In MRI, the main magnetic field polarizes the electron cloud of a molecule, generating chemical shift for observer protons within the molecule and magnetic susceptibility inhomogeneity field for observer protons outside the molecule. The number of water protons surrounding a molecule for detecting its magnetic susceptibility is vastly greater than the number of protons within the molecule for detecting its chemical shift. However, the study of tissue magnetic susceptibility has been hindered by poor molecular specificities of hitherto used methods based on MRI signal phase and T2* contrast, which depend convolutedly on surrounding susceptibility sources. Deconvolution of the MRI signal phase can determine tissue susceptibility but is challenged by the lack of background signal and by the zeroes in the dipole kernel. Recently, physically meaningful regularizations including the Bayesian approach have been developed to enable accurate quantitative susceptibility mapping (QSM) for studying iron distribution, metabolic oxygen consumption, blood degradation, calcification, demyelination and other pathophysiological susceptibility changes, as well as contrast agent biodistribution in MRI. This paper attempts to summarize the basic physical concepts and essential algorithmic steps in QSM, to describe clinical and technical issues under active development, and to provide references, codes and testing data for readers interested in QSM.

Photorealistic frontal view synthesis from a single face image has a wide range of applications in the field of face recognition. Although data-driven deep learning methods have been proposed to address this problem by seeking solutions from ample face data, this problem is still challenging because it is intrinsically ill-posed. This paper proposes a Two-Pathway Generative Adversarial Network (TP-GAN) for photorealistic frontal view synthesis by simultaneously perceiving global structures and local details. Four landmark located patch networks are proposed to attend to local textures in addition to the commonly used global encoderdecoder network. Except for the novel architecture, we make this ill-posed problem well constrained by introducing a combination of adversarial loss, symmetry loss and identity preserving loss. The combined loss function leverages both frontal face distribution and pre-trained discriminative deep face models to guide an identity preserving inference of frontal views from profiles. Different from previous deep learning methods that mainly rely on intermediate features for recognition, our method directly leverages the synthesized identity preserving image for downstream tasks like face recognition and attribution estimation. Experimental results demonstrate that our method not only presents compelling perceptual results but also outperforms state-of-theart results on large pose face recognition. * These two authors contributed equally.† Homepage

Aqueous zinc-based energy storage (ZES) devices are promising candidates for portable and grid-scale applications owing to their intrinsically high safety, low cost, and high theoretical energy density. However, the conventional...

Individuals with gallbladder carcinoma (GBC), the most aggressive malignancy of the biliary tract, have a poor prognosis. Here we report the identification of somatic mutations for GBC in 57 tumor-normal pairs through a combination of exome sequencing and ultra-deep sequencing of cancer-related genes. The mutation pattern is defined by a dominant prevalence of C>T mutations at TCN sites. Genes with a significant frequency (false discovery rate (FDR)<0.05) of non-silent mutations include TP53 (47.1%), KRAS (7.8%) and ERBB3 (11.8%). Moreover, ErbB signaling (including EGFR, ERBB2, ERBB3, ERBB4 and their downstream genes) is the most extensively mutated pathway, affecting 36.8% (21/57) of the GBC samples. Multivariate analyses further show that cases with ErbB pathway mutations have a worse outcome (P=0.001). These findings provide insight into the somatic mutational landscape in GBC and highlight the key role of the ErbB signaling pathway in GBC pathogenesis.

The demand for lithium-ion batteries (LIBs) with high mass-specific capacities, high rate capabilities and long-term cyclabilities is driving the research and development of LIBs with nickel-rich NMC (LiNi x Mn y Co 1−x−y O 2 , x ⩾ 0.5 ) cathodes and graphite (Li x C 6 ) anodes. Based on this, this review will summarize recently reported and widely recognized studies of the degradation mechanisms of Ni-rich NMC cathodes and graphite anodes. And with a broad collection of proposed mechanisms on both atomic and micrometer scales, this review can supplement previous degradation studies of Ni-rich NMC batteries. In addition, this review will categorize advanced mitigation strategies for both electrodes based on different modifications in which Ni-rich NMC cathode improvement strategies involve dopants, gradient layers, surface coatings, carbon matrixes and advanced synthesis methods, whereas graphite anode improvement strategies involve surface coatings, charge/discharge protocols and electrolyte volume estimations. Electrolyte components that can facilitate the stabilization of anodic solid electrolyte interfaces are also reviewed, and trade-offs between modification techniques as well as controversies are discussed for a deeper understanding of the mitigation strategies of Ni-rich NMC/graphite LIBs. Furthermore, this review will present various physical and electrochemical diagnostic tools that are vital in the elucidation of degradation mechanisms during operation to supplement future degradation studies. Finally, this review will summarize current research focuses and propose future research directions.

The mitochondrial pathway of apoptosis plays a critical role in estradiol-induced apoptosis in long-term estrogen-deprived breast cancer cells. Physiologic concentrations of estradiol could potentially be used to induce apoptosis and tumor regression in tumors that have developed resistance to aromatase inhibitors.

X-rays are widely used in probing inside information nondestructively, enabling broad applications in the medical radiography and electronic industries. X-ray imaging based on emerging lead halide perovskite scintillators has received extensive attention recently. However, the strong self-absorption, relatively low light yield and lead toxicity of these perovskites restrict their practical applications. Here, we report a series of nontoxic double-perovskite scintillators of Cs 2 Ag 0.6 Na 0.4 In 1-y Bi y Cl 6. By controlling the content of the heavy atom Bi 3+ , the X-ray absorption coefficient, radiative emission efficiency, light yield and light decay were manipulated to maximise the scintillator performance. A light yield of up to 39,000 ± 7000 photons/MeV for Cs 2 Ag 0.6 Na 0.4 In 0.85 Bi 0.15 Cl 6 was obtained, which is much higher than that for the previously reported lead halide perovskite colloidal CsPbBr 3 (21,000 photons/MeV). The large Stokes shift between the radioluminescence (RL) and absorption spectra benefiting from self-trapped excitons (STEs) led to a negligible selfabsorption effect. Given the high light output and fast light decay of this scintillator, static X-ray imaging was attained under an extremely low dose of ∼1 μGy air , and dynamic X-ray imaging of finger bending without a ghosting effect was demonstrated under a low-dose rate of 47.2 μGy air s −1. After thermal treatment at 85°C for 50 h followed by X-ray irradiation for 50 h in ambient air, the scintillator performance in terms of the RL intensity and X-ray image quality remained almost unchanged. Our results shed light on exploring highly competitive scintillators beyond the scope of lead halide perovskites, not only for avoiding toxicity but also for better performance.

The use of block copolymers as precursors revolutionizes the synthesis of porous carbon fibers with highly uniform pores.