Status of research results in chemistry of biologically active substances in Benin

Research on biologically active chemicals extracted from medicinal plants and essential oils from aromatic plants in the West African subregion is booming. Recognition of the clinical, pharmaceutical and economic value of herbal medicines continues to grow despite the growth of the pharmaceutical industry and the continued development of new, more effective synthetic and biological medical products. On the other hand, despite the improvement in food preservation technics, food preservatives nature remains one of the most important issues for public health. Indeed, several synthetic preservatives have been banned in some countries because of their long-term adverse toxicological effects. The current trend of consumers to seek for a more natural diet has prompted the research, development and application of new natural products with antimicrobial and antioxidant activities in order to use them as alternatives to synthetic preservatives. This review aims to do an inventory of the results of research in chemistry of biologically active substances in Benin

Is there pandemic vitamin D deficiency in the black population? A review of evidence

Although 1,25-dihydroxyvitamin D [1,25(OH)2D] is the biologically active form of vitamin D, measurement of the total serum 25-hydroxyvitamin D [25(OH)D] level is the gold standard used to define vitamin D status. Currently, it is widely accepted that serum 25 (OH) D levels below 20 ng/ml defines vitamin D deficiency. According to this definition, there appears to be pandemic vitamin D deficiency in the Black population. However, there is no evidence of higher-thannormal rates of common complications and symptomology of true vitamin D deficiency in the Black population. What is going on? We researched the MEDLINE databases to find studies, from 1967 to present, that directly compare between Blacks and Caucasians the following: serum vitamin D level, serum calcium level, serum parathyroid hormone level, bone mineral density and health, and non-skeletal risks associated with vitamin D deficiency. The available studies consistently show that Blacks tend to have serum 25(OH)D levels in the deficient range while their serum 1,25(OH)2D level is similar to, if not even slightly higher than that of Caucasians, and that the serum Ca2+ level in Blacks is virtually identical to that in Caucasians. Therefore, it appears that the serum 25(OH)D level is not the best marker of vitamin D sufficiency or deficiency in Blacks. In the future, clinical evaluation of the vitamin D status in the Black population needs to consider other serum biomarkers such as 1,25(OH)2D and/or bioavailable 25(OH)

Chemical and biological investigations of Syzygium aromaticum L. essential oil from Benin

The essential oil obtained by hydrodistillation from seeds of Syzygium aromaticum (Myrtaceae) growing in Benin was analyzed by gas chromatography (GC) and gas chromatography-mass spectrometry (GC/MS). Twenty-one components, which represented 99.4% of the total constituents of the oil were identified. The essential oil is rich in hydrocarbons monoterpenic. The major constituents found were eugenol (60.4%), trans-β-caryophyllene (24.0%). The oil extract revealed an important antiradical activity and a high antimicrobial activity.Keywords: Antimicrobial activity, antiradical activity, essential oil, eugenol, trans-β-caryophyllene, Syzygium aromaticu

Fitting IVIM with Variable Projection and Simplicial Optimization

Fitting multi-exponential models to Diffusion MRI (dMRI) data has always been challenging due to various underlying complexities. In this work, we introduce a novel and robust fitting framework for the standard two-compartment IVIM microstructural model. This framework provides a significant improvement over the existing methods and helps estimate the associated diffusion and perfusion parameters of IVIM in an automatic manner. As a part of this work we provide capabilities to switch between more advanced global optimization methods such as simplicial homology (SH) and differential evolution (DE). Our experiments show that the results obtained from this simultaneous fitting procedure disentangle the model parameters in a reduced subspace. The proposed framework extends the seminal work originated in the MIX framework, with improved procedures for multi-stage fitting. This framework has been made available as an open-source Python implementation and disseminated to the community through the DIPY project

Diffusional Kurtosis Imaging in the Diffusion Imaging in Python Project.

Diffusion-weighted magnetic resonance imaging (dMRI) measurements and models provide information about brain connectivity and are sensitive to the physical properties of tissue microstructure. Diffusional Kurtosis Imaging (DKI) quantifies the degree of non-Gaussian diffusion in biological tissue from dMRI. These estimates are of interest because they were shown to be more sensitive to microstructural alterations in health and diseases than measures based on the total anisotropy of diffusion which are highly confounded by tissue dispersion and fiber crossings. In this work, we implemented DKI in the Diffusion in Python (DIPY) project-a large collaborative open-source project which aims to provide well-tested, well-documented and comprehensive implementation of different dMRI techniques. We demonstrate the functionality of our methods in numerical simulations with known ground truth parameters and in openly available datasets. A particular strength of our DKI implementations is that it pursues several extensions of the model that connect it explicitly with microstructural models and the reconstruction of 3D white matter fiber bundles (tractography). For instance, our implementations include DKI-based microstructural models that allow the estimation of biophysical parameters, such as axonal water fraction. Moreover, we illustrate how DKI provides more general characterization of non-Gaussian diffusion compatible with complex white matter fiber architectures and gray matter, and we include a novel mean kurtosis index that is invariant to the confounding effects due to tissue dispersion. In summary, DKI in DIPY provides a well-tested, well-documented and comprehensive reference implementation for DKI. It provides a platform for wider use of DKI in research on brain disorders and in cognitive neuroscience

Bifurcated topological optimization for IVIM

In this work, we shed light on the issue of estimating Intravoxel Incoherent Motion (IVIM) for diffusion and perfusion estimation by characterizing the objective function using simplicial homology tools. We provide a robust solution via topological optimization of this model so that the estimates are more reliable and accurate. Estimating the tissue microstructure from diffusion MRI is in itself an ill-posed and a non-linear inverse problem. Using variable projection functional (VarPro) to fit the standard bi-exponential IVIM model we perform the optimization using simplicial homology based global optimization to better understand the topology of objective function surface. We theoretically show how the proposed methodology can recover the model parameters more accurately and consistently by casting it in a reduced subspace given by VarPro. Additionally we demonstrate that the IVIM model parameters cannot be accurately reconstructed using conventional numerical optimization methods due to the presence of infinite solutions in subspaces. The proposed method helps uncover multiple global minima by analyzing the local geometry of the model enabling the generation of reliable estimates of model parameters.The National Institute of Biomedical Imaging And Bioengineering (NIBIB) of the National Institutes of Health (NIH); University of Washington’s Royalty Research Fund; NIH grants; the German Research Foundation (DFG) and a grant from the Alfred P. Sloan Foundation and the Gordon & Betty Moore Foundation to the University of Washington eScience Institute Data Science Environment.http://www.frontiersin.org/Neuroscienceam2022Chemical Engineerin

brainlife.io: A decentralized and open source cloud platform to support neuroscience research

Neuroscience research has expanded dramatically over the past 30 years by advancing standardization and tool development to support rigor and transparency. Consequently, the complexity of the data pipeline has also increased, hindering access to FAIR data analysis to portions of the worldwide research community. brainlife.io was developed to reduce these burdens and democratize modern neuroscience research across institutions and career levels. Using community software and hardware infrastructure, the platform provides open-source data standardization, management, visualization, and processing and simplifies the data pipeline. brainlife.io automatically tracks the provenance history of thousands of data objects, supporting simplicity, efficiency, and transparency in neuroscience research. Here brainlife.io's technology and data services are described and evaluated for validity, reliability, reproducibility, replicability, and scientific utility. Using data from 4 modalities and 3,200 participants, we demonstrate that brainlife.io's services produce outputs that adhere to best practices in modern neuroscience research

brainlife.io: a decentralized and open-source cloud platform to support neuroscience research

Neuroscience is advancing standardization and tool development to support rigor and transparency. Consequently, data pipeline complexity has increased, hindering FAIR (findable, accessible, interoperable and reusable) access. brainlife.io was developed to democratize neuroimaging research. The platform provides data standardization, management, visualization and processing and automatically tracks the provenance history of thousands of data objects. Here, brainlife.io is described and evaluated for validity, reliability, reproducibility, replicability and scientific utility using four data modalities and 3,200 participants