Magnetic resonance imaging is a key diagnostic tool in modern healthcare, yet it can be cost-prohibitive given the high installation, maintenance and operation costs of the machinery. There are ...approximately seven scanners per million inhabitants and over 90% are concentrated in high-income countries. We describe an ultra-low-field brain MRI scanner that operates using a standard AC power outlet and is low cost to build. Using a permanent 0.055 Tesla Samarium-cobalt magnet and deep learning for cancellation of electromagnetic interference, it requires neither magnetic nor radiofrequency shielding cages. The scanner is compact, mobile, and acoustically quiet during scanning. We implement four standard clinical neuroimaging protocols (T1- and T2-weighted, fluid-attenuated inversion recovery like, and diffusion-weighted imaging) on this system, and demonstrate preliminary feasibility in diagnosing brain tumor and stroke. Such technology has the potential to meet clinical needs at point of care or in low and middle income countries.
Atomically dispersed transition metal active sites have emerged as one of the most important fields of study because they display promising performance in catalysis and have the potential to serve as ...ideal models for fundamental understanding. However, both the preparation and determination of such active sites remain a challenge. The structural engineering of carbon- and nitrogen-coordinated metal sites (M–N–C, M = Fe, Co, Ni, Mn, Cu, etc.) via employing new heteroatoms, e.g., P and S, remains challenging. In this study, carbon nanosheets embedded with nitrogen and phosphorus dual-coordinated iron active sites (denoted as Fe-N/P-C) were developed and determined using cutting edge techniques. Both experimental and theoretical results suggested that the N and P dual-coordinated iron sites were favorable for oxygen intermediate adsorption/desorption, resulting in accelerated reaction kinetics and promising catalytic oxygen reduction activity. This work not only provides efficient way to prepare well-defined single-atom active sites to boost catalytic performance but also paves the way to identify the dual-coordinated single metal atom sites.
The CuPS (Culture × Person × Situation) approach attempts to jointly consider culture and individual differences, without treating either as noise and without reducing one to the other. Culture is ...important because it helps define psychological situations and create meaningful clusters of behavior according to particular logics. Individual differences are important because individuals vary in the extent to which they endorse or reject a culture's ideals. Further, because different cultures are organized by different logics, individual differences mean something different in each. Central to these studies are concepts of honor-related violence and individual worth as being inalienable versus socially conferred. We illustrate our argument with 2 experiments involving participants from honor, face, and dignity cultures. The studies showed that the same "type" of person who was most helpful, honest, and likely to behave with integrity in one culture was the "type" of person least likely to do so in another culture. We discuss how CuPS can provide a rudimentary but integrated approach to understanding both within- and between-culture variation.
Alternative splicing (AS) is a regulated process that directs the generation of different transcripts from single genes. A computational model that can accurately predict splicing patterns based on ...genomic features and cellular context is highly desirable, both in understanding this widespread phenomenon, and in exploring the effects of genetic variations on AS.
Using a deep neural network, we developed a model inferred from mouse RNA-Seq data that can predict splicing patterns in individual tissues and differences in splicing patterns across tissues. Our architecture uses hidden variables that jointly represent features in genomic sequences and tissue types when making predictions. A graphics processing unit was used to greatly reduce the training time of our models with millions of parameters.
We show that the deep architecture surpasses the performance of the previous Bayesian method for predicting AS patterns. With the proper optimization procedure and selection of hyperparameters, we demonstrate that deep architectures can be beneficial, even with a moderately sparse dataset. An analysis of what the model has learned in terms of the genomic features is presented.
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