, advanced carbon materials and their applications in batteries, electrocatalysis and heterogeneous catalysis and finally an analysis of the chemicals in the liquid phase as well as a new family of fluorescent nanomaterials formed at the interface between the liquid and solid phases, known as hydrothermal carbon nanodots

KHGCN: Knowledge-Enhanced Recommendation with Hierarchical Graph Capsule Network

Knowledge graphs as external information has become one of the mainstream directions of current recommendation systems. Various knowledge-graph-representation methods have been proposed to promote the development of knowledge graphs in related fields. Knowledge-graph-embedding methods can learn entity information and complex relationships between the entities in knowledge graphs. Furthermore, recently proposed graph neural networks can learn higher-order representations of entities and relationships in knowledge graphs. Therefore, the complete presentation in the knowledge graph enriches the item information and alleviates the cold start of the recommendation process and too-sparse data. However, the knowledge graph’s entire entity and relation representation in personalized recommendation tasks will introduce unnecessary noise information for different users. To learn the entity-relationship presentation in the knowledge graph while effectively removing noise information, we innovatively propose a model named knowledge—enhanced hierarchical graph capsule network (KHGCN), which can extract node embeddings in graphs while learning the hierarchical structure of graphs. Our model eliminates noisy entities and relationship representations in the knowledge graph by the entity disentangling for the recommendation and introduces the attentive mechanism to strengthen the knowledge-graph aggregation. Our model learns the presentation of entity relationships by an original graph capsule network. The capsule neural networks represent the structured information between the entities more completely. We validate the proposed model on real-world datasets, and the validation results demonstrate the model’s effectiveness

Trade and Investment Among BRICS: Analysis of Impact of Tariff Reduction and Trade Facilitation Based on Dynamic Global CGE Model

So far, there are few researches based on GTAP model focusing on the inter-regional trade activities among BRICS countries. Specifically, few studies have paid special attention to tariff exemption or trade facilitation scenario analysis. On the other hand, these topics broadly exist in global, multilateral and bilateral trade agreements and dialogues. One of the most prominent issues calling for in-depth study is the dynamic changing characteristics of emerging economies’ trade activities. BRICS countries differ greatly with respect to their trade volume, structure, dependence and environment, which lead to diversified sensitivities to tariff and trade facilitation. As the largest export-oriented emerging economy, China is more sensitive to tariffs and trade facilitation due to its large trade volume of manufactured goods and primary goods. Brazil and Russia are traditional resources exporter and thus they are less sensitive to tariffs and trade facilities because of the monopoly power. India is more dependent on service trade and commodity trade market is usually protected. However, since all of the BRICS counties have joined WTO and the global trade context is transforming, we need to involve the political and economic dynamics into global trade model to simulate the economic impacts. In this paper, we established a dynamic global CGE model to analyze the effects of free trade and trade facilitation in BRICS countries. In the settings of our model, we use adaptive expectation other than pure rational expectation to reflect the situation that BRICS countries are in the midst of transformation. The results show that the dynamic trade changing paths of these countries are quite different from those of developed countries. When trade facilitation increases, the results of China show that China’s agricultural products will see a huge growth in the future. One reason is that agricultural products are very sensitive to trade facilitation, especially sensitive to factors like custom clearance time. Car trade will also see a huge growth under the scenario that car tariffs are reduced

Reduction of p11 in dorsal raphe nucleus serotonergic neurons mediates depression-like behaviors

Abstract The pathology of depression is related to the imbalance of various neurotransmitters. The dorsal raphe nucleus (DRN), the main brain region producing 5-HT, is crucially involved in the pathophysiology of depression. It contains several neuron types, in which GABAergic neurons are activated by stimuli associated with negative experiences and 5-HT neurons are activated by reward signals. However, little is known about its underlying molecular mechanisms. Here, we found that p11, a multifunctional protein associated with depression, was down-regulated by chronic social defeat stress in 5-HTDRN neurons. Knockdown of p11 in DRN induced depression-like behaviors, while its overexpression in 5-HTDRN neurons alleviated depression-like behavior caused by chronic social defeat stress. Further, p11 regulates membrane trafficking of glutamate receptors in 5-HTDRN neurons, suggesting a possible molecular mechanism underlying the participation of p11 in the pathological process of depression. This may facilitate the understanding of the molecular and cellular basis of depression