1,079 research outputs found

    The SS Collar

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    Localization of human breast-carcinoma xenografts using antibodies to carcinoembryonic antigen.

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    Affinity-purified antibodies to carcinoembryonic antigen (CEA) have been injected into immune-suppressed mice bearing xenografts of human breast tumours. It has been shown that the antibodies localized in the tumours but not in normal tissues. The degree of tumour localization correlates with the amount of tumour CEA, and is unaffected by levels of circulating CEA or CEA/anti-CEA immune complexes

    Corporate governance and financial constraints on strategic turnarounds

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    The paper extends the Robbins and Pearce (1992) two-stage turnaround response model to include governance factors. In addition to the retrenchment and recovery, the paper proposes the addition of a realignment stage, referring specifically to the re-alignment of expectations of principal and agent groups. The realignment stage imposes a threshold that must be crossed before the retrenchment and hence recovery stage can be entered. Crossing this threshold is problematic to the extent that the interests of governance-stakeholder groups diverge in a crisis situation. The severity of the crisis impacts on the bases of strategy contingent asset valuation leading to the fragmentation of stakeholder interests. In some cases the consequence may be that management are prevented from carrying out turnarounds by governance constraints. The paper uses a case study to illustrate these dynamics, and like the Robbins and Pearce study, it focuses on the textile industry. A longitudinal approach is used to show the impact of the removal of governance constraints. The empirical evidence suggests that such financial constraints become less serious to the extent that there is a functioning market for corporate control. Building on governance research and turnaround literature, the paper also outlines the general case necessary and sufficient conditions for successful turnarounds

    Evaluation of machine-learning methods for ligand-based virtual screening

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    Machine-learning methods can be used for virtual screening by analysing the structural characteristics of molecules of known (in)activity, and we here discuss the use of kernel discrimination and naive Bayesian classifier (NBC) methods for this purpose. We report a kernel method that allows the processing of molecules represented by binary, integer and real-valued descriptors, and show that it is little different in screening performance from a previously described kernel that had been developed specifically for the analysis of binary fingerprint representations of molecular structure. We then evaluate the performance of an NBC when the training-set contains only a very few active molecules. In such cases, a simpler approach based on group fusion would appear to provide superior screening performance, especially when structurally heterogeneous datasets are to be processed

    Enhanced methane reforming activity of a hydrothermally synthesised co-doped perovskite catalyst

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    A catalyst for the direct reforming of methane and simulated biogas has been prepared using a green and low temperature hydrothermal method. The nickel and iron co-doped SrZrO3 perovskite shows catalytic activity comparable to 10% Ni/Al2O3, but with an almost 50% saving in nickel content and a significant reduction in unwanted carbon deposition through thermal decomposition of methane and the Boudouard reaction. The use of a catalyst with a low active metal content produced via a hydrothermal route provides an attractive and sustainable method of production of synthesis gas from both methane and biogas for potential use in solid oxide fuel cells

    Differential Drug Survival of Biologic Therapies for the Treatment of Psoriasis: A Prospective Observational Cohort Study from the British Association of Dermatologists Biologic Interventions Register (BADBIR)

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    Drug survival reflects a drug’s effectiveness, safety, and tolerability. We assessed the drug survival of biologics used to treat psoriasis in a prospective national pharmacovigilance cohort (British Association of Dermatologists Biologic Interventions Register (BADBIR)). The survival rates of the first course of biologics for 3,523 biologic-naive patients with chronic plaque psoriasis were compared using survival analysis techniques and predictors of discontinuation analyzed using a multivariate Cox proportional hazards model. Data for patients on adalimumab (n=1,879), etanercept (n=1,098), infliximab (n=96), and ustekinumab (n=450) were available. The overall survival rate in the first year was 77%, falling to 53% in the third year. Multivariate analysis showed that female gender (hazard ratio (HR) 1.22; 95% confidence interval (CI): 1.09–1.37), being a current smoker (HR 1.19; 95% CI: 1.03–1.38), and a higher baseline dermatology life quality index (HR 1.01; 95% CI: 1.00–1.02) were predictors of discontinuation. Presence of psoriatic arthritis (HR 0.82; 95% CI: 0.71–0.96) was a predictor for drug survival. As compared with adalimumab, patients on etanercept (HR 1.63; 95% CI: 1.45–1.84) or infliximab (HR 1.56; 95% CI: 1.16–2.09) were more likely to discontinue therapy, whereas patients on ustekinumab were more likely to persist (HR 0.48; 95% CI: 0.37–0.62). After accounting for relevant covariates, ustekinumab had the highest first-course drug survival. The results of this study will aid clinical decision making when choosing biologic therapy for psoriasis patients
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