A Competing Risk Analysis of Executions and Cancellations in a Limit Order Market

The competing risks technique is applied to the analysis of times to execution and cancellation of limit orders submitted on an electronic trading platform. Time-to-execution is found to be more sensitive to the limit price variation than time-to-cancellation, even though it is less sensitive to the limit order size. More importantly, investors who aim to reduce the expected time-to-execution for their limit orders without inducing any significant increase in the risk of subsequent cancellation should submit their orders when the market depth is smaller on the side of their orders or when the market depth is greater on the opposite side of their orders. We also provide a new diagnostic plots method for evaluating the goodness-of-fit of different competing risks models.Market microstructure, limit order, competing risks, hazard rate, frailty

O-GlcNAc as an Integrator of Signaling Pathways

O-GlcNAcylation is an important posttranslational modification governed by a single pair of enzymes–O-GlcNAc transferase (OGT) and O-GlcNAcase (OGA). These two enzymes mediate the dynamic cycling of O-GlcNAcylation on a wide variety of cytosolic, nuclear and mitochondrial proteins in a nutrient- and stress-responsive fashion. While cellular functions of O-GlcNAcylation have been emerging, little is known regarding the precise mechanisms how the enzyme pair senses the environmental cues to elicit molecular and physiological changes. In this review, we discuss how the OGT/OGA pair acts as a metabolic sensor that integrates signaling pathways, given their capability of receiving signaling inputs from various partners, targeting multiple substrates with spatiotemporal specificity and translocating to different parts of the cell. We also discuss how the pair maintains homeostatic signaling within the cell and its physiological relevance. A better understanding of the mechanisms of OGT/OGA action would enable us to derive therapeutic benefits of resetting cellular O-GlcNAc levels within an optimal range

Growth responses of Ulva prolifera to inorganic and organic nutrients: Implications for macroalgal blooms in the southern Yellow Sea, China

International audienceThe marine macrophyte Ulva prolifera is the dominant green-tide-forming seaweed in the southern Yellow Sea, China. Here we assessed, in the laboratory, the growth rate and nutrient uptake responses of U. prolifera to different nutrient treatments. The growth rates were enhanced in incubations with added organic and inorganic nitrogen [i.e. nitrate (NO3−), ammonium (NH4+), urea and glycine] and phosphorus [i.e. phosphate (PO43−), adenosine triphosphate (ATP) and glucose 6-phosphate (G-6-P)], relative to the control. The relative growth rates of U. prolifera were higher when enriched with dissolved organic nitrogen (urea and glycine) and phosphorus (ATP and G-6-P) than inorganic nitrogen (NO3− and NH4+) and phosphorus (PO43−). In contrast, the affinity was higher for inorganic than organic nutrients. Field data in the southern Yellow Sea showed significant inverse correlations between macroalgal biomass and dissolved organic nutrients. Our laboratory and field results indicated that organic nutrients such as urea, glycine and ATP, may contribute to the development of macroalgal blooms in the southern Yellow Sea

Structure, expression differentiation and evolution of duplicated fiber developmental genes in Gossypium barbadense and G. hirsutum

<p>Abstract</p> <p>Background</p> <p>Both <it>Gossypium hirsutum </it>and <it>G. barbadense </it>probably originated from a common ancestor, but they have very different agronomic and fiber quality characters. Here we selected 17 fiber development-related genes to study their structures, tree topologies, chromosomal location and expression patterns to better understand the interspecific divergence of fiber development genes in the two cultivated tetraploid species.</p> <p>Results</p> <p>The sequence and structure of 70.59% genes were conserved with the same exon length and numbers in different species, while 29.41% genes showed diversity. There were 15 genes showing independent evolution between the A- and D-subgenomes after polyploid formation, while two evolved via different degrees of colonization. Chromosomal location showed that 22 duplicate genes were located in which at least one fiber quality QTL was detected. The molecular evolutionary rates suggested that the D-subgenome of the allotetraploid underwent rapid evolutionary differentiation, and selection had acted at the tetraploid level. Expression profiles at fiber initiation and early elongation showed that the transcripts levels of most genes were higher in Hai7124 than in TM-1. During the primary-secondary transition period, expression of most genes peaked earlier in TM-1 than in Hai7124. Homeolog expression profile showed that A-subgenome, or the combination of A- and D-subgenomes, played critical roles in fiber quality divergence of <it>G. hirsutum </it>and <it>G. barbadense</it>. However, the expression of D-subgenome alone also played an important role.</p> <p>Conclusion</p> <p>Integrating analysis of the structure and expression to fiber development genes, suggests selective breeding for certain desirable fiber qualities played an important role in divergence of <it>G. hirsutum </it>and <it>G. barbadense</it>.</p

Cost-Effective In-Context Learning for Entity Resolution: A Design Space Exploration

Entity resolution (ER) is an important data integration task with a wide spectrum of applications. The state-of-the-art solutions on ER rely on pre-trained language models (PLMs), which require fine-tuning on a lot of labeled matching/non-matching entity pairs. Recently, large languages models (LLMs), such as GPT-4, have shown the ability to perform many tasks without tuning model parameters, which is known as in-context learning (ICL) that facilitates effective learning from a few labeled input context demonstrations. However, existing ICL approaches to ER typically necessitate providing a task description and a set of demonstrations for each entity pair and thus have limitations on the monetary cost of interfacing LLMs. To address the problem, in this paper, we provide a comprehensive study to investigate how to develop a cost-effective batch prompting approach to ER. We introduce a framework BATCHER consisting of demonstration selection and question batching and explore different design choices that support batch prompting for ER. We also devise a covering-based demonstration selection strategy that achieves an effective balance between matching accuracy and monetary cost. We conduct a thorough evaluation to explore the design space and evaluate our proposed strategies. Through extensive experiments, we find that batch prompting is very cost-effective for ER, compared with not only PLM-based methods fine-tuned with extensive labeled data but also LLM-based methods with manually designed prompting. We also provide guidance for selecting appropriate design choices for batch prompting.Comment: 14 pages, 7 figure

Rotating Machinery Signal Analysis Method Based on EEMD and Spectrum Correction

Aiming at the problems of low accuracy of non-stationary signal spectrum analysis in rotating machinery vibration, this paper puts forward a kind of rotating mechanical signal analysis method based on EEMD and spectrum correction. Firstly, ensemble empirical mode decomposition (EEMD) is used to obtain the intrinsic mode functions (IMF) of the original signal; secondly, do correlation analysis for each IMF component and the original signal separately, and find out the IMF component with the largest correlation coefficient and calculate the frequency spectrum of the IMF; finally, spectrum correction algorithm is employed to get accurate spectrum for quantitative analysis. A practical vibration signal of rotor vibration platform is applied to testing the method of this paper, the EMD method and wavelet analysis method separately. The results show that the proposed new method can improve the precision of spectrum analysis for rotating mechanical signal significantly; therefore, it has a good application prospect

PreDiff: Precipitation Nowcasting with Latent Diffusion Models

Earth system forecasting has traditionally relied on complex physical models that are computationally expensive and require significant domain expertise. In the past decade, the unprecedented increase in spatiotemporal Earth observation data has enabled data-driven forecasting models using deep learning techniques. These models have shown promise for diverse Earth system forecasting tasks but either struggle with handling uncertainty or neglect domain-specific prior knowledge, resulting in averaging possible futures to blurred forecasts or generating physically implausible predictions. To address these limitations, we propose a two-stage pipeline for probabilistic spatiotemporal forecasting: 1) We develop PreDiff, a conditional latent diffusion model capable of probabilistic forecasts. 2) We incorporate an explicit knowledge control mechanism to align forecasts with domain-specific physical constraints. This is achieved by estimating the deviation from imposed constraints at each denoising step and adjusting the transition distribution accordingly. We conduct empirical studies on two datasets: N-body MNIST, a synthetic dataset with chaotic behavior, and SEVIR, a real-world precipitation nowcasting dataset. Specifically, we impose the law of conservation of energy in N-body MNIST and anticipated precipitation intensity in SEVIR. Experiments demonstrate the effectiveness of PreDiff in handling uncertainty, incorporating domain-specific prior knowledge, and generating forecasts that exhibit high operational utility.Comment: Technical repor

PivotE : revealing and visualizing the underlying entity structures for exploration

A Web-scale knowledge graph (KG) typically contains millions of entities and thousands of entity types. Due to the lack of a pre-defined data schema such as the ER model, entities in KGs are loosely coupled based on their relationships, which brings challenges for effective accesses of the KGs in a structured manner like SPARQL. This demonstration presents an entity-oriented exploratory search prototype system that is able to support search and explore KGs in a exploratory search manner, where local structures of KGs can be dynamically discovered and utilized for guiding users. The system applies a path-based ranking method for recommending similar entities and their relevant information as exploration pointers. The interface is designed to assist users to investigate a domain (particular type) of entities, as well as to explore the knowledge graphs in various relevant domains. The queries are dynamically formulated by tracing the users' dynamic clicking (exploration) behaviors. In this demonstration, we will show how our system visualize the underlying entity structures, as well as explain the semantic correlations among them in a unified interface, which not only assist users to learn about the properties of entities in many aspects but also guide them to further explore the information space.Peer reviewe

Impact of glyphosate on the rhizosphere microbial communities of a double-transgenic maize line D105

Plant roots shape the rhizosphere microbiome, recruiting microbes with beneficial functions. While genetically engineered crops offer yield advantages, their impacts on rhizosphere microbial communities remain understudied. This study evaluated the effects of transgenic maize, alongside a non-transgenic counterpart, on rhizosphere bacterial and fungal community composition using 16S rRNA and ITS amplicon sequencing. Additionally, glyphosate was used to evaluate its impact on microbial assembly and the magnitude of its effect at various maize growth stages. The results showed that transgenic maize D105 line significantly increased bacterial alpha diversity but not fungal diversity. Beta diversity analysis showed clear separation between bacterial and fungal communities at higher glyphosate treatment. Specific bacterial taxa such as Pseudomonas and Sphingomonas were enriched, while fungal taxa such as Ascomycota, Lasiosphaeriaceae, Verticillium were differentially abundant in glyphosate treatments. LEfSe analysis identified distinct enrichment patterns of bacterial (Proteobacteria and Actinobacteria) and fungal taxa (Verticillium and Guehomyces) associated with the transgenic line and glyphosate levels. KEGG functional analysis suggested potential impacts on bacterial metabolic pathways and shifts in fungal trophic modes (saprotrophs, pathogens) within the rhizosphere microbiome. This research provides insights into the classification, functional relationships, and underlying mechanisms shaping microbial communities carrying insect resistance and glyphosate resistance traits