The gap between predicted brain age using magnetic resonance imaging (MRI) and chronological age may serve as a biomarker for early-stage neurodegeneration. However, owing to the lack of large longitudinal studies, it has been challenging to validate this link. We aimed to investigate the utility of such a gap as a risk biomarker for incident dementia using a deep learning approach for predicting brain age based on MRI-derived gray matter (GM). We built a convolutional neural network (CNN) model to predict brain age trained on 3,688 dementia-free participants of the Rotterdam Study (mean age 66 ± 11 y, 55% women). Logistic regressions and Cox proportional hazards were used to assess the association of the age gap with incident dementia, adjusted for age, sex, intracranial volume, GM volume, hippocampal volume, white matter hyperintensities, years of education, and APOE e4 allele carriership. Additionally, we computed the attention maps, which shows which regions are important for age prediction. Logistic regression and Cox proportional hazard models showed that the age gap was significantly related to incident dementia (odds ratio [OR] = 1.11 and 95% confidence intervals [CI] = 1.05-1.16; hazard ratio [HR] = 1.11, and 95% CI = 1.06-1.15, respectively). Attention maps indicated that GM density around the amygdala and hippocampi primarily drove the age estimation. We showed that the gap between predicted and chronological brain age is a biomarker, complimentary to those that are known, associated with risk of dementia, and could possibly be used for early-stage dementia risk screening. deep learning | dementia | age prediction | magnetic resonance imaging | voxel-based morphometry T he human brain continuously changes throughout the entire lifespan. These changes partially reflect a normal aging process and are not necessarily pathological (1). However, neurodegenerative diseases, including dementia, also affect brain structure and function (2, 3). Therefore, a better understanding and modeling of normal brain aging can help to disentangle these two processes and improve the detection of early-stage neurodegeneration.Age prediction models based on brain MRI are a popular trend in neuroscience (4-7). The difference between predicted and chronological age is thought to serve as an important biomarker reflecting pathological processes in the brain. Several recent studies showed the relation between accelerated brain aging and various disorders, such as Alzheimer's disease (8), schizophrenia, epilepsy, or diabetes (7,9,10).In recent years, CNNs have become the methodology of choice for analyzing medical images. These models are able to learn complex relations between input data and desired outcomes. Recent studies (11, 12) were able to demonstrate that CNN models can be successfully applied in brain MRI-based age prediction (5, 6).Although cross-sectional studies have suggested that the gap between predicted and chronological age may serve as a biomarker for dementia diagnosis, it remains unclear whether this is also t...

In this work, we report the synthesis of an three-dimensional (3D) cone-shape CNT clusters (CCC) via chemical vapor deposition (CVD) with subsequent inductively coupled plasma (ICP) treatment. An innovative silicon decorated cone-shape CNT clusters (SCCC) is prepared by simply depositing amorphous silicon onto CCC via magnetron sputtering. The seamless connection between silicon decorated CNT cones and graphene facilitates the charge transfer in the system and suggests a binder-free technique of preparing lithium ion battery (LIB) anodes. Lithium ion batteries based on this novel 3D SCCC architecture demonstrates high reversible capacity of 1954 mAh g(-1) and excellent cycling stability (>1200 mAh g(-1) capacity with ≈ 100% coulombic efficiency after 230 cycles).

The rapid exploration of sp3‐enriched chemical space is facilitated by fragment‐coupling technologies that utilize simple and abundant alkyl precursors, among which alcohols are a highly desirable, commercially accessible, and synthetically versatile class of substrate. Herein, we describe an operationally convenient, N‐heterocyclic carbene (NHC)‐mediated deoxygenative Giese‐type addition of alcohol‐derived alkyl radicals to electron‐deficient alkenes under mild photocatalytic conditions. The fragment coupling accommodates a broad range of primary, secondary, and tertiary alcohol partners, as well as structurally varied Michael acceptors containing traditionally reactive sites, such as electrophilic or oxidizable moieties. We demonstrate the late‐stage diversification of densely functionalized molecular architectures, including drugs and biomolecules, and we further telescope our protocol with metallaphotoredox cross‐coupling for step‐economic access to sp3‐rich complexity.

A neighboring boronate group in the substrate provides a dramatic rate acceleration in transmetalation to copper and thereby enables organoboronic esters to participate in unprecedented site-selective cross-couplings. This cross-coupling operates under practical experimental conditions and allows for coupling between vicinal bis­(boronic esters) and allyl, alkynyl, and propargyl electrophiles as well as a simple proton. Because the reactive substrates are vicinal bis­(boronic esters), the cross-coupling described herein provides an expedient new method for the construction of boron-containing reaction products from alkenes. Mechanistic experiments suggest that chelated cyclic ate complexes may play a role in the transmetalation.