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.

A cooperação transfronteiriça como oportunidade de desenvolvimento das regiões de fronteira. Da Raia Ibérica à euroregião Galiza-Norte de Portugal

Tese de Doutoramento em Geografia Humana.

A novel tool for the assessment of pain: validation in low back pain.

BACKGROUND: Adequate pain assessment is critical for evaluating the efficacy of analgesic treatment in clinical practice and during the development of new therapies. Yet the currently used scores of global pain intensity fail to reflect the diversity of pain manifestations and the complexity of underlying biological mechanisms. We have developed a tool for a standardized assessment of pain-related symptoms and signs that differentiates pain phenotypes independent of etiology. METHODS AND FINDINGS: Using a structured interview (16 questions) and a standardized bedside examination (23 tests), we prospectively assessed symptoms and signs in 130 patients with peripheral neuropathic pain caused by diabetic polyneuropathy, postherpetic neuralgia, or radicular low back pain (LBP), and in 57 patients with non-neuropathic (axial) LBP. A hierarchical cluster analysis revealed distinct association patterns of symptoms and signs (pain subtypes) that characterized six subgroups of patients with neuropathic pain and two subgroups of patients with non-neuropathic pain. Using a classification tree analysis, we identified the most discriminatory assessment items for the identification of pain subtypes. We combined these six interview questions and ten physical tests in a pain assessment tool that we named Standardized Evaluation of Pain (StEP). We validated StEP for the distinction between radicular and axial LBP in an independent group of 137 patients. StEP identified patients with radicular pain with high sensitivity (92%; 95% confidence interval [CI] 83%-97%) and specificity (97%; 95% CI 89%-100%). The diagnostic accuracy of StEP exceeded that of a dedicated screening tool for neuropathic pain and spinal magnetic resonance imaging. In addition, we were able to reproduce subtypes of radicular and axial LBP, underscoring the utility of StEP for discerning distinct constellations of symptoms and signs. CONCLUSIONS: We present a novel method of identifying pain subtypes that we believe reflect underlying pain mechanisms. We demonstrate that this new approach to pain assessment helps separate radicular from axial back pain. Beyond diagnostic utility, a standardized differentiation of pain subtypes that is independent of disease etiology may offer a unique opportunity to improve targeted analgesic treatment.

Lipid composition of fingermark residue and donor classification using GC/MS

Lipids available in fingermark residue represent important targets for enhancement and dating techniques. While it is well known that lipid composition varies among fingermarks of the same donor (intra-variability) and between fingermarks of different donors (inter-variability), the extent of this variability remains uncharacterised. Thus, this worked aimed at studying qualitatively and quantitatively the initial lipid composition of fingermark residue of 25 different donors. Among the 104 detected lipids, 43 were reported for the first time in the literature. Furthermore, palmitic acid, squalene, cholesterol, myristyl myristate and myristyl myristoleate were quantified and their correlation within fingermark residue was highlighted. Ten compounds were then selected and further studied as potential targets for dating or enhancement techniques. It was shown that their relative standard deviation was significantly lower for the intra-variability than for the inter-variability. Moreover, the use of data pretreatments could significantly reduce this variability. Based on these observations, an objective donor classification model was proposed. Hierarchical cluster analysis was conducted on the pre-treated data and the fingermarks of the 25 donors were classified into two main groups, corresponding to "poor" and "rich" lipid donors. The robustness of this classification was tested using fingermark replicates of selected donors. 86% of these replicates were correctly classified, showing the potential of such a donor classification model for research purposes in order to select representative donors based on compounds of interest.

Derivaciones en los centros de salud de Andalucía según el sexo de profesionales y pacientes: un análisis de género.

BACKGROUND It has been identified differences of medical care practice in primary care related to physician's sex. Simultaneously, there are gender inequalities in the assignment of health resources. Both aspects give rise to an increasing growing interest in the management and provision of health services. OBJECTIVES To examine the differences in the referral practice made by female and male primary care physicians working in health centers in Andalusia, to consider whether there are disparities in referrals received by men and women, and to examine the interaction between patient's sex and physician's sex. METHODS Observational, cross-sectional, and multicenter study. POPULATION 4 health districts in Andalucía and their physicians. SAMPLE 382 physicians. MEASUREMENTS referral rate per visit (RV), referral rate per patient quota (RQ), patient's sex, physician: sex, age, postgraduate family medicine specialty, size of the patient quota by sex, mean number of patients/day by sex, mean age of the patient quota by sex, and proportion of men in the quota. Health center: urban / rural, size of the team, enrolled population, and postgraduate family medicine specialty's accreditation. SOURCES databases of health districts. PERIOD OF STUDY 2010. ANALYSIS Bivariate and multivariate multilevel analysis of the referral rate per visit with mixed Poisson model. RESULTS In 2010 382 physicians made 129,161 referrals to specialized care. The RQ was 23.47 and the RV was 4.92. The RQ in women and men was 27.23 and 19.78 for women physicians, being 27.37 and 19.51 for male physicians. The RV in women and men was 4.92 and 5.48 for women physicians, being 4.54 and 4.93 for male physicians. CONCLUSION There are no differences in referral according to physician's sex. However, there are signs that might indicate the existence of gender inequality, and women patient received less referrals. There are no physician-patient's sex interaction.

Undertreatment of acute pain (oligoanalgesia) and medical practice variation in prehospital analgesia of adult trauma patients: a 10 yr retrospective study.

BACKGROUND: Prehospital oligoanalgesia is prevalent among trauma victims, even when the emergency medical services team includes a physician. We investigated if not only patients' characteristics but physicians' practice variations contributed to prehospital oligoanalgesia. METHODS: Patient records of conscious adult trauma victims transported by our air rescue helicopter service over 10 yr were reviewed retrospectively. Oligoanalgesia was defined as a numeric rating scale (NRS) >3 at hospital admission. Multilevel logistic regression analysis was used to predict oligoanalgesia, accounting first for patient case-mix, and then physician-level clustering. The intraclass correlation was expressed as the median odds ratio (MOR). RESULTS: A total of 1202 patients and 77 physicians were included in the study. NRS at the scene was 6.9 (1.9). The prevalence of oligoanalgesia was 43%. Physicians had a median of 5.7 yr (inter-quartile range: 4.2-7.5) of post-graduate training and 27% were female. In our multilevel analysis, significant predictors of oligoanalgesia were: no analgesia [odds ratio (OR) 8.8], National Advisory Committee for Aeronautics V on site (OR 4.4), NRS on site (OR 1.5 per additional NRS unit >4), female physician (OR 2.0), and years of post-graduate experience [>4.0 to ≤5.0 (OR 1.3), >3.0 to ≤4.0 (OR 1.6), >2.0 to ≤3.0 (OR 2.6), and ≤2.0 yr (OR 16.7)]. The MOR was 2.6, and was statistically significant. CONCLUSIONS: Physicians' practice variations contributed to oligoanalgesia, a factor often overlooked in analyses of prehospital pain management. Further exploration of the sources of these variations may provide innovative targets for quality improvement programmes to achieve consistent pain relief for trauma victims.

Asessing public policies. The case of education in Europe and the interaction between personal and institutional factors

The paper deals with the comparative study of European citizens' satisfaction with the state of education in their respective countries. Individual and contextual effects are tested applying multilevel analysis. The results show that educational public policies (level of decentralization, degree of comprehensiveness and public spending) as well as the students' social environment (socioeconomic and cultural status) have a sound impact on the opinions about the state of education.

Asessing public policies. The case of education in Europe and the interaction between personal and institutional factors

The paper deals with the comparative study of European citizens satisfaction with thestate of education in their respective countries. Individual and contextual effects aretested applying multilevel analysis. The results show that educational public policies(level of decentralization, degree of comprehensiveness and public spending) as well asthe students social environment (socioeconomic and cultural status) have a soundimpact on the opinions about the state of education.

Public perceptions of animal experimentation across Europe

The goal of this article is to map out public perceptions of animal experimentation in 28 European countries. Postulating cross-cultural differences, this study mixes country-level variables (from the Eurostat database) and individual-level variables (from Eurobarometer Science and Technology 2010). It is shown that experimentation on animals such as mice is generally accepted in European countries, but perceptions are divided on dogs and monkeys. Between 2005 and 2010, we observe globally a change of approval on dogs and monkeys, with a significant decrease in nine countries. Multilevel analysis results show differences at country level (related to a post-industrialism model) and at individual level (related to gender, age, education, proximity and perceptions of science and the environment). These results may have consequences for public perceptions of science and we call for more cross-cultural research on press coverage of animal research and on the level of public engagement of scientists doing animal research

The role of incidental variables of time in mood assessment

Determining what influences mood is important for theories of emotion and research onsubjective well-being. We consider three sets of factors: activities in which people areengaged; individual differences; and incidental variables that capture when mood ismeasured, e.g., time-of-day. These three factors were investigated simultaneously in a studyinvolving 168 part-time students who each responded 30 times in an experience samplingstudy conducted over 10 working days. Respondents assessed mood on a simple bipolarscale from 1 (very negative) to 10 (very positive). Activities had significant effects but,with the possible exception of variability in the expression of mood, no systematicindividual differences were detected. Diurnal effects, similar to those already reported inthe literature, were found as was an overall Friday effect. However, these effects weresmall. Lastly, the weather had little or no influence. We conclude that simple measures ofoverall mood are not greatly affected by incidental variables.

Profiling of counterfeit medicines by vibrational spectroscopy

Counterfeit pharmaceutical products have become a widespread problem in the last decade. Various analytical techniques have been applied to discriminate between genuine and counterfeit products. Among these, Near-infrared (NIR) and Raman spectroscopy provided promising results.The present study offers a methodology allowing to provide more valuable information fororganisations engaged in the fight against counterfeiting of medicines.A database was established by analyzing counterfeits of a particular pharmaceutical product using Near-infrared (NIR) and Raman spectroscopy. Unsupervised chemometric techniques (i.e. principal component analysis - PCA and hierarchical cluster analysis - HCA) were implemented to identify the classes within the datasets. Gas Chromatography coupled to Mass Spectrometry (GC-MS) and Fourier Transform Infrared Spectroscopy (FT-IR) were used to determine the number of different chemical profiles within the counterfeits. A comparison with the classes established by NIR and Raman spectroscopy allowed to evaluate the discriminating power provided by these techniques. Supervised classifiers (i.e. k-Nearest Neighbors, Partial Least Squares Discriminant Analysis, Probabilistic Neural Networks and Counterpropagation Artificial Neural Networks) were applied on the acquired NIR and Raman spectra and the results were compared to the ones provided by the unsupervised classifiers.The retained strategy for routine applications, founded on the classes identified by NIR and Raman spectroscopy, uses a classification algorithm based on distance measures and Receiver Operating Characteristics (ROC) curves. The model is able to compare the spectrum of a new counterfeit with that of previously analyzed products and to determine if a new specimen belongs to one of the existing classes, consequently allowing to establish a link with other counterfeits of the database.

Evolution of spirituality and religiousness in chronic schizophrenia or schizo-affective disorders: A 3 years follow-up study

Purpose : Spirituality and religiousness have been shown to be highly prevalent in patients with schizophrenia. Religion can help instil a positive sense of self, decrease the impact of symptoms and provide social contacts. Religion may also be a source of suffering. In this context, this research explores whether religion remains stable over time. Methods : From an initial cohort of 115 out-patients, 80% completed the 3-years follow-up assessment. In order to study the evolution over time, a hierarchical cluster analysis using average linkage was performed on factorial scores at baseline and follow-up and their differences. A sensitivity analysis was secondarily performed to check if the outcome was influenced by other factors such as changes in mental states using mixed models. Results : Religion was stable over time for 63% patients; positive changes occurred for 20% (i.e., significant increase of religion as a resource or a transformation of negative religion to a positive one) and negative changes for 17% (i.e., decrease of religion as a resource or a transformation of positive religion to a negative one). Change in spirituality and/or religiousness was not associated with social or clinical status, but with reduced subjective quality of life and self-esteem; even after controlling for the influence of age, gender, quality of life and clinical factors at baseline. Conclusions : In this context of patients with chronic schizophrenia, religion appeared to be labile. Qualitative analyses showed that those changes expressed the struggles of patients and suggest that religious issues need to be discussed in clinical settings.

Magnetic susceptibility to identify landscape segments on a detailed scale in the region of Jaboticabal, São Paulo, Brazil

The agricultural potential is generally assessed and managed based on a one-dimensional vision of the soil profile, however, the increased appreciation of sustainable production has stimulated studies on faster and more accurate evaluation techniques and methods of the agricultural potential on detailed scales. The objective of this study was to investigate the possibility of using soil magnetic susceptibility for the identification of landscape segments on a detailed scale in the region of Jaboticabal, São Paulo State. The studied area has two slope curvatures: linear and concave, subdivided into three landscape segments: upper slope (US, concave), middle slope (MS, linear) and lower slope (LS, linear). In each of these segments, 20 points were randomly sampled from a database with 207 samples forming a regular grid installed in each landscape segment. The soil physical and chemical properties, CO2 emissions (FCO2) and magnetic susceptibility (MS) of the samples were evaluated represented by: magnetic susceptibility of air-dried fine earth (MS ADFE), magnetic susceptibility of the total sand fraction (MS TS) and magnetic susceptibility of the clay fraction (MS Cl) in the 0.00 - 0.15 m layer. The principal component analysis showed that MS is an important property that can be used to identify landscape segments, because the correlation of this property within the first principal component was high. The hierarchical cluster analysis method identified two groups based on the variables selected by principal component analysis; of the six selected variables, three were related to magnetic susceptibility. The landscape segments were differentiated similarly by the principal component analysis and by the cluster analysis using only the properties with higher discriminatory power. The cluster analysis of MS ADFE, MS TS and MS Cl allowed the formation of three groups that agree with the segment division established in the field. The grouping by cluster analysis indicated MS as a tool that could facilitate the identification of landscape segments and enable the mapping of more homogeneous areas at similar locations.

Development of a two-step screening ESI-TOF-MS method for rapid determination of significant stress-induced metabolome modifications in plant leaf extracts: the wound response in Arabidopsis thaliana as a case study.

To study the stress-induced effects caused by wounding under a new perspective, a metabolomic strategy based on HPLC-MS has been devised for the model plant Arabidopsis thaliana. To detect induced metabolites and precisely localise these compounds among the numerous constitutive metabolites, HPLC-MS analyses were performed in a two-step strategy. In a first step, rapid direct TOF-MS measurements of the crude leaf extract were performed with a ballistic gradient on a short LC-column. The HPLC-MS data were investigated by multivariate analysis as total mass spectra (TMS). Principal components analysis (PCA) and hierarchical cluster analysis (HCA) on principal coordinates were combined for data treatment. PCA and HCA demonstrated a clear clustering of plant specimens selecting the highest discriminating ions given by the complete data analysis, leading to the specific detection of discrete-induced ions (m/z values). Furthermore, pool constitution with plants of homogeneous behaviour was achieved for confirmatory analysis. In this second step, long high-resolution LC profilings on an UPLC-TOF-MS system were used on pooled samples. This allowed to precisely localise the putative biological marker induced by wounding and by specific extraction of accurate m/z values detected in the screening procedure with the TMS spectra.

Introduction

Although increasing our knowledge of the properties of networks of cities is essential, these properties can be measured at the city level, and must be assessed by analyzing actor networks. The present volume focuses less on individual characteristics and more on the interactions of actors and institutions that create functional territories in which the structure of existing links constrains emerging links. Rather than basing explanations on external factors, the goal is to determine the extent to which network properties reflect spatial distributions and create local synergies at the meso level that are incorporated into global networks at the macro level where different geographical scales occur. The paper introduces the way to use the graphs structure to identify empirically relevant groups and levels that explain dynamics. It defines what could be called âeurooemulti-levelâeuro, âeurooemulti-scaleâeuro, or âeurooemultidimensionalâeuro networks in the context of urban geography. It explains how the convergence of the network multi-territoriality paradigm collaboratively formulated, and manipulated by geographers and computer scientists produced the SPANGEO project, which is exposed in this volume.