Pyrazole or tris(pyrazolyl)ethanol oxo-vanadium(IV) complexes as homogeneous or supported catalysts for oxidation of cyclohexane under mild conditions

The oxovanadium(IV) complexes [VO(acac)(2)(Hpz)].HC(pz)(3) 1.HC(pz)(3) (acac= acetylacetonate, Hpz = pyrazole, pz = pyrazoly1) and [VOCl2{HOCH2C(pz)(3)}] 2 were obtained from reaction of [VO(acac)(2)] with hydrotris(1-pyrazolyl)methane or of VCl(3)with 2,2,2-tris(1-pyrazolyl)ethanol. The compounds were characterized by elemental analysis, IR, Far-IR and EPR spectroscopies, FAB or ESI mass-spectrometry and, for 1, by single crystal X-ray diffraction analysis. 1 and 2 exhibit catalytic activity for the oxidation of cyclohexane to the cyclohexanol and cyclohexanone mixture in homogeneous system (TONS up to 1100) under mild conditions (NCMe, 24h, room temperature) using benzoyl peroxide (BPO), tert-butyl hydroperoxide (TBHP), m-chloroperoxybenzoic acid (mCPBA), hydrogen peroxide or the urea-hydrogen peroxide adduct (UHP) as oxidants. 1 and 2 were also immobilized on a polydimethylsiloxane membrane (1-PDMS or 2-PDMS) and the systems acted as supported catalysts for the cyclohexane oxidation using the above oxidants (TONs up to 620). The best results were obtained with mCPBA or BP0 as oxidant. The effects of various parameters, such as the amount of catalyst, nitric acid, reaction time, type of oxidant and oxidant-to-catalyst molar ratio, were investigated, for both homogeneous and supported systems. (C) 2012 Elsevier B.V. All rights reserved

Predicting progression of mild cognitive impairment to dementia using neuropsychological data: a supervised learning approach using time windows

Background: Predicting progression from a stage of Mild Cognitive Impairment to dementia is a major pursuit in current research. It is broadly accepted that cognition declines with a continuum between MCI and dementia. As such, cohorts of MCI patients are usually heterogeneous, containing patients at different stages of the neurodegenerative process. This hampers the prognostic task. Nevertheless, when learning prognostic models, most studies use the entire cohort of MCI patients regardless of their disease stages. In this paper, we propose a Time Windows approach to predict conversion to dementia, learning with patients stratified using time windows, thus fine-tuning the prognosis regarding the time to conversion. Methods: In the proposed Time Windows approach, we grouped patients based on the clinical information of whether they converted (converter MCI) or remained MCI (stable MCI) within a specific time window. We tested time windows of 2, 3, 4 and 5 years. We developed a prognostic model for each time window using clinical and neuropsychological data and compared this approach with the commonly used in the literature, where all patients are used to learn the models, named as First Last approach. This enables to move from the traditional question "Will a MCI patient convert to dementia somewhere in the future" to the question "Will a MCI patient convert to dementia in a specific time window". Results: The proposed Time Windows approach outperformed the First Last approach. The results showed that we can predict conversion to dementia as early as 5 years before the event with an AUC of 0.88 in the cross-validation set and 0.76 in an independent validation set. Conclusions: Prognostic models using time windows have higher performance when predicting progression from MCI to dementia, when compared to the prognostic approach commonly used in the literature. Furthermore, the proposed Time Windows approach is more relevant from a clinical point of view, predicting conversion within a temporal interval rather than sometime in the future and allowing clinicians to timely adjust treatments and clinical appointments.FCT under the Neuroclinomics2 project [PTDC/EEI-SII/1937/2014, SFRH/BD/95846/2013]; INESC-ID plurianual [UID/CEC/50021/2013]; LASIGE Research Unit [UID/CEC/00408/2013

A“Dirty” Footprint: Macroinvertebrate diversity in Amazonian Anthropic Soils

International audienceAmazonian rainforests, once thought to be pristine wilderness, are increasingly known to have been widely inhabited, modified, and managed prior to European arrival, by human populations with diverse cultural backgrounds. Amazonian Dark Earths (ADEs) are fertile soils found throughout the Amazon Basin, created by pre-Columbian societies with sedentary habits. Much is known about the chemistry of these soils, yet their zoology has been neglected. Hence, we characterized soil fertility, macroinvertebrate communities, and their activity at nine archeological sites in three Amazonian regions in ADEs and adjacent reference soils under native forest (young and old) and agricultural systems. We found 673 morphospecies and, despite similar richness in ADEs (385 spp.) and reference soils (399 spp.), we identified a tenacious pre-Columbian footprint, with 49% of morphospecies found exclusively in ADEs. Termite and total macroinvertebrate abundance were higher in reference soils, while soil fertility and macroinvertebrate activity were higher in the ADEs, and associated with larger earthworm quantities and biomass. We show that ADE habitats have a unique pool of species, but that modern land use of ADEs decreases their populations, diversity, and contributions to soil functioning. These findings support the idea that humans created and sustained high-fertility ecosystems that persist today, altering biodiversity patterns in Amazonia

Neuropsychological predictors of conversion from mild cognitive impairment to Alzheimer’s disease: a feature selection ensemble combining stability and predictability

Background Predicting progression from Mild Cognitive Impairment (MCI) to Alzheimer’s Disease (AD) is an utmost open issue in AD-related research. Neuropsychological assessment has proven to be useful in identifying MCI patients who are likely to convert to dementia. However, the large battery of neuropsychological tests (NPTs) performed in clinical practice and the limited number of training examples are challenge to machine learning when learning prognostic models. In this context, it is paramount to pursue approaches that effectively seek for reduced sets of relevant features. Subsets of NPTs from which prognostic models can be learnt should not only be good predictors, but also stable, promoting generalizable and explainable models. Methods We propose a feature selection (FS) ensemble combining stability and predictability to choose the most relevant NPTs for prognostic prediction in AD. First, we combine the outcome of multiple (filter and embedded) FS methods. Then, we use a wrapper-based approach optimizing both stability and predictability to compute the number of selected features. We use two large prospective studies (ADNI and the Portuguese Cognitive Complaints Cohort, CCC) to evaluate the approach and assess the predictive value of a large number of NPTs. Results The best subsets of features include approximately 30 and 20 (from the original 79 and 40) features, for ADNI and CCC data, respectively, yielding stability above 0.89 and 0.95, and AUC above 0.87 and 0.82. Most NPTs learnt using the proposed feature selection ensemble have been identified in the literature as strong predictors of conversion from MCI to AD. Conclusions The FS ensemble approach was able to 1) identify subsets of stable and relevant predictors from a consensus of multiple FS methods using baseline NPTs and 2) learn reliable prognostic models of conversion from MCI to AD using these subsets of features. The machine learning models learnt from these features outperformed the models trained without FS and achieved competitive results when compared to commonly used FS algorithms. Furthermore, the selected features are derived from a consensus of methods thus being more robust, while releasing users from choosing the most appropriate FS method to be used in their classification task.PTDC/EEI-SII/1937/2014; SFRH/BD/95846/2013; SFRH/BD/118872/2016info:eu-repo/semantics/publishedVersio

Short-Term Withdrawal of Mitogens Prior to Plating Increases Neuronal Differentiation of Human Neural Precursor Cells

Background: Human neural precursor cells (hNPC) are candidates for neural transplantation in a wide range of neurological disorders. Recently, much work has been done to determine how the environment for NPC culture in vitro may alter their plasticity. Epidermal growth factor (EGF) and fibroblast growth factor-2 (FGF-2) are used to expand NPC; however, it is not clear if continuous exposure to mitogens may abrogate their subsequent differentiation. Here we evaluated if short-term removal of FGF-2 and EGF prior to plating may improve hNPC differentiation into neurons.Principal Findings: We demonstrate that culture of neurospheres in suspension for 2 weeks without EGF-FGF-2 significantly increases neuronal differentiation and neurite extension when compared to cells cultured using standard protocols. in this condition, neurons were preferentially located in the core of the neurospheres instead of the shell. Moreover, after plating, neurons presented radial rather than randomly oriented and longer processes than controls, comprised mostly by neurons with short processes. These changes were followed by alterations in the expression of genes related to cell survival.Conclusions: These results show that EGF and FGF-2 removal affects NPC fate and plasticity. Taking into account that a three dimensional structure is essential for NPC differentiation, here we evaluated, for the first time, the effects of growth factors removal in whole neurospheres rather than in plated cell culture.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Institutos do Milenio de Bioengenharia TecidualUniversidade Federal de São Paulo, Dept Physiol, São Paulo, BrazilUniversidade Federal de São Paulo, Dept Biophys, São Paulo, BrazilUniv Fed Rio de Janeiro, Inst Ciencias Biomed, BR-21941 Rio de Janeiro, BrazilUniversidade Federal de São Paulo, Dept Physiol, São Paulo, BrazilUniversidade Federal de São Paulo, Dept Biophys, São Paulo, BrazilFAPESP: fellowCNPq: fellowWeb of Scienc