The integration of different two-dimensional materials within a multilayer van der Waals (vdW) heterostructure offers a promising technology for high performance opto-electronic devices such as photodetectors and light sources. Here we report on the fabrication and electronic properties of vdW heterojunction diodes composed of the direct band gap layered semiconductors InSe and GaSe and transparent monolayer graphene electrodes. We show that the type II band alignment between the two layered materials and their distinctive spectral response, combined with the short channel length and low electrical resistance of graphene electrodes, enable efficient generation and extraction of photoexcited carriers from the heterostructure even when no external voltage is applied. Our devices are fast (∼2 μs), self-driven photodetectors with multicolor photoresponse ranging from the ultraviolet to the near-infrared and offer new routes to miniaturized optoelectronics beyond present semiconductor materials and technologies.

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Purpose: Early diagnosis or detection of Alzheimer's disease (AD) from the normal elder control (NC) is very important. However, the computer-aided diagnosis (CAD) was not widely used, and the classification performance did not reach the standard of practical use. We proposed a novel CAD system for MR brain images based on eigenbrains and machine learning with two goals: accurate detection of both AD subjects and AD-related brain regions.Method: First, we used maximum inter-class variance (ICV) to select key slices from 3D volumetric data. Second, we generated an eigenbrain set for each subject. Third, the most important eigenbrain (MIE) was obtained by Welch's t-test (WTT). Finally, kernel support-vector-machines with different kernels that were trained by particle swarm optimization, were used to make an accurate prediction of AD subjects. Coefficients of MIE with values higher than 0.98 quantile were highlighted to obtain the discriminant regions that distinguish AD from NC.Results: The experiments showed that the proposed method can predict AD subjects with a competitive performance with existing methods, especially the accuracy of the polynomial kernel (92.36 ± 0.94) was better than the linear kernel of 91.47 ± 1.02 and the radial basis function (RBF) kernel of 86.71 ± 1.93. The proposed eigenbrain-based CAD system detected 30 AD-related brain regions (Anterior Cingulate, Caudate Nucleus, Cerebellum, Cingulate Gyrus, Claustrum, Inferior Frontal Gyrus, Inferior Parietal Lobule, Insula, Lateral Ventricle, Lentiform Nucleus, Lingual Gyrus, Medial Frontal Gyrus, Middle Frontal Gyrus, Middle Occipital Gyrus, Middle Temporal Gyrus, Paracentral Lobule, Parahippocampal Gyrus, Postcentral Gyrus, Posterial Cingulate, Precentral Gyrus, Precuneus, Subcallosal Gyrus, Sub-Gyral, Superior Frontal Gyrus, Superior Parietal Lobule, Superior Temporal Gyrus, Supramarginal Gyrus, Thalamus, Transverse Temporal Gyrus, and Uncus). The results were coherent with existing literatures.Conclusion: The eigenbrain method was effective in AD subject prediction and discriminant brain-region detection in MRI scanning.

The fast-growing field of atomically thin semiconductors urges a new understanding of two-dimensional excitons, which entirely determine their optical responses. Here, taking layered lead halide perovskites as an example of unconventional two-dimensional semiconductors, by means of versatile optical spectroscopy measurements, we resolve fine-structure splitting of bright excitons of up to ∼2 meV, which is among the largest values in two-dimensional semiconducting systems. The large fine-structure splitting is attributed to the strong electron–hole exchange interaction in layered perovskites, which is proven by the optical emission in high magnetic fields of up to 30 T. Furthermore, we determine the g-factors for these bright excitons as ∼+1.8. Our findings suggest layered lead halide perovskites are an ideal platform for studying exciton spin-physics in atomically thin semiconductors that will pave the way toward exciton manipulation for novel device applications.

IMPORTANCE Impaired mitochondrial function impacts many biological processes that depend heavily on energy and metabolism and can lead to a wide range of neurodevelopmental disorders, including autism spectrum disorder (ASD). Although evidence that mitochondrial dysfunction is a biological subtype of ASD has grown in recent years, no study, to our knowledge, has demonstrated evidence of mitochondrial dysfunction in brain tissue in vivo in a large, well-defined sample of individuals with ASD.OBJECTIVES To assess brain lactate in individuals with ASD and typically developing controls using high-resolution, multiplanar spectroscopic imaging; to map the distribution of lactate in the brains of individuals with ASD; and to assess correlations of elevated brain lactate with age, autism subtype, and intellectual ability.

Highlights We analysed over 450 references from all well-famed databases. We provided a comprehensive survey on multimodal data fusion in neuroimaging. This review encompassed current challenges & applications, strengths &limitations. Fundamental fusion rules, and fusion quality assessment methods were reviewed. Atlas-based fusion segmentation, quantification, & applications were reviewed.

The use of multi-channel coils can efficiently increase the signal-to-noise ratio (SNR) of magnetic resonance spectroscopy data if the signals from multiple channels are optimally combined. Combining multi-channel signals requires proper alignment of the phases of the signals from each of the elements of the coil and then accurately weighting the summation of those signals. We present a procedure for acquiring proton magnetic resonance spectroscopic imaging (MRSI) data using an eight-channel coil without water suppression and a rapid and robust method that uses unsuppressed water signal as a reference both for aligning the phases and for weighting the summation of signals that originate in the multiple coil elements. We use both computer simulation and in vivo proton MRSI data to demonstrate the advantages of our method for optimizing the SNR of the combined signal compared with the SNRs of signals that were acquired either using a standard volume head coil or using an eight-channel coil with a metabolite signal as the reference for combination.