Relationships between nutrient composition of flowers and fruit quality in orange trees grown in calcareous soil

To determine if flower nutrient composition can be used to predict fruit quality, a field experiment was conducted over three seasons (1996-1999) in a commercial orange orchard (Citrus sinensis (L.) Osbeck cv. 'Valencia Late', budded on Troyer citrange rootstock) established on a calcareous soil in southern Portugal. Flowers were collected from 20 trees during full bloom in April and their nutrient composition determined, and fruits were harvested the following March and their quality evaluated. Patterns of covariation in flower nutrient concentrations and in fruit quality variables were evaluated by principal component analysis. Regression models relating fruit quality variables to flower nutrient composition were developed by stepwise selection procedures. The predictive power of the regression models was evaluated with an independent data set. Nutrient composition of flowers at full bloom could be used to predict the fruit quality variables fresh fruit mass and maturation index in the following year. Magnesium, Ca and Zn concentrations measured in flowers were related to fruit fresh mass estimations and N, P, Mg and Fe concentrations were related to fruit maturation index. We also established reference values for the nutrient composition of flowers based on measurements made in trees that produced large (> 76 mm in diameter) fruit.info:eu-repo/semantics/publishedVersio

Structure of naturally hydrated ferrihydrite revealed through neutron diffraction and first-principles modeling

Ferrihydrite, with a ‘‘two-line’’ x-ray diffraction pattern (2L-Fh), is the most amorphous of the iron oxides and is ubiquitous in both terrestrial and aquatic environments. It also plays a central role in the regulation and metabolism of iron in bacteria, algae, higher plants, and animals, including humans. In this study, we present a single-phase model for ferrihydrite that unifies existing analytical data while adhering to fundamental chemical principles. The primary particle is small (20–50 Å) and has a dynamic and variably hydrated surface, which negates long-range order; collectively, these features have hampered complete characterization and frustrated our understanding of the mineral's reactivity and chemical/biochemical function. Near and intermediate range neutron diffraction (NIMROD) and first-principles density functional theory (DFT) were employed in this study to generate and interpret high-resolution data of naturally hydrated, synthetic 2L-Fh at standard temperature. The structural optimization overcomes transgressions of coordination chemistry inherent within previously proposed structures, to produce a robust and unambiguous single-phase model

Prescription of medicines by medical students of Karachi, Pakistan: A cross-sectional study

<p>Abstract</p> <p>Background</p> <p>Prescription of medicines by non-doctors is an issue with serious global implications. To our knowledge prescription of drugs by medical and non-medical students has not been studied before. We aimed to determine the practice and attitudes of drug prescription by medical students and: a) how non-medical students respond to this practice, b) How this compares with the attitudes and practices of non-medical students.</p> <p>Methods</p> <p>A cross-sectional study was conducted on a sample of 600 students randomly selected from 2 medical and 2 non-medical universities. Ethical requirements were ensured and data was collected using self administered questionnaires. The Chi square tests and logistic univariate regression analyses were performed using SPSS v 14 to identify associations and differences.</p> <p>Results</p> <p>A total of 572 forms were completed and the sample consisted of 295 medical students and 277 non-medical students with no significant difference in their demographic profile. Of the 295 medical students 163 (55.3%) had prescribed a medicine independently and most (48.5%) said that they did this 2–3 times a year. The commonest reasons for this were 'previous experience' (68.7%), 'problem too trivial' (34.4%) and 'we knew everything about the condition' (31.3%). One-third (33.6%) of the undergraduate medical students thought that it was alright to independently diagnose an illness while a vast majority (78.3%) thought that it was alright for them to prescribe medicines to others. Common prescriptions were pain-killers, antipyretics, antiallergics and antibiotics. Medical students who prescribed medicines were of lesser age (CI = 1.366–1.887) and more likely to belong to the 1^st(CI = 3.588–21.731), 2^nd(CI = 2.059– 10.869) or 3^rd(CI = 4.331–26.374) year of medical college. One-third (33.9%) of the non-medical students reported that a medical student had prescribed medicines to them and 21.3% said that they trusted medical students and would follow their advice blindly. Many students thought it alright for medical students to diagnose and treat illnesses. A similar proportion of non-medical students (58.5%) reported prescribing medicines to others.</p> <p>Conclusion</p> <p>Prescription of medicines by non-doctors is rampant and urgent corrective measures are warranted. We have highlighted areas for future research and intervention and have given a few recommendations.</p

Reductions in the Prevalence and Incidence of Geohelminth Infections following a City-wide Sanitation Program in a Brazilian Urban Centre

In the city of Salvador, a large urban centre in Northeast Brazil, a city-wide sanitation intervention started in 1997, aiming to improve the sewerage coverage of households from 26% to 80%. Our aim was to study the impact of the intervention on the prevalence and incidence of geohelminths in the school-aged population. The longitudinal study comprised two cohorts: from the beginning of 1997 to 1998, where data was collected before the intervention, and at the end of 2003 to 2004, after the intervention. Copro-parasitological examinations were carried out on every individual from both cohorts, at the start and nine months later. Demographic, socio-economic, and environmental data were collected using semi-structured questionnaires. The variables utilized to demonstrate the effects of intervention, when utilized together in a multivariate model, accounted for a 100% observed reduction in the prevalence ratio (PR) and incidence ratio (IR). As well as proving that the variables associated with the effect of the program intervention were mediators in this reduction, the reduction in the PR and IR between these periods demonstrated that modifications to the urban environment, particularly those associated with sanitary sewage systems, affected the health of the population, significantly reducing the prevalence of geohelminths

Turing learning: : A metric-free approach to inferring behavior and its application to swarms

We propose Turing Learning, a novel system identification method for inferring the behavior of natural or artificial systems. Turing Learning simultaneously optimizes two populations of computer programs, one representing models of the behavior of the system under investigation, and the other representing classifiers. By observing the behavior of the system as well as the behaviors produced by the models, two sets of data samples are obtained. The classifiers are rewarded for discriminating between these two sets, that is, for correctly categorizing data samples as either genuine or counterfeit. Conversely, the models are rewarded for 'tricking' the classifiers into categorizing their data samples as genuine. Unlike other methods for system identification, Turing Learning does not require predefined metrics to quantify the difference between the system and its models. We present two case studies with swarms of simulated robots and prove that the underlying behaviors cannot be inferred by a metric-based system identification method. By contrast, Turing Learning infers the behaviors with high accuracy. It also produces a useful by-product - the classifiers - that can be used to detect abnormal behavior in the swarm. Moreover, we show that Turing Learning also successfully infers the behavior of physical robot swarms. The results show that collective behaviors can be directly inferred from motion trajectories of individuals in the swarm, which may have significant implications for the study of animal collectives. Furthermore, Turing Learning could prove useful whenever a behavior is not easily characterizable using metrics, making it suitable for a wide range of applications.Comment: camera-ready versio

Gut microbiota modulation accounts for the neuroprotective properties of anthocyanins

High-fat (HF) diets are thought to disrupt the profile of the gut microbiota in a manner that may contribute to the neuroinflammation and neurobehavioral changes observed in obesity. Accordingly, we hypothesize that by preventing HF-diet induced dysbiosis it is possible to prevent neuroinflammation and the consequent neurological disorders. Anthocyanins are flavonoids found in berries that exhibit anti-neuroinflammatory properties in the context of obesity. Here, we demonstrate that the blackberry anthocyanin-rich extract (BE) can modulate gut microbiota composition and counteract some of the features of HF-diet induced dysbiosis. In addition, we show that the modifications in gut microbial environment are partially linked with the anti-neuroinflammatory properties of BE. Through fecal metabolome analysis, we unravel the mechanism by which BE participates in the bilateral communication between the gut and the brain. BE alters host tryptophan metabolism, increasing the production of the neuroprotective metabolite kynurenic acid. These findings strongly suggest that dietary manipulation of the gut microbiota with anthocyanins can attenuate the neurologic complications of obesity, thus expanding the classification of psychobiotics to anthocyanins