Discrimination of Brazilian propolis according to the seasoning using chemometrics and machine learning based on UV-Vis scanning data

Propolis is a chemically complex biomass produced by honeybees (Apis mellifera) from plant resins added of salivary enzymes, beeswax, and pollen. The biological activities described for propolis were also identified for donor plants resin, but a big challenge for the standardization of the chemical composition and biological effects of propolis remains on a better understanding of the influence of seasonality on the chemical constituents of that raw material. Since propolis quality depends, among other variables, on the local flora which is strongly influenced by (a)biotic factors over the seasons, to unravel the harvest season effect on the propolis chemical profile is an issue of recognized importance. For that, fast, cheap, and robust analytical techniques seem to be the best choice for large scale quality control processes in the most demanding markets, e.g., human health applications. For that, UV-Visible (UV-Vis) scanning spectrophotometry of hydroalcoholic extracts (HE) of seventy-three propolis samples, collected over the seasons in 2014 (summer, spring, autumn, and winter) and 2015 (summer and autumn) in Southern Brazil was adopted. Further machine learning and chemometrics techniques were applied to the UV-Vis dataset aiming to gain insights as to the seasonality effect on the claimed chemical heterogeneity of propolis samples determined by changes in the flora of the geographic region under study. Descriptive and classification models were built following a chemometric approach, i.e. principal component analysis (PCA) and hierarchical clustering analysis (HCA) supported by scripts written in the R language. The UV-Vis profiles associated with chemometric analysis allowed identifying a typical pattern in propolis samples collected in the summer. Importantly, the discrimination based on PCA could be improved by using the dataset of the fingerprint region of phenolic compounds ( = 280-400m), suggesting that besides the biological activities of those secondary metabolites, they also play a relevant role for the discrimination and classification of that complex matrix through bioinformatics tools. Finally, a series of machine learning approaches, e.g., partial least square-discriminant analysis (PLS-DA), k-Nearest Neighbors (kNN), and Decision Trees showed to be complementary to PCA and HCA, allowing to obtain relevant information as to the sample discrimination.

Devices and Techniques for Blood Pressure Measurement and Criteria for Hypertension Adopted by Brazilian Physicians: Exploratory Study

OBJECTIVE: To determine technical procedures and criteria used by Brazilian physicians for measuring blood pressure and diagnosing hypertension. METHODS: A questionnaire with 5 questions about practices and behaviors regarding blood pressure measurement and the diagnosis of hypertension was sent to 25,606 physicians in all Brazilian regions through a mailing list. The responses were compared with the recommendations of a specific consensus and descriptive analysis. RESULTS: Of the 3,621 (14.1%) responses obtained, 57% were from the southeastern region of Brazil. The following items were reported: use of an aneroid device by 67.8%; use of a mercury column device by 14.6%; 11.9% of the participants never calibrated the devices; 35.7% calibrated the devices at intervals < 1 year; 85.8% measured blood pressure in 100% of the medical visits; 86.9% measured blood pressure more than once and on more than one occasion. For hypertension diagnosis, 55.7% considered the patient's age, and only 1/3 relied on consensus statements. CONCLUSION: Despite the adequate frequency of both practices, it was far from that expected, and some contradictions between the diagnostic criterion for hypertension and the number of blood pressure measurements were found. The results suggest that, to include the great majority of the medical professionals, disclosure of consensus statements and techniques for blood pressure measurement should go beyond the boundaries of medical events and specialized journals.

Olfactory traces and spatial learning in rats.

We conducted an experiment to assess the use of olfactory traces for spatial orientation in an open environment in rats, Rattus norvegicus. We trained rats to locate a food source at a fixed location from different starting points, in the presence or absence of visual information. A single food source was hidden in an array of 19 petri dishes regularly arranged in an open-field arena. Rats were trained to locate the food source either in white light (with full access to distant visuospatial information) or in darkness (without any visual information). In both cases, the goal was in a fixed location relative to the spatial frame of reference. The results of this experiment revealed that the presence of noncontrolled olfactory traces coherent with the spatial frame of reference enables rats to locate a unique position as accurately in darkness as with full access to visuospatial information. We hypothesize that the olfactory traces complement the use of other orientation mechanisms, such as path integration or the reliance on visuospatial information. This experiment demonstrates that rats can rely on olfactory traces for accurate orientation, and raises questions about the establishment of such traces in the absence of any other orientation mechanism. Copyright 1998 The Association for the Study of Animal Behaviour.

Unilateral Emissions Mitigation, Spillovers, and Global Learning

What's the role of unilateral measures in global climate change mitigation in a post-Durban, post 2012 global policy regime? We argue that under conditions of preference heterogeneity, unilateral emissions mitigation at a subnational level may exist even when a nation is unwilling to commit to emission cuts. As the fraction of individuals unilaterally cutting emissions in a global strongly connected network of countries evolves over time, learning the costs of cutting emissions can result in the adoption of such activities globally and we establish that this will indeed happen under certain assumptions. We analyze the features of a policy proposal that could accelerate convergence to a low carbon world in the presence of global learning.

Public and private learning from prices, strategic substitutability and complementarity, and equilibrium multiplicity

We study a general static noisy rational expectations model where investors have private information about asset payoffs, with common and private components, and about their own exposure to an aggregate risk factor, and derive conditions for existence and uniqueness (or multiplicity) of equilibria. We find that a main driver of the characterization of equilibria is whether the actions of investors are strategic substitutes or complements. This latter property in turn is driven by the strength of a private learning channel from prices, arising from the multidimensional sources of asymmetric information, in relation to the usual public learning channel. When the private learning channel is strong (weak) in relation to the public we have strong (weak) strategic complementarity in actions and potentially multiple (unique) equilibria. The results enable a precise characterization of whether information acquisition decisions are strategic substitutes or complements. We find that the strategic substitutability in information acquisition result obtained in Grossman and Stiglitz (1980) is robust. JEL Classification: D82, D83, G14 Keywords: Rational expectations equilibrium, asymmetric information, risk exposure, hedging, supply information, information acquisition.

Extreme Precipitation Modelling Using Geostatistics and Machine Learning Algorithms

The paper presents an approach for mapping of precipitation data. The main goal is to perform spatial predictions and simulations of precipitation fields using geostatistical methods (ordinary kriging, kriging with external drift) as well as machine learning algorithms (neural networks). More practically, the objective is to reproduce simultaneously both the spatial patterns and the extreme values. This objective is best reached by models integrating geostatistics and machine learning algorithms. To demonstrate how such models work, two case studies have been considered: first, a 2-day accumulation of heavy precipitation and second, a 6-day accumulation of extreme orographic precipitation. The first example is used to compare the performance of two optimization algorithms (conjugate gradients and Levenberg-Marquardt) of a neural network for the reproduction of extreme values. Hybrid models, which combine geostatistical and machine learning algorithms, are also treated in this context. The second dataset is used to analyze the contribution of radar Doppler imagery when used as external drift or as input in the models (kriging with external drift and neural networks). Model assessment is carried out by comparing independent validation errors as well as analyzing data patterns.

Literacy and Numeracy Learning for Life

Literacy and Numeracy for Learning and Life is the national strategy to improve literacy and numeracy standards among children and young people in the education system. This strategy seeks to address significant concerns about how well our young people are developing the literacy and numeracy skills that they will need to participate fully in the education system, to live satisfying and rewarding lives, and to participate as active and informed citizens in our society.

Literacy and Numeracy Learning for Life - Summary

In July 2011 the Minister for education launched Literacy and Numeracy for Learning and Life – the national strategy to improve literacy and numeracy among children and young people. The strategy was developed following an extensive consultation process and contributions from individuals, schools, groups and organisations. This leaflet gives a flavour of the key parts of the Strategy with access to the full document on the Department’s website.

Postmortem circulation: a new model for testing endovascular devices and training clinicians in their use.

The development of new medical devices, such as aortic valves, requires numerous preliminary studies on animals and training of personnel on cadavers before the devices can be used in patients. Postmortem circulation, a technique used for postmortem angiography, allows the vascular system to be reperfused in a way similar to that in living persons. This technique is used for postmortem investigations to visualize the human vascular system and to make vascular diagnoses. Specific material for reperfusing a human body was developed recently. Our aim was to investigate whether postmortem circulation that imitates in vivo conditions allows for the testing of medical materials on cadavers. We did this by delivering an aortic valve using minimally invasive methods. Postmortem circulation was established in eight corpses to recreate an environment as close as possible to in vivo conditions. Mobile fluoroscopy and a percutaneous catheterization technique were used to deliver the material to the correct place. Once the valve was implanted, the heart and primary vessels were extracted to confirm its position. Postmortem circulation proved to be essential in several of the cadavers because it helped the clinicians to deliver the material and improve their implantation techniques. Due to the intravascular circulation, sites with substantial arteriosclerotic stenosis could be bypassed, which would have been impossible without perfusion. Although originally developed for postmortem investigations, this reperfusion technique could be useful for testing new medical devices intended for living patients.

Preventive medicine for epidemic outbreaks and risky behavior through mobile devices and ubiquitous computing

In this work, we have developed the first free software for mobile devices with the Android operating system that can preventively mitigate the number of contagions of sexually transmitted infections (STI), associated with risk behavior. This software runs in two modes. The normal mode allows the user to see the alerts and nearby health centers. The second mode enables the service to work in the background. This software reports the health risks, as well as the location of different test centers.

Attention, memory and verbal learning and their relation to schizotypal traits in unaffected parents of schizophrenic patients

The main objective of this ex post facto study is to compare the differencesin cognitive functions and their relation to schizotypal personality traits between agroup of unaffected parents of schizophrenic patients and a control group. A total of 52unaffected biological parents of schizophrenic patients and 52 unaffected parents ofunaffected subjects were assessed in measures of attention (Continuous PerformanceTest- Identical Pairs Version, CPT-IP), memory and verbal learning (California VerbalLearning Test, CVLT) as well as schizotypal personality traits (Oxford-Liverpool Inventoryof Feelings and Experiences, O-LIFE). The parents of the patients with schizophreniadiffer from the parents of the control group in omission errors on the ContinuousPerformance Test- Identical Pairs, on a measure of recall and on two contrast measuresof the California Verbal Learning Test. The associations between neuropsychologicalvariables and schizotpyal traits are of a low magnitude. There is no defined pattern ofthe relationship between cognitive measures and schizotypal traits

A Formal Framework for Specifying, Reusing and Implementing Learning Processes. First level

This file contains the ontology of patterns of educational settings, as part of the formal framework for specifying, reusing and implementing educational settings. Furthermore, it includes the set of rules that extend the ontology of educational scenarios as well as a brief description of the level of patters of such ontological framework.

Intrauterine devices and endometrial cancer risk : a pooled analysis of the Epidemiology of Endometrial Cancer Consortium

Intrauterine devices (IUDs), long-acting and reversible contraceptives, induce a number of immunological and biochemical changes in the uterine environment that could affect endometrial cancer (EC) risk. We addressed this relationship through a pooled analysis of data collected in the Epidemiology of Endometrial Cancer Consortium. We combined individual-level data from 4 cohort and 14 case-control studies, in total 8,801 EC cases and 15,357 controls. Using multivariable logistic regression, we estimated pooled odds ratios (pooled-ORs) and 95% confidence intervals (CIs) for EC risk associated with ever use, type of device, ages at first and last use, duration of use and time since last use, stratified by study and adjusted for confounders. Ever use of IUDs was inversely related to EC risk (pooled-OR = 0.81, 95% CI = 0.74-0.90). Compared with never use, reduced risk of EC was observed for inert IUDs (pooled-OR = 0.69, 95% CI = 0.58-0.82), older age at first use (≥35 years pooled-OR = 0.53, 95% CI = 0.43-0.67), older age at last use (≥45 years pooled-OR = 0.60, 95% CI = 0.50-0.72), longer duration of use (≥10 years pooled-OR = 0.61, 95% CI = 0.52-0.71) and recent use (within 1 year of study entry pooled-OR = 0.39, 95% CI = 0.30-0.49). Future studies are needed to assess the respective roles of detection biases and biologic effects related to foreign body responses in the endometrium, heavier bleeding (and increased clearance of carcinogenic cells) and localized hormonal changes.

eKnowledge Project: Developing and piloting a forum tool to support the collaborative construction of knowledge on the UOC's Virtual Campus

Final report of the eKnowledge's project, an online forum tool that offers consultants and students the chance to create spaces for asynchronous communication and collaboration.