Indicators for All?:Monitoring Quality and Equity for a Broad and Bold Post-2015 Global Education Agenda

This paper sets out to reclaim rights-based thinking on for the current proposals forthe education Sustainable Development Goal (SDG). The proposed indicators areintended to contribute to a broad and bold agenda for education quality compatiblewith the targets set out in two proposals for a post-2015 education goals – the MuscatAgreement (Global Education For All Meeting 2014) and Open Working Group (OWG)proposal (Open Working Group for Sustainable Development Goals 2014). Two targetsare addressed, the "relevant learning outcomes" component of the basic educationtarget and the target for qualified teachers.The paper is organized into two parts. Part I looks back at experience with the educationMDG, EFA goals, and understandings of quality within the EFA movement to arrive at aframework for formulating indicators. Suggestions for post-2015 indicators are set out inPart II. The conclusion argues that the development goal should not only monitor whathas been achieved against a pre-determined agenda but support stakeholders across alllevels to create and implement a broad and bold agenda for education

Controlling for P-value inflation in allele frequency change in experimental evolution and artificial selection experiments

acceptedVersio

Development and Use of a Planter for Simultaneous Application of Seed, Fertilizer and Compost in Pearl Millet Production in Niger—Effects on Labor Use, Yield and Economic Return

Sowing and application of mineral and organic fertilizer is generally done manually in the Sahel, resulting in low precision and delayed application. The objective of this paper is to present a new mechanical planter (Gangaria) for the combined application of seeds and soil amendments (mineral fertilizer, compost, etc.), and to assess the effects of using this planter in pearl millet on labor use, yield and economic return. The labor study showed that the mechanized application of seeds and compost reduced time use by a factor of more than six. The on-station experiments were completely randomized experiments with six replications and six treatments: T0 (control), T1 (0.3 g NPK hill−1), T2 (25 g compost hill−1), T3 (25 g compost + 0.3 g NPK hill−1), T4 (50 g compost hill−1) and T5 (50 g compost + 0.3 g NPK hill−1). Treatments T1 to T5 were sown by the planter with seeds that were primed in combination with coating of seeds with a fungicide/insecticide. The treatment T5 increased grain yield and economic return compared to the control by 113% and 106%, respectively. The advantages for farmers using this approach of agricultural intensification are timelier sowing of dryland cereal crops, easy application of organic fertilizer and more precise delivery of input, thereby making this cropping system more productive and less vulnerable to drought.publishedVersio

Farmers' Soil Fertility Management in Niger and Opportunities for Improvements Through Mechanization, Microdosing, and Seed Coating

The objective of this study was to characterize pearl millet production in Niger and to assess the potential impact of a low-cost production package on land- and labor productivity. The survey showed that 62% of the farmers used manure, while 22% used mineral fertilizer. Of those who used mineral fertilizer only 18% practiced microdosing. High labor demand was given as the reason why 89% of the farmers did not practice microdosing. In field experiments, we tested at three sites and over 2 years a control (no fertilizer and manual sowing) against two improved production packages consisting of mechanized sowing, seed priming, seed treatment with fungicide and NPK fertilizer in treatment 1 (T1), or phosphate coating in treatment 2 (T2). In the production package T1, seed and NPK fertilizer were mixed in a 1:1 ratio and this mixture was thereafter applied by a planter giving a fertilizer rate of 0.3 g NPK hill−1. In treatment T2, the seeds were coated with rock phosphate, and were thereafter sown by a planter giving a rate of 0.35 g rock phosphate hill−1. Compared to the control, the T1 and T2 treatments increased yield by 70.9 and 42.7%, respectively. The two improved production packages reduced time to maturity by 10 days. The net benefit increased for the T1 and T2 treatments compared to the control by 111.8 and 72.8%, respectively. This increase was particularly due to the higher grain and stover yield as well as lower weeding costs. These technologies will also render pearl millet production more resistant to climate change due to timelier sowing and weeding, a better crop establishment, and a shortened growing season.publishedVersio

Rethinking paraneoplastic eosinophilia

Not available

Norwegian og Sterlings allianse på det norske markedet : en analyse av styringsmekanismer basert på spillteoretiske utfordringer

Denne utredningen ønsker å gi svar på hvordan styringsmekanismer kan skape økonomiske gevinster og redusere sannsynligheten for opportunistisk atferd i Norwegian og Sterlings allianse. Herunder hvordan selskapene kan påvirke hverandre til et gjensidig samarbeid som realiserer potensielle gevinster i relasjonen. For å kunne belyse problemstillinger rundt gjensidig samarbeid på en best mulig måte er det teoretiske rammeverket bygget opp av perspektiver fra forskjellige fagfelt. Mens Porters rammeverk vektlegger høyere profitt og markedsmakt som motiver for samarbeid, understreker transaksjonskostnadsteori at samarbeid må styres ut fra effektivitetsfremmende- og kostnadsreduserende hensyn. Spillteori fanger opp det dynamiske aspektet som Porters rammeverk og transaksjonskostnadsteori mangler, og viser sammen med ulike styringsmekanismer hvordan gjensidig samarbeid kan oppnås. Da Norwegian og Sterling lanserte sitt samarbeid 23. november 2004 var avtalen ment som en kommersiell allianse uten relasjonsspesifikke investeringer, og skulle omfatte 13 av selskapenes ruter. Motivet var økt tilbud og reduserte driftskostnader gjennom økt kapasitetsutnyttelse. Partene ønsket ikke å utvikle en felles merkevare, og alle støtteaktiviteter skulle fortsatt utføres av hvert enkelt flyselskap. Innenfor rutesamarbeidet skulle selskapene kjøpe seter av hverandre basert på selvkost, og således konkurrere på pris om de samme kundene innenfor det samme geografiske markedet. Analyser basert på dybdeintervjuer, observasjoner og sekundærdata, avdekker flere av de forutsette muligheter og trusler. Den forvirring som ble skapt omkring samarbeidet på ruten Oslo – Paris antyder for eksempel en ”fangens dilemma”-situasjon der Sterling tjente penger som konkurrent, og samtidig høstet fordeler som alliansepartner. På den annen side viser analysen av markedet at lønnsomhetsstrukturen reduserer sannsynligheten for opportunistisk atferd i alliansen. Et mindre marked med færre aktører innebærer mindre gevinster ved ensidige avvik, og skaper en mer fordelaktig lønnsomhetsstruktur ettersom den relative forskjell i fortjeneste mellom gjensidig samarbeid og ensidig avvik reduseres. I tråd med utredningens syntese vil fraværet av relasjonsspesifikke investeringer kreve at alliansens transaksjonskostnader holdes på et lavest mulig nivå. Et eventuelt 3 innkjøpssamarbeidet bør i hvert fall være basert på grundige kalkulasjoner. Stiger transaksjonskostnadene relativt mer enn gevinstene vil de andre styringsmekanismenes mulighet til å oppnå et gjensidig samarbeid bli begrenset. Med andre ord; når fortjenesten fra alliansen blir tilstrekkelig lav, vil verken signaler, handlingsregler eller uformelle forsikringer være nok til å forhindre opportunistisk atferd. Resultatet blir at Norwegian og Sterling mister tilgangen til en komplementær flyflåte, og må kjøpe disse flyene gjennom tradisjonelle markedstransaksjoner. Videre kan Norwegians skreddersydde it-system fungere som en formell forsikring og skape økonomiske gevinster ved at samarbeidet framstår som en strategisk allianse der selskapene hjelper hverandres kunder. Ikke minst kan uformelle forsikringer skape gevinster gjennom økt fleksibilitet og reduserte transaksjonskostnader fra sterkere relasjoner og tillit. Fraværet av relasjonsspesifikke investeringer gjør derimot at utgangsbarrierene er lave, og er med på å prege usikkerheten i alliansen. Det bør derfor også etableres en formalisert kontakt som sikrer kontroll og oppfølging med lønnsomhetsberegningene på en bedre måte enn i dag. Usikkerhet omkring Sterlings nye eiere, og den manglende tilliten som ble kommunisert under intervjuene, bør uansett lede til at Norwegian søker strengere kontroll over alliansens fremtidige utvikling med formelle forsikringer av en eller annen form. Ettersom Norwegian nå har opparbeidet seg en kapasitet på 20 fly kan det imidlertid tenkes at selskapet ønsker å kontrahere større flytyper for å redusere egne variable kostnader. For å sikre at dette blir en ”allianse for fremtiden” må derfor styringsmekanismene være sterke nok til å forhindre at samarbeidet blir et læringskappløp mot lavest mulig driftskostnader ved høy kapasitetsutnyttelse. Det er derfor under alle omstendigheter, og ved alle anledninger, viktig at selskapene diskuterer alliansens videre fremtid

AI for predicting chemical-effect associations at the chemical universe level – deepFPlearn

Many chemicals are out there in our environment, and all living species are exposed. However, numerous chemicals pose risks, such as developing severe diseases, if they occur at the wrong time in the wrong place. For the majority of the chemicals, these risks are not known. Chemical risk assessment and subsequent regulation of use require efficient and systematic strategies. Lab-based methods – even if high throughput – are too slow to keep up with the pace of chemical innovation. Existing computational approaches are designed for specific chemical classes or sub-problems but not usable on a large scale. Further, the application range of these approaches is limited by the low amount of available labeled training data.We present the ready-to-use and stand-alone program deepFPlearn that predicts the association between chemical structures and effects on the gene/pathway level using a combined deep learning approach. deepFPlearn uses a deep autoencoder for feature reduction before training a deep feedforward neural network to predict the target association. We received good prediction qualities and showed that our feature compression preserves relevant chemical structural information. Using a vast chemical inventory (unlabeled data) as input for the autoencoder did not reduce our prediction quality but allowed capturing a much more comprehensive range of chemical structures. We predict meaningful - experimentally verified-associations of chemicals and effects on unseen data. deepFPlearn classifies hundreds of thousands of chemicals in seconds.We provide deepFPlearn as an open-source and flexible tool that can be easily retrained and customized to different application settings at https://github.com/yigbt/deepFPlearn.Supplementary information Supplementary data are available at bioRxiv online.Contact jana.schor{at}ufz.deCompeting Interest StatementThe authors have declared no competing interest

AI for predicting chemical-effect associations at the chemical universe level: DeepFPlearn

Many chemicals are present in our environment, and all living species are exposed to them. However, numerous chemicals pose risks, such as developing severe diseases, if they occur at the wrong time in the wrong place. For the majority of the chemicals, these risks are not known. Chemical risk assessment and subsequent regulation of use require efficient and systematic strategies. Lab-based methods-even if high throughput-are too slow to keep up with the pace of chemical innovation. Existing computational approaches are designed for specific chemical classes or sub-problems but not usable on a large scale. Further, the application range of these approaches is limited by the low amount of available labeled training data. We present the ready-to-use and stand-alone program deepFPlearn that predicts the association between chemical structures and effects on the gene/pathway level using a combined deep learning approach. deepFPlearn uses a deep autoencoder for feature reduction before training a deep feed-forward neural network to predict the target association. We received good prediction qualities and showed that our feature compression preserves relevant chemical structural information. Using a vast chemical inventory (unlabeled data) as input for the autoencoder did not reduce our prediction quality but allowed capturing a much more comprehensive range of chemical structures. We predict meaningful-experimentally verified-associations of chemicals and effects on unseen data. deepFPlearn classifies hundreds of thousands of chemicals in seconds. We provide deepFPlearn as an open-source and flexible tool that can be easily retrained and customized to different application settings at https://github.com/yigbt/deepFPlearn

A random set scoring model for prioritization of disease candidate genes using protein complexes and data-mining of GeneRIF, OMIM and PubMed records.

BACKGROUND: Prioritizing genetic variants is a challenge because disease susceptibility loci are often located in genes of unknown function or the relationship with the corresponding phenotype is unclear. A global data-mining exercise on the biomedical literature can establish the phenotypic profile of genes with respect to their connection to disease phenotypes. The importance of protein-protein interaction networks in the genetic heterogeneity of common diseases or complex traits is becoming increasingly recognized. Thus, the development of a network-based approach combined with phenotypic profiling would be useful for disease gene prioritization. RESULTS: We developed a random-set scoring model and implemented it to quantify phenotype relevance in a network-based disease gene-prioritization approach. We validated our approach based on different gene phenotypic profiles, which were generated from PubMed abstracts, OMIM, and GeneRIF records. We also investigated the validity of several vocabulary filters and different likelihood thresholds for predicted protein-protein interactions in terms of their effect on the network-based gene-prioritization approach, which relies on text-mining of the phenotype data. Our method demonstrated good precision and sensitivity compared with those of two alternative complex-based prioritization approaches. We then conducted a global ranking of all human genes according to their relevance to a range of human diseases. The resulting accurate ranking of known causal genes supported the reliability of our approach. Moreover, these data suggest many promising novel candidate genes for human disorders that have a complex mode of inheritance. CONCLUSION: We have implemented and validated a network-based approach to prioritize genes for human diseases based on their phenotypic profile. We have devised a powerful and transparent tool to identify and rank candidate genes. Our global gene prioritization provides a unique resource for the biological interpretation of data from genome-wide association studies, and will help in the understanding of how the associated genetic variants influence disease or quantitative phenotypes. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1471-2105-15-315) contains supplementary material, which is available to authorized users