Grid multi-category response logistic models.

BackgroundMulti-category response models are very important complements to binary logistic models in medical decision-making. Decomposing model construction by aggregating computation developed at different sites is necessary when data cannot be moved outside institutions due to privacy or other concerns. Such decomposition makes it possible to conduct grid computing to protect the privacy of individual observations.MethodsThis paper proposes two grid multi-category response models for ordinal and multinomial logistic regressions. Grid computation to test model assumptions is also developed for these two types of models. In addition, we present grid methods for goodness-of-fit assessment and for classification performance evaluation.ResultsSimulation results show that the grid models produce the same results as those obtained from corresponding centralized models, demonstrating that it is possible to build models using multi-center data without losing accuracy or transmitting observation-level data. Two real data sets are used to evaluate the performance of our proposed grid models.ConclusionsThe grid fitting method offers a practical solution for resolving privacy and other issues caused by pooling all data in a central site. The proposed method is applicable for various likelihood estimation problems, including other generalized linear models

Optimal design of blade parameters for fracturing tea-picking machine

The blade is one of the most critical components in the fracturing tea-picking machine, and this study is conducted to optimize the blade's working parameters. In this study, the effects of blade width, blade thickness, and cutting angle on the maximum fracturing force of tea stems were analyzed using the L9 (34) standard orthogonal table, with the maximum fracturing force used as the evaluation index. The results indicate that the main factors affecting the maximum fracturing force (MFF) of tea stems are cutting angle (CA), blade width (BW), and blade thickness (BT) in that order. Furthermore, microscopic observation of the fracture surface revealed that compared with the thickness of the other two blades, the thickness of 0 mm caused the cross-section uneven and had lots of burrs, correspondingly resulting in the section's oxidation and the deterioration of tea leaf quality. Therefore, the optimal combination of design parameters was a cutting angle of 90°, a blade width of 2.0 mm, and a blade thickness of 0.5 mm. The findings of this study can provide reference for blade design to reduce the fracturing force of tea-picking machines, lower the working power consumption, and improve the quality of freshly plucked tea leaves

中国における農産加工食品産業の空間経済分析

筑波大学 (University of Tsukuba)201

Measurement of a topological edge invariant in a microwave network

We report on the measurement of topological invariants in an electromagnetic topological insulator analog formed by a microwave network, consisting of the winding numbers of scattering matrix eigenvalues. The experiment can be regarded as a variant of a topological pump, with non-zero winding implying the existence of topological edge states. In microwave networks, unlike most other systems exhibiting topological insulator physics, the winding can be directly observed. The effects of loss on the experimental results, and on the topological edge states, is discussed.Comment: 10 pages, 10 figure

Exploring the role of the transactive memory system in virtual team resilience: Evidence from online medical teams

The capacity to resist and recover from challenges and adversities (i.e., resilience capacity) is critical for a virtual team to survive. However, our knowledge of what influence the development of resilient virtual teams have yet to be fully developed. Drawing on the transactive memory system (TMS) theory, we propose that TMS will enhance a virtual team resilience capacity. Applying discontinuous growth modeling, results of an empirical study involving 1974 online medical teams from a popular online healthcare platform in China provide available evidence. We found inconsistent effects of the three dimensions of TMS on online medical team resilience capacity. Specifically, specialization shows no significant impact. Credibility can enhance online medical team resilience capacity for both process and outcome performance. For coordination, voice centralization positively affects online medical team resilience capacity for process performance. These findings advance virtual team resilience literature and inform practitioners about how to build resilient virtual teams

OCT1 regulates the migration of colorectal cancer cells by acting on LDHA

Colorectal cancer is one of the most common cancers with high morbidity and mortality. Effective treatments to improve the prognosis are still lacking. The results of online analysis tools showed that OCT1 and LDHA were highly expressed in colorectal cancer, and the high expression of OCT1 was associated with poor prognosis. Immunofluorescence demonstrated that OCT1 and LDHA co-localized in colorectal cancer cells. In colorectal cancer cells, OCT1 and LDHA were upregulated by OCT1 overexpression, but downregulated by OCT1 knockdown. OCT1 overexpression promoted cell migration. OCT1 or LDHA knockdown inhibited the migration, and the downregulation of LDHA restored the promoting effect of OCT1 overexpression. OCT1 upregulation increased the levels of HK2, GLUT1 and LDHA proteins in colorectal cancer cells. Consequently, OCT1 promoted the migration of colorectal cancer cells by upregulating LDHA

High glucose upregulates connective tissue growth factor expression in human vascular smooth muscle cells

BACKGROUND: Connective tissue growth factor (CTGF) is a potent profibrotic factor, which is implicated in fibroblast proliferation, angiogenesis and extracellular matrix (ECM) synthesis. It is a downstream mediator of some of the effects of transforming growth factor β (TGFβ) and is potentially induced by hyperglycemia in human renal mesangial cells. However, whether high glucose could induce the CTGF expression in vascular smooth muscle cells (VSMCs) remains unknown. Therefore, this study was designed to test whether high glucose could regulate CTGF expression in human VSMC. The effect of modulating CTGF expression on VSMC proliferation and migration was further investigated. RESULTS: Expression of CTGF mRNA was up-regulated as early as 6 hours in cultured human VSMCs after exposed to high glucose condition, followed by ECM components (collagen type I and fibronectin) accumulation. The upregulation of CTGF mRNA appears to be TGFβ-dependent since anti-TGFβ antibody blocks the effect of high glucose on CTGF gene expression. A small interference RNA (siRNA) targeting CTGF mRNA (CTGF-siRNA) effectively suppressed CTGF up-regulation stimulated by high glucose up to 79% inhibition. As a consequence of decreased expression of CTGF gene, the deposition of ECM proteins in the VSMC was also declined. Moreover, CTGF-siRNA expressing vector partially inhibited the high glucose-induced VSMC proliferation and migration. CONCLUSION: Our data suggest that in the development of macrovascular complications in diabetes, CTGF might be an important factor involved in the patho-physiological responses to high glucose in human VSMCs. In addition, the modulatory effects of CTGF-siRNA during this process suggest that specific targeting CTGF by RNA interference could be useful in preventing intimal hyperplasia in diabetic macrovascular complications

Hypoxia stimulates the expression of macrophage migration inhibitory factor in human vascular smooth muscle cells via HIF-1α dependent pathway

<p>Abstract</p> <p>Background</p> <p>Hypoxia plays an important role in vascular remodeling and directly affects vascular smooth muscle cells (VSMC) functions. Macrophage migration inhibitory factor (MIF) is a well known proinflammatory factor, and recent evidence suggests an important role of MIF in the progression of atherosclerosis and restenosis. However, the potential link between hypoxia and MIF in VSMC has not been investigated. The current study was designed to test whether hypoxia could regulate MIF expression in human VSMC. The effect of modulating MIF expression on hypoxia-induced VSMC proliferation and migration was also investigated at the same time.</p> <p>Results</p> <p>Expression of MIF mRNA and protein was up-regulated as early as 2 hours in cultured human VSMCs after exposed to moderate hypoxia condition (3% O₂). The up-regulation of MIF expression appears to be dependent on hypoxia-inducible transcription factor-1α(HIF-1α) since knockdown of HIF-1α inhibits the hypoxia induction of MIF gene and protein expression. The hypoxia induced expression of MIF was attenuated by antioxidant treatment as well as by inhibition of extracellular signal-regulated kinase (ERK). Under moderate hypoxia conditions (3% O₂), both cell proliferation and cell migration were increased in VSMC cells. Blocking the MIF by specific small interference RNA to MIF (MIF-shRNA) resulted in the suppression of proliferation and migration of VSMCs.</p> <p>Conclusion</p> <p>Our results demonstrated that in VSMCs, hypoxia increased MIF gene expression and protein production. The hypoxia-induced HIF-1α activation, reactive oxygen species (ROS) generation and ERK activation might be involved in this response. Both MIF and HIF-1α mediated the hypoxia response of vascular smooth muscle cells, including cell migration and proliferation.</p

Automated Bug Removal for Software-Defined Networks

When debugging an SDN application, diagnosing the problem is merely the first step: the operator must still find a fix that solves the problem, without causing new problems elsewhere. However, most existing debuggers focus exclusively on diagnosis and offer the network operator little or no help with finding an effective fix. Finding a suitable fix is difficult because the number of candidates can be enormous. In this paper, we propose a step towards automated repair for SDN applications. Our approach consists of two elements. The first is a data structure that we call meta provenance, which can be used to efficiently find good candidate repairs. Meta provenance is inspired by the provenance concept from the database community; however, whereas standard provenance can only reason about changes to data, meta provenance can also reason about changes to programs. The second element is a system that can efficiently backtest a set of candidate repairs using historical data from the network. This is used to eliminate candidate repairs that do not work well, or that cause other problems. We have implemented a system that maintains meta provenance for SDNs, as well as a prototype debugger that uses the meta provenance to automatically suggest repairs. Results from several case studies show that, for problems of moderate complexity, our debugger can find high-quality repairs within one minute