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In shape analysis, the interpolation of shapes' trajectories is often performed by means of geodesics in an appropriate Riemannian Shape Space. Over the past several decades, different metrics and shape spaces have been proposed, including Kendall shape space, LDDMM based approaches, and elastic contour, among others. Once a Riemannian space is chosen, geodesics and parallel transports can be used to build splines or piecewise geodesics paths. In a recent paper, we introduced a new Riemannian shape space named TPS Space based on the Thin Plate Spline interpolant and characterized by an appropriate metric and parallel transport rule. In the present paper, we further explore the geometry of the TPS Space by characterizing the properties of its geodesics. Several applications show the capability of the proposed formulation to conserve important physical properties of deformation, such as local strains and global elastic energy. [ABSTRACT FROM AUTHOR]

A framework is developed for inference concerning the covariance operator of a functional random process, where the covariance operator itself is an object of interest for statistical analysis. Distances for comparing positive-definite covariance matrices are either extended or shown to be inapplicable to functional data. In particular, an infinite-dimensional analogue of the Procrustes size-and-shape distance is developed. Convergence of finite-dimensional approximations to the infinite-dimensional distance metrics is also shown. For inference, a Fréchet estimator of both the covariance operator itself and the average covariance operator is introduced. A permutation procedure to test the equality of the covariance operators between two groups is also considered. Additionally, the use of such distances for extrapolation to make predictions is explored. As an example of the proposed methodology, the use of covariance operators has been suggested in a philological study of cross-linguistic dependence as a way to incorporate quantitative phonetic information. It is shown that distances between languages derived from phonetic covariance functions can provide insight into the relationships between the Romance languages. [ABSTRACT FROM PUBLISHER]

fMRI studies of brain activity at rest study slow (<0.1 Hz) intrinsic fluctuations in the blood-oxygenation-level-dependent (BOLD) signal that are observed in a temporal scale of several minutes. The origin of these fluctuations is not clear but has previously been associated with slow changes in rhythmic neuronal activity resulting from changes in cortical excitability or neuronal synchronization. In this work, we show that individual spontaneous BOLD events occur during rest, in addition to slow fluctuations. Individual spontaneous BOLD events were identified by deconvolving the hemodynamic impulse response function for each time point in the fMRI time series, thus requiring no information on timing or a-priori spatial information of events. The patterns of activation detected were related to the motor, visual, default-mode, and dorsal attention networks. The correspondence between spontaneous events and slow fluctuations in these networks was assessed using a sliding window, seed-correlation analysis, where seed regions were selected based on the individual spontaneous event BOLD activity maps. We showed that the correlation varied considerably over time, peaking at the time of spontaneous events in these networks. By regressing spontaneous events out of the fMRI signal, we showed that both the correlation strength and the power in spectral frequencies <0.1 Hz decreased, indicating that spontaneous activation events contribute to low-frequency fluctuations observed in resting state networks with fMRI. This work provides new insights into the origin of signals detected in fMRI studies of functional connectivity. Hum Brain Mapp, 2013. © 2012 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

In landmarks-based Shape Analysis size is measured, in most cases, with Centroid Size. Changes in shape are decomposed in affine and non affine components. Furthermore the non affine component can be in turn decomposed in a series of local deformations (partial warps). If the extent of deformation between two shapes is small, the difference between Centroid Size and m-Volume increment is barely appreciable. In medical imaging applied to soft tissues bodies can undergo very large deformations, involving large changes in size. The cardiac example, analyzed in the present paper, shows changes in m-Volume that can reach the 60%. We show here that standard Geometric Morphometrics tools (landmarks, Thin Plate Spline, and related decomposition of the deformation) can be generalized to better describe the very large deformations of biological tissues, without losing a synthetic description. In particular, the classical decomposition of the space tangent to the shape space in affine and non affine components is enriched to include also the change in size, in order to give a complete description of the tangent space to the size-and-shape space. The proposed generalization is formulated by means of a new Riemannian metric describing the change in size as change in m-Volume rather than change in Centroid Size. This leads to a redefinition of some aspects of the Kendall’s size-and-shape space without losing Kendall’s original formulation. This new formulation is discussed by means of simulated examples using 2D and 3D platonic shapes as well as a real example from clinical 3D echocardiographic data. We demonstrate that our decomposition based approaches discriminate very effectively healthy subjects from patients affected by Hypertrophic Cardiomyopathy. [ABSTRACT FROM AUTHOR]

Most functional MRI (fMRI) studies map task-driven brain activity using a block or event-related paradigm. Sparse paradigm free mapping (SPFM) can detect the onset and spatial distribution of BOLD events in the brain without prior timing information, but relating the detected events to brain function remains a challenge. In this study, we developed a decoding method for SPFM using a coordinate-based meta-analysis method of activation likelihood estimation (ALE). We defined meta-maps of statistically significant ALE values that correspond to types of events and calculated a summation overlap between the normalized meta-maps and SPFM maps. As a proof of concept, this framework was applied to relate SPFM-detected events in the sensorimotor network (SMN) to six motor functions (left/right fingers, left/right toes, swallowing, and eye blinks). We validated the framework using simultaneous electromyography (EMG)-fMRI experiments and motor tasks with short and long duration, and random interstimulus interval. The decoding scores were considerably lower for eye movements relative to other movement types tested. The average successful rate for short and long motor events were 77 ± 13% and 74 ± 16%, respectively, excluding eye movements. We found good agreement between the decoding results and EMG for most events and subjects, with a range in sensitivity between 55% and 100%, excluding eye movements. The proposed method was then used to classify the movement types of spontaneous single-trial events in the SMN during resting state, which produced an average successful rate of 22 ± 12%. Finally, this article discusses methodological implications and improvements to increase the decoding performance. Hum Brain Mapp 38:5778-5794, 2017. © 2017 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Modern shape analysis allows the fine comparison of shape changes occurring between different objects. Very often the classic machineries of generalized Procrustes analysis and principal component analysis are used in order to contrast the shape change occurring among configurations represented by homologous landmarks. However, if size and shape data are structured in different groups thus constituting different morphological trajectories, a data centering is needed if one wants to compare solely the deformation representing the trajectories. To do that, inter-individual variation must be filtered out. This maneuver is rarely applied in studies using simulated or real data. A geometrical procedure named parallel transport, that can be based on various connection types, is necessary to perform such kind of data centering. Usually, the Levi Civita connection is used for interpolation of curves in a Riemannian space. It can also be used to transport a deformation. We demonstrate that this procedure does not preserve some important characters of the deformation, even in the affine case. We propose a novel procedure called 'TPS Direct Transport' which is able to perfectly transport deformation in the affine case and to better approximate non affine deformation in comparison to existing tools. We recommend to center shape data using the methods described here when the differences in deformation rather than in shape are under study. [ABSTRACT FROM AUTHOR]

Human mesenchymal stem cells (hMSCs) are widely represented in regenerative medicine clinical strategies due to their compatibility with autologous implantation. Effective bone regeneration involves crosstalk between macrophages and hMSCs, with macrophages playing a key role in the recruitment and differentiation of hMSCs. However, engineered biomaterials able to simultaneously direct hMSC fate and modulate macrophage phenotype have not yet been identified. A novel combinatorial chemistry-topography screening platform, the ChemoTopoChip, is used here to identify materials suitable for bone regeneration by screening 1008 combinations in each experiment for human immortalized mesenchymal stem cell (hiMSCs) and human macrophage response. The osteoinduction achieved in hiMSCs cultured on the "hit" materials in basal media is comparable to that seen when cells are cultured in osteogenic media, illustrating that these materials offer a materials-induced alternative to osteo-inductive supplements in bone-regeneration. Some of these same chemistry-microtopography combinations also exhibit immunomodulatory stimuli, polarizing macrophages towards a pro-healing phenotype. Maximum control of cell response is achieved when both chemistry and topography are recruited to instruct the required cell phenotype, combining synergistically. The large combinatorial library allows us for the first time to probe the relative cell-instructive roles of microtopography and material chemistry which we find to provide similar ranges of cell modulation for both cues. Machine learning is used to generate structure-activity relationships that identify key chemical and topographical features enhancing the response of both cell types, providing a basis for a better understanding of cell response to micro topographically patterned polymers. [ABSTRACT FROM AUTHOR]

Morbidities generally show patterns of concentration that vary by space and time. Disease mapping models are useful in estimating the spatiotemporal patterns of disease risks and are therefore pivotal for effective disease surveillance, resource allocation, and the development of prevention strategies. This study considers six spatiotemporal Bayesian hierarchical models based on two spatial conditional autoregressive priors. It could serve as a guideline on the development and application of Bayesian hierarchical models to assess the emerging risk trends, risk clustering, and spatial inequality trends, with estimation of covariables' effects on the interested disease risk. The method is applied to the Florida Birth Record data between 2006 and 2015 to study two cardiovascular risk factors: preeclampsia and gestational diabetes. High-risk clusters were detected in North Central Florida for preeclampsia and in Central Florida for gestational diabetes. While the adjusted disease trend was stable, spatial inequality peaked in 2011-2012 for both diseases. Exposure to PM2.5 at first or/and second trimester increased the risk of preeclampsia and gestational diabetes, but the magnitude is less severe compared to previous studies. In conclusion, this study underscores the significance of selecting appropriate disease mapping models in estimating the intricate spatiotemporal patterns of disease risk and suggests the importance of localized interventions to reduce health disparities. The result also identified an opportunity to study potential risk factors of preeclampsia, as the spike of risk in North Central Florida cannot be explained by current covariables., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Sporadic Creutzfeldt-Jakob disease (sCJD) is the most commonly diagnosed human prion disease caused by the abnormal misfolding of the 'cellular' prion protein (PrP C ) into the transmissible 'scrapie-type' prion form (PrP Sc ). Neuropathologic evaluation of brains with sCJD reveals abnormal PrP Sc deposits primarily in grey matter structures, often associated with micro-vacuolar spongiform changes in neuropil, neuronal loss, and gliosis. Abnormal PrP Sc deposits have also been reported in the retina of patients with sCJD, but few studies have characterized the morphology of these retinal PrP Sc deposits or evaluated for any retinal neurodegenerative changes. We performed histopathologic and morphometric analyses of retinal and brain prion deposits in 14 patients with sCJD. Interestingly, we discovered that the morphology of retinal PrP Sc deposits generally differs from that of brain PrP Sc deposits in terms of size and shape. We found that retinal PrP Sc deposits consistently localize to the outer plexiform layer of the retina. Additionally, we observed that the retinal PrP Sc deposits are not associated with the spongiform change, neuronal loss, and gliosis often seen in the brain. The stereotypic morphology and location of PrP Sc deposits in sCJD retinas may help guide the use of ocular imaging devices in the detection of these deposits for a clinical diagnosis., Competing Interests: Declaration of competing interest None., (Published by Elsevier Ltd.)

Introduction: Uveal melanoma (UM) is the most common primary intraocular malignancy in adults, and despite treatment of the primary tumor, approximately 15%-50% of patients will develop metastatic disease. Based on gene expression profiling (GEPs), UM can be categorized as Class 1A (low metastatic risk), Class 1B (intermediate metastatic risk), or Class 2 (high metastatic risk). PReferentially expressed Antigen in MElanoma (PRAME) status is an independent prognostic UM biomarker and a potential target for immunotherapy in metastatic UM. PRAME expression status can be detected in tumors using reverse-transcription polymerase chain reaction (RT-PCR). More recently, immunohistochemistry (IHC) has been developed to detect PRAME protein expression. Here, we employed both techniques to evaluate PRAME expression in 18 UM enucleations., Methods: Tumor material from the 18 UM patients who underwent enucleation was collected by fine-needle aspiration before or during enucleation and sent for GEP and PRAME analysis by RT-PCR. Histologic sections from these patients were stained with an anti-PRAME monoclonal antibody. We collected patient demographics and tumor characteristics and included this with our analysis of GEP class, PRAME status by RT-PCR, and PRAME status by IHC. PRAME IHC and RT-PCR results were compared., Results: Twelve males (12/18) and 6 females (6/18) with an average age of 60.6 years underwent enucleation for UM. TNM staging of the UM diagnosed Stage I in 2 patients (2/18), Stage II in 7 patients (7/18), Stage III in 8 patients (8/18), and Stage IV in 1 (1/18). GEP was Class 1A in 6 tumors (6/18), Class 1B in 6 tumors (6/18), and Class 2 in 6 tumors (6/18). PRAME IHC showed diffusely positive labeling of all UM cells in 2/18 enucleations; negative IHC labeling of UM cells in 9/18 enucleations; and IHC labeling of subsets of UM cells in 7/18 enucleations. Eleven of the 17 UMs tested for PRAME by both RT-PCR and IHC had consistent PRAME results. In the remaining 6/17 cases tested by both modalities, PRAME results were discordant between RT-PCR and IHC., Conclusions: We find that PRAME IHC distinguishes PRAME-positive and PRAME-negative UM tumor cells. Interestingly, IHC reveals focal PRAME expression in subsets of tumor cells consistent with tumor heterogeneity. PRAME RT-PCR and IHC provide concordant results in most of our cases. We suggest that discordance in PRAME results could arise from spatial or temporal variation in PRAME expression between tumor cells. Further studies are required to determine the prognostic implications of PRAME IHC in UM., Competing Interests: All authors: no reported conflicts., (Copyright © 2022 by S. Karger AG, Basel.)

We describe a general approach to transforming molecular models between different levels of resolution, based on machine learning methods. The approach uses a matched set of models at both levels of resolution for training, but requires only the coordinates of their particles and no side information (e.g., templates for substructures, defined mappings, or molecular mechanics force fields). Once trained, the approach can transform further molecular models of the system between the two levels of resolution in either direction with equal facility.

Current treatments for chronic pain rely largely on opioids despite their substantial side effects and risk of addiction. Genetic studies have identified in humans key targets pivotal to nociceptive processing. In particular, a hereditary loss-of-function mutation in Na V 1.7, a sodium channel protein associated with signaling in nociceptive sensory afferents, leads to insensitivity to pain without other neurodevelopmental alterations. However, the high sequence and structural similarity between Na V subtypes has frustrated efforts to develop selective inhibitors. Here, we investigated targeted epigenetic repression of Na V 1.7 in primary afferents via epigenome engineering approaches based on clustered regularly interspaced short palindromic repeats (CRISPR)-dCas9 and zinc finger proteins at the spinal level as a potential treatment for chronic pain. Toward this end, we first optimized the efficiency of Na V 1.7 repression in vitro in Neuro2A cells and then, by the lumbar intrathecal route, delivered both epigenome engineering platforms via adeno-associated viruses (AAVs) to assess their effects in three mouse models of pain: carrageenan-induced inflammatory pain, paclitaxel-induced neuropathic pain, and BzATP-induced pain. Our results show effective repression of Na V 1.7 in lumbar dorsal root ganglia, reduced thermal hyperalgesia in the inflammatory state, decreased tactile allodynia in the neuropathic state, and no changes in normal motor function in mice. We anticipate that this long-lasting analgesia via targeted in vivo epigenetic repression of Na V 1.7 methodology we dub pain LATER, might have therapeutic potential in management of persistent pain states., (Copyright © 2021 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.)

Solid dispersions can be a successful way to enhance the bioavailability of poorly soluble drugs. Here 60 solid dispersion formulations were produced using ten chemically diverse, neutral, poorly soluble drugs, three commonly used polymers, and two manufacturing techniques, spray-drying and melt extrusion. Each formulation underwent a six-month stability study at accelerated conditions, 40 °C and 75% relative humidity (RH). Significant differences in times to crystallization (onset of crystallization) were observed between both the different polymers and the two processing methods. Stability from zero days to over one year was observed. The extensive experimental data set obtained from this stability study was used to build multiple linear regression models to correlate physicochemical properties of the active pharmaceutical ingredients (API) with the stability data. The purpose of these models is to indicate which combination of processing method and polymer carrier is most likely to give a stable solid dispersion. Six quantitative mathematical multiple linear regression-based models were produced based on selection of the most influential independent physical and chemical parameters from a set of 33 possible factors, one model for each combination of polymer and processing method, with good predictability of stability. Three general rules are proposed from these models for the formulation development of suitably stable solid dispersions. Namely, increased stability is correlated with increased glass transition temperature ( T g ) of solid dispersions, as well as decreased number of H-bond donors and increased molecular flexibility (such as rotatable bonds and ring count) of the drug molecule.

Root architecture impacts water and nutrient uptake efficiency. Identifying exactly which root architectural properties influence these agronomic traits can prove challenging. In this paper, approximately 300 wheat (Triticum aestivum) plants were divided into four groups using two binary classifications, high versus low nitrogen uptake efficiency (NUpE), and high versus low nitrate in the growth medium. The root system architecture for each wheat plant was captured using 16 quantitative variables. The multivariate analysis tool, linear discriminant analysis, was used to construct composite variables, each a linear combination of the original variables, such that the score of the plants on the new variables showed the maximum between-group variability. The results show that the distribution of root-system architecture traits differs between low- and high-NUpE plants and, less strongly, between low-NUpE plants grown on low versus high nitrate media., (© The Author 2017. Published by Oxford University Press on behalf of the Society for Experimental Biology.)

We consider the problem of comparing sizes and shapes of objects when landmark data are prone to measurement error. We show that naive implementation of ordinary Procrustes analysis that ignores measurement error can compromise inference. To account for measurement error, we propose the conditional score method for matching configurations, which guarantees consistent inference under mild model assumptions. The effects of measurement error on inference from naive Procrustes analysis and the performance of the proposed method are illustrated via simulation and application in three real data examples. Supplementary materials for this article are available online.

A general framework for a novel non-geodesic decomposition of high-dimensional spheres or high-dimensional shape spaces for planar landmarks is discussed. The decomposition, principal nested spheres, leads to a sequence of submanifolds with decreasing intrinsic dimensions, which can be interpreted as an analogue of principal component analysis. In a number of real datasets, an apparent one-dimensional mode of variation curving through more than one geodesic component is captured in the one-dimensional component of principal nested spheres. While analysis of principal nested spheres provides an intuitive and flexible decomposition of the high-dimensional sphere, an interesting special case of the analysis results in finding principal geodesics, similar to those from previous approaches to manifold principal component analysis. An adaptation of our method to Kendall's shape space is discussed, and a computational algorithm for fitting principal nested spheres is proposed. The result provides a coordinate system to visualize the data structure and an intuitive summary of principal modes of variation, as exemplified by several datasets.

The placenta is an essential component of the life-support system for the developing foetus, enabling nutrients and waste to be exchanged between the foetal and maternal circulations. Maternal blood flows between the densely packed branches of villous trees, within which are foetal vessels. Here, we explore some of the challenges in modelling maternal haemodynamic transport using homogenization approaches. We first show how two measures can be used to estimate the minimum distance over which the distribution of villous branches appears statistically homogeneous. We then analyse a simplified model problem (solute transport by a unidirectional flow past a distribution of point sinks) to assess the accuracy of homogenization approximations as a function of governing parameters (Péclet and Damköhler numbers) and the statistical properties of the sink distribution. The difference between the leading-order homogenization approximation and the exact solute distribution is characterized by large spatial gradients at the scale of individual villi and substantial fluctuations that can be correlated over lengthscales comparable to the whole domain. This study highlights the importance of quantifying errors owing to spatial disorder in multi-scale approximations of physiological systems.

We consider Bayesian methodology for comparing two or more unlabeled point sets. Application of the technique to a set of steroid molecules illustrates its potential utility involving the comparison of molecules in chemoinformatics and bioinformatics. We initially match a pair of molecules, where one molecule is regarded as random and the other fixed. A type of mixture model is proposed for the point set coordinates, and the parameters of the distribution are a labeling matrix (indicating which pairs of points match) and a concentration parameter. An important property of the likelihood is that it is invariant under rotations and translations of the data. Bayesian inference for the parameters is carried out using Markov chain Monte Carlo simulation, and it is demonstrated that the procedure works well on the steroid data. The posterior distribution is difficult to simulate from, due to multiple local modes, and we also use additional data (partial charges on atoms) to help with this task. An approximation is considered for speeding up the simulation algorithm, and the approximating fast algorithm leads to essentially identical inference to that under the exact method for our data. Extensions to multiple molecule alignment are also introduced, and an algorithm is described which also works well on the steroid data set. After all the steroid molecules have been matched, exploratory data analysis is carried out to examine which molecules are similar. Also, further Bayesian inference for the multiple alignment problem is considered.