Culture medium pH and growth of brazilian ginseng in vitro cultured plantlets

O presente trabalho objetivou avaliar o efeito do pH do meio de cultivo sobre alguns parâmetros de crescimento da Pfaffia glomerata (Spreng.) Pedersen cultivada in vitro, bem como checar se o crescimento dos explantes altera o pH do meio ao longo do período de cultivo. Foram testados quatro tratamentos constituídos de distintos valores de pH (3,7; 5,0; 6,0 e 7,5) do meio de cultivo. O pH do meio de cultivo foi ajustado antes da inclusão do agar (6g L-1 - Merck) e da autoclavagem. Como fonte de explantes foram utilizadas segmentos nodais de plantas previamente estabelecidas in vitro em meio MS. Dos nove aos 15 dias após a inoculação (DAI) dos segmentos nodais, verificou-se maior número de raízes em pH 6,0 e o menor no pH 7,5. Aos 35 DAI, o comprimento da maior brotação e o número total de segmentos nodais por planta foram maiores em torno de pH 6,0. Aos 35 DAI, observou-se menor crescimento em biomassa de raízes em pH 3,7. Já a parte aérea apresentou menor biomassa em pH 7,5. Aos 35 DAI, a produção de matéria fresca e seca total da plântula foi maior em pH próximo a 6,0. Concluiu-se que valores de pH do meio de cultivo próximos a 6,0, ajustados antes da autoclavagem, são ideais para o crescimento da P. glomerata cultivada in vitro. Também se verificou que o crescimento da plântula modificou significativamente o pH do meio de cultivo.The present research aimed to evaluate the effect of culture medium pH on some growth parameter of Pfaffia glomerata (Spreng.) Pedersen in vitro cultured plantlets, as well as to check whether the explant´s growth alters the culture medium pH. Four treatments consisted of different values (3.7; 5.0; 6.0 and 7.5) of culture medium pH were tested. The culture medium pH was adjusted prior to the addition of agar (6g L-1 - Merck) and autoclaving. Nodal segments from asseptic plants grown in MS medium were used as explants. From 9 to 15 days after inoculation (DAI) of nodal segments, the higher number of roots was obtained at pH 6.0, and the lower at pH 7.5. At 35 DAI, both length of the higher sprout and total number of nodal segments per plantlet were greater at about pH 6.0. At 35 DAI, roots biomass was lower at pH 3.7. On the other hand, shoots biomass was lower at pH 7.5. Fresh and dry matter of the whole plantlet was greater at pH around 6.0. In conclusion, values of culture medium pH near to 6.0, adjusted prior to autoclaving, are ideal for the growth of P. glomerata in vitro cultured plantlets. Moreover, the in vitro growth of plantlet modified significantly the culture medium pH

A SuperLearner-enforced approach for the estimation of treatment effect in pediatric trials

Background: Randomized Clinical Trials (RCT) represent the gold standard among scientific evidence. RCTs are tailored to control selection bias and the confounding effect of baseline characteristics on the effect of treatment. However, trial conduction and enrolment procedures could be challenging, especially for rare diseases and paediatric research. In these research frameworks, the treatment effect estimation could be compromised. A potential countermeasure is to develop predictive models on the probability of the baseline disease based on previously collected observational data. Machine learning (ML) algorithms have recently become attractive in clinical research because of their flexibility and improved performance compared to standard statistical methods in developing predictive models. Objective: This manuscript proposes an ML-enforced treatment effect estimation procedure based on an ensemble SuperLearner (SL) approach, trained on historical observational data, to control the confounding effect. Methods: The REnal SCarring Urinary infEction trial served as a motivating example. Historical observational study data have been simulated through 10,000 Monte Carlo (MC) runs. Hypothetical RCTs have been also simulated, for each MC run, assuming different treatment effects of antibiotics combined with steroids. For each MC simulation, the SL tool has been applied to the simulated observational data. Furthermore, the average treatment effect (ATE), has been estimated on the trial data and adjusted for the SL predicted probability of renal scar. Results: The simulation results revealed an increased power in ATE estimation for the SL-enforced estimation compared to the unadjusted estimates for all the algorithms composing the ensemble SL

Volatile organic compounds detection by electrical sensors using polyalkylthiophene-based Langmuir–Blodgett films

Despite their environmental and health hazards, volatile organic compounds (VOCs) are present in our everyday life (e.g., fuels and solvents). Their detection is of paramount importance for environmental and occupational hazards monitoring. Among other technologies, semiconducting polymers show good capabilities in VOCs detection and identification, thanks to the variation of their electronic properties upon VOC exposure. We fabricated and characterized VOCs sensors depositing thin films of regioregular poly(3-alkylthiophene) derivatives (rr-P3ATs) and stearic acid (SA) onto gold interdigitated electrodes by Langmuir–Blodgett technique. Poly(3-butylthiophene) and poly(3-hexylthiophene) (P3HT) were mixed with SA at different ratios, and their electrical conductivity was used to optimize the film composition. We characterized the optoelectronic and morphological properties of these films, as well as their electrical response to dichloromethane (DCM), tetrahydrofuran and toluene VOC exposure. Both P3AT sensors showed distinct and characteristic responses, highlighting their ability to recognize different VOCs. Moreover, we investigated the sensors saturation and sensitivity to different VOCs. The sensors were still able to detect the VOCs after six cycles, with P3HT remarkably showing no saturation at all for DCM, and the characteristic single VOC response for subsequent exposure to the different VOCs

Complexity dichotomy on partial grid recognition

Deciding whether a graph can be embedded in a grid using only unit-length edges is NP-complete, even when restricted to binary trees. However, it is not difficult to devise a number of graph classes for which the problem is polynomial, even trivial. A natural step, outstanding thus far, was to provide a broad classification of graphs that make for polynomial or NP-complete instances. We provide such a classification based on the set of allowed vertex degrees in the input graphs, yielding a full dichotomy on the complexity of the problem. As byproducts, the previous NP-completeness result for binary trees was strengthened to strictly binary trees, and the three-dimensional version of the problem was for the first time proven to be NP-complete. Our results were made possible by introducing the concepts of consistent orientations and robust gadgets, and by showing how the former allows NP-completeness proofs by local replacement even in the absence of the latter

A Bayesian Sample Size Estimation Procedure Based on a B-Splines Semiparametric Elicitation Method

Sample size estimation is a fundamental element of a clinical trial, and a binomial experiment is the most common situation faced in clinical trial design. A Bayesian method to determine sample size is an alternative solution to a frequentist design, especially for studies conducted on small sample sizes. The Bayesian approach uses the available knowledge, which is translated into a prior distribution, instead of a point estimate, to perform the final inference. This procedure takes the uncertainty in data prediction entirely into account. When objective data, historical information, and literature data are not available, it may be indispensable to use expert opinion to derive the prior distribution by performing an elicitation process. Expert elicitation is the process of translating expert opinion into a prior probability distribution. We investigated the estimation of a binomial sample size providing a generalized version of the average length, coverage criteria, and worst outcome criterion. The original method was proposed by Joseph and is defined in a parametric framework based on a Beta-Binomial model. We propose a more flexible approach for binary data sample size estimation in this theoretical setting by considering parametric approaches (Beta priors) and semiparametric priors based on B-splines

Compostagem no Brasil sob a perspectiva da legislação ambiental

A partir da compostagem é possível transformar resíduos orgânicos em fertilizantes orgânicos. No Brasil, todavia, esse tipo de reciclagem ocorre em apenas 4% da fração orgânica gerada, sendo que mais de 60% da massa total dos resíduos gerados pela população são classificados como resíduos orgânicos. Assim, objetivou-se analisar o estado da arte da reciclagem dos resíduos orgânicos na forma de compostagem sob a perspectiva da legislação ambiental brasileira. Um estudo bibliográfico, documental e sistemático do Art. 225 da Constituição da República Federativa do Brasil, Política Nacional do Meio Ambiente, Política Nacional de Resíduos Sólidos, Políticas Estaduais de Resíduos Sólidos e lei e decreto federal sobre fertilizantes destinados à agricultura. Observou-se que em âmbito federal, os documentos legais analisados tratam da compostagem nitidamente e de forma prioritária em relação à disposição em aterro sanitário; já em âmbito estadual não foi verificado uniformidade no tratamento do assunto, sendo notado que as desigualdades regionais existentes no país refletem na existência ou não de Políticas Estaduais de Resíduos Sólidos e apenas 55% destas Políticas existentes tratam da prioridade  da compostagem. Portanto, verifica-se que o dispositivo legislativo estadual deixa uma lacuna em relação à gestão de resíduos orgânicos que deve ser suprida pela legislação federal

Ocorrência de coronavírus entérico de ferrets no Brasil: nota prévia

Ferret enteric coronavirus (FECV) is associated to the epizootic catarrhal enteritis (ECE) in ferrets (Mustela putorius furo). In this study, we report the occurrence of this agent in four diarrheic stool samples of domestic ferrets, analyzed by negative staining transmission electron microscopy and a specific RT-PCR assay targeting the nucleocapsid (N) gene. These findings are the first report of FECV in Brazil and address the importance of this virus on the etiology of enteric disorders in ferrets.Coronavírus entérico de furões (FECV) é associado à enterite catarral epizoótica (ECE) em furões (Mustela putorius furo). Neste estudo, relatamos a ocorrência deste agente em quatro amostras fecais diarreicas de furões domésticos, analisadas por microscopia eletrônica de transmissão (contrastação negativa) e RT-PCR específica e direcionada ao gene de nucleocapsídeo (N). Estes achados constituem o primeiro relato de FECV no Brasil e remetem para a importância deste vírus na etiologia de quadros entéricos nestes animais

A CONSTANTE VIOLAÇÃO DOS CONHECIMENTOS TRADICIONAIS ASSOCIADOS À BIODIVERSIDADE E O NOVO CONSTITUCIONALISMO LATINO-AMERICANO COMO CAMINHO A SER TRILHADO PARA A SUA EFETIVA PROTEÇÃO

O desrespeito aos conhecimentos tradicionais associados à biodiversidade é latente, mesmo havendo previsão para a sua proteção. Assim, cabe perquirir em que medida o novo constitucionalismo latino-americano pode ser considerado como um caminho apto a efetivar proteção de referidos conhecimentos? Para tanto, utilizar-se-á o método de abordagem dedutivo e como método de procedimento o estruturalista. Ademais, dividir-se-á o artigo em duas seções. Na primeira, analisar-se-á os conhecimentos tradicionais e a ineficácia dos mecanismos protetivos e, na segunda, a possibilidade de adoção do novo constitucionalismo latino-americano como sendo uma forma de proteção desses conhecimentos

POLÍTICAS PÚBLICAS PARA O DESENVOLVIMENTO AMBIENTAL: A COMPLEXIDADE DOS DESAFIOS AMBIENTAIS NA SOCIEDADE MODERNA

Diante da magnitude dos desafios ambientais na sociedade moderna, depara-se com a imprescindibilidade de políticas públicas para o desenvolvimento ambiental, de modo que possa ser efetivado uma expansão e/ou alteração das percepções globais no que se refere às organizações sociais. Perante a mudança paradigmática mecanicista para ecológica, é mister a abertura a uma visão sistêmica, de forma que seja percebido o mundo como um todo integrado. Imprescindível, portanto, a efetivação de políticas públicas atentas à nova visão ecológica emergente na sociedade moderna. Neste sentido, esta produção abordará a respeito das políticas públicas para o desenvolvimento ambiental, por meio de uma análise a partir do pensamento sistêmico, atenta à complexidade dos desafios ambientais na sociedade moderna. Tratar-se-á quanto à problemática ambiental de uma forma contextual de modo que possa ser avaliado as relações e interações entre homem-natureza bem como a efetividade do direito ambiental e a eficácia das políticas públicas nesta seara. Aplicou-se o método de abordagem dedutivo, método de procedimento monográfico e teoria de base sistêmico complexa com fundamento em Capra e Morin

Pediatric Injury Surveillance From Uncoded Emergency Department Admission Records in Italy: Machine Learning-Based Text-Mining Approach

Background: Unintentional injury is the leading cause of death in young children. Emergency department (ED) diagnoses are a useful source of information for injury epidemiological surveillance purposes. However, ED data collection systems often use free-text fields to report patient diagnoses. Machine learning techniques (MLTs) are powerful tools for automatic text classification. The MLT system is useful to improve injury surveillance by speeding up the manual free-text coding tasks of ED diagnoses. Objective: This research aims to develop a tool for automatic free-text classification of ED diagnoses to automatically identify injury cases. The automatic classification system also serves for epidemiological purposes to identify the burden of pediatric injuries in Padua, a large province in the Veneto region in the Northeast Italy. Methods: The study includes 283, 468 pediatric admissions between 2007 and 2018 to the Padova University Hospital ED, a large referral center in Northern Italy. Each record reports a diagnosis by free text. The records are standard tools for reporting patient diagnoses. An expert pediatrician manually classified a randomly extracted sample of approximately 40, 000 diagnoses. This study sample served as the gold standard to train an MLT classifier. After preprocessing, a document-term matrix was created. The machine learning classifiers, including decision tree, random forest, gradient boosting method (GBM), and support vector machine (SVM), were tuned by 4-fold cross-validation. The injury diagnoses were classified into 3 hierarchical classification tasks, as follows: injury versus noninjury (task A), intentional versus unintentional injury (task B), and type of unintentional injury (task C), according to the World Health Organization classification of injuries. Results: The SVM classifier achieved the highest performance accuracy (94.14%) in classifying injury versus noninjury cases (task A). The GBM method produced the best results (92% accuracy) for the unintentional and intentional injury classification task (task B). The highest accuracy for the unintentional injury subclassification (task C) was achieved by the SVM classifier. The SVM, random forest, and GBM algorithms performed similarly against the gold standard across different tasks. Conclusions: This study shows that MLTs are promising techniques for improving epidemiological surveillance, allowing for the automatic classification of pediatric ED free-text diagnoses. The MLTs revealed a suitable classification performance, especially for general injuries and intentional injury classification. This automatic classification could facilitate the epidemiological surveillance of pediatric injuries by also reducing the health professionals' efforts in manually classifying diagnoses for research purposes