Organisation and management of shared documents in super-peer networks based semantic hierarchical cluster trees

A super-peer semantic network defines the semantic groups that client peers and their super peers use to connect with each other according to the semantic similarity of shared documents in peers. This will result in a query that can be evaluated locally thus reducing query process time and the amount of communication required to obtain related data. In this study, new algorithms are proposed to arrange shared documents in peers into hierarchical cluster tree structures, and links will be set up to connect semantically related super peers. During query evaluation, only related peers will be searched thus the complexity of computing is significantly reduced. Consideration is also given to automatically deciding the threshold for clustering and the construction and other essential operations for hierarchical cluster trees. The experiment results illustrate the features of the approach developed in this study.     

Integration of data systems and technology improves research and collaboration for a superfund research center

Large collaborative centers are a common model for accomplishing integrated environmental health research. These centers often include various types of scientific domains (e.g., chemistry, biology, bioinformatics) that are integrated to solve some of the nation's key economic or public health concerns. The Superfund Research Center (SRP) at Oregon State University (OSU) is one such center established in 2008 to study the emerging health risks of polycyclic aromatic hydrocarbons while using new technologies both in the field and laboratory. With outside collaboration at remote institutions, success for the center as a whole depends on the ability to effectively integrate data across all research projects and support cores. Therefore, the OSU SRP center developed a system that integrates environmental monitoring data with analytical chemistry data and downstream bioinformatics and statistics to enable complete "source-to-outcome" data modeling and information management. This article describes the development of this integrated information management system that includes commercial software for operational laboratory management and sample management in addition to open-source custom-built software for bioinformatics and experimental data management.     

Ultrasound image analysis technology under deep belief networks in evaluation on the effects of diagnosis and chemotherapy of cervical cancer

The purpose of this study was to explore the value of extraction of tumor features in contrast-enhanced ultrasonography (CEUS) images based on the deep belief networks (DBN) for the diagnosis of cervical cancer patients and realize the intelligent evaluation on effects of diagnosis and chemotherapy of the cervical cancer. An automatic extraction algorithm with the time-intensity curve (TIC) was proposed based on Sparse nonnegative matrix factorization (SNMF) in this study, and was applied to the framework of automatic analysis of cervical cancer tumors based on the deep belief networks, to assist doctors in the analysis of cervical cancer tumors. The framework was applied to the real clinical diagnostic data, and the feasibility of the method was verified by comparing the accuracy, sensitivity, and specificity. Later, the parameters of patients’ time to peak (TP), peak intensity (PI), mean transit time (MTT), and area under the curve (AUC) were obtained by drawing TICs, and the changes of p53 protein and ki-67 protein obtained by pathological section staining were analyzed to evaluate the therapeutic effect in the patients. It was found that the proposed model of tumor feature extraction based on the DBN had the higher accuracy (86.36%), sensitivity (83.33%), and specificity (87.50%). The related parameters of TIC curve obtained based on SNMF showed that there was a significant difference in p53 content between tissues with different degrees of disease (p?<?0.05), the PI of poorly differentiated tissues was significantly higher than that of those with high to medium differentiation (p?<?0.05). In addition, PI and AUC of patients after chemotherapy were significantly lower than that before chemotherapy (p?<?0.05), while MTT was significantly higher than that before chemotherapy (p?<?0.05). Therefore, the proposed TIC feature extraction of CEUS images based on SNMF and the automatic tumor classification based on deep learning can be used in the diagnosis and efficacy evaluation of cervical cancer patients.

     

On enabling integrated process compliance with semantic constraints in process management systems

Key to broad use of process management systems (PrMS) in practice is their ability to foster and ease the implementation, execution, monitoring, and adaptation of business processes while still being able to ensure robust and error-free process enactment. To meet these demands a variety of mechanisms has been developed to prevent errors at the structural level (e.g., deadlocks). In many application domains, however, processes often have to comply with business level rules and policies (i.e., semantic constraints) as well. Hence, to ensure error-free executions at the semantic level, PrMS need certain control mechanisms for validating and ensuring the compliance with semantic constraints. In this paper, we discuss fundamental requirements for a comprehensive support of semantic constraints in PrMS. Moreover, we provide a survey on existing approaches and discuss to what extent they are able to meet the requirements and which challenges still have to be tackled. In order to tackle the particular challenge of providing integrated compliance support over the process lifecycle, we introduce the SeaFlows framework. The framework introduces a behavioural level view on processes which serves a conceptual process representation for constraint specification approaches. Further, it provides general compliance criteria for static compliance validation but also for dealing with process changes. Altogether, the SeaFlows framework can serve as formal basis for realizing integrated support of semantic constraints in PrMS.     

An illusion of memory: False recognition influenced by unconscious perception.

The results of two experiments showed that an illusion of memory can be produced by unconscious perception. In a first phase of those experiments, a long list of words was presented for study. For the test of recognition memory given in the second phase of each experiment, presentation of a "context" word preceded that of most recognition test words. Ss were to judge whether or not the test words had been presented during the earlier study phase of the experiment. Effects of a context word on this recognition memory decision were opposite when Ss were aware vs. unaware of its presentation. For example, as compared to a condition in which no context word was presented, the probability of false recognition was increased when Ss were unaware but decreased when Ss were aware of the presentation of a context word that matched the recognition test word. Results are discussed in terms of unconscious influences on an attribution process. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

A Novel Volume-Stable Collagen Matrix Induces Changes in the Behavior of Primary Human Oral Fibroblasts,Periodontal Ligament,and Endothelial Cells

The aim of the present study was to investigate the influence of a novel volume-stable collagen matrix (vCM) on early wound healing events including cellular migration and adhesion, protein adsorption and release, and the dynamics of the hemostatic system. For this purpose, we utilized transwell migration and crystal violet adhesion assays, ELISAs for quantification of adsorbed and released from the matrix growth factors, and qRT-PCR for quantification of gene expression in cells grown on the matrix. Our results demonstrated that primary human oral fibroblasts, periodontal ligament, and endothelial cells exhibited increased migration toward vCM compared to control cells that migrated in the absence of the matrix. Cellular adhesive properties on vCM were significantly increased compared to controls. Growth factors TGF-β1, PDGF-BB, FGF-2, and GDF-5 were adsorbed on vCM with great efficiency and continuously delivered in the medium after an initial burst release within hours. We observed statistically significant upregulation of genes encoding the antifibrinolytic thrombomodulin, plasminogen activator inhibitor type 1, thrombospondin 1, and thromboplastin, as well as strong downregulation of genes encoding the profibrinolytic tissue plasminogen activator, urokinase-type plasminogen activator, its receptor, and the matrix metalloproteinase 14 in cells grown on vCM. As a general trend, the stimulatory effect of the vCM on the expression of antifibrinolytic genes was synergistically enhanced by TGF-β1, PDGF-BB, or FGF-2, whereas the strong inhibitory effect of the vCM on the expression of profibrinolytic genes was reversed by PDGF-BB, FGF-2, or GDF-5. Taken together, our data strongly support the effect of the novel vCM on fibrin clot stabilization and coagulation/fibrinolysis equilibrium, thus facilitating progression to the next stages of the soft tissue healing process.     

The Effects of Insulin-Like Growth Factor I and BTP-2 on Acute Lung Injury

Acute lung injury (ALI) afflicts approximately 200,000 patients annually and has a 40% mortality rate. The COVID-19 pandemic has massively increased the rate of ALI incidence. The pathogenesis of ALI involves tissue damage from invading microbes and, in severe cases, the overexpression of inflammatory cytokines such as tumor necrosis factor-α (TNF-α) and interleukin-1β (IL-1β). This study aimed to develop a therapy to normalize the excess production of inflammatory cytokines and promote tissue repair in the lipopolysaccharide (LPS)-induced ALI. Based on our previous studies, we tested the insulin-like growth factor I (IGF-I) and BTP-2 therapies. IGF-I was selected, because we and others have shown that elevated inflammatory cytokines suppress the expression of growth hormone receptors in the liver, leading to a decrease in the circulating IGF-I. IGF-I is a growth factor that increases vascular protection, enhances tissue repair, and decreases pro-inflammatory cytokines. It is also required to produce anti-inflammatory 1,25-dihydroxyvitamin D. BTP-2, an inhibitor of cytosolic calcium, was used to suppress the LPS-induced increase in cytosolic calcium, which otherwise leads to an increase in proinflammatory cytokines. We showed that LPS increased the expression of the primary inflammatory mediators such as toll like receptor-4 (TLR-4), IL-1β, interleukin-17 (IL-17), TNF-α, and interferon-γ (IFN-γ), which were normalized by the IGF-I + BTP-2 dual therapy in the lungs, along with improved vascular gene expression markers. The histologic lung injury score was markedly elevated by LPS and reduced to normal by the combination therapy. In conclusion, the LPS-induced increases in inflammatory cytokines, vascular injuries, and lung injuries were all improved by IGF-I + BTP-2 combination therapy.     

An SDP Primal-Dual Algorithm for Approximating the Lovász-Theta Function

The Lovász ?-function (Lovász in IEEE Trans. Inf. Theory, 25:1–7, 1979) of a graph G=(V,E) can be defined as the maximum of the sum of the entries of a positive semidefinite matrix X, whose trace Tr(X) equals 1, and X _ij =0 whenever {i,j}∈E. This function appears as a subroutine for many algorithms for graph problems such as maximum independent set and maximum clique. We apply Arora and Kale’s primal-dual method for SDP to design an algorithm to approximate the ?-function within an additive error of δ>0, which runs in time $O(\frac{\vartheta ^{2} n^{2}}{\delta^{2}} \log n \cdot M_{e})$ , where ?=?(G) and M _e =O(n ³) is the time for a matrix exponentiation operation. It follows that for perfect graphs G, our primal-dual method computes ?(G) exactly in time O(? ² n ⁵logn). Moreover, our techniques generalize to the weighted Lovász ?-function, and both the maximum independent set weight and the maximum clique weight for vertex weighted perfect graphs can be approximated within a factor of (1+?) in time O(? ^?2 n ⁵logn).     

A Comparison of the Levels of Lutein and Zeaxanthin in Corn Germ Oil,Corn Fiber Oil and Corn Kernel Oil

All commercial corn oil is obtained by pressing corn germ and/or extracting the germ with hexane. In the current study, six types of corn oil were prepared by extracting corn germ, corn fiber and ground corn, each with hexane or with ethanol. The levels of lutein, zeaxanthin and other carotenoids were quantitatively analyzed in the six corn oils. The levels of lutein + zeaxanthin in the oil ranged from 2.3 μg/g for hexane-extracted corn germ oil to 220.9 μg/g for ethanol-extracted ground corn oil. These results indicate that a diet that includes 30 g (~2 tbsp) per day of the unrefined corn oil obtained by extracting ground corn with ethanol would provide ~6 mg of lutein + zeaxanthin, the daily dosage that is currently considered to be necessary to slow the progression of age-related macular degeneration. Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the U.S. Department of Agriculture.