Deep Learning for Inflexible Multi-Asset Hedging of incomplete market

Models trained under assumptions in the complete market usually don't take effect in the incomplete market. This paper solves the hedging problem in incomplete market with three sources of incompleteness: risk factor, illiquidity, and discrete transaction dates. A new jump-diffusion model is proposed to describe stochastic asset prices. Three neutral networks, including RNN, LSTM, Mogrifier-LSTM are used to attain hedging strategies with MSE Loss and Huber Loss implemented and compared.As a result, Mogrifier-LSTM is the fastest model with the best results under MSE and Huber Loss

An Efficient Dynamic Multi-Sources To Single-Destination (DMS-SD) Algorithm In Smart City Navigation Using Adjacent Matrix

Dijkstra's algorithm is one of the most popular classic path planning algorithms, achieving optimal solutions across a wide range of challenging tasks. However, it only calculates the shortest distance from one vertex to another, which is hard to directly apply to the Dynamic Multi-Sources to Single-Destination (DMS-SD) problem. This paper proposes a modified Dijkstra algorithm to address the DMS-SD problem, where the destination can be dynamically changed. Our method deploys the concept of Adjacent Matrix from Floyd's algorithm and achieves the goal with mathematical calculations. We formally show that all-pairs shortest distance information in Floyd's algorithm is not required in our algorithm. Extensive experiments verify the scalability and optimality of the proposed method.Comment: International Conference On Human-Centered Cognitive Systems (HCCS) 202

Bioorthogonal photocatalytic decaging-enabled spatiotemporal proteomics

Spatiotemporally resolved dissection of subcellular proteome is crucial to our understanding of cellular functions in health and disease. Although enzyme-based proximity labeling strategies have emerged as powerful methods to portray the compartmentalized proteome of diverse organelles, current approaches still suffer from limitations such as the genetic operation that is not compatible with hard-to-transfect cells as well as overexpression of the fusion protein that may cause altered intracellular localization. We herein report a non-genetic strategy termed bioorthogonal and photocatalytic decaging-mediated proximity labeling (CAT-Prox) for spatiotemporally resolved proteome profiling in living cells. Our systematic survey of the organometallic photocatalysts has led to the identification of Ir(ppy)2bpy as a bioorthogonal and mitochondria-targeting complex that allowed photo-controlled, rapid rescue of azidobenzyl-caged quinone methide as a highly reactive Michael acceptor for proximity-based protein labelings. By coupling with quantitative mass spectrometry, CAT-Prox revealed the dynamic mitochondria proteome of cancer cells and macrophage cells under normal and stressed conditions. Furthermore, by targeting the photocatalyst to cell membrane receptors, CAT-Prox allowed microdomain proteome profiling on live cell surface. Together, CAT-Prox integrated the advantages of both enzymatic and chemical-based proximity labeling approaches as a general spatiotemporal proteomics platform for diverse subcellular spaces and cell types

Variations of Urban Thermal Risk with Local Climate Zones

Due to the differences in land cover and natural surroundings within cities, residents in various regions face different thermal risks. Therefore, this study combined multi-source data to analyze the relationship between urban heat risk and local climate zones (LCZ). We found that in downtown Shenyang, the building-type LCZ was mainly found in urban centers, while the natural- type LCZ was mainly found in suburbs. Heat risk was highest in urban centers, gradually decreasing along the suburban direction. The thermal risk indices of the building-type LCZs were significantly higher than those of the natural types. Among the building types of LCZs, LCZ 8 (open middle high-rise) had the highest average thermal risk index (0.48), followed by LCZ 3 (0.46). Among the natural types of LCZs, LCZ E (bare rock and paved) and LCZ F (bare soil and sand) had the highest thermal risk indices, reaching 0.31 and 0.29, respectively. This study evaluated the thermal risk of the Shenyang central urban area from the perspective of LCZs and combined it with high-resolution remote sensing data to provide a reference for thermal risk mitigation in future urban planning

Additional file 1 of Estrogen receptor β deficiency impairs gut microbiota: a possible mechanism of IBD-induced anxiety-like behavior

Additional file 1: Table S1. Scoring system for histological changes in the colon. Table S2. The sequences of primers used in this study. Figure S1. ERβ deficiency did not influence the sensorimotor function, memory function, or social interactions in mice following induced experimental colitis. (A) Nest score in the nest building test was performed among the four groups to detect the sensorimotor functions. (B) Spatial memory was assessed by percentage of spontaneous alterations in the Y maze test. (C) Recognition memory was detected by the discrimination index in the novel object recognition test. (D, E) Time spent in each chamber and time spent in sniffing a novel mouse or novel object were used to test the sociability in the social approach period (D). Social recognition was evaluated by the time spent in each chamber and time sniffing familiar mouse or novel mouse in social novelty period (E). Data are presented as mean ± SEM. Statistical comparisons were performed by two-way ANOVA or paired t-test for the three-chamber test. n = 8/group. *P < 0.05, **P < 0.01. Figure S2. Fecal microbiota of WT and ERβ−/− mice under baseline and inflammatory states at the class and order levels. (A) Bar graph of bacterial abundance at the class level. (B) Relative abundances of substantially changed bacterial taxa at the class level. (C) Bar graph of bacterial abundances at the order level. (D) Relative abundances of substantially changed bacterial taxa at the order level. Data are presented as boxplots. Statistical comparisons were performed using the non-parametric Wilcoxon rank sum test. n = 9/group, except for n = 8 in the WT DSS group. *P < 0.05. Figure S3. ERβ deficiency does not influence gut microbiota composition in adult female mice. (A) Community richness calculated by observed OTUs. (B, C) Principal coordinates analysis of microbial unweighted UniFrac compositional differences (B), quantified by UniFrac distance (C) between WT and ERβ−/− female mice. (D) Taxonomic cladogram obtained using LEfSe analysis. (E–G) Bar graph of bacterial abundances at the phylum (E), family (F), and genus (G) levels. Data are presented as boxplots. Statistical comparisons were performed using the non-parametric Wilcoxon rank sum test. n = 5/group. Figure S4. Tight junctions in WT and ERβ−/− mice under the baseline and inflammatory states on day 5 post-DSS treatment. (A) Representative images of immunofluorescence staining for tight junction proteins (occludin and ZO-1) in the distal colon of WT and ERβ−/− mice under homeostatic conditions and 5 days following DSS treatment. Scale bar = 100 μm. (B–C) Quantitative real-time PCR analysis of mRNA expressions of occludin and ZO-1 in whole colon tissues of WT and ERβ−/− male mice under homeostatic conditions and 5 days following DSS treatment. n = 7-8/group. Data are presented as mean ± SEM. *P < 0.05, **P < 0.01. Figure S5. ERβ deficiency aggravated the development of DSS-induced colitis on day 10 after initial DSS exposure. (A, B) Mice were sacrificed on day 10 after DSS treatment to measure the colon length. n = 7/group. (C) Histology of distal colon tissues collected at day 10 was examined by hematoxylin and eosin (HE) and Alcian Blue Periodic Acid Schiff (AB-PAS) staining. Scale bars = 100 μm. (D–G) Composite score of histopathology (inflammation, ulceration, and crypt damage scores). n = 7/group. *P < 0.05, **P < 0.01, ***P < 0.001. Figure S6. Tight junctions in WT and ERβ−/− mice under baseline and inflammatory states on day 10 post-DSS treatment. (A) Tight junctions and villi in the colonic epithelium were examined under an electron microscope (scale bar = 2 or 1 μm as indicated in figure), and representative images of immunofluorescence staining (scale bars = 100 μm) of tight junction proteins (occludin and ZO-1) in the distal colon of WT and ERβ−/− mice under homeostasis conditions and day 10 following DSS treatment. (B–C) Quantitative real-time PCR analysis of mRNA expressions of occludin and ZO-1 in whole colon tissues of WT and ERβ−/− male mice under homeostatic conditions and 10 days following DSS treatment. n = 9/group. Data are presented as mean ± SEM. *P < 0.05, **P < 0.01. Figure S7. ERβ deficiency did not significantly influence the neuroinflammation status compared with WT mice after DSS treatment. (A, B) Diagrams, representative images (A), and quantitative analysis (B) of Iba1-positive cells in mPFC. (C, D) Diagrams, representative images (C), and quantitative analysis (D) of Iba1-positive cells in the amygdala. (E, F) Diagrams, representative images (E), and quantitative analysis (F) of Iba1-positive cells in the ventral hippocampus (including CA1, DG, and CA3 areas). (G, H) Diagrams, representative images (G), and quantitative analysis (H) of Iba1-positive cells in the dorsal hippocampus (including CA1, DG, and CA3 areas). Scale bars = 200 μm for lower magnification, and 100 μm for the higher magnification. n = 4/group. Data are presented as mean ± SEM. Statistical comparisons were performed using two-way ANOVA. *P < 0.05, **P < 0.01. Figure S8. mRNA expression levels of hypothalamic neuropeptides and hierarchical clustering of the 934 overlapping genes. (A) Hierarchical clustering heatmap of several hypothalamic neuropeptide gene expression profiles (Crh, Sst, Npy, Agrp, Vip, Avp, Gal, Oxt, and Trh) of WT and ERβ−/− mice under homeostasis conditions and treatment with DSS. n = 3/group. (B) The gene expression profile of the overlapping genes in hypothalamus of WT and ERβ−/− mice under the homeostasis conditions and DSS treatment. Figure S9. Fecal microbiota of SiHo WT, SiHo ERβ−/−, CoHo WT, and CoHo ERβ−/− mice before DSS treatment. (A) UniFrac distances showing microbiota compositional differences among SiHo WT, SiHo ERβ−/−, CoHo WT and CoHo ERβ−/− mice. (B) Taxonomic cladogram obtained using LEfSe analysis. (C) Relative abundances of substantially changed bacterial taxa at the genus level. (D) Relative abundances of substantially changed bacterial taxa at the family level. Data are presented as boxplots. Statistical comparisons were performed using the non-parametric Wilcoxon rank sum test. n = 9/group, except for n = 8 for the CoHo WT group. *P < 0.05, **P < 0.01, ***P < 0.001. Supplemental materials and methods