Data mining and analysis of lung cancer data.

Lung cancer is the leading cause of cancer death in the United States and the world, with more than 1.3 million deaths worldwide per year. However, because of a lack of effective tools to diagnose Lung Cancer, more than half of all cases are diagnosed at an advanced stage, when surgical resection is unlikely to be feasible. The main purpose of this study is to examine the relationship between patient outcomes and conditions of the patients undergoing different treatments for lung cancer and to develop models to predict the mortality of lung cancer. This study will identify the demographic, finance, and clinical factors related to the diagnosis or mortality of Lung Cancer to help physicians and patients in their decision-making. We combined Text Miner and Cluster analysis to identify the claim data for Lung Cancer and to determine the category of diagnosis, treatment procedures and medication treatments for those patients. Moreover, the claims data were used to define severity level and treatment categories. Compared with using diagnosis codes directly, the combination of text mining and cluster analysis is more efficient and captures more useful information for further analysis. In order to analyze the mortality of Lung Cancer, we also found that survival analysis is appropriate to preprocess the data for the relationship between a predictor variable of interest and the time of an event. The proportional hazard model examined the effects of different treatment clusters using a hazard ratio and the proportional effect of a treatment cluster (treatment procedure or medication treatment) may vary with time. A decision tree was built to generate rules for identifying high risk lung cancer cases among the regular inpatient population. Two primary data sets have been used in this study, the Nationwide Inpatient Sample (NIS) and the Thomson MedStat MarketScan data. Kernel density estimation was used for NIS to examine the relationship between Age, Length of stay, Diagnosis Categories, Total Cost and Lung Cancer by visualization. The Kaplan-Meier method and Cox proportional hazard model are used for the Medstat data to discover the relationship between the factors and the target variable for more detail. Time series and predictive modeling are used to predict the total cost for hospital decision making, the mortality of Lung cancer based on the historical data and to generate rules to identify the diagnosis of Lung cancer. Older patients are more likely to have lung cancers that would lead to a higher probability of longer stay and higher costs for the treatment. Within 7 defined clusters of diagnosis for Lung Cancer, the malignant neoplasm of lobe, bronchus or lung is under higher risk. Age, length of stay, admit type, clusters of diagnosis, and clusters of treatment procedures and Major Diagnostic Categories (MDC) were identified as significant factors for the mortality of lung cancer

Mussel-Inspired Polyglycerol Coatings for Surface Modification with Tunable Architecture

Mussel-inspired coatings, known for their outstanding substrate-independent adhesive capabilities, have numerous potential applications in materials science and biomedical fields. To improve the understanding of how these polymers’ molecular structure and chemical composition affect their coating mechanisms and resulting coating properties, herein three mussel-inspired polymers are developed: dendritic polyglycerol with 40% catechol groups and 60% amines (dPG40), linear polyglycerol with 80% catechols and 20% amines (lPG80), and finally lPG40 with 40% catechols and 60% amines. After a series of characterizations, it is found that chemical surface modification with a monolayer coating can be easily achieved with lPG40, and that robust and well-defined nano- to micro-structural surface coatings are possible with lPG80 and dPG40. Tunable properties are found to include not only coating speed, but coating thickness, roughness, and surficial topography. This diverse suite of controllable attributes enables mussel-inspired polyglycerol (MiPG) coatings to satisfy a wide-range of applications on multiple material

Pre-training Graph Transformer with Multimodal Side Information for Recommendation

Side information of items, e.g., images and text description, has shown to be effective in contributing to accurate recommendations. Inspired by the recent success of pre-training models on natural language and images, we propose a pre-training strategy to learn item representations by considering both item side information and their relationships. We relate items by common user activities, e.g., co-purchase, and construct a homogeneous item graph. This graph provides a unified view of item relations and their associated side information in multimodality. We develop a novel sampling algorithm named MCNSampling to select contextual neighbors for each item. The proposed Pre-trained Multimodal Graph Transformer (PMGT) learns item representations with two objectives: 1) graph structure reconstruction, and 2) masked node feature reconstruction. Experimental results on real datasets demonstrate that the proposed PMGT model effectively exploits the multimodality side information to achieve better accuracies in downstream tasks including item recommendation, item classification, and click-through ratio prediction. We also report a case study of testing the proposed PMGT model in an online setting with 600 thousand users

Unifying Vision, Text, and Layout for Universal Document Processing

We propose Universal Document Processing (UDOP), a foundation Document AI model which unifies text, image, and layout modalities together with varied task formats, including document understanding and generation. UDOP leverages the spatial correlation between textual content and document image to model image, text, and layout modalities with one uniform representation. With a novel Vision-Text-Layout Transformer, UDOP unifies pretraining and multi-domain downstream tasks into a prompt-based sequence generation scheme. UDOP is pretrained on both large-scale unlabeled document corpora using innovative self-supervised objectives and diverse labeled data. UDOP also learns to generate document images from text and layout modalities via masked image reconstruction. To the best of our knowledge, this is the first time in the field of document AI that one model simultaneously achieves high-quality neural document editing and content customization. Our method sets the state-of-the-art on 8 Document AI tasks, e.g., document understanding and QA, across diverse data domains like finance reports, academic papers, and websites. UDOP ranks first on the leaderboard of the Document Understanding Benchmark.Comment: CVPR 202

A general route via formamide condensation to prepare atomically dispersed metal-nitrogen-carbon electrocatalysts for energy technologies

Single-atom electrocatalysts (SAECs) have gained tremendous attention due to their unique active sites and strong metal–substrate interactions. However, the current synthesis of SAECs mostly relies on costly precursors and rigid synthetic conditions and often results in very low content of single-site metal atoms. Herein, we report an efficient synthesis method to prepare metal–nitrogen–carbon SAECs based on formamide condensation and carbonization, featuring a cost-effective general methodology for the mass production of SAECs with high loading of atomically dispersed metal sites. The products with metal inclusion were termed as formamide-converted metal–nitrogen–carbon (shortened as f-MNC) materials. Seven types of single-metallic f-MNC (Fe, Co, Ni, Mn, Zn, Mo and Ir), two bi-metallic (ZnFe and ZnCo) and one tri-metallic (ZnFeCo) SAECs were synthesized to demonstrate the generality of the methodology developed. Remarkably, these f-MNC SAECs can be coated onto various supports with an ultrathin layer as pyrolysis-free electrocatalysts, among which the carbon nanotube-supported f-FeNC and f-NiNC SAECs showed high performance for the O2 reduction reaction (ORR) and the CO2 reduction reaction (CO2RR), respectively. Furthermore, the pyrolysis products of supported f-MNC can still render isolated metallic sites with excellent activity, as exemplified by the bi-metallic f-FeCoNC SAEC, which exhibited outstanding ORR performance in both alkaline and acid electrolytes by delivering ∼70 and ∼20 mV higher half-wave potentials than that of commercial 20 wt% Pt/C, respectively. This work offers a feasible approach to design and manufacture SAECs with tuneable atomic metal components and high density of single-site metal loading, and thus may accelerate the deployment of SAECs for various energy technology applications

Auditor of State Mary Mosiman today released a reaudit report on the Mason City Community School District (District) for the period July 1, 2014 through June 30, 2015. The reaudit also covered items applicable to the years ended June 30, 2016 and June 30, 2017.

Auditor of State Mary Mosiman today released a reaudit report on the Mason City Community School District (District) for the period July 1, 2014 through June 30, 2015. The reaudit also covered items applicable to the years ended June 30, 2016 and June 30, 2017

Monitoring the Size and Lateral Dynamics of ErbB1 Enriched Membrane Domains through Live Cell Plasmon Coupling Microscopy

To illuminate the role of the spatial organization of the epidermal growth factor receptor (ErbB1) in signal transduction quantitative information about the receptor topography on the cell surface, ideally on living cells and in real time, are required. We demonstrate that plasmon coupling microscopy (PCM) enables to detect, size, and track individual membrane domains enriched in ErbB1 with high temporal resolution. We used a dendrimer enhanced labeling strategy to label ErbB1 receptors on epidermoid carcinoma cells (A431) with 60 nm Au nanoparticle (NP) immunolabels under physiological conditions at 37°C. The statistical analysis of the spatial NP distribution on the cell surface in the scanning electron microscope (SEM) confirmed a clustering of the NP labels consistent with a heterogeneous distribution of ErbB1 in the plasma membrane. Spectral shifts in the scattering response of clustered NPs facilitated the detection and sizing of individual NP clusters on living cells in solution in an optical microscope. We tracked the lateral diffusion of individual clusters at a frame rate of 200 frames/s while simultaneously monitoring the configurational dynamics of the clusters. Structural information about the NP clusters in their membrane confinements were obtained through analysis of the electromagnetic coupling of the co-confined NP labels through polarization resolved PCM. Our studies show that the ErbB1 receptor is enriched in membrane domains with typical diameters in the range between 60–250 nm. These membrane domains exhibit a slow lateral diffusion with a diffusion coefficient of  = |0.0054±0.0064| µm2/s, which is almost an order of magnitude slower than the mean diffusion coefficient of individual NP tagged ErbB1 receptors under identical conditions

Primer registro de anomalía intersexual gonadal de Trachurus mediterraneus (Steindachner, 1868) desde el Mar de Alborán.

El objetivo principal de este trabajo es dar a conocer el primer registro de una anomalía intersexual gonadal de Trachurus mediterraneus desde el mar de Alborán (Mediterráneo occidental). Este espécimen es el primer registro de intersexualidad para un jurel en el mundo.Postprin

Regulation of C-Type Lectin Receptor-Mediated Antifungal Immunity

Of all the pathogen recognition receptor families, C-type lectin receptor (CLR)-induced intracellular signal cascades are indispensable for the initiation and regulation of antifungal immunity. Ongoing experiments over the last decade have elicited diverse CLR functions and novel regulatory mechanisms of CLR-mediated-signaling pathways. In this review, we highlight novel insights in antifungal innate and adaptive-protective immunity mediated by CLRs and discuss the potential therapeutic strategies against fungal infection based on targeting the mediators in the host immune system

Study on the Electron Transfer Process of Cytochrome C with Different Charge Transfer Promoters

报道了应用近年来发展的薄层光谱电化学技术，对几种促进剂（４，４＇－二硫基联吡啶、腺嘌呤、Ｌ－半胱氨酸）存在下细胞色素Ｃ电子迁移过程进行了探讨。The diffusion coefficient D. standard rate constant and transfer coefficient afor electron transfer process of cytochrome C at OTTLE in the presence of 4,4'-Dithiedipyridine(PySSPy),Adenine and L-Cysteine were determined by the chronoadsorptometric technique with Au-OTTLE. A computer fitting method was used to calculated these kinetic parameters.The mechanismsof PysSPy,Adenine and L-Cysteine were used as the promoters,and were also discussed.作者联系地址：上海化工高等专科学校精细化工系，上海师范大学化学系Author's Address: Dept. of Fine Chem.Eng .Shanghai Inst.of Chem.Tech.,Shanghai 200233Wu XiaqinElec. Division, Dept.of Chem. Shanghai Teacher's,Univ.Shanghai 20023