THE KINETICS OF THE REACTION OF MALEIC ANHYDRIDE AND OLEIC ACID IN A MEDIUM OF THE ACIDIC SOLVENT

Изучена кинетика процесса образования аддукта малеинового ангидрида и остатка олеиновой кислоты в среде кислотного органического растворителя – перхлорэтилена. Показано, что порядок реакции по малеиновому ангидриду – второй, а от концентрации олеиновой кислоты скорость реакции не зависит. Отмечено, что малеиновый ангидрид также расходуется на образование побочных продуктов: фумаровую кислоту и аддукт малеинового ангидрида и олеиновой кислоты. Определены оптимальные условия проведения синтеза при кипении в среде перхлорэтилена:избыток малеинового ангидрида 20-25 % и время проведения процесса 4-4,5 часа.Вивчена кінетика процесу утворення аддукта малеїнового ангідриду і залишку олеїнової кислоти в середовищі кислотного органічного розчинника - перхлоретілену. Показано, що порядок реакції за малеїновим ангідридом – другий, а від концентрації олеїнової кислоти швидкість реакції не залежить. Відзначено, що малеїновий ангідрид також витрачається на утворення побічних продуктів: фумарову кислоту і аддукт малеїнового ангідриду і олеїнової кислоти. Визначено оптимальні умови проведення синтезу при кипінні в середовищі перхлоретілену: надлишок малеїнового ангідриду 20-25% і час проведення процесу 4-4,5 години.In the article the interaction the kinetics of the reaction of maleic anhydride and oleic acid in a medium of the acidic solvent. We prove that the second order reaction of the maleic anhydride. The oleic acid does not affect the reaction rate. The optimal reaction conditions: the excess of maleic anhydride is 20-25 % and the time of reaction is 4-4,5 hours.The reaction takes place during boiling in the tetrachloroethene

Soil organisms in organic and conventional cropping systems.

Apesar do crescente interesse pela agricultura orgânica, são poucas as informações de pesquisa disponíveis sobre o assunto. Assim, num Argissolo Vermelho-Amarelo distrófico foram comparados os efeitos de sistemas de cultivo orgânico e convencional, para as culturas do tomate (Lycopersicum esculentum) e do milho (Zea mays), sobre a comunidade de organismos do solo e suas atividades. As populações de fungos,bactérias e actinomicetos, determinadas pela contagem de colônias em meio de cultura, foram semelhantes para os dois sistemas de produção. A atividade microbiana, avaliada pela evolução de CO2, manteve-se superior no sistema orgânico, sendo que em determinadas avaliações foi o dobro da evolução verificada no sistema convencional. O número de espécimes de minhoca foi praticamente dez vezes maior no sistema orgânico. Não foi observada diferença na taxa de decomposição de matéria orgânica entre os dois sistemas. De modo geral, o número de indivíduos de microartrópodos foi superior no sistema orgânico do que no sistema convencional, refletindo no maior índice de diversidade de Shannon. As maiores populações de insetos foram as da ordem Collembola, enquanto para os ácaros a maior população foi a da superfamília Oribatuloidea. Indivíduos dos grupos Aranae, Chilopoda, Dyplopoda, Pauropoda, Protura e Symphyla foram ocasionalmente coletados e de forma similar entre os sistemas

Discovery of distinct immune phenotypes using machine learning in pulmonary arterial hypertension

RATIONALE: Accumulating evidence implicates inflammation in pulmonary arterial hypertension (PAH) and therapies targeting immunity are under investigation, though it remains unknown if distinct immune phenotypes exist. OBJECTIVE: Identify PAH immune phenotypes based on unsupervised analysis of blood proteomic profiles. METHODS AND RESULTS: In a prospective observational study of Group 1 PAH patients evaluated at Stanford University (discovery cohort, n=281) and University of Sheffield (validation cohort, n=104) between 2008-2014, we measured a circulating proteomic panel of 48 cytokines, chemokines, and factors using multiplex immunoassay. Unsupervised machine learning (consensus clustering) was applied in both cohorts independently to classify patients into proteomic immune clusters, without guidance from clinical features. To identify central proteins in each cluster, we performed partial correlation network analysis. Clinical characteristics and outcomes were subsequently compared across clusters. Four PAH clusters with distinct proteomic immune profiles were identified in the discovery cohort. Cluster 2 (n=109) had low cytokine levels similar to controls. Other clusters had unique sets of upregulated proteins central to immune networks- cluster 1 (n=58)(TRAIL, CCL5, CCL7, CCL4, MIF), cluster 3 (n=77)(IL-12, IL-17, IL-10, IL-7, VEGF), and cluster 4 (n=37)(IL-8, IL-4, PDGF-β, IL-6, CCL11). Demographics, PAH etiologies, comorbidities, and medications were similar across clusters. Non-invasive and hemodynamic surrogates of clinical risk identified cluster 1 as high-risk and cluster 3 as low-risk groups. Five-year transplant-free survival rates were unfavorable for cluster 1 (47.6%, CI 35.4-64.1%) and favorable for cluster 3 (82.4%, CI 72.0-94.3%)(across-cluster p<0.001). Findings were replicated in the validation cohort, where machine learning classified four immune clusters with comparable proteomic, clinical, and prognostic features. CONCLUSIONS: Blood cytokine profiles distinguish PAH immune phenotypes with differing clinical risk that are independent of World Health Organization Group 1 subtypes. These phenotypes could inform mechanistic studies of disease pathobiology and provide a framework to examine patient responses to emerging therapies targeting immunity

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types