Stagnant ice and age modelling in the Dome C region, Antarctica

The European Beyond EPICA project aims to extract a continuous ice core of up to 1.5 Ma, with a maximum age density of 20 kyr m-1 at Little Dome C (LDC). We present a 1D numerical model which calculates the age of the ice around Dome C. The model inverts for basal conditions and accounts either for melting or for a layer of stagnant ice above the bedrock. It is constrained by internal reflecting horizons traced in radargrams and dated using the EPICA Dome C (EDC) ice core age profile. We used three different radar datasets ranging from a 10 000 km2 airborne survey down to 5 km long ground-based radar transects over LDC. We find that stagnant ice exists in many places, including above the LDC relief where the new Beyond EPICA drill site (BELDC) is located. The modelled thickness of this layer of stagnant ice roughly corresponds to the thickness of the basal unit observed in one of the radar surveys and in the autonomous phase-sensitive radio-echo sounder (ApRES) dataset. At BELDC, the modelled stagnant ice thickness is 198±44 m and the modelled oldest age of ice is 1.45±0.16 Ma at a depth of 2494±30 m. This is very similar to all sites situated on the LDC relief, including that of the Million Year Ice Core project being conducted by the Australian Antarctic Division. The model was also applied to radar data in the area 10-15 km north of EDC (North Patch), where we find either a thin layer of stagnant ice (generally <60 m) or a negligible melt rate (<0.1 mm yr-1). The modelled maximum age at North Patch is over 2 Ma in most places, with ice at 1.5 Ma having a resolution of 9-12 kyr m-1, making it an exciting prospect for a future Oldest Ice drill site

Good practices for a literature survey are not followed by authors while preparing scientific manuscripts

The number of citations received by authors in scientific journals has become a major parameter to assess individual researchers and the journals themselves through the impact factor. A fair assessment therefore requires that the criteria for selecting references in a given manuscript should be unbiased with respect to the authors or the journals cited. In this paper, we advocate that authors should follow two mandatory principles to select papers (later reflected in the list of references) while studying the literature for a given research: i) consider similarity of content with the topics investigated, lest very related work should be reproduced or ignored; ii) perform a systematic search over the network of citations including seminal or very related papers. We use formalisms of complex networks for two datasets of papers from the arXiv repository to show that neither of these two criteria is fulfilled in practice

Nonnegative principal component analysis for mass spectral serum profiles and biomarker discovery

<p>Abstract</p> <p>Background</p> <p>As a novel cancer diagnostic paradigm, mass spectroscopic serum proteomic pattern diagnostics was reported superior to the conventional serologic cancer biomarkers. However, its clinical use is not fully validated yet. An important factor to prevent this young technology to become a mainstream cancer diagnostic paradigm is that robustly identifying cancer molecular patterns from high-dimensional protein expression data is still a challenge in machine learning and oncology research. As a well-established dimension reduction technique, PCA is widely integrated in pattern recognition analysis to discover cancer molecular patterns. However, its global feature selection mechanism prevents it from capturing local features. This may lead to difficulty in achieving high-performance proteomic pattern discovery, because only features interpreting global data behavior are used to train a learning machine.</p> <p>Methods</p> <p>In this study, we develop a nonnegative principal component analysis algorithm and present a nonnegative principal component analysis based support vector machine algorithm with sparse coding to conduct a high-performance proteomic pattern classification. Moreover, we also propose a nonnegative principal component analysis based filter-wrapper biomarker capturing algorithm for mass spectral serum profiles.</p> <p>Results</p> <p>We demonstrate the superiority of the proposed algorithm by comparison with six peer algorithms on four benchmark datasets. Moreover, we illustrate that nonnegative principal component analysis can be effectively used to capture meaningful biomarkers.</p> <p>Conclusion</p> <p>Our analysis suggests that nonnegative principal component analysis effectively conduct local feature selection for mass spectral profiles and contribute to improving sensitivities and specificities in the following classification, and meaningful biomarker discovery.</p

Optimal management of an insurer's exposure in a competitive general insurance market

The qualitative behavior of the optimal premium strategy is determined for an insurer in a finite and an infinite market using a deterministic general insurance model. The optimization problem leads to a system of forward-backward differential equations obtained from Pontryagin’s Maximum Principle. The focus of the modelling is on how this optimization problem can be simplified by the choice of demand function and the insurer’s objective. Phase diagrams are used to characterize the optimal control. When the demand is linear in the relative premium, the structure of the phase diagram can be determined analytically. Two types of premium strategy are identified for an insurer in an infinite market, and which is optimal depends on the existence of equilibrium points in the phase diagram. In a finite market there are four more types of premium strategy, and optimality depends on the initial exposure of the insurer and the position of a saddle point in the phase diagram. The effect of a nonlinear demand function is examined by perturbing the linear price function. An analytical optimal premium strategy is also found using inverse methods when the price function is nonlinear

Soft systems methodology: a context within a 50-year retrospective of OR/MS

Soft systems methodology (SSM) has been used in the practice of operations research and management science OR/MS) since the early 1970s. In the 1990s, it emerged as a viable academic discipline. Unfortunately, its proponents consider SSM and traditional systems thinking to be mutually exclusive. Despite the differences claimed by SSM proponents between the two, they have been complementary. An extensive sampling of the OR/MS literature over its entire lifetime demonstrates the richness with which the non-SSM literature has been addressing the very same issues as does SSM

Cisplatin and carboplatin pharmacokinetics in a pediatric patient with hepatoblastoma receiving peritoneal dialysis

PURPOSE: Cisplatin and carboplatin are frequently used drugs in the treatment of pediatric hepatoblastoma. Dosing guidelines for these drugs in children requiring peritoneal dialysis are lacking. Here, we describe the case of a 3-year-old boy with pre-existing end-stage renal disease on peritoneal dialysis, requiring treatment with cisplatin and carboplatin for hepatoblastoma. METHODS: Pharmacokinetic data were generated to support clinical dosing decisions, with the aim of adequate exposure and minimal toxicity. In the first chemotherapy cycle, 25% of the standard cisplatin dose and 75% of the carboplatin dose, calculated using the pediatric Calvert formula, were administered. Free platinum concentrations were determined in plasma ultrafiltrate and dialysate samples drawn after administration of cis- and carboplatin. RESULTS: Cisplatin was well tolerated and the observed AUC of cisplatin were 15.3 and 14.3 mg/L h in cycles 1 and 3, respectively. The calculated AUC of carboplatin in cycle 1 (9.8 mg/mL min) exceeded target AUC of 6.5 mg/mL min and toxicity was observed; therefore, the dose was reduced in cycles 2 and 3. The observed AUC in cycles 2 and 3 was 5.4 and 5.7 mg/mL min respectively. Platinum concentrations in the dialysate showed that 3-4% of the total dose of cisplatin and 10-12% of the total dose of carboplatin were excreted via peritoneal dialysis. Chemotherapy enabled extended hemihepatectomy and complete remission was achieved. CONCLUSION: This report shows that it is feasible to measure AUCs for both drugs and to individualize the dose of these drugs according to the PK results and clinical parameters. Our advice for future cases would be to calculate the starting dose of carboplatin using the (pediatric) Calvert formula, assuming a dialytic clearance of zero, and to adjust the dose if required, based on therapeutic drug monitoring

Volatility in the Housing Market: Evidence on Risk and Return in the London Sub-market

The impact of volatility in housing market analysis is reconsidered via examination of the risk-return relationship in the London housing market is examined. In addition to providing the first empirical results for the relationship between risk (as measured by volatility) and returns for this submarket, the analysis offers a more general message to empiricists via a detailed and explicit evaluation of the impact of empirical design decisions upon inferences. In particular, the negative risk-return relationship discussed frequently in the housing market literature is examined and shown to depend upon typically overlooked decisions concerning components of the empirical framework from which statistical inferences are drawn