Pointwise Differences Research Articles

Deep learning-based prediction of the dose-volume histograms for volumetric modulated arc therapy of left-sided breast cancer.

The advancements in artificial intelligence and computational power have made deep learning an attractive tool for radiotherapy treatment planning. Deep learning has the potential to significantly simplify the trial-and-error process involved in inverse planning required by modern treatment techniques such as volumetric modulated arc therapy (VMAT). In this study, we explore the ability of deep learning to predict organ-at-risk (OAR) dose-volume histograms (DVHs) of left-sided breast cancer patients undergoing VMAT treatment based solely on their anatomical characteristics. The predicted DVHs could be used to derive patient-specific dose constraints and dose objectives, streamlining the treatment planning process, standardizing the quality of the plans, and personalizing the treatment planning. This study aimed to develop a deep learning-based framework for the prediction of organ-specific dose-volume histograms (DVH) based on structures delineated for left-sided breast cancer treatment. We used a dataset of 249 left-sided breast cancer patients treated with tangential VMAT fields. We extracted delineated structures and dose distributions for each patient and derived slice-by-slice DVHs for planning target volume (PTV) and organs-at-risk. The patients were divided into training (70%, n=174), validation (10%, n=24), and test (20%, n=51) sets. Collected data were used to train a deep learning model for the prediction of the DVHs based on the delineated structures. The developed deep learning model comprised a modified DenseNet architecture followed by a recurrent neural network. In the independent test set (n=51), the point-wise differences in the slice-by-slice DVHs between the clinical and predicted DVHs were small; the mean squared errors were 3.53, 1.58, 2.28, 3.37, and 1.44 [×10-4] for PTV, heart, ipsilateral lung, contralateral lung, and contralateral breast, respectively. With the derived cumulative DVHs, the mean absolute difference±standard deviation of mean doses between the clinical and the predicted DVH were 0.08± 0.04Gy, 0.24± 0.22Gy, 0.73± 0.46Gy, 0.07± 0.06Gy, and 0.14± 0.14Gy for PTV, heart, ipsilateral lung, contralateral lung, and contralateral breast, respectively. The deep learning-based approach enabled automatic and reliable prediction of the DVH based on delineated structures. The predicted DVHs could potentially serve as patient-specific clinical goals used to aid treatment planning and avoid suboptimal plans or to derive optimization objectives and constraints for automated treatment planning.

Omnipolar conduction velocity mapping for ventricular substrate characterization: Impact of CV estimation method and EGM type on in vivo conduction velocity measurements

BackgroundAreas of abnormal or heterogeneous conduction velocity (CV) are important ablation targets for ventricular tachycardias, yet precise assessment of CV in clinical contact mapping remains challenging. Numerous different CV estimation methods have been proposed. ObjectiveThis study aimed to compare the automated local activation time (LAT)–independent omnipolar-based CV estimation method termed wave speed (WS) with 4 established LAT-based methods to formally establish the quantitative differences between them. MethodsHigh-density contact maps in patients with structurally normal hearts during sinus rhythm (SR) and ventricular ectopy (VE) were retrospectively analyzed. CV was assessed and compared by 5 methods: omnipolar WS, gradient method, planar wavefront fitting, circular wavefront fitting, and radial basis function. CV variations based on electrogram (EGM) type (unipolar, bipolar, and omnipolar), catheter movement, and surrogate markers for catheter contact were analyzed. ResultsThe study included 23 patients (47.8% male; 45.7 ± 17.3 years) with 22 SR maps (11 left ventricle, 11 right ventricle) and 16 VE maps (9 left ventricle, 7 right ventricle). The WS algorithm yielded statistically significant higher CV estimates in SR (mean, 1.41 ± 0.18 m/s) and VE (mean, 1.23 ± 0.18 m/s) maps compared with all LAT-based estimation methods, with absolute differences ranging from 0.1 m/s to 0.81 m/s. Median pointwise differences in SR and VE between WS and LAT-based methods were high, ranging from 0.55 ± 0.15 m/s (WS vs planar wavefront fitting) to 0.67 ± 0.16 m/s (WS vs radial basis function). For LAT-based methods, use of unipolar EGMs yielded significantly higher CV estimates than bipolar or omnipolar EGMs in SR. ConclusionThe CV estimation method has an important, statistically significant impact on ventricular CV measurements. Future work will focus on how these differences affect identification of pathologic conduction slowing in scar-related substrate.

Segmental disturbance of white matter microstructure in predicting mild cognitive impairment in idiopathic Parkinson's disease: An individualized study based on automated fiber quantification tractography

PurposeThe neurobiological mechanisms and an early identification of MCI in idiopathic Parkinson's disease (IPD) remain unclear. To investigate the abnormalities of types of white matter (WM) fiber tracts segmentally and establish reliable indicator in IPD-MCI. MethodsForty IPD with normal cognition (IPD-NCI), thirty IPD-MCI, and thirty healthy controls were included. Automated fiber quantification was applied to extract the fractional anisotropy (FA) and mean diffusivity (MD) values at 100 locations along the major fibers. Partial correlation was performed between diffusion values and cognitive performance. Furthermore, machine learning analyses were conducted to determine the imaging biomarker of MCI. Permutation tests were performed to evaluate the pointwise differences under the FWE correction. ResultsIPD-MCI had similar but more severe and widespread WM degeneration in the association, projection, and commissural fibers compared with IPD-NCI. Meanwhile, IPD-MCI showed distinct degeneration pattern in the association fibers. The FA of the anterior segment of right inferior fronto-occipital fasciculus (IFOF) was positively correlated with MoCA (P < 0.05) and executive function (P < 0.05). The MD of the middle and posterior segment of left superior longitudinal fasciculus (SLF) was negatively correlated with MoCA P < 0.05), executive (P < 0.05), visuospatial function (P < 0.05). Furthermore, the AUC of support vector machine model was 0.80 in the validation dataset. The FA of anterior segment of right IFOF contribute the most. ConclusionThis study demonstrated that regional tract-specific microstructural degeneration, especially the association fibers, can be used to predict MCI in IPD. Especially, the right IFOF may be a significant imaging biomarker in predicting IPD with MCI.

On the exact and efficient solution of the Huber function for measurement applications

The findings of this paper are summarized as follows: (1) We point out that the recently published conclusion that Iteratively Reweighted Least-Squares (IRLS) cannot guarantee the global solution of the well-known Huber function is incorrect. We present that the Huber function has only one minimum and IRLS will obtain the exact minimum unless it is incorrectly implemented. (2) We revisit the variants of the IRLS algorithm and compare them with the exact IRLS algorithm in the geodetic free trilateration networks adjustment. Notably, the pointwise differences between the variants of IRLS and the exact IRLS are even beyond the differences between the Least-Squares solution and the exact IRLS for over 75% points. (3) For rigorously and efficiently solving the Huber M−estimation problem, we propose the Quasi-Newton approach and its limited memory version. The time expense of our proposed algorithm is quite low compared with the time expense of the existing exact IRLS in the cases of a large number of unknown parameters and observations. Our results show that the computation efficiency is improved by one or two orders of magnitude.

Anomaly Detection for Internet of Things Time Series Data Using Generative Adversarial Networks With Attention Mechanism in Smart Agriculture.

More recently, smart agriculture has received widespread attention, which is a deep combination of modern agriculture and the Internet of Things (IoT) technology. To achieve the aim of scientific cultivation and precise control, the agricultural environments are monitored in real time by using various types of sensors. As a result, smart agricultural IoT generated a large amount of multidimensional time series data. However, due to the limitation of applied scenarios, smart agricultural IoT often suffers from data loss and misrepresentation. Moreover, some intelligent decision-makings for agricultural management also require the detailed analysis of data. To address the above problems, this article proposes a new anomaly detection model based on generative adversarial networks (GAN), which can process the multidimensional time series data generated by smart agricultural IoT. GAN is a deep learning model to learn the distribution patterns of normal data and capture the temporal dependence of time series and the potential correlations between features through learning. For the problem of generator inversion, an encoder–decoder structure incorporating the attention mechanism is designed to improve the performance of the model in learning normal data. In addition, we also present a new reconstruction error calculation method that measures the error in terms of both point-wise difference and curve similarity to improve the detection effect. Finally, based on three smart agriculture-related datasets, experimental results show that our proposed model can accurately achieve anomaly detection. The experimental precision, recall, and F1 score exceeded the counterpart models by reaching 0.9351, 0.9625, and 0.9482, respectively.

Ocean mover's distance: using optimal transport for analysing oceanographic data.

Remote sensing observations from satellites and global biogeochemical models have combined to revolutionize the study of ocean biogeochemical cycling, but comparing the two data streams to each other and across time remains challenging due to the strong spatial-temporal structuring of the ocean. Here, we show that the Wasserstein distance provides a powerful metric for harnessing these structured datasets for better marine ecosystem and climate predictions. The Wasserstein distance complements commonly used point-wise difference methods such as the root-mean-squared error, by quantifying differences in terms of spatial displacement in addition to magnitude. As a test case, we consider chlorophyll (a key indicator of phytoplankton biomass) in the northeast Pacific Ocean, obtained from model simulations, in situ measurements, and satellite observations. We focus on two main applications: (i) comparing model predictions with satellite observations, and (ii) temporal evolution of chlorophyll both seasonally and over longer time frames. The Wasserstein distance successfully isolates temporal and depth variability and quantifies shifts in biogeochemical province boundaries. It also exposes relevant temporal trends in satellite chlorophyll consistent with climate change predictions. Our study shows that optimal transport vectors underlying the Wasserstein distance provide a novel visualization tool for testing models and better understanding temporal dynamics in the ocean.

Tractography in Type 2 Diabetes Mellitus With Subjective Memory Complaints: A Diffusion Tensor Imaging Study.

The brain white matter (WM) structural injury caused by type 2 diabetes mellitus (T2DM) has been linked to cognitive impairment. However, the focus was mainly on the mild cognitive impairment (MCI) stage in most previous studies, with little attention made to subjective memory complaints (SMC). The main purpose of the current study was to investigate the characteristics of WM injury in T2DM patients and its correlation with SMC symptoms. In a group of 66 participants (33 HC and 33 T2DM-S), pointwise differences along WM tracts were identified using the automated fiber quantification (AFQ) approach. Then we investigated the utility of DTI properties along major WM tracts as features to distinguish patients with T2DM-S from HC via the support vector machine (SVM). Based on AFQ analysis, 10 primary fiber tracts that represent the subtle alterations of WM in T2DM-S were identified. Lower fractional anisotropy (FA) in the right SLF tract (r = −0.538, p = 0.0013), higher radial diffusivity (RD) in the thalamic radiation (TR) tract (r = 0.433, p = 0.012), and higher mean diffusivity (MD) in the right inferior fronto-occipital fasciculus (IFOF) tract (r = 0.385, p = 0.0029) were significantly associated with a long period of disease. Decreased axial diffusivity (AD) in the left arcuate was associated with HbA1c (r = −0.368, p = 0.049). In addition, we found a significant negative correlation between delayed recall and abnormal MD in the left corticospinal tract (r = −0.546, p = 0.001). The FA of the right SLF tracts and bilateral arcuate can be used to differentiate the T2DM-S and the HC at a high accuracy up to 88.45 and 87.8%, respectively. In conclusion, WM microstructure injury in T2DM may be associated with SMC, and these abnormalities identified by DTI can be used as an effective biomarker.

GNSS and Photogrammetric UAV Derived Data for Coastal Monitoring: A Case of Study in Emilia-Romagna, Italy

GNSS real-time techniques and UAV photogrammetry can be alternative methods for the monitoring of sand beaches. This activity is particularly important in environments such as the Emilia-Romagna coastline. In this paper, two couples of surveys (year 2019 and 2020) performed using GNSS or a low-cost UAV equipment over a common area were compared in order to analyse: point-wise height differences, profile shapes along defined sections, and volumes variations over time. Both surveys were aligned to the same reference benchmark through GNSS measurements. The highest discrepancies between the two surveying methods (tens of cm) were found in vegetated areas and along the shoreline, otherwise, the height differences are mainly within the 10 cm level. In terms of volumes, excluding the most critical areas, differences close to zero can be found. Obtained results show that GNSS and UAV photogrammetry provides similar results, at least for quite flat terrains and when decimetre-level accuracy is required.

Demographic, Comorbid, and Clinical Variables Associated With Pointwise Visual Field Damage in Glaucoma: Data From the AGIS and CIGTS Clinical Trials.

PurposeTo investigate differences across the visual field (VF) in the rate of glaucomatous progression, the likelihood of defect in four disease severity cross-sections, and comparisons of subgroups in each of between 12 demographic, comorbid, and clinical variables.MethodsTwo long-term glaucoma clinical trials used Humphrey Field Analyzer 24-2 VFs to calculate pointwise deviations from age-matched normal controls. Slopes of glaucomatous progression over time were calculated per participant using linear mixed models. Pointwise differences between subgroups in slopes and cross-sectional categories were tested, adjusting for multiple comparisons using false discovery rate (FDR) and Q values.ResultsPointwise data were available for 1118 patients who had 15,073 VFs. On average, defects were seen at all VF points. Of the 12 variables, six had average pointwise slopes where Subgroup 1 had significantly faster progression than Subgroup 2 at all or many of the 52 VF points: participants who were older (≥65 vs. younger), 52/52; were male, 13/52; had diabetes, 29/52; had hypertension, 46/52; had a larger cup-to-disc ratio (≥0.7), 36/52; or had larger differences in absolute mean deviation (MD) between eyes (>3 dB), 52/52. Cross-sectional patterns at MD severity of −12 to −6.1 dB showed strong midline effects for gender and other patterns for hypertension, cup-to-disc ratio, absolute difference in MD between eyes, and disc notching.ConclusionsThe approach used provides new longitudinal and cross-sectional insights into variation across the VF associated with demographic, comorbid, and clinical variables.Translational RelevanceThis exploration and characterization of variable effects in the setting of pointwise VF testing may enable clinicians to anticipate patterns of VF loss based on demographic, comorbid, and clinical associations.

Aberrant White Matter Microstructure in Depressed Patients with Suicidality.

Depression is a common psychiatric disorder affecting 264 million people globally, and the worst outcome is suicide. While regional brain alterations in depressed suicidal brain have previously been reported, knowledge about white matter (WM) microstructure is limited. Automated fiber quantification (AFQ) acquired by magnetic resonance imaging was used to calculate diffusion properties of fiber tracks to explore the structural alteration of WM associated with suicidality in depressive patients. Cross-sectional. Forty-five depressive patients without suicidality (DS- group, 60.00% females), 53 depressed patients with suicidality (DS+ group, 66.04% females), and 59 healthy controls (HC group, 67.80% females). 3.0 T; single-shot echo-planar imaging sequence. The point-wise group difference of the fiber tracts was determined by diffusion properties including fractional anisotropy, mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) of 18 specific WM tracts. Analysis of covariance (ANCOVA) and partial correlation analysis were used. A threshold of P < 0.05 was considered statistically significant. The significantly different diffusion properties were found in callosum forceps, left inferior fronto-occipital fasciculus (IFOF), right anterior thalamic radiation (ATR), and left cingulum cingulate in DS- and DS+ groups. The correlation analysis results showed that MD of right ATR was significantly positively correlated with Hamilton Depression Rating Scale (HAMD) scores (r=0.363). In addition, AD of right ATR (r=0.372), MD of callosum forceps minor (r=0.511), RD of left IFOF (r=0.429), and RD of callosum forceps minor (r=0.515) were significantly positively correlated with suicide item scores of HAMD. Our demonstration of decreased WM tract integrity including callosum forceps, IFOF, and ATR confirms the central involvement of the frontal cortex and limbic system with suicidality in depression. 3 TECHNICAL EFFICACY STAGE: 3.

The intensity-based measure approach to “Weberize” L2-based methods of signal and image approximation

We consider the problem of modifying $$L^2$$ -based approximations so that they “conform” in a better way to Weber’s model of perception: Given a greyscale background intensity $$I > 0$$ , the minimum change in intensity $$\varDelta I$$ perceived by the human visual system (HVS) is $$\varDelta I / I^a = C$$ , where $$a > 0$$ and $$C > 0$$ are constants. A “Weberized distance” between two image functions u and v should tolerate greater (lesser) differences over regions in which they assume higher (lower) intensity values in a manner consistent with the above formula. In this paper, we modify the usual integral formulas used to define $$L^2$$ distances between functions. The pointwise differences $$|u(x)-v(x)|$$ which comprise the $$L^2$$ (or $$L^p$$ ) integrands are replaced with measures of the appropriate greyscale intervals $$\nu _a ( \min \{ u(x),v(x) \}, \max \{ u(x),v(x) \} ]$$ . These measures $$\nu _a$$ are defined in terms of density functions $$\rho _a(y)$$ which decrease at rates that conform with Weber’s model of perception. The existence of such measures is proved in the paper. We also define the “best Weberized approximation” of a function in terms of these metrics and also prove the existence and uniqueness of such an approximation.

Comparison and evaluation of pair distribution functions, using a similarity measure based on cross-correlation functions.

An approach for the comparison of pair distribution functions (PDFs) has been developed using a similarity measure based on cross-correlation functions. The PDF is very sensitive to changes in the local structure, i.e. small deviations in the structure can cause large signal shifts and significant discrepancies between the PDFs. Therefore, a comparison based on pointwise differences (e.g. R values and difference curves) may lead to the assumption that the investigated PDFs as well as the corresponding structural models are not in agreement at all, whereas a careful visual inspection of the investigated structural models and corresponding PDFs may reveal a relatively good match. To quantify the agreement of different PDFs for those cases an alternative approach is introduced: the similarity measure based on cross-correlation functions. In this paper, the power of this application of the similarity measure to the analysis of PDFs is highlighted. The similarity measure is compared with the classical R wp values as representative of the comparison based on pointwise differences as well as with the Pearson product-moment correlation coefficient, using polymorph IV of barbituric acid as an example.

Coupled simulation of flow-induced viscous and elastic anisotropy of short-fiber reinforced composites

The present work discusses the impact of the back coupling of the fiber orientation distribution on the base flow and on the fiber orientation itself during mold filling simulations. Flows through a channel and over a backward-facing step are investigated. Different closure approximations are considered for modeling the flow-induced evolution of anisotropy. The results corresponding to the decoupled approach, in which the effect of fibers on local fluid properties is neglected, build the basis of comparison. The modeling is limited to a laminar, incompressible, and isothermal flow of a fiber suspension consisting of rigid short fibers suspended in an isotropic Newtonian matrix fluid. A linear, anisotropic constitutive law is used in combination with a uniform fiber volume fraction of 10% and an aspect ratio of 10. To evaluate the impact of back coupling and of different closure methods in view of the manufactured solid composite the resulting anisotropic elastic properties are investigated based on the Mori–Tanaka method combined with an orientation average scheme. Relative to the range [0, 1] the pointwise difference in fiber orientation between the decoupled and the coupled approach is found to be pm 5{%} in the channel and pm 30{%} in the backward-facing step, respectively. The viscosity and the elasticity tensor show both significant flow-induced anisotropies as well as a strong dependence on closure and coupling.

Comparative analysis of continuum angiogenesis models

Although discrete approaches are increasingly employed to model biological phenomena, it remains unclear how complex, population-level behaviours in such frameworks arise from the rules used to represent interactions between individuals. Discrete-to-continuum approaches, which are used to derive systems of coarse-grained equations describing the mean-field dynamics of a microscopic model, can provide insight into such emergent behaviour. Coarse-grained models often contain nonlinear terms that depend on the microscopic rules of the discrete framework, however, and such nonlinearities can make a model difficult to mathematically analyse. By contrast, models developed using phenomenological approaches are typically easier to investigate but have a more obscure connection to the underlying microscopic system. To our knowledge, there has been little work done to compare solutions of phenomenological and coarse-grained models. Here we address this problem in the context of angiogenesis (the creation of new blood vessels from existing vasculature). We compare asymptotic solutions of a classical, phenomenological “snail-trail” model for angiogenesis to solutions of a nonlinear system of partial differential equations (PDEs) derived via a systematic coarse-graining procedure (Pillay et al. in Phys Rev E 95(1):012410, 2017. https://doi.org/10.1103/PhysRevE.95.012410). For distinguished parameter regimes corresponding to chemotaxis-dominated cell movement and low branching rates, both continuum models reduce at leading order to identical PDEs within the domain interior. Numerical and analytical results confirm that pointwise differences between solutions to the two continuum models are small if these conditions hold, and demonstrate how perturbation methods can be used to determine when a phenomenological model provides a good approximation to a more detailed coarse-grained system for the same biological process.

Poisson approximation with applications to stochastic geometry

This article compares the distributions of integer-valued random variables and Poisson random variables. It considers the total variation and the Wasserstein distance and provides, in particular, explicit bounds on the pointwise difference between the cumulative distribution functions. Special attention is dedicated to estimating the difference when the cumulative distribution functions are evaluated at 0. This permits to approximate the minimum (or maximum) of a collection of random variables by a suitable random variable in the Kolmogorov distance. The main theoretical results are obtained by combining the Chen-Stein method with size-bias coupling and a generalization of size-bias coupling for integer-valued random variables developed herein. A wide variety of applications are then discussed with a focus on stochastic geometry. In particular, transforms of the minimal circumscribed radius and the maximal inradius of Poisson-Voronoi tessellations as well as the minimal inter-point distance of the points of a Poisson process are considered and bounds for their Kolmogorov distances to extreme value distributions are derived.

Characterizing white matter connectivity in Alzheimer's disease and mild cognitive impairment: An automated fiber quantification analysis with two independent datasets

Alzheimer's disease (AD) is a chronic neurodegenerative disease characterized by progressive dementia. Diffusion tensor imaging (DTI) has been widely used to show structural integrity and delineate white matter (WM) degeneration in AD. The automated fiber quantification (AFQ) method is a fully automated approach that can rapidly and reliably identify major WM fiber tracts and evaluate WM properties. The main aim of this study was to assess WM integrity and abnormities in a cohort of patients with amnestic mild cognitive impairment (aMCI) and AD as well as normal controls (NCs). For this purpose, we first used AFQ to identify 20 major WM tracts and assessed WM integrity and abnormalities in a cohort of 120 subjects (39 NCs, 34 aMCI patients and 47 AD patients) in a discovery dataset and 122 subjects (43 NCs, 37 aMCI patients and 42 AD patients) in a replicated dataset. Pointwise differences along WM tracts were identified in the discovery dataset and simultaneously confirmed in the replicated dataset. Next, we investigated the utility of DTI measures along WM tracts as features to distinguish patients with AD from NCs via multilevel cross validation using a support vector machine. Correlation analysis revealed the identified microstructural WM alterations and classification output to be highly associated with cognitive ability in the patient groups, suggesting that they may be a robust biomarker of AD. This systematic study provides a pipeline to examine WM integrity and its potential clinical application in AD and may be useful for studying other neurological and psychiatric disorders.

Round robin comparison study on the form measurement of optical freeform surfaces

A round robin comparison of freeform form measurements was carried out by the project partners and stakeholders of a European metrology research project. Altogether six measuring instruments were considered: five different (pointwise and areal) optical devices and one tactile device. Three optical freeform surfaces were used for the comparison measurements, where two specimens were measured by five instruments and one specimen by four instruments. In this paper, the evaluation methods and results of this round robin are presented for the three freeform surfaces made from a temperature-stable material, Super Invar ®. The freeforms had diameters of 40 mm, 50 mm and 100 mm and best-fit radii of 39.75 mm (convex), 40.9 mm (convex) and 423.5 mm (concave). For comparison, the bilateral pointwise differences between the available measurements were calculated. The root-mean-square values of these differences ranged from 15 nm to 110 nm (neglecting spherical contributions) and provided an insight into the status of typical freeform measurement capabilities for optical surfaces.

Characterizing White Matter Connectivity in Alzheimer's Disease and Mild Cognitive Impairment: Automated Fiber Quantification Analysis with Two Independent Datasets

Alzheimer's disease (AD) is a chronic neurodegenerative disease characterized by progressive dementia. Diffusion tensor imaging (DTI) has been widely used to show structural integrity and delineate white matter (WM) degeneration in AD. The automated fiber quantification (AFQ) method is a fully automated method that can rapidly and reliably identify major WM fiber tracts and evaluate white matter properties. The main aim of this study was to assess WM integrity and abnormities in a cohort of patients with amnestic mild cognitive impairment (aMCI) and AD as well as normal controls (NCs). For this purpose, we first used AFQ to identify 20 major WM tracts and assessed WM integrity and abnormities in a cohort of patients with 120 participants (39 NCs, 34 aMCI and 47 AD) in the discovery dataset, as well as 122 participants (43 NCs, 37aMCI and 42 AD) in the replicated dataset. Pointwise differences along the WM tracts were identified in the discovery dataset and simultaneously confirmed by the replicated dataset. Next, we investigated the utility of DTI measures along WM tracts as features to classify patients with AD from NCs with multilevel cross validations using a support vector machine. The correlation analysis revealed that the identified impaired microstructural WM alterations and classification output were highly associated with cognitive ability in the patient groups, indicating that this credible, robust potential early biomarker may be useful for clinical application in AD. This systematic study provides a pipeline to examine WM integrity and its potential clinical application in AD and may be useful in studying other neurological and psychiatric disorders. Funding: This work was partially supported by the National Key Research and Development Program of China (No. 2016YFC1305904), the National Natural Science Foundation of China (Grant Nos. 81871438, 81571062, 81471120), and the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDB32020200). Declaration of Interest: The authors declare no conflicts of interest. Ethical Approval: This study was approved by the Medical Ethics Committee of the Chinese PLA General Hospital.

An atomistic fingerprint algorithm for learning ab initio molecular force fields.

Molecular fingerprints, i.e., feature vectors describing atomistic neighborhood configurations, is an important abstraction and a key ingredient for data-driven modeling of potential energy surface and interatomic force. In this paper, we present the density-encoded canonically aligned fingerprint algorithm, which is robust and efficient, for fitting per-atom scalar and vector quantities. The fingerprint is essentially a continuous density field formed through the superimposition of smoothing kernels centered on the atoms. Rotational invariance of the fingerprint is achieved by aligning, for each fingerprint instance, the neighboring atoms onto a local canonical coordinate frame computed from a kernel minisum optimization procedure. We show that this approach is superior over principal components analysis-based methods especially when the atomistic neighborhood is sparse and/or contains symmetry. We propose that the "distance" between the density fields be measured using a volume integral of their pointwise difference. This can be efficiently computed using optimal quadrature rules, which only require discrete sampling at a small number of grid points. We also experiment on the choice of weight functions for constructing the density fields and characterize their performance for fitting interatomic potentials. The applicability of the fingerprint is demonstrated through a set of benchmark problems.

Effects of Time-Dependent Inflow Perturbations on Turbulent Flow in a Street Canyon

Urban flow and turbulence are driven by atmospheric flows with larger horizontal scales. Since building-resolving computational fluid dynamics models typically employ steady Dirichlet boundary conditions or forcing, the accuracy of numerical simulations may be limited by the neglect of perturbations. We investigate the sensitivity of flow within a unit-aspect-ratio street canyon to time-dependent perturbations near the inflow boundary. Using large-eddy simulation, time-periodic perturbations to the streamwise velocity component are incorporated via the nudging technique. Spatial averages of pointwise differences between unperturbed and perturbed velocity fields (i.e., the error kinetic energy) show a clear dependence on the perturbation period, though spatial structures are largely insensitive to the time-dependent forcing. The response of the error kinetic energy is maximized for perturbation periods comparable to the time scale of the mean canyon circulation. Frequency spectra indicate that this behaviour arises from a resonance between the inflow forcing and the mean motion around closed streamlines. The robustness of the results is confirmed using perturbations derived from measurements of roof-level wind speed.