Vaterite Dissolution: Mechanism and Kinetics

The dissolution of porous spherulitic, vaterite particles in aqueous solution is investigated via microscopic monitoring of their size as a function of time. The latter is shown to provide a clear, generic distinction between dissolution controlled either by the rate of a surface-controlled reaction or via dissolution under conditions where the concentration of calcium and carbonate ions is pinned locally by the solubility product of vaterite followed by diffusion away from the dissolving interface into the bulk solution. Dissolution under “thermodynamic control” is shown to be the case for vaterite particles, allowing a value of the solubility product to be determined in the light of the known solution phase equilibria, including the ion pairs CaCO3, CaOH+, and CaHCO3 +

An Efficient Source Model Selection Framework in Model Databases

With the explosive increase of big data, training a Machine Learning (ML) model becomes a computation-intensive workload, which would take days or even weeks. Thus, reusing an already trained model has received attention, which is called transfer learning. Transfer learning avoids training a new model from scratch by transferring knowledge from a source task to a target task. Existing transfer learning methods mostly focus on how to improve the performance of the target task through a specific source model, and assume that the source model is given. Although many source models are available, it is difficult for data scientists to select the best source model for the target task manually. Hence, how to efficiently select a suitable source model in a model database for model reuse is an interesting but unsolved problem. In this paper, we propose SMS, an effective, efficient, and flexible source model selection framework. SMS is effective even when the source and target datasets have significantly different data labels, and is flexible to support source models with any type of structure, and is efficient to avoid any training process. For each source model, SMS first vectorizes the samples in the target dataset into soft labels by directly applying this model to the target dataset, then uses Gaussian distributions to fit for clusters of soft labels, and finally measures the distinguishing ability of the source model using Gaussian mixture-based metric. Moreover, we present an improved SMS (I-SMS), which decreases the output number of the source model. I-SMS can significantly reduce the selection time while retaining the selection performance of SMS. Extensive experiments on a range of practical model reuse workloads demonstrate the effectiveness and efficiency of SMS

Calcium carbonate dissolution from the laboratory to the ocean: kinetics and mechanism

The ultimate fate, over the course of millennia, of nearly all of the carbon dioxide formed by humankind is for it to react with calcium carbonate in the world's oceans. Although, this reaction is of global relevance, aspects of the calcite dissolution reaction remain poorly described with apparent contradictions present throughout the expansive literature. In this perspective we aim to evidence how a lack of appreciation of the role of mass-transport may have hampered developments in this area. These insights have important implications for both idealised experiments performed under laboratory conditions and for the measurement and modelling of oceanic calcite sediment dissolution

RNA-seq liver transcriptome analysis reveals an activated MHC-I pathway and an inhibited MHC-II pathway at the early stage of vaccine immunization in zebrafish

BACKGROUND: Zebrafish (Danio rerio) is a prominent vertebrate model of human development and pathogenic disease and has recently been utilized to study teleost immune responses to infectious agents threatening the aquaculture industry. In this work, to clarify the host immune mechanisms underlying the protective effects of a putative vaccine and improve its immunogenicity in the future efforts, high-throughput RNA sequencing technology was used to investigate the immunization-related gene expression patterns of zebrafish immunized with Edwardsiella tarda live attenuated vaccine. RESULTS: Average reads of 18.13 million and 14.27 million were obtained from livers of zebrafish immunized with phosphate buffered saline (mock) and E. tarda vaccine (WED), respectively. The reads were annotated with the Ensembl zebrafish database before differential expressed genes sequencing (DESeq) comparative analysis, which identified 4565 significantly differentially expressed genes (2186 up-regulated and 2379 down-regulated in WED; p<0.05). Among those, functional classifications were found in the Gene Ontology database for 3891 and in the Kyoto Encyclopedia of Genes and Genomes database for 3467. Several pathways involved in acute phase response, complement activation, immune/defense response, and antigen processing and presentation were remarkably affected at the early stage of WED immunization. Further qPCR analysis confirmed that the genes encoding the factors involved in major histocompatibility complex (MHC)-I processing pathway were up-regulated, while those involved in MHC-II pathway were down-regulated. CONCLUSION: These data provided insights into the molecular mechanisms underlying zebrafish immune response to WED immunization and might aid future studies to develop a highly immunogenic vaccine against gram-negative bacteria in teleosts

AI facilitated fluoro-electrochemical phytoplankton classification

Marine phytoplankton is extremely diverse. Counting and characterising phytoplankton is essential for understanding climate change and ocean health not least since phytoplankton extensively biomineralize carbon dioxide whilst generating 50% of the planet's oxygen. We report the use of fluoro-electrochemical microscopy to distinguish different taxonomies of phytoplankton by the quenching of their chlorophyll-a fluorescence using chemical species oxidatively electrogenerated in situin seawater. The rate of chlorophyll-a quenching of each cell is characteristic of the species-specific structural composition and cellular content. But with increasing diversity and extent of phytoplankton species under study, human interpretation and distinction of the resulting fluorescence transients becomes increasingly and prohibitively difficult. Thus, we further report a neural network to analyse these fluorescence transients, with an accuracy >95% classifying 29 phytoplankton strains to their taxonomic orders. This method transcends the state-of-the-art. The success of the fluoro-electrochemical microscopy combined with AI provides a novel, flexible and highly granular solution to phytoplankton classification and is adaptable for autonomous ocean monitoring

DESIGN AND DEVELOPMENT OF A RELIABILITY ANALYSIS TOOL BASED ON MULTILEVEL FLOW MODELS

ABSTRACT This paper proposes a universal graphical tool for the modeling and reliability analysis of complex industrial process system based on Multilevel Flow Models (MFM). An Extensible Markup Language (XML) is used for structuring the MFM model. An editor is developed and an executor can implement reliability analysis in terms of the established MFM models. The proposed reliability analysis tool is meaningful for further research of the MFM based system reliability analysis and will be useful for more practical applications such as online risk monitoring by integrating the existed algorithms of alarm analysis and fault diagnosis based on MFM. INTRODUCTION Reliability analysis is the key to effective, reliable and safe design and operation of nuclear power system. Several system reliability analysis techniques have been proposed and commonly used to characterize the probabilistic behavior of nuclear power plant. Among these, the well-known and the one extensively employed is Fault Tree Analysis (FTA). However, the main limitation of FTA is the difficulty of handing the problems of systems with multiple states and /or timesequential signals. In addition, a fault tree would become too large and too complex and different analysts may use different representations, which cause the difficulties in building, validating and modifying fault tree logic models. Multilevel Flow Models is a new and promising system modeling method which is developed by Lin

Calcifying coccolithophore: an evolutionary advantage against extracellular oxidative damage

The evolutionary advantages afforded by phytoplankton calcification remain enigmatic. In this work, fluoroelectrochemical experiments reveal that the presence of a CaCO3 shell of a naturally calcifying coccolithophore, Coccolithus braarudii, offers protection against extracellular oxidants as measured by the time required for the switch-off in their chlorophyll signal, compared to the deshelled equivalents, suggesting the shift toward calcification offers some advantages for survival in the surface of radical-rich seawater

A novel fluoro-electrochemical technique for classifying diverse marine nanophytoplankton

To broaden our understanding of pelagic ecosystem responses to environmental change, it is essential that we improve the spatiotemporal resolution of in situ monitoring of phytoplankton communities. A key challenge for existing methods is in classifying and quantifying cells within the nanophytoplankton size range (2–20 μm). This is particularly difficult when there are similarities in morphology, making visual differentiation difficult for both trained taxonomists and machine learning-based approaches. Here we present a rapid fluoro-electrochemical technique for classifying nanophytoplankton, and using a library of 52 diverse strains of nanophytoplankton we assess the accuracy of this technique based on two measurements at the individual level: charge required to reduce per cell chlorophyll a fluorescence by 50% and cell radius. We demonstrate a high degree of accuracy overall (92%) in categorizing cells belonging to widely recognized key functional groups; however, this is reduced when we consider the broader diversity of “nano-phytoflagellates'.” Notably, we observe that some groups, for example, calcifying Isochrysidales, have much greater resilience to electrochemically driven oxidative conditions relative to others of a similar size, making them more easily categorized by the technique. The findings of this study present a promising step forward in advancing our toolkit for monitoring phytoplankton communities. We highlight that, for improved categorization accuracy, future iterations of the method can be enhanced by measuring additional predictor variables with minimal adjustments to the set-up. In doing so, we foresee this technique being highly applicable, and potentially invaluable, for in situ classification and enumeration of the nanophytoplankton size fraction

Genome-Wide Expression Analysis in Down Syndrome: Insight into Immunodeficiency

Down syndrome (DS) is caused by triplication of Human chromosome 21 (Hsa21) and associated with an array of deleterious phenotypes, including mental retardation, heart defects and immunodeficiency. Genome-wide expression patterns of uncultured peripheral blood cells are useful to understanding of DS-associated immune dysfunction. We used a Human Exon microarray to characterize gene expression in uncultured peripheral blood cells derived from DS individuals and age-matched controls from two age groups: neonate (N) and child (C). A total of 174 transcript clusters (gene-level) with eight located on Hsa21 in N group and 383 transcript clusters including 56 on Hsa21 in C group were significantly dysregulated in DS individuals. Microarray data were validated by quantitative polymerase chain reaction. Functional analysis revealed that the dysregulated genes in DS were significantly enriched in two and six KEGG pathways in N and C group, respectively. These pathways included leukocyte trans-endothelial migration, B cell receptor signaling pathway and primary immunodeficiency, etc., which causally implicated dysfunctional immunity in DS. Our results provided a comprehensive picture of gene expression patterns in DS at the two developmental stages and pointed towards candidate genes and molecular pathways potentially associated with the immune dysfunction in DS