The impact of buyer-supplier relationships’ social capital on bi-directional information sharing in the supply chain

Purpose The purpose of this paper is to understand how the development of social capital can promote buyer’s bi-directional (inflow and outflow) information sharing. The authors examined buyers’ perceptional differences in information sharing: when they receive information from suppliers and when they provide information to suppliers, and how such inequivalent perception in information sharing can be resolved by the level of social capital and its’ sub-dimensions. Design/methodology/approach Theoretical model and the hypotheses were developed through literature review. Data were collected from Korean manufacturers in supply chains and structural equation modelling was used for analysis. Findings The results show that each dimension of social capital has a different effect on bi-directional information sharing. For the information inflow, all of the facets of social capital were significant; for the information outflow, however, only relational capital was significant. That is, with cognitive and structural capital, buyers perceive that they can receive appropriate information from suppliers yet be reluctant to provide information to suppliers. Practical implications Given that relational capital is essential for the balanced information sharing in buyer-supplier relationship, firms should pay attention to having social interactions with partners to promote trust in the relationship for efficacy in information sharing. Originality/value This is one of the first studies to explore the role of social capital in facilitating equivalent information sharing. This study develops a framework that social capital can provide valuable guidance in achieving equivalency of bi-directional information sharing

Dispersion of Vascular Plant in Kumo-do, Korea

AbstractThe vascular plants observed in the area were composed of a total of 228 taxa; 72 families, 172 genus, 201 species, 25 varieties, 1 sub-species and 1 cross species. The only endangered plants found in the area were Milletia japonica (Siebold & Zucc.) A.Gray. The endemic plants growing in the Geumodo except transplanted plants were Lespedeza x maritima Nakai and Carpinus coreana Nakai. which accounted for 0.8% of the vascular plants in Geumodo, 228 taxa. Specialized plants of Geumodo were a total of 41 species; 30 taxa in Grade I, 1 taxon in Grade II, 9 taxa in Grade III and 1 taxon in Grade V. Milletia japonica (Siebold & Zucc.) A.Gray was the only species found in important Grade IV to V. Currently, ferries ply to the island, attracting many tourists. This poses a threat to the rare plants living in the island and presses down the island to develop. Therefore, in the long-term perspective, the conservation plan such as comprehensive research and monitoring on the ecosystem shall be established to protect evergreen broad-leaved forests

The prognostic factors of resected non-small cell lung cancer with chest wall invasion

<p>Abstract</p> <p>Background</p> <p>We retrospectively reviewed the clinical features and surgical outcomes of patients with a surgically resected NSCLC invading chest wall in order to identify prognostic factors that impact long term survival.</p> <p>Methods</p> <p>Between January 1990 and December 2009, 107 patients who underwent surgical resection for chest wall invading NSCLC were reviewed. Tumors invading only the parietal pleura were defined as superficial invasions, and those involving the soft tissue or ribs were defined as deep invasions.</p> <p>Results</p> <p>There were 91 men and 16 women; median age was 64 years (range 30 to 80 years). Overall 5 year survival rate was 26.3%. The univariate prognostic factors for survival included gender, extent of resection (pneumonectomy vs lobectomy), tumor size(> 5 cm vs ≤ 5 cm), nodal status (N0 or N1 vs N2), completeness of resection (complete vs incomplete) and completeness of adjuvant chemotherapy. At multivariate analysis, five independent prognostic factors were shown; depth of invasion (superficial vs deep), tumor size, nodal status, completeness of resection, and completeness of adjuvant chemotherapy. In patients with completely resected T3N0 NSCLC, completion of chemotherapy is the only prognostic factor for long term survival.</p> <p>Conclusions</p> <p>Completeness of resection, nodal status, depth of invasion, tumor size, and adjuvant chemotherapy were prognostic factors for long-term survival in NSCLC patients with chest wall invasion. Because of poor prognosis in cases with chest wall invasion that have N2 positive LN, that is difficult to achieve complete resection and that need pneumonectomy, definite chemoradiotherapy or neoadjuvant chemoradiotherapy should be considered first in these cases.</p

Electronic structure and charge-density wave transition in monolayer VS_{2}

Vanadium disulfide (VS_{2}) attracts elevated interests for its charge-density wave (CDW) phase transition, ferromagnetism, and catalytic reactivity, but the electronic structure of monolayer has not been well understood yet. Here we report synthesis of epitaxial 1T VS_{2} monolayer on bilayer graphene grown by molecular-beam epitaxy (MBE). Angle-resolved photoemission spectroscopy (ARPES) measurements reveal that Fermi surface with six elliptical pockets centered at the M points shows gap opening at low temperature. Temperature-dependence of the gap size suggests existence of CDW phase transition above room temperature. Our observations provide important evidence to understand the strongly correlated electron physics and the related surface catalytic properties in two-dimensional transition-metal dichalcogenides (TMDCs).Comment: 25 pages, 4 figures, accepted in Current Applied Physic

Development of a Specific and Rapid Diagnostic Method for Detecting Influenza A (H1N1) pdm09 Virus Infection Using Immunochromatographic Assay

AbstractObjectivesThe aim of this study was to develop an immunochromatographic assay (ICA) for the detection of influenza A (H1N1) pdm09 virus infection.Materials and methodsSeveral monoclonal antibodies against influenza A (H1N1) pdm09 virus were generated and an ICA (pdm09-ICA) was developed for the rapid and specific detection of influenza A (H1N1) pdm09 virus infection. The specificity and sensitivity of the developed assay were compared with that of hemagglutination assay and real-time reverse-transcription polymerase chain reaction (rRT-PCR).ResultsThe detection limit was estimated to be 1/2 (8) hemagglutinating unit; the sensitivity and specificity rates of pdm09-ICA were 75.86% (110/145) and 100% (43/43), respectively, compared with rRT-PCR. The cross-reactivity for 20 influenza viruses, including seasonal H1N1 viruses, was found to be negative except for the H1N1 virus (A/Swine/Korea/GC0503/2005).ConclusionThese results indicate that the proposed method can be easily used for rapid and specific detection of the pdm09 infection. The assay developed in this study would be a useful tool for distinguishing the pdm09 infection from seasonal influenza A and B infections

Actinomycosis of the Gallbladder Mimicking Carcinoma: a Case Report with US and CT Findings

We describe a case of actinomycosis of the gallbladder mimicking carcinoma. Sonography showed a hypoechoic mass replacing gallbladder lumen and engulfing a stone; contrast-enhanced computed tomography showed a heterogeneously enhanced thickened gallbladder wall with subtle, disrupted luminal surface enhancement, which formed a mass. As a result of the clinical and radiologic presentation, our impression was of gallbladder carcinoma. Actinomycosis should be included in the differential diagnosis when sonography and computed tomography findings show a mass engulfing the stone in the gallbladder and extensive pericholecystic infiltration with extension to neighboring abdominal wall muscle

Automatic segmentation of multiple cardiovascular structures from cardiac computed tomography angiography images using deep learning.

OBJECTIVES:To develop, demonstrate and evaluate an automated deep learning method for multiple cardiovascular structure segmentation. BACKGROUND:Segmentation of cardiovascular images is resource-intensive. We design an automated deep learning method for the segmentation of multiple structures from Coronary Computed Tomography Angiography (CCTA) images. METHODS:Images from a multicenter registry of patients that underwent clinically-indicated CCTA were used. The proximal ascending and descending aorta (PAA, DA), superior and inferior vena cavae (SVC, IVC), pulmonary artery (PA), coronary sinus (CS), right ventricular wall (RVW) and left atrial wall (LAW) were annotated as ground truth. The U-net-derived deep learning model was trained, validated and tested in a 70:20:10 split. RESULTS:The dataset comprised 206 patients, with 5.130 billion pixels. Mean age was 59.9 ± 9.4 yrs., and was 42.7% female. An overall median Dice score of 0.820 (0.782, 0.843) was achieved. Median Dice scores for PAA, DA, SVC, IVC, PA, CS, RVW and LAW were 0.969 (0.979, 0.988), 0.953 (0.955, 0.983), 0.937 (0.934, 0.965), 0.903 (0.897, 0.948), 0.775 (0.724, 0.925), 0.720 (0.642, 0.809), 0.685 (0.631, 0.761) and 0.625 (0.596, 0.749) respectively. Apart from the CS, there were no significant differences in performance between sexes or age groups. CONCLUSIONS:An automated deep learning model demonstrated segmentation of multiple cardiovascular structures from CCTA images with reasonable overall accuracy when evaluated on a pixel level

Mechanism of Humoral and Cellular Immune Modulation Provided by Porcine Sertoli Cells

The understanding of main mechanisms that determine the ability of immune privilege related to Sertoli cells (SCs) will provide clues for promoting a local tolerogenic environment. In this study, we evaluated the property of humoral and cellular immune response modulation provided by porcine SCs. Porcine SCs were resistant to human antibody and complement-mediated formation of the membrane attack complex (38.41±2.77% vs. 55.02±5.44%, p=0.027) and cell lysis (42.95±1.75% vs. 87.99±2.25%, p<0.001) compared to immortalized aortic endothelial cells, suggesting that porcine SCs are able to escape cellular lysis associated with complement activation by producing one or more immunoprotective factors that may be capable of inhibiting membrane attack complex formation. On the other hand, porcine SCs and their culture supernatant suppressed the up-regulation of CD40 expression (p<0.05) on DCs in the presence of LPS stimulation. These novel findings, as we know, suggest that immune modulatory effects of porcine SCs in the presence of other antigen can be obtained from the first step of antigen presentation. These might open optimistic perspectives for the use of porcine SCs in tolerance induction eliminating the need for chronic immunosuppressive drugs

Machine Learning Framework to Identify Individuals at Risk of Rapid Progression of Coronary Atherosclerosis: From the PARADIGM Registry.

Background Rapid coronary plaque progression (RPP) is associated with incident cardiovascular events. To date, no method exists for the identification of individuals at risk of RPP at a single point in time. This study integrated coronary computed tomography angiography-determined qualitative and quantitative plaque features within a machine learning (ML) framework to determine its performance for predicting RPP. Methods and Results Qualitative and quantitative coronary computed tomography angiography plaque characterization was performed in 1083 patients who underwent serial coronary computed tomography angiography from the PARADIGM (Progression of Atherosclerotic Plaque Determined by Computed Tomographic Angiography Imaging) registry. RPP was defined as an annual progression of percentage atheroma volume ≥1.0%. We employed the following ML models: model 1, clinical variables; model 2, model 1 plus qualitative plaque features; model 3, model 2 plus quantitative plaque features. ML models were compared with the atherosclerotic cardiovascular disease risk score, Duke coronary artery disease score, and a logistic regression statistical model. 224 patients (21%) were identified as RPP. Feature selection in ML identifies that quantitative computed tomography variables were higher-ranking features, followed by qualitative computed tomography variables and clinical/laboratory variables. ML model 3 exhibited the highest discriminatory performance to identify individuals who would experience RPP when compared with atherosclerotic cardiovascular disease risk score, the other ML models, and the statistical model (area under the receiver operating characteristic curve in ML model 3, 0.83 [95% CI 0.78-0.89], versus atherosclerotic cardiovascular disease risk score, 0.60 [0.52-0.67]; Duke coronary artery disease score, 0.74 [0.68-0.79]; ML model 1, 0.62 [0.55-0.69]; ML model 2, 0.73 [0.67-0.80]; all P&lt;0.001; statistical model, 0.81 [0.75-0.87], P=0.128). Conclusions Based on a ML framework, quantitative atherosclerosis characterization has been shown to be the most important feature when compared with clinical, laboratory, and qualitative measures in identifying patients at risk of RPP