Two-dimensional Projection Research Articles

Real-Time Map Matching: A New Algorithm Integrating Spatio-Temporal Proximity and Improved Weighted Circle

Abstract Previous real-time map matching algorithms for in-vehicle navigation systems had some efficiencies and defects on time lagging and low accuracy. As a response, this paper proposes a new algorithm that integrates STP (spatio-temporal proximity) and IWC (improved weighted circle), in which the new algorithm proposes STP to dynamically refine candidate matching roads, and IWC to adaptively identify the optimal matching road. Specifically, three spatio-temporal proximity indicators are defined in STP to build a three-dimensional stereoscopic cone, and then the two-dimensional projection of the cone are adopted to dynamically select the candidate matching roads. Further, by adaptively setting the weight, the IWC algorithm is developed to integrate three new parameters to adaptively determine the optimal matching road. The test results show that the matching accuracy of the algorithm is over 95%, much higher than that of the existing algorithm, which demonstrates the feasibility and efficiency of the new algorithm.

Utilization of an optimum low-pass filter during filtered back-projection in the reconstruction of single photon emission computed tomography images of small structures

Introduction:Low-pass filters eliminate noise, and accordingly improve the quality of filtered back-projection (FBP) in the reconstruction of single photon emission computed tomography (SPECT) images. This study aimed at selection of an optimum low-pass filter for FBP reconstruction of SPECT images of small structures. Material and Methods:Spheres A, B, and C (16 mm, 12 mm, and 11 mm in diameter, respectively) attached to capillary stems were filled with technetium-99m solution (activity concentration 300 kBq/mL). They were then mounted inside a Jaszczak Phantom forming a V-shaped structure. The phantom was then filled with distilled water. Two-dimensional (2D) projections were acquired on 128 128 pixels using a Siemens E-Cam dual-head gamma camera. The Parzen, Shepp-Logan, Low Pass Cosine filters (cut-off frequencies: 0.2-0.9), and Butterworth filter (order: 1-9; cut-off frequencies: 0.3-0.9) were employed during FBP reconstruction. The line command of ImageJ software was used to draw the point spread functions of acquired 2D transaxial central slices and for the measurement of their full-width at half-maximum (FWHM). Results:The FWHM of 2D central image slices of spheres A, B, and C reconstructed using a Butterworth filter measured 20, 20, and 10 pixels, respectively. In comparison, the reconstructed images using the Parzen, Low Pass Cosine, and Shepp-Logan filters measured to 27, 25 and, 22 pixels for sphere A, 24, 22, 20 pixels for B, and 22, 20, 18 pixels for C, respectively. Conclusion: The low-pass filters successfully suppressed noise during the FBP reconstruction of SPECT images of small structures. Accordingly, the Butterworth is a suitable choice.

Abstract 3757: Probing glioblastoma and its microenvironment at single cell resolution

Abstract Single-cell sequencing (scSeq) is a powerful tool to investigate cancer genomics at single cell resolution. Multiple studies have recently illuminated intratumoral heterogeneity in glioblastoma, however, the majority focused on molecular complexity of tumor cells, without taking into account unexplored host cell types that contribute to the microenvironment around GBM tumor. To address the glioblastoma microenvironment composition and potential tumor-host interactions, we performed deep coverage (176k average reads per cell) scSeq of freshly resected primary GBM patient tissue without implementing any tumor cell enrichment strategies. scSeq libraries for 902 cells were prepared using 10X Gemcode platform and sequenced on Illumina NextSeq 500. This run was of high quality with 2,663 median genes per cell and low mitochondrial gene percentage (median < 5%). We used Cell Ranger analysis pipelines and Seurat packages to classify individual cells into 10 clusters and visualize them using t-SNE two-dimensional projections. We then identified the signature gene set for each cluster, relative to all other cells. Pathway analysis of each cluster signature along with known GBM microenvironment cell signatures revealed glioma tumor population along with surrounding microglia/marcophages, astrocytes, pericytes, oligodendrocytes, T cells and endothelial cells. Cell type markers identified by single cell transcriptomics were validated by IHC analysis. Microenvironmental composition and single cell signature will be confirmed through single nuclei sequencing of preserved (Frozen) tumor sample. Our results demonstrate the cellular diversity of brain tumor microenvironment and lay a foundation to further investigate the individual tumor and host cell transcriptomes that are influenced not only by their cell identity but also by their interaction with surrounding microenvironment. Citation Format: Sen Peng, Sanhita Rath, Saumya Bollam, Jenny Eschbacher, Shwetal Mehta, Nader Sanai, Michael Berens, Seungchan Kim, Harshil Dhruv. Probing glioblastoma and its microenvironment at single cell resolution [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3757.

Compressive spectral imaging via deformable mirror and colored-mosaic detector

Compressive Spectral Imaging (CSI) is an emerging technology that aims at reconstructing a spectral image from a limited set of two-dimensional projections. To capture these projections, CSI architectures often combine light dispersive elements with coded apertures or programmable spatial light modulators. This work introduces a novel and compact CSI architecture based on a deformable mirror and a colored-filter detector to produce compressive spatio-spectral projections without the need of a grating or prism. Alongside, we propose a tensor-based reconstruction algorithm to recover the spatial-spectral information from the compressed measurements. Experimental results on both simulated and real datasets demonstrate efficacy of the proposed acquisition architecture and the especially crafted inversion algorithms.

A direct comparison of high-speed methods for the numerical Abel transform.

The Abel transform is a mathematical operation that transforms a cylindrically symmetric three-dimensional (3D) object into its two-dimensional (2D) projection. The inverse Abel transform reconstructs the 3D object from the 2D projection. Abel transforms have wide application across numerous fields of science, especially chemical physics, astronomy, and the study of laser-plasma plumes. Consequently, many numerical methods for the Abel transform have been developed, which makes it challenging to select the ideal method for a specific application. In this work, eight published transform methods have been incorporated into a single, open-source Python software package (PyAbel) to provide a direct comparison of the capabilities, advantages, and relative computational efficiency of each transform method. Most of the tested methods provide similar, high-quality results. However, the computational efficiency varies across several orders of magnitude. By optimizing the algorithms, we find that some transform methods are sufficiently fast to transform 1-megapixel images at more than 100 frames per second on a desktop personal computer. In addition, we demonstrate the transform of gigapixel images.

The relation between the true and observed fractal dimensions of turbulent clouds

Observations of interstellar gas clouds are typically limited to two-dimensional (2D) projections of the intrinsically three-dimensional (3D) structure of the clouds. In this study, we present a novel method for relating the 2D projected fractal dimension ($\mathcal{D}_{\text{p}}$) to the 3D fractal dimension ($\mathcal{D}_{\text{3D}}$) of turbulent clouds. We do this by computing the fractal dimension of clouds over two orders of magnitude in turbulent Mach number $(\mathcal{M} = 1-100)$, corresponding to seven orders of magnitude in spatial scales within the clouds. This provides us with the data to create a new empirical relation between $\mathcal{D}_{\text{p}}$ and $\mathcal{D}_{\text{3D}}$. The proposed relation is $\mathcal{D}_{\text{3D}}(\mathcal{D}_{\text{p}}) = \Omega_1 erfc ( \xi_1 erfc^{-1}[ (\mathcal{D}_{\text{p}} - \mathcal{D}_{\text{p,min}})/\Omega_2 ] + \xi_2 ) + \mathcal{D}_{\text{3D,min}}$, where the minimum 3D fractal dimension, $\mathcal{D}_{\text{3D,min}} = 2.06 \pm 0.35$, the minimum projected fractal dimension, $\mathcal{D}_{\text{p,min}} = 1.55 \pm 0.13$, $\Omega_1 = 0.47 \pm 0.18$, $\Omega_2 = 0.22 \pm 0.07$, $\xi_1 = 0.80 \pm 0.18$ and $\xi_2 = 0.26 \pm 0.19$. The minimum 3D fractal dimension, $\mathcal{D}_{\text{3D,min}} = 2.06 \pm 0.35$, indicates that in the high $\mathcal{M}$ limit the 3D clouds are dominated by planar shocks. The relation between $\mathcal{D}_{\text{p}}$ and $\mathcal{D}_{\text{3D}}$ of molecular clouds may be a useful tool for those who are seeking to understand the 3D structures of molecular clouds, purely based upon 2D projected data and shows promise for relating the physics of the turbulent clouds to the fractal dimension.

Comparison of Adrenal Tumor Size in Ultrasound Examinations with and without the Use of a Contrast Agent.

Background and objectives: Patients diagnosed with incidentally found adrenal tumors (incidentaloma) that do not meet the criteria for surgical treatment require follow-ups with repeated imaging. The aim of this study is to compare the accuracy of the measurements of the adrenal tumor size in ultrasound (US) with and without contrast in comparison to computed tomography (CT) or magnetic resonance (MRI). Further, this study attempts to answer the question of whether contrast-enhanced ultrasound (CEUS) can improve imaging accuracy and replace CT/MRI in the monitoring of patients with adrenal tumors. Materials and Methods: The retrospective analysis included 79 adult patients with adrenal incidentalomas not exceeding a dimension of 6 cm who underwent a CT or MRI scan, US, and CEUS with the use of SonoVue in two-dimensional (2D) and three-dimensional (3D) projections and Doppler techniques. Tumor vascularization in CEUS was classified as follows: peripheral, peripheral-central, central, or poor. Results: Of 79 adrenal tumors, 48.1% showed peripheral, 29.1% showed poor, 21.5% showed peripheral-central, and only 1.3% showed central vascularization. The median volume of tumors detected with CEUS (69.9 cm3) was significantly higher than with US (44.5 cm3) and CT or MRI (57.1 cm3). The relative error of the adrenal volume with CEUS compared with CT or MRI was significantly higher than with standard US, regardless of the type of tumor vascularization. Conclusions: CEUS does not improve the accuracy of adrenal tumor size assessment regardless of the type of vascularization.

The approach angle to the interoptic triangle limits surgical workspace when targeting the contralateral internal carotid artery.

The interoptic triangle (IOT) offers a key access to the contralateral carotid artery's ophthalmic segment (oICA) and its perforating branches (PB), the ophthalmic artery (OA), and the superior hypophyseal artery (SHA). It has been previously reported that the assessment of IOT's size is relevant when attempting approaches to the contralateral oICA. However, previous studies have overseen that, since the oICA is a paramedian structure and a lateralized contralateral approach trajectory is then required, the real access to the oICA is further limited by the approach angle adopted by the surgeon with respect to the IOT's plane. For this reason, we determined the surgical accessibility to the contralateral oICA and its branches though the IOT by characterizing the morphometry of this triangle relative to the optimal contralateral approach angle. We defined the "relative interoptic triangle" (rIOT) as the two-dimensional projection of the IOT to the surgeon's view, when the microscope has been positioned with a certain angle with respect to the midline to allow the maximal contralateral oICA visualization. We correlated the surface of the rIOT to the visualization of oICA, OA, SHA, and PBs on 8 cadavers and 10 clinical datasets, using for the last a 3D-virtual reality system. A larger rIOT correlated positively with the exposure of the contralateral oICA (R = 0.967, p < 0.001), OA (R = 0.92, p < 0.001), SHA (R = 0.917, p < 0.001), and the number of perforant vessels of the oICA visible (R = 0.862, p < 0.001). The exposed length of oICA, OA, SHA, and number PB observed increased as rIOT's surface enlarged. The correlation patterns observed by virtual 3D-planning matched the anatomical findings closely. The exposure of contralateral oICA, OA, SHA, and PB directly correlates to rIOT's surface. Therefore, preoperative assessment of rIOT's surface is helpful when considering contralateral approaches to the oICA. A virtual 3D planning tool greatly facilitates this assessment.

Characterization of dynamic morphological changes of tin anode electrode during (de)lithiation processes using in operando synchrotron transmission X-ray microscopy

The morphological evolution of tin particles with different sizes during the first lithiation and delithiation processes has been visualized by an in operando synchrotron transmission X-ray microscope (TXM). The in operando lithium ion battery cell was operated at constant current condition during TXM imaging. Two-dimensional projection images with 40 nm resolution showing morphological evolution were obtained and analyzed. The analysis of relative area change shows that the morphology of tin particles with different sizes changed simultaneously. This phenomenon is mainly due to a negative feedback mechanism among tin particles in the battery electrode at a constant current operating condition. For irregular-shaped tin particles, the contour analysis shows that the regions with higher curvature started volume expansion first, and then the entire particle expanded almost homogeneously. This study provides insights for understanding the dynamic morphological change and the particle-particle interactions in high capacity lithium ion battery electrodes.

A novel edge-enabled SLAM solution using projected depth image information

Environmental mapping is the key step for mobile robots to perform tasks independently and perfectly. In recent years, visual SLAM, laser-based SLAM and simultaneous localization and mapping (SLAM) have aroused the interest of many people. Unfortunately, those technologies are not widely used, limited by the computational complexity, data processing and very low and predictable latency. This paper had mainly completed the following work and edge-enabled computing-based edge computing (Shi and Dustdar in Computer 49(5):78–81, 2016) is used as a solution to accelerate calculation. First of all, this research design works with inertial unit mobile robotic navigation systems, and all sensors are connected in edge layers in the framework of edge computing and explore the accelerometer, electronic compass, and gyroscope data. The accelerometer data are integrated using the Kalman filter data fusion algorithm to filter the random drift error caused by the gyroscope and the electronic compass. The state of the machine is determined by calculation of the corresponding attitude angle and position information. Second, a low-cost distance sensor is used to detect the depth and upload to the other fog node for computation. Next, the 3D point coordinate information is projected onto the two-dimensional coordinate extraction feature point to establish the feature map. Third, the extended Kalman filter SLAM is used to achieve simultaneous positioning and mapping. Finally, the method is validated in the experiment, proving that the method is feasible. The main improvement in this article is as follows: First, the multi-sensor data fusion algorithm is used to reduce the positioning error. Second, we use low-cost distance sensors to measure the depth of the model environment and reduce the cost. Third, we would take advantage of translating the three-dimensional depth information into a flat two-dimensional projection information to reduce the calculation of load and computing time. Fourth, our computation is distributed in different layers and focuses on edge-enabled platform to decrease the latency and redundancy.

Statistical Analysis of the Effect of Deuteration on Quantum Delocalization in CH5.

Statistical approaches are applied to an investigation of how deuterated forms of CH5+ sample the potential energy surface for this ion. In its ground state, CH5+ has been shown to have amplitude in all of the 120 equivalent minima in the potential as well as in the region of each of the 180 low-energy saddle points that connect these minima. Although deuteration quenches this delocalization, the multidimensional nature of the isomerization pathways makes it difficult to visualize and quantify the delocalization of the ground state wave function. In this work, the localization of the ground state wave function is explored through an analysis of projections of the ground state probability amplitude onto various atom-atom distances. Specifically, analysis of two-dimensional projections of the ground state probability amplitude on to the distances between pairs of hydrogen atoms reveals that the ground state probability amplitude is localized near structures in which the hydrogen atoms in the ion can be divided into those that make up a CH3+ part and those that make up in a H2 part. Analysis of fits of projections of the probability amplitude onto distances between pairs of hydrogen atoms, between pairs of deuterium atoms, and between hydrogen and deuterium atoms to sums of Gaussian functions allows us to quantify the increased localization of hydrogen and deuterium atoms in the CH3+ and H2 parts of the ion. In addition, the amplitude of the wave function in the regions of the potential that correspond to the transition state for exchange of hydrogen or deuterium atoms between these two parts of the ion is found to decrease in the partially deuterated forms of CH5+ and is smallest for CH2D3+ and CH3D2+.

Quantitative assessment of breast density using transmission ultrasound tomography.

PurposeBreast density is important in the evaluation of breast cancer risk. At present, breast density is evaluated using two‐dimensional projections from mammography with or without tomosynthesis using either (a) subjective assessment or (b) a computer‐aided approach. The purpose of this work is twofold: (a) to establish an algorithm for quantitative assessment of breast density using quantitative three‐dimensional transmission ultrasound imaging; and (b) to determine how these quantitative assessments compare with both subjective and objective mammographic assessments of breast density.MethodsWe described and verified a threshold‐based segmentation algorithm to give a quantitative breast density (QBD) on ultrasound tomography images of phantoms of known geometric forms. We also used the algorithm and transmission ultrasound tomography to quantitatively determine breast density by separating fibroglandular tissue from fat and skin, based on imaged, quantitative tissue characteristics, and compared the quantitative tomography segmentation results with subjective and objective mammographic assessments.ResultsQuantitative breast density (QBD) measured in phantoms demonstrates high quantitative accuracy with respect to geometric volumes with average difference of less than 0.1% of the total phantom volumes. There is a strong correlation between QBD and both subjective mammographic assessments of Breast Imaging ‐ Reporting and Data System (BI‐RADS) breast composition categories and Volpara density scores — the Spearman correlation coefficients for the two comparisons were calculated to be 0.90 (95% CI: 0.71–0.96) and 0.96 (95% CI: 0.92–0.98), respectively.ConclusionsThe calculation of breast density using ultrasound tomography and the described segmentation algorithm is quantitatively accurate in phantoms and highly correlated with both subjective and Food and Drug Administration (FDA)‐cleared objective assessments of breast density.

Performance evaluation of methods for integrative dimension reduction

Dimension reduction (DR) methods play an inevitable role in analyzing and visualizing high-dimensional multi-source data. In the recent decades many variants of these methods have been developed in various disciplines and domains. Due to the diversity and an ever-increasing number of developed techniques, choosing the right method for the given problem is a difficult task. In this study we benchmark 87 methods for integrative dimension reduction of mRNA expression and DNA methylation data, which is a common problem in biology and medicine. Our ranking is obtained based on four main factors: quality of dimension reduction (local, global, and local-global neighborhood preservation), clustering quality, speed and sensitivity to input parameters on multiple datasets generated by InterSIM (a semi-realistic multi-source data simulator in the genomics domain). The results are later validated on a real dataset for breast cancer through visual evaluation metrics such as co-ranking matrices, inspection of true cancer sub-types in two-dimensional projections, and LCMC curves. We also demonstrate the relationship between the methods via network inference. The findings in this study can be useful in algorithm selection and planning of experimental design in multi-source data analysis.

Active Thermochemical Tables: The Partition Function of Hydroxymethyl (CH2OH) Revisited.

The best currently available set of temperature-dependent nonrigid rotor anharmonic oscillator (NRRAO) thermochemical and thermophysical properties of hydroxymethyl radical is presented. The underlying partition function relies on a critically evaluated complement of accurate experimental and theoretical data and is constructed using a two-pronged strategy that combines contributions from large amplitude motions obtained from direct counts, with contributions from the other internal modes of motion obtained from analytic NRRAO expressions. The contributions from the two strongly coupled large-amplitude motions of CH2OH, OH torsion and CH2 wag, are based on energy levels obtained by solving the appropriate two-dimensional projection of a fully dimensional potential energy surface that was recently obtained at the CCSD(T)/cc-pVTZ level of theory. The contributions of the remaining seven, more rigid, vibrational modes and of the external rotations are captured by NRRAO corrections to the standard rigid rotor harmonic oscillator (RRHO) treatment, which include corrections for vibrational anharmonicities, rotation-vibration interaction, Coriolis effects, and low temperature. The basic spectroscopic constants needed for the construction of the initial RRHO partition function rely on experimental ground-state rotational constants and the best available experimental fundamentals, additionally complemented by fundamentals obtained from the variational solution of the full-dimensional potential energy surface using a recently developed two-component multilayer Lanczos algorithm. The higher-order spectroscopic constants necessary for the NRRAO corrections are extracted from a second-order variational perturbation treatment (VPT2) of the same potential energy surface. The Lanczos solutions of the fully dimensional surface are validated against available experimental data, and the VPT2 results and the solutions of the reduced dimensionality surface are validated both against the Lanczos solutions and available experiments. The NRRAO thermophysical and thermochemical properties, given both in tabular form and as seven- and nine-coefficient NASA polynomials, are compared to previous results. In addition, the latest ATcT values for the enthalpy of formation of CH2OH at 298.15 K (0 K), -16.75 ± 0.27 kJ/mol (-10.45 ± 0.27 kJ/mol), and of other related CH nO m species ( n = 0-4, m = 0,1) are reported, together with a plethora of related bond dissociation enthalpies (BDEs), such as the C-H, O-H, and C-O bond dissociation enthalpies of methanol, 402.16 ± 0.26 kJ/mol (395.61 ± 0.26 kJ/mol), 440.34 ± 0.26 kJ/mol (434.86 ± 0.26 kJ/mol), and 384.85 ± 0.15 kJ/mol (377.14 ± 0.15 kJ/mol), respectively, and analogous BDEs for hydroxymethyl, 343.67 ± 0.37 kJ/mol (339.16 ± 0.37 kJ/mol), 125.54 ± 0.28 kJ/mol (121.11 ± 0.28 kJ/mol), and 445.86 ± 0.29 kJ/mol (438.76 ± 0.29 kJ/mol), respectively. The reasons governing the alternation between strong and weak sequential H atom BDEs of methanol are also discussed.

Evaluation methods and indexes of morphological characteristics of coarse aggregates for road materials: A comprehensive review

Abstract The morphological characteristics of coarse aggregates, namely shape profile, angularity and surface texture, have a significant effect on the mechanical properties of asphalt mixtures. The identification methods and evaluation indexes of the morphological characteristics are crucial for the coarse aggregates. First, the visual identification technologies, including charged coupled device (CCD) image processing, X-ray tomography imaging and laser scanning technology, are summarized in term of the characteristics, research status and existing problems. Based on the deficiencies of visual identification technologies, it is proposed to improve the three-dimensional reconstruction of aggregate particles by increasing the number of two-dimensional projection directions or scanning sections. Moreover, the evaluation indexes of coarse aggregate morphology were summarized from the three aspects, namely shape profile, angularity and surface texture. It is found that evaluation of the indexes based on two-dimensional profile images or unrelated to pavement performance could not adequately evaluate the morphological characteristics of coarse aggregates. Therefore, the evaluation methods and indexes of coarse aggregate morphological characteristics have been recommended based on the performance of asphalt mixtures in this paper, which is helpful to establish a complete set of an evaluation system for coarse aggregate morphology.

Algorithm of reconstruction of a three-dimensional crystal structure from two-dimensional projections

The article deals with a problem of three-dimensional crystal lattice reconstruction, which is an important stage in the X-ray structural analysis. The accuracy of parametric and structural identification of crystals directly depends on the quality of crystal lattice reconstruction. The proposed algorithm of reconstruction of a three-dimensional crystal lattice is based on minimizing the distances from each node to a line projected onto a specified plane. Three sets of two-dimensional node coordinates, obtained from three two-dimensional projections, are used as input data. We performed an analytical calculation of the reconstruction error, allowing the total reconstruction accuracy to be estimated. The results of computational experiments confirmed the high quality of the proposed reconstruction algorithms and its stability against the distortion of node coordinates. In addition, we revealed a problem of lattice system separability, with the identification accuracy for monoclinic, rhombic and tetragonal systems found to be 34%, 53% and 10%, respectively.

Universal N-Partite d-Level Pure-State Entanglement Witness Based on Realistic Measurement Settings.

Entanglement witnesses are operators that are crucial for confirming the generation of specific quantum systems, such as multipartite and high-dimensional states. For this reason, many witnesses have been theoretically derived which commonly focus on establishing tight bounds and exhibit mathematical compactness as well as symmetry properties similar to that of the quantum state. However, for increasingly complex quantum systems, established witnesses have lacked experimental achievability, as it has become progressively more challenging to design the corresponding experiments. Here, we present a universal approach to derive entanglement witnesses that are capable of detecting the presence of any targeted complex pure quantum system and that can be customized towards experimental restrictions or accessible measurement settings. Using this technique, we derive experimentally optimized witnesses that are able to detect multipartite d-level cluster states, and that require only two measurement settings. We present explicit examples for customizing the witness operators given different realistic experimental restrictions, including witnesses for high-dimensional entanglement that use only two-dimensional projection measurements. Our work enables us to confirm the presence of probed quantum states using methods that are compatible with practical experimental realizations in different quantum platforms.

THE APPLICATION OF VIRTUAL REALITY IN GERIATRIC MENTAL HEALTH: THE STATE OF THE EVIDENCE

Introduction In recent years, there has been a rise in the prevalence of cognitive and affective disorders in the ageing population. This has led to an impetus on innovative, scalable and engaging interventions for these disorders, and technology has proved to be a promising frontier. In the past decade, virtual reality (VR) technology has seen a shift from two-dimensional non-immersive projections to three-dimensional fully immersive experiences, allowing for the development of more sophisticated simulations. There have been successful applications of VR in clinical settings with children and teens, including social cognition training for autism, sustained attention in attention-deficit hyperactivity disorder (ADHD), exposure-based therapies for anxiety disorders, and depression pathology. However, VR has been less studied in the geriatric population. The purpose of this study is to determine the existing levels of evidence for use of VR in clinical settings and identify areas where more evidence may guide translation of existing VR interventions for older adults. Methods This study is a systematic review. We conducted a search on PubMed in December 2018 for peer-reviewed journal articles published in English within the last 10 years. We narrowed the search to VR technology and its applications in older adults, as well as studies of VR in normal aged adults for comparative value. We rejected articles if the title and abstract did not meet inclusion criteria and authors LM, AR and MS agreed by consensus on which articles were relevant. We also used the bibliographies of selected articles to find additional articles that we may have missed in the initial search. We then reviewed article content to classify the articles into appropriate domains of geriatric mental health (content domains) and research objectives (research domains). Results Based on our review, a total number of 426 articles were found, and 49 met our inclusion criteria related to older adults. We conducted a yearly count for the number of search results to identify if there has been an increase in research in the field. The number of search results by year are positively correlated (see Figure 1). We also identified 5 additional articles which focused on younger adults which we opted to include in the review based on internal consensus amongst all authors that they reflected VR application in domains of mental health that are known to apply to older adults as well. We found that the articles fell across several content domains: Cognition, conditions along the mood spectrum, anxiety, post-traumatic stress disorder (PTSD), and caregiver burden. The research domain with the highest number of articles was VR validity testing (26), followed by cognitive training (18), indicating that these are the VR applications for which the best evidence exists currently. Conclusions Currently, existing evidence offers clear support for the use of VR as a screening tool for cognitive impairment in older adults, and as a training tool to improve cognitive skills. VR-based tasks demonstrated validity comparable to some traditional paper-based assessments of cognition. Furthermore, there are indications that VR can play a role in delaying the onset of Alzheimer's Disease (AD), mild cognitive impairment (MCI), and other forms of dementia. More work is needed to refine VR screening capabilities for more specific diagnoses, but the potential for innovation in VR environments and tasks makes VR a promising medium to achieve this specificity. Additionally, there were many different VR environments used across studies, which may show a need for standardization of environments before comparisons can be made between VR simulations. Given the robust evidence of VR as a treatment in affective disorders in younger adults, we see potential for the same intervention in older adult mood spectrum disorders and other psychiatric conditions. Finally, future studies should address key issues surrounding VR use in clinical settings, such as usability, data privacy, and confidentiality. This research was funded by Anonymous philanthropic donation to McLean Hospital.

To the Solution of the Inverse Problem of X-Ray Topo-Tomography. Computer Algorithms and 3D Reconstruction on the Example of a Crystal with a Point Defect of Coulomb Type

A successive approach to the solution of the inverse problem of diffraction X-ray topo-tomography is proposed. It is based on the semikinematic solution to the Takagi–Taupin equations for the amplitude of diffracted σ-polarized wave. An example of a point defect of Coulomb type in a Si(111) single-crystal plate under conditions of symmetric Laue diffraction and a set of oblique two-dimensional topographic projections corresponding to the rotation of plane-parallel sample crystal around the diffraction vector [ $$\bar {2}20$$ ] is considered. Iterative algorithms for simulated annealing (SA) and quasi-Newton-type algorithms are used for computer reconstruction of the three-dimensional function of the displacement field around a point defect. The results of computer simulation of the displacement field function based on the data for one 2D projection, corresponding to the point defect image in an X-ray topogram in classical X-ray diffraction topography, are presented.

Two-dimensional joint local and nonlocal discriminant analysis-based 2D image feature extraction for deep learning

This paper proposes a new two-dimensional manifold learning algorithm called two-dimensional joint local/nonlocal discriminant analysis (2DJLNDA) for 2D image feature extraction, which directly extracts projective vectors from 2D image matrices rather than image vectors. Different from other typical 2D methods, e.g., two-dimensional principal component analysis (2DPCA), two-dimensional linear discriminative analysis (2DLDA), two-dimensional locality-preserving projection (2DLPP), 2DJLNDA preserves not only local/nonlocal intrinsic structure but also local/nonlocal penalization structure of the image data in the high-dimensional space, which can be powerful in extracting intrinsic information of the image data in the low-dimensional space. The experimental results on the ORL, Yale, AR and UMIST face datasets indicate that 2DJLNDA is capable of extracting effective image features and outperforms 2DPCA, 2DLDA and 2DLPP. The 2D image features extracted by 2DJLNDA further improve the performance of deep neural networks (DNNs), e.g., stacked denoising autoencoder, and convolutional neural network (CNN) significantly. These studying results illustrate that the feature face images will provide more discriminant features than the original face images for DNNs. Therefore, 2DJLNDA-based 2D feature image extraction can be used as an effective preprocessing of DNNs (e.g., CNN) for face recognition.