Ultrasound Technologies for Imaging and Modulating Neural Activity

Visualizing and perturbing neural activity on a brain-wide scale in model animals and humans is a major goal of neuroscience technology development. Established electrical and optical techniques typically break down at this scale due to inherent physical limitations. In contrast, ultrasound readily permeates the brain, and in some cases the skull, and interacts with tissue with a fundamental resolution on the order of 100 μm and 1 ms. This basic ability has motivated major efforts to harness ultrasound as a modality for large-scale brain imaging and modulation. These efforts have resulted in already-useful neuroscience tools, including high-resolution hemodynamic functional imaging, focused ultrasound neuromodulation, and local drug delivery. Furthermore, recent breakthroughs promise to connect ultrasound to neurons at the genetic level for biomolecular imaging and sonogenetic control. In this article, we review the state of the art and ongoing developments in ultrasonic neurotechnology, building from fundamental principles to current utility, open questions, and future potential

Ultrasound Technologies for Imaging and Modulating Neural Activity

Visualizing and perturbing neural activity on a brain-wide scale in model animals and humans is a major goal of neuroscience technology development. Established electrical and optical techniques typically break down at this scale due to inherent physical limitations. In contrast, ultrasound readily permeates the brain, and in some cases the skull, and interacts with tissue with a fundamental resolution on the order of 100 μm and 1 ms. This basic ability has motivated major efforts to harness ultrasound as a modality for large-scale brain imaging and modulation. These efforts have resulted in already-useful neuroscience tools, including high-resolution hemodynamic functional imaging, focused ultrasound neuromodulation, and local drug delivery. Furthermore, recent breakthroughs promise to connect ultrasound to neurons at the genetic level for biomolecular imaging and sonogenetic control. In this article, we review the state of the art and ongoing developments in ultrasonic neurotechnology, building from fundamental principles to current utility, open questions, and future potential

Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems

Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences. Today, AI has started to advance natural sciences by improving, accelerating, and enabling our understanding of natural phenomena at a wide range of spatial and temporal scales, giving rise to a new area of research known as AI for science (AI4Science). Being an emerging research paradigm, AI4Science is unique in that it is an enormous and highly interdisciplinary area. Thus, a unified and technical treatment of this field is needed yet challenging. This work aims to provide a technically thorough account of a subarea of AI4Science; namely, AI for quantum, atomistic, and continuum systems. These areas aim at understanding the physical world from the subatomic (wavefunctions and electron density), atomic (molecules, proteins, materials, and interactions), to macro (fluids, climate, and subsurface) scales and form an important subarea of AI4Science. A unique advantage of focusing on these areas is that they largely share a common set of challenges, thereby allowing a unified and foundational treatment. A key common challenge is how to capture physics first principles, especially symmetries, in natural systems by deep learning methods. We provide an in-depth yet intuitive account of techniques to achieve equivariance to symmetry transformations. We also discuss other common technical challenges, including explainability, out-of-distribution generalization, knowledge transfer with foundation and large language models, and uncertainty quantification. To facilitate learning and education, we provide categorized lists of resources that we found to be useful. We strive to be thorough and unified and hope this initial effort may trigger more community interests and efforts to further advance AI4Science

Genomic Analyses Reveal Mutational Signatures and Frequently Altered Genes in Esophageal Squamous Cell Carcinoma

Esophageal squamous cell carcinoma (ESCC) is one of the most common cancers worldwide and the fourth most lethal cancer in China. However, although genomic studies have identified some mutations associated with ESCC, we know little of the mutational processes responsible. To identify genome-wide mutational signatures, we performed either whole-genome sequencing (WGS) or whole-exome sequencing (WES) on 104 ESCC individuals and combined our data with those of 88 previously reported samples. An APOBEC-mediated mutational signature in 47% of 192 tumors suggests that APOBEC-catalyzed deamination provides a source of DNA damage in ESCC. Moreover, PIK3CA hotspot mutations (c.1624G>A [p.Glu542Lys] and c.1633G>A [p.Glu545Lys]) were enriched in APOBEC-signature tumors, and no smoking-associated signature was observed in ESCC. In the samples analyzed by WGS, we identified focal (<100 kb) amplifications of CBX4 and CBX8. In our combined cohort, we identified frequent inactivating mutations in AJUBA, ZNF750, and PTCH1 and the chromatin-remodeling genes CREBBP and BAP1, in addition to known mutations. Functional analyses suggest roles for several genes (CBX4, CBX8, AJUBA, and ZNF750) in ESCC. Notably, high activity of hedgehog signaling and the PI3K pathway in approximately 60% of 104 ESCC tumors indicates that therapies targeting these pathways might be particularly promising strategies for ESCC. Collectively, our data provide comprehensive insights into the mutational signatures of ESCC and identify markers for early diagnosis and potential therapeutic targets

Subspace Feature Analysis of Local Manifold Learning for Hyperspectral Remote Sensing Images Classification

Abstract: Dimensionality reduction and segmentation have been used as methods to reduce the complexity of the representation of hyperspectral remote sensing images. In this study, a new object-oriented mapping approach is proposed based on nonlinear subspace feature analysis of hyperspectral remote sensing images. Nonlinear local manifold learning approaches for feature extraction were utilized to obtain subspace feature representation of hyperspectral remote sensing images. Afterwards, with a proper selection of parameters, the extracted subspace feature images were fed into the object-oriented system. Texture features derived from gray level co-occurrence matrix and wavelet filter with the use of SVM classifier at the pixel level of the feature images were also used to evaluate the proposed algorithm. Experiments are conducted on the AVIRIS dataset with 220 spectral bands, covering an agricultural area. Classification results show that the proposed object-oriented subspace analysis approach can give significantly higher accuracies than the traditional pixel-level and texture-based subspace feature classification

Subspace Feature Analysis of Local Manifold Learning for Hyperspectral Remote Sensing Images Classification

Dimensionality reduction and segmentation have been used as methods to reduce the complexity of the representation of hyperspectral remote sensing images. In this study, a new object-oriented mapping approach is proposed based on nonlinear subspace feature analysis of hyperspectral remote sensing images. Nonlinear local manifold learning approaches for feature extraction were utilized to obtain subspace feature representation of hyperspectral remote sensing images. Afterwards, with a proper selection of parameters, the extracted subspace feature images were fed into the object-oriented system. Texture features derived from gray level co-occurrence matrix and wavelet filter with the use of SVM classifier at the pixel level of the feature images were also used to evaluate the proposed algorithm. Experiments are conducted on the AVIRIS dataset with 220 spectral bands, covering an agricultural area. Classification results show that the proposed object-oriented subspace analysis approach can give significantly higher accuracies than the traditional pixel-level and texture-based subspace feature classification

Preliminary Study on Nano Particle/Photopolymer Hybrid for 3D Inkjet Printing

3D inkjet printing is one of the new generation Additive Manufacturing technologies for the production of multi-material and multifunctional 3D products. Due to the critical condition for ink deposition and the stringent requirements for ink solidification, only limited ranges of materials are suitable for 3D inkjet printing. These include low viscosity photo curable polymers, and low melting temperature materials like wax. In this study, a hybrid printing ink prepared by mixing nano silica particles into photopolymer resin was developed for 3D printing, and the influence of silica particle concentrations on the viscosity, surface tension, printability and mechanical properties of cured polymer was studied. Results show that untreated nano silica particles would influence the rheological properties which is critical to the printability, but did not improve the mechanical properties due to the aggregation of nanoparticles during photo curing.ASTAR (Agency for Sci., Tech. and Research, S’pore)Published versio

The mechanical and electrical properties of Nb

It is well known that certain amounts of oxides doping can improve the ionic conductivity and mechanical properties of the Na-β"-Al2O3 solid electrolytes. In order to obtain high quality Na-β"-Al2O3 solid electrolytes, Nb2O5 doped Na-β"-Al2O3 solid electrolytes have been synthesized by double-zeta process. The effects of the Nb2O5 content on the microstructure, mechanical property and ionic conductivity of the Na-β"-Al2O3 are studied. The results show that the doping amount is the key factor to affect the properties of the final products. The optimal doping amount is 1 wt.%. The sample β"−1 contains 96.82% of β" phase, which is higher than that of the non-doped sample (95.31%). In addition, the relative density of the sample β"−1 is 98.93% (3.225 g/cm3), which contains a more uniform and compact microstructure. What is more, the sample β"−1 displays a bending strength up to 295 MPa and an ionic conductivity up to 0.153 S/cm (300 °C)