Synthesis, structure and dioxygen reactivity of a bis(µ-iodo)dicopper(I) complex supported by the [N-(3,5-di-tert-butyl-2-hydroxybenzyl)-N,N-di-(2-pyridylmethyl)]amine ligand

The air-sensitive bis(µ-iodo)dicopper(I) complex 1 supported by [N-(3,5-di-tert-butyl-2-hydroxybenzyl)-N,N-di-(2-pyridylmethyl)]amine (L) has been prepared by treating copper(I) iodide with L in anhydrous THF. Compound 1 crystallizes as a dimer in space group C2/c. Each copper(I) center has distorted tetrahedral N2I2 coordination geometry with Cu–N(pyridyl) distances 2.061(3) and 2.063(3) Å, Cu–I distances 2.6162(5) and 2.7817(5) and a CuCu distance of 2.9086(8) Å. Complex 1 is rapidly oxidized by dioxygen in CH2Cl2 with a 1 : 1 stoichiometry giving the bis(µ-iodo)peroxodicopper(II) complex [Cu(L)(µ-I)]2O2 (2). The reaction of 1 with dioxygen has been characterized by UV-vis, mass spectrometry, EPR and Cu K-edge X-ray absorption spectroscopy at low temperature (193 K) and above. The mass spectrometry and low temperature EPR measurements suggested an equilibrium between the bis(µ-iodo)peroxodicopper(II) complex 2 and its dimer, namely, the tetranuclear (peroxodicopper(II))2 complex [Cu(L)(µ-I)]4O4 (2). Complex 2 undergoes an effective oxo-transfer reaction converting PPh3 into OPPh3 under anaerobic conditions. At sufficiently high concentration of PPh3, the oxygen atom transfer from 2 to PPh3 was followed by the formation of [Cu(PPh3)3I]. The dioxygen reactivity of 1 was compared with that known for other halo(amine)copper(I) dimers

A histone acetylome-wide association study of Alzheimer’s disease identifies disease-associated H3K27ac differences in the entorhinal cortex

We quantified genome-wide patterns of lysine H3K27 acetylation (H3K27ac) in entorhinal cortex samples from Alzheimer’s disease (AD) cases and matched controls using chromatin immunoprecipitation and highly parallel sequencing. We observed widespread acetylomic variation associated with AD neuropathology, identifying 4,162 differential peaks (false discovery rate < 0.05) between AD cases and controls. Differentially acetylated peaks were enriched in disease-related biological pathways and included regions annotated to genes involved in the progression of amyloid-β and tau pathology (for example, APP, PSEN1, PSEN2, and MAPT), as well as regions containing variants associated with sporadic late-onset AD. Partitioned heritability analysis highlighted a highly significant enrichment of AD risk variants in entorhinal cortex H3K27ac peak regions. AD-associated variable H3K27ac was associated with transcriptional variation at proximal genes including CR1, GPR22, KMO, PIM3, PSEN1, and RGCC. In addition to identifying molecular pathways associated with AD neuropathology, we present a framework for genome-wide studies of histone modifications in complex disease

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset