A diagnostic model based on bioinformatics and machine learning to differentiate bipolar disorder from schizophrenia and major depressive disorder.

Bipolar disorder (BD) showed the highest suicide rate of all psychiatric disorders, and its underlying causative genes and effective treatments remain unclear. During diagnosis, BD is often confused with schizophrenia (SC) and major depressive disorder (MDD), due to which patients may receive inadequate or inappropriate treatment, which is detrimental to their prognosis. This study aims to establish a diagnostic model to distinguish BD from SC and MDD in multiple public datasets through bioinformatics and machine learning and to provide new ideas for diagnosing BD in the future. Three brain tissue datasets containing BD, SC, and MDD were chosen from the Gene Expression Omnibus database (GEO), and two peripheral blood datasets were selected for validation. Linear Models for Microarray Data (Limma) analysis was carried out to identify differentially expressed genes (DEGs). Functional enrichment analysis and machine learning were utilized to identify. Least absolute shrinkage and selection operator (LASSO) regression was employed for identifying candidate immune-associated central genes, constructing protein-protein interaction networks (PPI), building artificial neural networks (ANN) for validation, and plotting receiver operating characteristic curve (ROC curve) for differentiating BD from SC and MDD and creating immune cell infiltration to study immune cell dysregulation in the three diseases. RBM10 was obtained as a candidate gene to distinguish BD from SC. Five candidate genes (LYPD1, HMBS, HEBP2, SETD3, and ECM2) were obtained to distinguish BD from MDD. The validation was performed by ANN, and ROC curves were plotted for diagnostic value assessment. The outcomes exhibited the prediction model to have a promising diagnostic value. In the immune infiltration analysis, Naive B, Resting NK, and Activated Mast Cells were found to be substantially different between BD and SC. Naive B and Memory B cells were prominently variant between BD and MDD. In this study, RBM10 was found as a candidate gene to distinguish BD from SC; LYPD1, HMBS, HEBP2, SETD3, and ECM2 serve as five candidate genes to distinguish BD from MDD. The results obtained from the ANN network showed that these candidate genes could perfectly distinguish BD from SC and MDD (76.923% and 81.538%, respectively).

Three-dimensional nanoscale metal, metal oxide, and semiconductor frameworks through DNA-programmable assembly and templating.

Controlling the three-dimensional (3D) nanoarchitecture of inorganic materials is imperative for enabling their novel mechanical, optical, and electronic properties. Here, by exploiting DNA-programmable assembly, we establish a general approach for realizing designed 3D ordered inorganic frameworks. Through inorganic templating of DNA frameworks by liquid- and vapor-phase infiltrations, we demonstrate successful nanofabrication of diverse classes of inorganic frameworks from metal, metal oxide and semiconductor materials, as well as their combinations, including zinc, aluminum, copper, molybdenum, tungsten, indium, tin, and platinum, and composites such as aluminum-doped zinc oxide, indium tin oxide, and platinum/aluminum-doped zinc oxide. The open 3D frameworks have features on the order of nanometers with architecture prescribed by the DNA frames and self-assembled lattice. Structural and spectroscopic studies reveal the composition and organization of diverse inorganic frameworks, as well as the optoelectronic properties of selected materials. The work paves the road toward establishing a 3D nanoscale lithography.

Symmetry-specific characterization of bond orientation order in DNA-assembled nanoparticle lattices.

Bond-orientational order in DNA-assembled nanoparticles lattices is explored with the help of recently introduced Symmetry-specific Bond Order Parameters (SymBOPs). This approach provides a more sensitive analysis of local order than traditional scalar BOPs, facilitating the identification of coherent domains at the single bond level. The present study expands the method initially developed for assemblies of anisotropic particles to the isotropic ones or cases where particle orientation information is unavailable. The SymBOP analysis was applied to experiments on DNA-frame-based assembly of nanoparticle lattices. It proved highly sensitive in identifying coherent crystalline domains with different orientations, as well as detecting topological defects, such as dislocations. Furthermore, the analysis distinguishes individual sublattices within a single crystalline domain, such as pair of interpenetrating FCC lattices within a cubic diamond. The results underscore the versatility and robustness of SymBOPs in characterizing ordering phenomena, making them valuable tools for investigating structural properties in various systems.

A general image misalignment correction method for tomography experiments.

Tomography experiments generate three-dimensional (3D) reconstructed slices from a series of two-dimensional (2D) projection images. However, the mechanical system generates joint offsets that result in unaligned 2D projections. This misalignment affects the reconstructed images and reduces their actual spatial resolution. In this study, we present a novel method called outer contour-based misalignment correction (OCMC) for correcting image misalignments in tomography. We use the sample's outer contour structure as auxiliary information to estimate the extent of misalignment in each image. This method is generic and can be used with various tomography imaging techniques. We validated our method with five datasets collected from different samples and across various tomography techniques. The OCMC method demonstrated significant advantages in terms alignment accuracy and time efficiency. As an end-to-end correction method, OCMC can be easily integrated into an online tomography data processing pipeline and facilitate feedback control in future synchrotron tomography experiments.

Nanoscale heterogeneity of arsenic and selenium species in coal fly ash particles: analysis using enhanced spectroscopic imaging and speciation techniques.

Coal combustion byproducts are known to be enriched in arsenic (As) and selenium (Se). This enrichment is a concern during the handling, disposal, and reuse of the ash as both elements can be harmful to wildlife and humans if mobilized into water and soils. The leaching potential and bioaccessibility of As and Se in coal fly ash depends on the chemical forms of these elements and their association with the large variety of particles that comprise coal fly ash. The overall goal of this research was to determine nanoscale and microscale solid phase mineral associations and oxidation states of As and Se in fly ash. We utilized nanoscale 2D imaging (30-50 nm spot size) with the Hard X-ray Nanoprobe (HXN) in combination with microprobe X-ray capabilities (∼5 µm resolution) to determine the As and Se elemental associations. Speciation of As and Se was also measured at the nano- to microscale with X-ray absorption spectroscopy. The enhanced resolution of HXN showed As and Se as either diffusely located around or comingled with Ca- and Fe-rich particles. The results also showed nanoparticles of Se attached to the surface of fly ash grains. Overall, a comparison of As and Se species across scales highlights the heterogeneity and complexity of chemical associations for these trace elements of concern in coal fly ash.

Deep learning enables nanoscale X-ray 3D imaging with limited data.

Deep neural network can greatly improve tomography reconstruction with limited data. A recent effort of combining ptycho-tomography model with the 3D U-net demonstrated a significant reduction in both the number of projections and computation time, and showed its potential for integrated circuit imaging that requires high-resolution and fast measurement speed.

Self-absorption correction on 2D X-ray fluorescence maps.

X-ray fluorescence mapping (XRF) is a highly efficient and non-invasive technique for quantifying material composition with micro and nanoscale spatial resolutions. Quantitative XRF analysis, however, confronts challenges from the long-lasting problem called self-absorption. Moreover, correcting two-dimensional XRF mapping datasets is particularly difficult because it is an ill-posed inverse problem. Here we report a semi-empirical method that can effectively correct 2D XRF mapping data. The correction error is generally less than 10% from a comprehensive evaluation of the accuracy in various configurations. The proposed method was applied to quantify the composition distribution around the grain boundaries in an electrochemically corroded stainless steel sample. Highly localized Cr enrichment was found around the crack sites, which was invisible before the absorption correction.

RadVolViz: An Information Display-Inspired Transfer Function Editor for Multivariate Volume Visualization.

In volume visualization transfer functions are widely used for mapping voxel properties to color and opacity. Typically, volume density data are scalars which require simple 1D transfer functions to achieve this mapping. If the volume densities are vectors of three channels, one can straightforwardly map each channel to either red, green or blue, which requires a trivial extension of the 1D transfer function editor. We devise a new method that applies to volume data with more than three channels. These types of data often arise in scientific scanning applications, where the data are separated into spectral bands or chemical elements. Our method expands on prior work in which a multivariate information display, RadViz, was fused with a radial color map, in order to visualize multi-band 2D images. In this work, we extend this joint interface to blended volume rendering. The information display allows users to recognize the presence and value distribution of the multivariate voxels and the joint volume rendering display visualizes their spatial distribution. We design a set of operators and lenses that allow users to interactively control the mapping of the multivariate voxels to opacity and color. This enables users to isolate or emphasize volumetric structures with desired multivariate properties. Furthermore, it turns out that our method also enables more insightful displays even for RGB data. We demonstrate our method with three datasets obtained from spectral electron microscopy, high energy X-ray scanning, and atmospheric science.

Multimodal X-ray nano-spectromicroscopy analysis of chemically heterogeneous systems.

Understanding the nanoscale chemical speciation of heterogeneous systems in their native environment is critical for several disciplines such as life and environmental sciences, biogeochemistry, and materials science. Synchrotron-based X-ray spectromicroscopy tools are widely used to understand the chemistry and morphology of complex material systems owing to their high penetration depth and sensitivity. The multidimensional (4D+) structure of spectromicroscopy data poses visualization and data-reduction challenges. This paper reports the strategies for the visualization and analysis of spectromicroscopy data. We created a new graphical user interface and data analysis platform named XMIDAS (X-ray multimodal image data analysis software) to visualize spectromicroscopy data from both image and spectrum representations. The interactive data analysis toolkit combined conventional analysis methods with well-established machine learning classification algorithms (e.g. nonnegative matrix factorization) for data reduction. The data visualization and analysis methodologies were then defined and optimized using a model particle aggregate with known chemical composition. Nanoprobe-based X-ray fluorescence (nano-XRF) and X-ray absorption near edge structure (nano-XANES) spectromicroscopy techniques were used to probe elemental and chemical state information of the aggregate sample. We illustrated the complete chemical speciation methodology of the model particle by using XMIDAS. Next, we demonstrated the application of this approach in detecting and characterizing nanoparticles associated with alveolar macrophages. Our multimodal approach combining nano-XRF, nano-XANES, and differential phase-contrast imaging efficiently visualizes the chemistry of localized nanostructure with the morphology. We believe that the optimized data-reduction strategies and tool development will facilitate the analysis of complex biological and environmental samples using X-ray spectromicroscopy techniques.

Three-dimensional visualization of nanoparticle lattices and multimaterial frameworks.

Advances in nanoscale self-assembly have enabled the formation of complex nanoscale architectures. However, the development of self-assembly strategies toward bottom-up nanofabrication is impeded by challenges in revealing these structures volumetrically at the single-component level and with elemental sensitivity. Leveraging advances in nano-focused hard x-rays, DNA-programmable nanoparticle assembly, and nanoscale inorganic templating, we demonstrate nondestructive three-dimensional imaging of complexly organized nanoparticles and multimaterial frameworks. In a three-dimensional lattice with a size of 2 micrometers, we determined the positions of about 10,000 individual nanoparticles with 7-nanometer resolution, and identified arrangements of assembly motifs and a resulting multimaterial framework with elemental sensitivity. The real-space reconstruction permits direct three-dimensional imaging of lattices, which reveals their imperfections and interfaces and also clarifies the relationship between lattices and assembly motifs.

Design nanoporous metal thin films via solid state interfacial dealloying.

Thin-film solid-state interfacial dealloying (thin-film SSID) is an emerging technique to design nanoarchitecture thin films. The resulting controllable 3D bicontinuous nanostructure is promising for a range of applications including catalysis, sensing, and energy storage. Using a multiscale microscopy approach, we combine X-ray and electron nano-tomography to demonstrate that besides dense bicontinuous nanocomposites, thin-film SSID can create a very fine (5-15 nm) nanoporous structure. Not only is such a fine feature among one of the finest fabrications by metal-agent dealloying, but a multilayer thin-film design enables creating nanoporous films on a wider range of substrates for functional applications. Through multimodal synchrotron diffraction and spectroscopy analysis with which the materials' chemical and structural evolution in this novel approach is characterized in details, we further deduce that the contribution of change in entropy should be considered to explain the phase evolution in metal-agent dealloying, in addition to the commonly used enthalpy term in prior studies. The discussion is an important step leading towards better explaining the underlying design principles for controllable 3D nanoarchitecture, as well as exploring a wider range of elemental and substrate selections for new applications.

Hierarchical nickel valence gradient stabilizes high-nickel content layered cathode materials.

High-nickel content cathode materials offer high energy density. However, the structural and surface instability may cause poor capacity retention and thermal stability of them. To circumvent this problem, nickel concentration-gradient materials have been developed to enhance high-nickel content cathode materials' thermal and cycling stability. Even though promising, the fundamental mechanism of the nickel concentration gradient's stabilization effect remains elusive because it is inseparable from nickel's valence gradient effect. To isolate nickel's valence gradient effect and understand its fundamental stabilization mechanism, we design and synthesize a LiNi0.8Mn0.1Co0.1O2 material that is compositionally uniform and has a hierarchical valence gradient. The nickel valence gradient material shows superior cycling and thermal stability than the conventional one. The result suggests creating an oxidation state gradient that hides the more capacitive but less stable Ni3+ away from the secondary particle surfaces is a viable principle towards the optimization of high-nickel content cathode materials.

Tunable hard x-ray nanofocusing with Fresnel zone plates fabricated using deep etching.

Fresnel zone plates are widely used for x-ray nanofocusing, due to their ease of alignment and energy tunability. Their spatial resolution is limited in part by their outermost zone width dr N , while their efficiency is limited in part by their thickness t zp. We demonstrate the use of Fresnel zone plate optics for x-ray nanofocusing with dr N = 16 nm outermost zone width and a thickness of about t zp = 1.8 µm (or an aspect ratio of 110) with an absolute focusing efficiency of 4.7% at 12 keV, and 6.2% at 10 keV. Using partially coherent illumination at 12 keV, the zone plate delivered a FWHM focus of 46 × 60 nm at 12 keV, with the first order coherent mode in a ptychographic reconstruction showing a probe size of 16 nm FWHM. These optics were fabricated using a combination of metal assisted chemical etching and atomic layer deposition for the diffracting structures, and silicon wafer back-thinning to produce optics useful for real applications. This approach should enable new higher resolution views of thick materials, especially when energy tunability is required.

Micromachined Silicon Platform for Precise Assembly of 2D Multilayer Laue Lenses for High-Resolution X-ray Microscopy.

We report on a developed micromachined silicon platform for the precise assembly of 2D multilayer Laue lenses (MLLs) for high-resolution X-ray microscopy. The platform is 10 × 10 mm2 and is fabricated on ~500 µm thick silicon wafers through multiple steps of photolithography and deep reactive-ion etching. The platform accommodates two linear MLLs in a pre-defined configuration with precise angular and lateral position control. In this work, we discuss the design and microfabrication of the platform, and characterization regarding MLLs assembly, position control, repeatability, and stability. The results demonstrate that a micromachined platform can be used for the assembly of a variety of MLLs with different dimensions and optical parameters. The angular misalignment of 2D MLLs is well controlled in the range of the designed accuracy, down to a few millidegrees. The separation distance between MLLs is adjustable from hundreds to more than one thousand micrometers. The use of the developed platform greatly simplifies the alignment procedure of the MLL optics and reduces the complexity of the X-ray microscope. It is a significant step forward for the development of monolithic 2D MLL nanofocusing optics for high-resolution X-ray microscopy.

Ptychographic X-ray speckle tracking with multi-layer Laue lens systems.

The ever-increasing brightness of synchrotron radiation sources demands improved X-ray optics to utilize their capability for imaging and probing biological cells, nano-devices and functional matter on the nanometre scale with chemical sensitivity. Hard X-rays are ideal for high-resolution imaging and spectroscopic applications owing to their short wavelength, high penetrating power and chemical sensitivity. The penetrating power that makes X-rays useful for imaging also makes focusing them technologically challenging. Recent developments in layer deposition techniques have enabled the fabrication of a series of highly focusing X-ray lenses, known as wedged multi-layer Laue lenses. Improvements to the lens design and fabrication technique demand an accurate, robust, in situ and at-wavelength characterization method. To this end, a modified form of the speckle tracking wavefront metrology method has been developed. The ptychographic X-ray speckle tracking method is capable of operating with highly divergent wavefields. A useful by-product of this method is that it also provides high-resolution and aberration-free projection images of extended specimens. Three separate experiments using this method are reported, where the ray path angles have been resolved to within 4 nrad with an imaging resolution of 45 nm (full period). This method does not require a high degree of coherence, making it suitable for laboratory-based X-ray sources. Likewise, it is robust to errors in the registered sample positions, making it suitable for X-ray free-electron laser facilities, where beam-pointing fluctuations can be problematic for wavefront metrology.

Complete Strain Mapping of Nanosheets of Tantalum Disulfide.

Quasi-two-dimensional (quasi-2D) materials hold promise for future electronics because of their unique band structures that result in electronic and mechanical properties sensitive to crystal strains in all three dimensions. Quantifying crystal strain is a prerequisite to correlating it with the performance of the device and calls for high resolution but spatially resolved rapid characterization methods. Here, we show that using fly-scan nano X-ray diffraction, we can accomplish a tensile strain sensitivity below 0.001% with a spatial resolution of better than 80 nm over a spatial extent of 100 µm on quasi-2D flakes of 1T-TaS2. Coherent diffraction patterns were collected from a ∼100 nm thick sheet of 1T-TaS2 by scanning a 12 keV focused X-ray beam across and rotating the sample. We demonstrate that the strain distribution around micron- and submicron-sized "bubbles" that are present in the sample may be reconstructed from these images. The experiments use state-of-the-art synchrotron instrumentation and will allow rapid and nonintrusive strain mapping of thin-film samples and electronic devices based on quasi-2D materials.

2D MEMS-based multilayer Laue lens nanofocusing optics for high-resolution hard x-ray microscopy.

We report on the development of 2D integrated multilayer Laue lens (MLL) nanofocusing optics used for high-resolution x-ray microscopy. A Micro-Electro-Mechanical-Systems (MEMS) - based template has been designed and fabricated to accommodate two linear MLL optics in pre-aligned configuration. The orthogonality requirement between two MLLs has been satisfied to a better than 6 millidegrees level, and the separation along the x-ray beam direction was controlled on a micrometer scale. Developed planar 2D MLL structure has demonstrated astigmatism free point focus of ∼14 nm by ∼13 nm in horizontal and vertical directions, respectively, at 13.6 keV photon energy. Approaching 10 nm resolution with integrated 2D MLL optic is a significant step forward in applications of multilayer Laue lenses for high-resolution hard x-ray microscopy and their adoption by the general x-ray microscopy community.

Perovskite neural trees.

Trees are used by animals, humans and machines to classify information and make decisions. Natural tree structures displayed by synapses of the brain involves potentiation and depression capable of branching and is essential for survival and learning. Demonstration of such features in synthetic matter is challenging due to the need to host a complex energy landscape capable of learning, memory and electrical interrogation. We report experimental realization of tree-like conductance states at room temperature in strongly correlated perovskite nickelates by modulating proton distribution under high speed electric pulses. This demonstration represents physical realization of ultrametric trees, a concept from number theory applied to the study of spin glasses in physics that inspired early neural network theory dating almost forty years ago. We apply the tree-like memory features in spiking neural networks to demonstrate high fidelity object recognition, and in future can open new directions for neuromorphic computing and artificial intelligence.

Lanthanide-Binding Tags for 3D X-ray Imaging of Proteins in Cells at Nanoscale Resolution.

We report the application of lanthanide-binding tags (LBTs) for two- and three-dimensional X-ray imaging of individual proteins in cells with a sub-15 nm beam. The method combines encoded LBTs, which are tags of minimal size (ca. 15-20 amino acids) affording high-affinity lanthanide ion binding, and X-ray fluorescence microscopy (XFM). This approach enables visualization of LBT-tagged proteins while simultaneously measuring the elemental distribution in cells at a spatial resolution necessary for visualizing cell membranes and eukaryotic subcellular organelles.

Synthesis, Characterization, and Stability Studies of Ge-Based Perovskites of Controllable Mixed Cation Composition, Produced with an Ambient Surfactant-Free Approach.

In this report, we have applied a facile, ligand-free, ambient synthesis protocol toward the fabrication of not only a series of lead-free Ge-based perovskites with the general formulation of MA1-x FA x GeI3 (where x was changed from 0, 0.25, 0.5, 0.75, to 1) but also CsGeI3. Specifically, our methodology for producing ABX3 systems is generalizable, regardless of the identity of either the A site cation or the X site halide ion. Moreover, it incorporates many advantages, including (i) the possibility of efficiently generating pure Ge-based perovskite particles of any desired chemical composition, (ii) the use of readily available, commercial precursors and comparatively lower toxicity solvents, (iii) the practicality of scale up, and (iv) the elimination of the need for any superfluous organic surface ligands or surfactants. In addition to providing mechanistic insights into their formation, we have examined the chemical composition, crystallite size, morphology, surface attributes, oxidation states, and optical properties of our as-prepared perovskites using a combination of diffraction, microscopy, and spectroscopy techniques. Specifically, we noted that the optical band gap could be reliably tuned as a function of chemical composition, via the identity of the A site cation. Moreover, we have probed their stability, not only under standard storage conditions but also, for the first time, when subjected to both e-beam- and X-ray-induced degradation, using cumulative data from sources such as synchrotron-based scanning hard X-ray microscopy. Importantly, of relevance for the potential practical incorporation of these Pb-free perovskites, our work has emphasized the possibility of controlling the chemical composition within Ge-based perovskites as a means of rationally tuning their observed band gaps and optical behavior.