Introduction for the Special Issue on Beyond the Hypes of Geospatial Big Data: Theories, Methods, Analytics, and Applications

We live in the era of ‘Big Data’. In particular, Geospatial data, whether captured through remote sensors (e.g., satellite imagery) or generated from large-scale simulations (e.g., climate change models) have always been significantly large in size. Over the last decade however, advances in instrumentation and computation has seen the volume, variety, velocity, and veracity of this data increase exponentially. Of the 2.5 quintillion (1018) bytes of data that are generated on a daily basis across the globe, a large portion (arguably as much as 80%) is found to be geo-referenced. Therefore, this special issue is dedicated to the innovative theories, methods, analytics, and applications of geospatial big data

Sparse integrative clustering of multiple omics data sets

High resolution microarrays and second-generation sequencing platforms are powerful tools to investigate genome-wide alterations in DNA copy number, methylation and gene expression associated with a disease. An integrated genomic profiling approach measures multiple omics data types simultaneously in the same set of biological samples. Such approach renders an integrated data resolution that would not be available with any single data type. In this study, we use penalized latent variable regression methods for joint modeling of multiple omics data types to identify common latent variables that can be used to cluster patient samples into biologically and clinically relevant disease subtypes. We consider lasso [J. Roy. Statist. Soc. Ser. B 58 (1996) 267-288], elastic net [J. R. Stat. Soc. Ser. B Stat. Methodol. 67 (2005) 301-320] and fused lasso [J. R. Stat. Soc. Ser. B Stat. Methodol. 67 (2005) 91-108] methods to induce sparsity in the coefficient vectors, revealing important genomic features that have significant contributions to the latent variables. An iterative ridge regression is used to compute the sparse coefficient vectors. In model selection, a uniform design [Monographs on Statistics and Applied Probability (1994) Chapman & Hall] is used to seek "experimental" points that scattered uniformly across the search domain for efficient sampling of tuning parameter combinations. We compared our method to sparse singular value decomposition (SVD) and penalized Gaussian mixture model (GMM) using both real and simulated data sets. The proposed method is applied to integrate genomic, epigenomic and transcriptomic data for subtype analysis in breast and lung cancer data sets.Comment: Published in at http://dx.doi.org/10.1214/12-AOAS578 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

An Agile Protocol for E-Commerce

With merchant information increasing rapidly, customers have to spend a lot of time and energies to search for goods they need, as a result, they maybe find nothing. If merchant can respond customer’s requirements agilely when the event defined by customer appears, blindness of customer can be avoided, merchant wins business opportunities too. Protocol is the technology fundament for e-commerce. An idea for agile mechanism is introduced, this paper designs an e-commerce protocol with agility, and it discusses its security. This protocol improves e-commerce efficiency and solves activity problems

Team up GBDTs and DNNs: Advancing Efficient and Effective Tabular Prediction with Tree-hybrid MLPs

Tabular datasets play a crucial role in various applications. Thus, developing efficient, effective, and widely compatible prediction algorithms for tabular data is important. Currently, two prominent model types, Gradient Boosted Decision Trees (GBDTs) and Deep Neural Networks (DNNs), have demonstrated performance advantages on distinct tabular prediction tasks. However, selecting an effective model for a specific tabular dataset is challenging, often demanding time-consuming hyperparameter tuning. To address this model selection dilemma, this paper proposes a new framework that amalgamates the advantages of both GBDTs and DNNs, resulting in a DNN algorithm that is as efficient as GBDTs and is competitively effective regardless of dataset preferences for GBDTs or DNNs. Our idea is rooted in an observation that deep learning (DL) offers a larger parameter space that can represent a well-performing GBDT model, yet the current back-propagation optimizer struggles to efficiently discover such optimal functionality. On the other hand, during GBDT development, hard tree pruning, entropy-driven feature gate, and model ensemble have proved to be more adaptable to tabular data. By combining these key components, we present a Tree-hybrid simple MLP (T-MLP). In our framework, a tensorized, rapidly trained GBDT feature gate, a DNN architecture pruning approach, as well as a vanilla back-propagation optimizer collaboratively train a randomly initialized MLP model. Comprehensive experiments show that T-MLP is competitive with extensively tuned DNNs and GBDTs in their dominating tabular benchmarks (88 datasets) respectively, all achieved with compact model storage and significantly reduced training duration.Comment: Accepted at KDD 2024 Research Track, codes will be available at https://github.com/jyansir/tml

Evaluation of Plaque Stability of Advanced Atherosclerotic Lesions in Apo E-Deficient Mice after Treatment with the Oral Factor Xa Inhibitor Rivaroxaban

Aim. Thrombin not only plays a central role in thrombus formation and platelet activation, but also in induction of inflammatory processes. Activated factor X (FXa) is traditionally known as an important player in the coagulation cascade responsible for thrombin generation. We assessed the hypothesis that rivaroxaban, a direct FXa inhibitor, attenuates plaque progression and promotes stability of advanced atherosclerotic lesions in an in vivo model. Methods and Results. Rivaroxaban (1 or 5 mg/kg body weight/day) or standard chow diet was administered for 26 weeks to apolipoprotein E-deficient mice (n = 20 per group) with already established atherosclerotic lesions. There was a nonsignificant reduction of lesion progression in the high-concentration group, compared to control mice. FXa inhibition with 5 mg Rivaroxaban/kg/day resulted in increased thickness of the protective fibrous caps (12.3 ± 3.8 μm versus 10.1 ± 2.7 μm; P < .05), as well as in fewer medial erosions and fewer lateral xanthomas, indicating plaque stabilizing properties. Real time-PCR from thoracic aortas revealed that rivaroxaban (5 mg/kg/day) treatment reduced mRNA expression of inflammatory mediators, such of IL-6, TNF-α, MCP-1, and Egr-1 (P < .05). Conclusions. Chronic administration of rivaroxaban does not affect lesion progression but downregulates expression of inflammatory mediators and promotes lesion stability in apolipoprotein E-deficient mice

Rhizoma Coptidis Inhibits LPS-Induced MCP-1/CCL2 Production in Murine Macrophages via an AP-1 and NFκB-Dependent Pathway

Introduction. The Chinese extract Rhizoma coptidis is well known for its anti-inflammatory, antioxidative, antiviral, and antimicrobial activity. The exact mechanisms of action are not fully understood. Methods. We examined the effect of the extract and its main compound, berberine, on LPS-induced inflammatory activity in a murine macrophage cell line. RAW 264.7 cells were stimulated with LPS and incubated with either Rhizoma coptidis extract or berberine. Activation of AP-1 and NFκB was analyzed in nuclear extracts, secretion of MCP-1/CCL2 was measured in supernatants. Results. Incubation with Rhizoma coptidis and berberine strongly inhibited LPS-induced monocyte chemoattractant protein (MCP)-1 production in RAW cells. Activation of the transcription factors AP-1 and NFκB was inhibited by Rhizoma coptidis in a dose- and time-dependent fashion. Conclusions. Rhizoma coptidis extract inhibits LPS-induced MCP-1/CCL2 production in vitro via an AP-1 and NFκB-dependent pathway. Anti-inflammatory action of the extract is mediated mainly by its alkaloid compound berberine

The effects of sample handling on proteomics assessed by reverse phase protein arrays (RPPA):Functional proteomic profiling in leukemia

Reverse phase protein arrays (RPPA) can assess protein expression and activation states in large numbers of samples (n > 1000) and evidence suggests feasibility in the setting of multi-institution clinical trials. Despite evidence in solid tumors, little is known about protein stability in leukemia. Proteins collected from leukemia cells in blood and bone marrow biopsies must be sufficiently stable for analysis. Using 58 leukemia samples, we initially assessed protein/phospho-protein integrity for the following preanalytical variables: 1) shipping vs local processing, 2) temperature (4 degrees C vs ambient temperature), 3) collection tube type (heparin vs Cell Save (CS) preservation tubes), 4) treatment effect (prevs post-chemotherapy) and 5) transit time. Next, we assessed 1515 samples from the Children's Oncology Group Phase 3 AML clinical trial (AAML1031, NCT01371981) for the effects of transit time and tube type. Protein expression from shipped blood samples was stable if processed in Significance: RPPA can assess protein abundance and activation states in large numbers of samples using small amounts of material, making this method ideal for use in multi-institution clinical trials. However, there is little known about the effect of preanalytical handling variables on protein stability and the integrity of protein concentrations after sample collection and shipping. In this study, we used RPPA to assess preanalytical variables that could potentially affect protein concentrations. We found that the preanalytical variables of shipping, transit time, and temperature had minimal effects on RPPA protein concentration distributions in peripheral blood and bone marrow, demonstrating that these preanalytical variables could be successfully managed in a multi-site clinical trial setting