Agricultura apoiada pela comunidade: poderia a experiência dos agricultores americanos ser útil para os agricultores urbanos brasileiros?

O objetivo deste trabalho foi obter algumas informações sobre o projeto Agricultura Apoiada pela Comunidade (AAC) nos EUA e avaliar se consumidores e produtores urbanos pobres de uma cidade brasileira de baixa renda aceitariam se engaja neste tipo de projeto

Out-of-phase oscillation between superfluid and thermal components for a trapped Bose condensate under oscillatory excitation

The vortex nucleation and the emergence of quantum turbulence induced by oscillating magnetic fields, introduced by Henn E A L, et al. 2009 (Phys. Rev. A 79, 043619) and Henn E A L, et al. 2009 (Phys. Rev. Lett. 103, 045301), left a few open questions concerning the basic mechanisms causing those interesting phenomena. Here, we report the experimental observation of the slosh dynamics of a magnetically trapped $^{87}$Rb Bose-Einstein condensate (BEC) under the influence of a time-varying magnetic field. We observed a clear relative displacement in between the condensed and the thermal fraction center-of-mass. We have identified this relative counter move as an out-of-phase oscillation mode, which is able to produce ripples on the condensed/thermal fractions interface. The out-of-phase mode can be included as a possible mechanism involved in the vortex nucleation and further evolution when excited by time dependent magnetic fields.Comment: 5 pages, 5 figures, 25 reference

Extraction, selection and comparison of features for an effective automated computer-aided diagnosis of Parkinson's disease based on [123I]FP-CIT SPECT images

Purpose This work aimed to assess the potential of a set of features extracted from [I-123] FP-CIT SPECT brain images to be used in the computer-aided "in vivo" confirmation of dopaminergic degeneration and therefore to assist clinical decision to diagnose Parkinson's disease.Methods Seven features were computed from each brain hemisphere: five standard features related to uptake ratios on the striatum and two features related to the estimated volume and length of the striatal region with normal uptake. The features were tested on a dataset of 652 [I-123] FP-CIT SPECT brain images from the Parkinson's Progression Markers Initiative. The discrimination capacities of each feature individually and groups of features were assessed using three different machine learning techniques: support vector machines (SVM), k-nearest neighbors and logistic regression.Results Cross-validation results based on SVM have shown that, individually, the features that generated the highest accuracies were the length of the striatal region (96.5%), the putaminal binding potential (95.4%) and the striatal binding potential (93.9%) with no statistically significant differences among them. The highest classification accuracy was obtained using all features simultaneously (accuracy 97.9%, sensitivity 98% and specificity 97.6%). Generally, slightly better results were obtained using the SVM with no statistically significant difference to the other classifiers for most of the features.Conclusions The length of the striatal region uptake is clinically useful and highly valuable to confirm dopaminergic degeneration "in vivo" as an aid to the diagnosis of Parkinson's disease. It compares fairly well to the standard uptake ratio-based features, reaching, at least, similar accuracies and is easier to obtain automatically. Thus, we propose its day to day clinical use, jointly with the uptake ratio-based features, in the computer-aided diagnosis of dopaminergic degeneration in Parkinson's disease

The neural basis of fatigue in multiple sclerosis: A multimodal MRI approach

BACKGROUND: Fatigue is a frequent disabling symptom in multiple sclerosis (MS), but its pathophysiology remains incompletely understood. This study aimed to explore the underlying neural basis of fatigue in patients with MS. METHODS: We enrolled 60 consecutive patients with MS and 60 healthy controls (HC) matched on age, sex, and education. Fatigue was assessed using the Portuguese version of the Modified Fatigue Impact Scale (MFIS). All participants underwent 3T brain MRI (conventional and diffusion tensor imaging [DTI] sequences). White matter (WM) focal lesions were identified and T1/T2 lesion volumes were computed. Tract-based spatial statistics were applied for voxel-wise analysis of DTI metrics fractional anisotropy and mean diffusivity (MD) on normal-appearing WM (NAWM). Using Freesurfer software, total and regional volumes of cortical and subcortical gray matter (GM) were calculated. RESULTS: Compared to HC, patients with MS scored significantly higher on MFIS (33.8 ± 19.7 vs 16.5 ± 15.1, p < 0.001). MFIS scores were not significantly correlated with T1/T2 lesion volumes, total GM volume, or any regional volume of cortical and subcortical GM. Significant correlations were found between global scores of MFIS and MD increase of the NAWM skeleton, including corona radiata, internal capsule, external capsule, corticospinal tract, cingulum, corpus callosum, fornix, superior longitudinal fasciculus, superior fronto-occipital fasciculus, sagittal stratum, posterior thalamic radiation, cerebral peduncle, and uncinate fasciculus. CONCLUSIONS: In this study, fatigue was associated with widespread NAWM damage but not with lesion load or GM atrophy. Functional disconnection, caused by diffuse microstructural WM damage, might be the main neural basis of fatigue in MS.info:eu-repo/semantics/publishedVersio

Endothelial Progenitor Cells influence acute and subacute stroke hemodynamics

BACKGROUND: Endothelial Progenitor Cells (EPCs) are a circulating stem cell population with in vivo capacity of promoting angiogenesis after ischemic events. Despite the promising preclinical data, their potential integration with reperfusion therapies and hemodynamic evolution of stroke patients is still unknown. Our aim was to determine the association of EPCs with acute, subacute and chronic hemodynamic features. METHODS: In this prospective study, we included consecutive patients with ages between 18 and 80years and non-lacunar ischemic stroke within the territory of a middle cerebral artery. All patients were subject to hemodynamic evaluation by ultrasound at baseline, seven days and three months. We quantified cerebral blood flow (CBF) and assessed early recanalization and collateral flow. Hemorrhagic transformation was graded in Magnetic Resonance imaging performed at seven days. EPCs were isolated from peripheral venous blood collected in the first 24h and seven days, counted and submitted to functional in vitro tests. RESULTS: We included 45 patients with a median age of 70±10years. The angiogenic and migratory capacities of EPCs were associated with increased collateral flow in the acute stage and day seven CBF, without statistically significant associations with recanalization nor haemorrhagic transformation. The number of EPCs was not associated with any hemodynamic variable. CONCLUSIONS: The functional properties of EPCs are associated with acute and subacute stroke hemodynamics, with no effect on haemorrhagic transformation.info:eu-repo/semantics/publishedVersio

Three-vortex configurations in trapped Bose-Einstein condensates

We report on the creation of three-vortex clusters in a $^{87}Rb$ Bose-Einstein condensate by oscillatory excitation of the condensate. This procedure can create vortices of both circulation, so that we are able to create several types of vortex clusters using the same mechanism. The three-vortex configurations are dominated by two types, namely, an equilateral-triangle arrangement and a linear arrangement. We interpret these most stable configurations respectively as three vortices with the same circulation, and as a vortex-antivortex-vortex cluster. The linear configurations are very likely the first experimental signatures of predicted stationary vortex clusters.Comment: 4 pages, 4 figure

Effects of serine proteases inhibitors in bovine sperm cryopreservation.

Proceedings of the 30th Annual Meeting of the Brazilian Embryo Technology Society (SBTE); Foz do Iguaçu, PR, Brazil, August 25th to 27th, 2016, and 32nd Meeting of the European Embryo Transfer Association (AETE); Barcelona, Spain, September 9th and 10th, 2016

Artificial intelligence for predictive biomarker discovery in immuno-oncology: a systematic review

Background: The widespread use of immune checkpoint inhibitors (ICIs) has revolutionised treatment of multiple cancer types. However, selecting patients who may benefit from ICI remains challenging. Artificial intelligence (AI) approaches allow exploitation of high-dimension oncological data in research and development of precision immuno-oncology. Materials and methods: We conducted a systematic literature review of peer-reviewed original articles studying the ICI efficacy prediction in cancer patients across five data modalities: genomics (including genomics, transcriptomics, and epigenomics), radiomics, digital pathology (pathomics), and real-world and multimodality data. Results: A total of 90 studies were included in this systematic review, with 80% published in 2021-2022. Among them, 37 studies included genomic, 20 radiomic, 8 pathomic, 20 real-world, and 5 multimodal data. Standard machine learning (ML) methods were used in 72% of studies, deep learning (DL) methods in 22%, and both in 6%. The most frequently studied cancer type was non-small-cell lung cancer (36%), followed by melanoma (16%), while 25% included pan-cancer studies. No prospective study design incorporated AI-based methodologies from the outset; rather, all implemented AI as a post hoc analysis. Novel biomarkers for ICI in radiomics and pathomics were identified using AI approaches, and molecular biomarkers have expanded past genomics into transcriptomics and epigenomics. Finally, complex algorithms and new types of AI-based markers, such as meta-biomarkers, are emerging by integrating multimodal/multi-omics data. Conclusion: AI-based methods have expanded the horizon for biomarker discovery, demonstrating the power of integrating multimodal data from existing datasets to discover new meta-biomarkers. While most of the included studies showed promise for AI-based prediction of benefit from immunotherapy, none provided high-level evidence for immediate practice change. A priori planned prospective trial designs are needed to cover all lifecycle steps of these software biomarkers, from development and validation to integration into clinical practice