Multilabel Consensus Classification

In the era of big data, a large amount of noisy and incomplete data can be collected from multiple sources for prediction tasks. Combining multiple models or data sources helps to counteract the effects of low data quality and the bias of any single model or data source, and thus can improve the robustness and the performance of predictive models. Out of privacy, storage and bandwidth considerations, in certain circumstances one has to combine the predictions from multiple models or data sources to obtain the final predictions without accessing the raw data. Consensus-based prediction combination algorithms are effective for such situations. However, current research on prediction combination focuses on the single label setting, where an instance can have one and only one label. Nonetheless, data nowadays are usually multilabeled, such that more than one label have to be predicted at the same time. Direct applications of existing prediction combination methods to multilabel settings can lead to degenerated performance. In this paper, we address the challenges of combining predictions from multiple multilabel classifiers and propose two novel algorithms, MLCM-r (MultiLabel Consensus Maximization for ranking) and MLCM-a (MLCM for microAUC). These algorithms can capture label correlations that are common in multilabel classifications, and optimize corresponding performance metrics. Experimental results on popular multilabel classification tasks verify the theoretical analysis and effectiveness of the proposed methods

Ultrastructural analysis of transitional endoplasmic reticulum and pre-Golgi intermediates: a highway for cars and trucks

Cargo selection and export from the endoplasmic reticulum occurs at specialized sites in cells. Export complexes consist of transitional elements of the endoplasmic reticulum and pre-Golgi intermediates. It is generally assumed that 60 to 80 nm initially COPII-coated vesicles derived from the transitional endoplasmic reticulum are the main carriers for transport of cargo to the Golgi apparatus. We have analyzed on serial ultrathin sections the transitional endoplasmic reticulum and pre-Golgi intermediates of beta cells of islets of Langerhans in mouse pancreas. In addition to Golgi-associated complexes, others were observed in the periphery of the cells or close to the nuclear envelope. Upon three-dimensional reconstruction, non-coated ribosome-free tubules with an average diameter of 115nm (range 60-195nm) and a length of up to 500nm were detected in the pre-Golgi intermediates in addition to small vesiculo-tubular elements. Furthermore, evidence was found that the large tubular elements may directly arise from transitional elements of the endoplasmic reticulum. In a given cell, pre-Golgi intermediates were found to be composed solely of small vesiculo-tubular elements or additionally of tubules or solely of tubules. Immunogold labeling for proinsulin indicated that the large tubular elements contained cargo and thus appear to take part in ER-to-Golgi transpor

Expression of mutant Ins2C96Y results in enhanced tubule formation causing enlargement of pre-Golgi intermediates of CHO cells

Misfolded proteins are recognized by the protein quality control and eventually degraded by the ubiquitin-proteasome system. Previously, we demonstrated accumulation of a misfolded non-glycosylated protein, namely proinsulin, in enlarged pre-Golgi intermediates and dilated rough endoplasmic reticulum (ER) domains in pancreatic β-cells of Akita mice. In order to exclude effects possibly due to coexisting wild type and mutant proinsulin in pancreatic β-cells, CHO cells expressing singly wild type or mutant C96Y proinsulin 2 were now analyzed by electron microscopic morphometry and immunogold labeling as well as serial section 3D analysis. We found a significant increase in volume density of pre-Golgi intermediates in CHO Ins2C96Y cells which was principally due to an increase of its tubular elements, and no significant changes of the ER. The average diameter of the pre-Golgi intermediates of CHO Ins2C96Y cells was about twice that of CHO Ins2wt cells. The enlarged pre-Golgi intermediates and the ER of CHO Ins2C96Y cells were positive for proinsulin, which was not detectable in the significantly enlarged Golgi cisternal stack. Treatment of CHO Ins2C96Y cells with proteasome inhibitors resulted in the formation of proinsulin-containing aggresomes. We conclude that misfolded proinsulin causes enlargement of pre-Golgi intermediates which indicates their involvement in protein quality contro

Medical Big Data Analysis in Hospital Information System

The rapidly increasing medical data generated from hospital information system (HIS) signifies the era of Big Data in the healthcare domain. These data hold great value to the workflow management, patient care and treatment, scientific research, and education in the healthcare industry. However, the complex, distributed, and highly interdisciplinary nature of medical data has underscored the limitations of traditional data analysis capabilities of data accessing, storage, processing, analyzing, distributing, and sharing. New and efficient technologies are becoming necessary to obtain the wealth of information and knowledge underlying medical Big Data. This chapter discusses medical Big Data analysis in HIS, including an introduction to the fundamental concepts, related platforms and technologies of medical Big Data processing, and advanced Big Data processing technologies

Charge 4e superconductivity and chiral metal in the 45∘45^\circ-twisted bilayer cuprates and similar materials

The vestigial phase above the $T_c$ of a multi-component pairing state is a hot topic recently. Here we study the vestigial phases of a class of materials made through stacking a homo-bilayer with the largest twist angle, dubbed as the twist-bilayer quasi-crystal (TB-QC), exampled by the 45$^\circ$-twisted bilayer cuprates and 30$^\circ$-twisted bilayer graphene. When each mononlayer hosts a pairing state with the largest pairing angular momentum, e.g. $d$-wave for the cuprates or $f$-wave for some members in the graphene family, previous studies yield that the second-order interlayer Josephson coupling would drive chiral $d+id$ or $f+if$ topological superconductivity (TSC) in the TB-QC. Here we propose that, above the $T_c$ of the chiral TSC phase, either the total- or relative- pairing phase of the two layers can be unilateral quasi-ordered or ordered. In the form case, a Cooper pair from the top layer pairs with a Cooper pair from the bottom layer to form the charge-4e SC; in the latter case, a time-reversal symmetry breaking chiral metal phase is formed. Based on a thorough symmetry analysis, we arrive at the low-energy effective Hamiltonian describing the pairing-phase fluctuations. Our combined renormalization group and Monte-Carlo studies reveal the presence of the charge-4e SC and chiral metal phases in certain regimes in the phase diagram. These vestigial phases are characterized by various temperature-dependent quantities and spatial-dependent correlations.Comment: 4.2 pages plus Appendi

A graphical simulator for modeling complex crowd behaviors

Abnormal crowd behaviors of varied real-world settings could represent or pose serious threat to public safety. The video data required for relevant analysis are often difficult to acquire due to security, privacy and data protection issues. Without large amounts of realistic crowd data, it is difficult to develop and verify crowd behavioral models, event detection techniques, and corresponding test and evaluations. This paper presented a synthetic method for generating crowd movements and tendency based on existing social and behavioral studies. Graph and tree searching algorithms as well as game engine-enabled techniques have been adopted in the study. The main outcomes of this research include a categorization model for entity-based behaviors following a linear aggregation approach; and the construction of an innovative agent-based pipeline for the synthesis of A-Star path-finding algorithm and an enhanced Social Force Model. A Spatial-Temporal Texture (STT) technique has been adopted for the evaluation of the model's effectiveness. Tests have highlighted the visual similarities between STTs extracted from the simulations and their counterparts - video recordings - from the real-world