Effect of nitrogen and potassium fertilizers on melon plant productivity

The objective of this investigation was to evaluate the effect of different doses of nitrogen and potassium fertilizers on some production parameters of melon plants. The experiment was carried out in San Roque, which is 24 km from the city of Concepción, Paraguay, at the coordinates 57°14´10.29´´ South and 23°19´12.05´´ West. The study design was the completely randomized block with three replications in a split plot scheme 4 x 4. The dose used in the main plot was of N (0, 50, 100 and 150 kg ha-1) and in the sub-plot, K (0, 95, 190 and 285 kg ha-1). A light harrow was carried out to prepare the soil, seedlings were produced in 1500 cm3 pots of 60-micron thickness and the transplant was carried out when the seedlings had between 4 and 6 true leaves at 30 days after planting. Urea with 45% N was used as the source of nitrogen and potassium chloride 60% was used as the source of potassium. Fertilisation was carried out in September 2017 on two occasions: at 15 and 30 days after transplanting. The spacing used was of 1.5 m between rows and 1.5 m between plants, giving a total of 4356 plants ha-1. Harvesting began 90 days after planting and was carried out three times as the fruits reached commercial ripeness. The variables that were measured were average fruit weight (AFW), total soluble solids (TSS), polar diameter (PD), equatorial diameter (ED), fruit weight per plant (FWP). The data of the evaluated variables were subjected to the analysis of variance using the Fisher test where significant differences were found in: AFW, TSS, PD, ED and FWP. Subsequently, the regression analysis was performed (AFW, TSS and PD) and response surface (ED and FWP). The dose combination that produced the best values for equatorial diameter and fruit weight per plant was 71.9495 kg ha-1 of N and 160.554 kg ha-1 of K, 77.5921 kg ha-1 of N and 147.369 kg ha-1 of K, respectively.&nbsp

An Incentives Model Based on Reputation for P2P Systems, Journal of Telecommunications and Information Technology, 2013, nr 4

In this paper an incentive model to improve the collaboration in peer-to-peer networks is introduced. The proposed solution uses an incentives model associated with reputation issues as a way to improve the performance of a P2P system. The reputation of the all peers in the system is based on their donated resources and on their behavior. Supplying peers use these rules as a way to assign its outgoing bandwidth to the requesting peers during a content distribution. Each peer can build its best paths by using a best-neighbor policy within its neighborhood. A peer can use its best paths to obtain best services related to content search or download. The obtained results show that proposed scheme insulates the misbehaving peers and reduces the free-riding so that the systems performance is maximized

Risk assessment and suicide by patients with schizophrenia in secondary mental healthcare: a case-control study

Objectives: To investigate the role of risk assessment in predicting suicide in patients with schizophrenia spectrum disorders (SSDs) receiving secondary mental healthcare. We postulated that risk assessment plays a limited role in predicting suicide in these patients. Design: Retrospective case–control study. Setting: Anonymised electronic mental health record data from the South London and Maudsley National Health Service (NHS) Foundation Trust (SLaM) (London, UK) linked with national mortality data. Participants: In 242 227 SLaM service users up to 31 December 2013, 635 suicides were identified. 96 (15.1%) had a SSD diagnosis. Those who died before 1 January 2007 (n=25) were removed from the analyses. Thus, 71 participants with SSD who died from suicide over the study period (cases) were compared with 355 controls. Main outcome measure: Risk of suicide in relation to risk assessment ratings. Results: Cases were younger at first contact with services (mean±SD 34.5±12.6 vs 39.2±15.2) and with a higher preponderance of males (OR=2.07, 95% CI 1.18 to 3.65, p=0.01) than controls. Also, suicide occurred within 10 days after last contact with services in half of cases, with the most common suicide methods being hanging (14) and jumping (13). Cases were more likely to have the following ‘risk assessment’ items previously recorded: suicidal history (OR=4.42, 95% CI 2.01 to 9.65, p<0.001), use of violent method (OR=3.37, 95% CI 1.47 to 7.74, p=0.01), suicidal ideation (OR=3.57, 95% CI 1.40 to 9.07, p=0.01) and recent hospital discharge (OR=2.71, 95% CI 1.17 to 6.28, p=0.04). Multiple regression models predicted only 21.5% of the suicide outcome variance. Conclusions: Predicting suicide in schizophrenia is highly challenging due to the high prevalence of risk factors within this diagnostic group irrespective of outcome, including suicide. Nevertheless, older age at first contact with mental health services and lack of suicidal history and suicidal ideation are useful protective markers indicative of those less likely to end their own lives

Predicting prostate cancer treatment choices: The role of numeracy, time discounting, and risk attitudes

Prostate cancer is the most common cancer among males in the United States and there is lack of consensus as to whether active surveillance (AS) or radical prostatectomy (RP) is the best course of treatment. In this study we examined the role of three overlooked determinants of decision making about prostate cancer treatment in a hypothetical experiment—numeracy, time discounting, and risk taking in 279 men over age 50 without a prior prostate cancer diagnosis. Results showed that AS was the most frequently chosen option. Furthermore, numeracy and time discounting significantly predicted participants’ preference for AS, whereas a propensity to take risks was associated with a preference for RP. Such insights into the factors that affects cancer treatment preferences may improve tailored decision aids and help physicians be better poised to engage in shared decision-making to improve both patient-reported and clinical outcomes

Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline

From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm. However, the past decade has marked a long and arduous transformation bringing healthcare into the digital age. Ranging from electronic health records, to digitized imaging and laboratory reports, to public health datasets, today, healthcare now generates an incredible amount of digital information. Such a wealth of data presents an exciting opportunity for integrated machine learning solutions to address problems across multiple facets of healthcare practice and administration. Unfortunately, the ability to derive accurate and informative insights requires more than the ability to execute machine learning models. Rather, a deeper understanding of the data on which the models are run is imperative for their success. While a significant effort has been undertaken to develop models able to process the volume of data obtained during the analysis of millions of digitalized patient records, it is important to remember that volume represents only one aspect of the data. In fact, drawing on data from an increasingly diverse set of sources, healthcare data presents an incredibly complex set of attributes that must be accounted for throughout the machine learning pipeline. This chapter focuses on highlighting such challenges, and is broken down into three distinct components, each representing a phase of the pipeline. We begin with attributes of the data accounted for during preprocessing, then move to considerations during model building, and end with challenges to the interpretation of model output. For each component, we present a discussion around data as it relates to the healthcare domain and offer insight into the challenges each may impose on the efficiency of machine learning techniques.Comment: Healthcare Informatics, Machine Learning, Knowledge Discovery: 20 Pages, 1 Figur

Results of the MAJORANA DEMONSTRATOR's Search for Double-Beta Decay of 76Ge to Excited States of 76Se

The MAJORANA DEMONSTRATOR is searching for double-beta decay of 76Ge to excited states (E.S.) in 76Se using a modular array of high purity Germanium detectors. 76Ge can decay into three E.S.s of 76Se. The E.S. decays have a clear event signature consisting of a ββ-decay with the prompt emission of one or two γ-rays, resulting in with high probability in a multi-site event. The granularity of the DEMONSTRATOR detector array enables powerful discrimination of this event signature from backgrounds. Using 21.3 kg-y of isotopic exposure, the DEMONSTRATOR has set world leading limits for each E.S. decay, with 90% CL lower half-life limits in the range of (0.56 2.1) ⋅ 1024 y. In particular, for the 2v transition to the first 0+ E.S. of 76Se, a lower half-life limit of 0.68 ⋅ 1024 at 90% CL was achieved

Macro-Climatic Distribution Limits Show Both Niche Expansion and Niche Specialization among C4 Panicoids

Grasses are ancestrally tropical understory species whose current dominance in warm open habitats is linked to the evolution of C4 photosynthesis. C4 grasses maintain high rates of photosynthesis in warm and water stressed environments, and the syndrome is considered to induce niche shifts into these habitats while adaptation to cold ones may be compromised. Global biogeographic analyses of C4 grasses have, however, concentrated on diversity patterns, while paying little attention to distributional limits. Using phylogenetic contrast analyses, we compared macro-climatic distribution limits among ~1300 grasses from the subfamily Panicoideae, which includes 4/5 of the known photosynthetic transitions in grasses. We explored whether evolution of C4 photosynthesis correlates with niche expansions, niche changes, or stasis at subfamily level and within the two tribes Paniceae and Paspaleae. We compared the climatic extremes of growing season temperatures, aridity, and mean temperatures of the coldest months. We found support for all the known biogeographic distribution patterns of C4 species, these patterns were, however, formed both by niche expansion and niche changes. The only ubiquitous response to a change in the photosynthetic pathway within Panicoideae was a niche expansion of the C4 species into regions with higher growing season temperatures, but without a withdrawal from the inherited climate niche. Other patterns varied among the tribes, as macro-climatic niche evolution in the American tribe Paspaleae differed from the pattern supported in the globally distributed tribe Paniceae and at family level.Fil: Aagesen, Lone. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Botánica Darwinion. Academia Nacional de Ciencias Exactas, Físicas y Naturales. Instituto de Botánica Darwinion; ArgentinaFil: Biganzoli, Fernando. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Métodos Cuantitativos y Sistemas de Información; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Bena, María Julia. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Botánica Darwinion. Academia Nacional de Ciencias Exactas, Físicas y Naturales. Instituto de Botánica Darwinion; ArgentinaFil: Godoy Bürki, Ana Carolina. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Botánica Darwinion. Academia Nacional de Ciencias Exactas, Físicas y Naturales. Instituto de Botánica Darwinion; ArgentinaFil: Reinheimer, Renata. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Agrobiotecnología del Litoral. Universidad Nacional del Litoral. Instituto de Agrobiotecnología del Litoral; ArgentinaFil: Zuloaga, Fernando Omar. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Botánica Darwinion. Academia Nacional de Ciencias Exactas, Físicas y Naturales. Instituto de Botánica Darwinion; Argentin

Kinetic modelling of competition and depletion of shared miRNAs by competing endogenous RNAs

Non-conding RNAs play a key role in the post-transcriptional regulation of mRNA translation and turnover in eukaryotes. miRNAs, in particular, interact with their target RNAs through protein-mediated, sequence-specific binding, giving rise to extended and highly heterogeneous miRNA-RNA interaction networks. Within such networks, competition to bind miRNAs can generate an effective positive coupling between their targets. Competing endogenous RNAs (ceRNAs) can in turn regulate each other through miRNA-mediated crosstalk. Albeit potentially weak, ceRNA interactions can occur both dynamically, affecting e.g. the regulatory clock, and at stationarity, in which case ceRNA networks as a whole can be implicated in the composition of the cell's proteome. Many features of ceRNA interactions, including the conditions under which they become significant, can be unraveled by mathematical and in silico models. We review the understanding of the ceRNA effect obtained within such frameworks, focusing on the methods employed to quantify it, its role in the processing of gene expression noise, and how network topology can determine its reach.Comment: review article, 29 pages, 7 figure