Strong duality in conic linear programming: facial reduction and extended duals

The facial reduction algorithm of Borwein and Wolkowicz and the extended dual of Ramana provide a strong dual for the conic linear program $$(P) \sup {<c, x> | Ax \leq_K b}$$ in the absence of any constraint qualification. The facial reduction algorithm solves a sequence of auxiliary optimization problems to obtain such a dual. Ramana's dual is applicable when (P) is a semidefinite program (SDP) and is an explicit SDP itself. Ramana, Tuncel, and Wolkowicz showed that these approaches are closely related; in particular, they proved the correctness of Ramana's dual using certificates from a facial reduction algorithm. Here we give a clear and self-contained exposition of facial reduction, of extended duals, and generalize Ramana's dual: -- we state a simple facial reduction algorithm and prove its correctness; and -- building on this algorithm we construct a family of extended duals when $K$ is a {\em nice} cone. This class of cones includes the semidefinite cone and other important cones.Comment: A previous version of this paper appeared as "A simple derivation of a facial reduction algorithm and extended dual systems", technical report, Columbia University, 2000, available from http://www.unc.edu/~pataki/papers/fr.pdf Jonfest, a conference in honor of Jonathan Borwein's 60th birthday, 201

Genetic Variability of Yield and Its Components in Winter Forage Pea Cultivars

The genus Pisum (peas) is rich in variability of morphological traits. It provides an excellent basis for breeding, but is also one of the reasons for the still undefined status of Pisum taxons (Mihailović et al., 2004a). The majority of forage pea cultivars used belongs to subspecies sativum and variety arvense (Maxted & Ambrose, 2000). The objective of our study was to determine genetic variability of yield and its components in six winter forage pea cultivars of different origin and to evaluate their breeding potential

Effect of Cutting Date on Quality of Red Clover Forage

Development stage or plant age is an important factor determining the chemical composition and quality of red clover forage (Ignjatovic et al., 2001). In early spring, young red clover plants have large leaf mass, high contents of moisture, protein and minerals and a low fibre content. In the course of the growing season, under the effects of long days and high temperatures, the plant undergoes morphological changes: leaves grow more slowly, the stem elongates, dry matter yield increases and quality drops, especially digestibility and the contents of protein and minerals

Monitoring Space Weather: Using Automated, Accurate Neural Network Based Whistler Segmentation for Whistler Inversion

It is challenging, yet important, to measure the - ever-changing - cold electron density in the plasmasphere. The cold electron density inside and outside of the plasmapause is a key parameter for radiation belt dynamics. One indirect measurement is through finding the velocity dispersion relation exhibited by lightning induced whistlers. The main difficulty of the method comes from low signal-to-noise ratios for most of the ground-based whistler components. To provide accurate electron density and L-shell measurements, whistler components need to be detectable in the noisy background, and their characteristics need to be reliably determined. For this reason precise segmentation is needed on a spectrogram image. Here we present a fully automated way to perform such an image segmentation by leveraging the power of convolutional neural networks, a state-of-the-art method for computer vision tasks. Testing the proposed method against a manually, and semi-manually segmented whistler dataset achieved <10% relative electron density prediction error for 80% of the segmented whistler traces, while for the L-shell, the relative error is <5% for 90% of the cases. By segmenting more than 1 million additional real whistler traces from Rothera station Antarctica, logged over 9 years, seasonal changes in the average electron density were found. The variations match previously published findings, and confirm the capabilities of the image segmentation technique

Nanofibrous solid dosage form of living bacteria prepared by electrospinning

The aim of this work was to investigate the suitability of electrospinning for biodrug delivery and to develop an electrospinning-based method to produce vaginal drug delivery systems. Lactobacillus acidophilus bacteria were encapsulated into nanofibers of three different polymers (polyvinyl alcohol and polyvinylpyrrolidone with two different molar masses). Shelf life of the bacteria could be enhanced by the exclusion of water and by preparing a solid dosage form, which is an advantageous and patient-friendly way of administration. The formulations were stored at –20, 7 and 25°C, respectively. Viability testing showed that the nanofibers can provide long term stability for huge amounts of living bacteria if they are kept at (or below) 7°C. Furthermore, all kinds of nanowebs prepared in this work dissolved instantly when they got in contact with water, thus the developed biohybrid nanowebs can provide new potential ways for curing bacterial vaginosis

Selecting appropriate methods of knowledge synthesis to inform biodiversity policy

Responding to different questions generated by biodiversity and ecosystem services policy or management requires different forms of knowledge (e.g. scientific, experiential) and knowledge synthesis. Additionally, synthesis methods need to be appropriate to policy context (e.g. question types, budget, timeframe, output type, required scientific rigour). In this paper we present a range of different methods that could potentially be used to conduct a knowledge synthesis in response to questions arising from knowledge needs of decision makers on biodiversity and ecosystem services policy and management. Through a series of workshops attended by natural and social scientists and decision makers we compiled a range of question types, different policy contexts and potential methodological approaches to knowledge synthesis. Methods are derived from both natural and social sciences fields and reflect the range of question and study types that may be relevant for syntheses. Knowledge can be available either in qualitative or quantitative form and in some cases also mixed. All methods have their strengths and weaknesses and we discuss a sample of these to illustrate the need for diversity and importance of appropriate selection. To summarize this collection, we present a table that identifies potential methods matched to different combinations of question types and policy contexts, aimed at assisting teams undertaking knowledge syntheses to select appropriate methods

Accelerating surveillance and research of antimicrobial resistance - an online repository for sharing of antimicrobial susceptibility data associated with whole-genome sequences

Antimicrobial resistance (AMR) is an emerging threat to modern medicine. Improved diagnostics and surveillance of resistant bacteria require the development of next-generation analysis tools and collabor

Evaluating methane inventories by isotopic analysis in the London region

A thorough understanding of methane sources is necessary to accomplish methane reduction targets. Urban environments, where a large variety of methane sources coexist, are one of the most complex areas to investigate. Methane sources are characterised by specific δ13C-CH4 signatures, so high precision stable isotope analysis of atmospheric methane can be used to give a better understanding of urban sources and their partition in a source mix. Diurnal measurements of methane and carbon dioxide mole fraction, and isotopic values at King’s College London, enabled assessment of the isotopic signal of the source mix in central London. Surveys with a mobile measurement system in the London region were also carried out for detection of methane plumes at near ground level, in order to evaluate the spatial allocation of sources suggested by the inventories. The measured isotopic signal in central London (−45.7 ±0.5‰) was more than 2‰ higher than the isotopic value calculated using emission inventories and updated δ13C-CH4 signatures. Besides, during the mobile surveys, many gas leaks were identified that are not included in the inventories. This suggests that a revision of the source distribution given by the emission inventories is needed

Understanding and predicting ciprofloxacin minimum inhibitory concentration in Escherichia coli with machine learning

It is important that antibiotics prescriptions are based on antimicrobial susceptibility data to ensure effective treatment outcomes. The increasing availability of next-generation sequencing, bacterial whole genome sequencing (WGS) can facilitate a more reliable and faster alternative to traditional phenotyping for the detection and surveillance of AMR. This work proposes a machine learning approach that can predict the minimum inhibitory concentration (MIC) for a given antibiotic, here ciprofloxacin, on the basis of both genome-wide mutation profiles and profiles of acquired antimicrobial resistance genes. We analysed 704 Escherichia coli genomes combined with their respective MIC measurements for ciprofloxacin originating from different countries. The four most important predictors found by the model, mutations in gyrA residues Ser83 and Asp87, a mutation in parC residue Ser80 and presence of the qnrS1 gene, have been experimentally validated before. Using only these four predictors in a linear regression model, 65% and 93% of the test samples' MIC were correctly predicted within a two- and a four-fold dilution range, respectively. The presented work does not treat machine learning as a black box model concept, but also identifies the genomic features that determine susceptibility. The recent progress in WGS technology in combination with machine learning analysis approaches indicates that in the near future WGS of bacteria might become cheaper and faster than a MIC measurement

Epidemiology of influenza-associated hospitalization in adults, Toronto, 2007/8

The purpose of this investigation was to identify when diagnostic testing and empirical antiviral therapy should be considered for adult patients requiring hospitalization during influenza seasons. During the 2007/8 influenza season, six acute care hospitals in the Greater Toronto Area participated in active surveillance for laboratory-confirmed influenza requiring hospitalization. Nasopharyngeal (NP) swabs were obtained from patients presenting with acute respiratory or cardiac illness, or with febrile illness without clear non-respiratory etiology. Predictors of influenza were analyzed by multivariable logistic regression analysis and likelihoods of influenza infection in various patient groups were calculated. Two hundred and eighty of 3,917 patients were found to have influenza. Thirty-five percent of patients with influenza presented with a triage temperature ≥38.0°C, 80% had respiratory symptoms in the emergency department, and 76% were ≥65 years old. Multivariable analysis revealed a triage temperature ≥38.0°C (odds ratio [OR] 3.1; 95% confidence interval [CI] 2.3–4.1), the presence of respiratory symptoms (OR 1.7; 95% CI 1.2–2.4), admission diagnosis of respiratory infection (OR 1.8; 95% CI 1.3–2.4), admission diagnosis of exacerbation of chronic obstructive pulmonary disease (COPD)/asthma or respiratory failure (OR 2.3; 95% CI 1.6–3.4), and admission in peak influenza weeks (OR 4.2; 95% CI 3.1–5.7) as independent predictors of influenza. The likelihood of influenza exceeded 15% in patients with respiratory infection or exacerbation of COPD/asthma if the triage temperature was ≥38.0°C or if they were admitted in the peak weeks during the influenza season. During influenza season, diagnostic testing and empiric antiviral therapy should be considered in patients requiring hospitalization if respiratory infection or exacerbation of COPD/asthma are suspected and if either the triage temperature is ≥38.0°C or admission is during the weeks of peak influenza activity