Representation For Use Research Articles

Multifaceted modeling of small intestinal neuroendocrine tumors

Small intestinal neuroendocrine tumors, siNETs, are a group of rare cancers that arise from neuroendocrine cells in the lining of the jejunum and ileum, which are either classified as tumors, siNETs, or carcinomas, siNECs. Current treatment strategies for low grade tumors include surgical resection, peptide radionucleotide receptor therapy, and somatostatin analogues, while high grade and recurrent tumors may receive cytotoxic chemotherapy. These limited treatment options are linked to the lack of representative models that can both reflect the biology of the tumor and are amenable to mid-to-high throughput experimentation. Cell line generation is challenging considering the indolent nature of primary lesions, although some attempts have been successful using a variety of methods and include the primary P-STS line and those derived from metastatic lesions including GOT1, CNDT2.5 and HC45. Patient-derived modeling, including organoids and xenografting, have allowed for multicellular and three-dimensional representations of the original tumor. These specific models allow for multicellular populations derived from the tumor, providing better tumor representation for use in drug screening and in vitro assays. Currently, there are limited, although increasing, published models of siNETs implanted as xenografts in mice and zebrafish. As these cellular and animal models provide insights on siNET biology, theragnostic modeling has provided key information on the clinical progression and treatment of this disease. Significant strides toward more representative models have been made throughout the last decade. In this review, details of these attempts as well as future directions and strategies for more robust models will be addressed.

Seeing the unseen: adapting Landscape and Visual Impact Assessment (LVIA) to examine Neolithic stone circles in the English Lake District

This article presents an in-depth exploration of the application of 3D techniques frequently used in Landscape and Visual Impact Assessment, with a focus on the adaptation of Accurate Visual Representation for use in landscape archaeology. Specifically, this paper explores the visual environment surrounding mid-to-late Neolithic enclosures in the English county of Cumbria.This study explores how 3D modelling, photography, and GIS analyses can enhance our understanding of archaeological landscapes. It focuses on the relationship between stone circles and their visual context, using the concept of ‘visual magnitude’ as a phenomenological lens. The results indicate that the mid-to-late Neolithic Cumbrian stone circles (circa. 3200 – 2500 BC) exhibit similarities to contemporary ‘henge enclosures’ across England.

Comparison and evaluation of resolved resonance region covariance representations

ABSTRACT The covariance matrices for the evaluated resolved resonance region parameters are typically processed into a grouped covariance representation for use in uncertainty propagation studies. An alternative method for performing uncertainty propagation would be to use the resonance parameters covariance matrix to sample many realizations of resonance parameters, and then perform a simulation with each sample, employing the total Monte Carlo method. In this paper, we demonstrate that these two techniques can yield drastically different estimations of the uncertainty on k eff . Furthermore, we provide reasoning as to why the grouped covariance approximation is a nonphysical approximation for the resolved resonance region, and how sampling resonance parameters is able to capture variations in the structure of the point-wise cross sections. Our recommendation is that the community move away from the grouped covariance approximation and towards a continuous energy representation, which could be generated by sampling resonance parameters, and ensure that the resonance parameter covariance matrices in evaluated libraries yield realistic grouped cross section uncertainties, regardless of group structure.

ACORN SDOH survey: Terminological representation for use with NLP and CDS.

Social Determinants of Health (SDOH) greatly influence health outcomes. SDOH surveys, such as the Assessing Circumstances & Offering Resources for Needs (ACORN) survey, have been developed to screen for SDOH in Veterans. The purpose of this study is to determine the terminological representation of the ACORN survey, to aid in natural language processing (NLP). Each ACORN survey question was read to determine its concepts. Next, Solor was searched for each of the concepts and for the appropriate attributes. If no attributes or concepts existed, they were proposed. Then, each question's concepts and attributes were arranged into subject-relation-object triples. Eleven unique attributes and 18 unique concepts were proposed. These results demonstrate a gap in representing SDOH with terminologies. We believe that using these new concepts and relations will improve NLP, and thus, the care provided to Veterans.

Author multidisciplinarity and disciplinary roles in field of study networks

When studying large research corpora, “distant reading” methods are vital to understand the topics and trends in the corresponding research space. In particular, given the recognised benefits of multidisciplinary research, it may be important to map schools or communities of diverse research topics, and to understand the multidisciplinary role that topics play within and between these communities. This work proposes Field of Study (FoS) networks as a novel network representation for use in scientometric analysis. We describe the formation of FoS networks, which relate research topics according to the authors who publish in them, from corpora of articles in which fields of study can be identified. FoS networks are particularly useful for the distant reading of large datasets of research papers when analysed through the lens of exploring multidisciplinary science. In an evolving scientific landscape, modular communities in FoS networks offer an alternative categorisation strategy for research topics and sub-disciplines, when compared to traditional prescribed discipline classification schemes. Furthermore, structural role analysis of FoS networks can highlight important characteristics of topics in such communities. To support this, we present two case studies which explore multidisciplinary research in corpora of varying size and scope; namely, 6323 articles relating to network science research and 4,184,011 articles relating to research on the COVID-19-pandemic.

ASE

The incredible feats of athleticism demonstrated by humans are made possible in part by a vast repertoire of general-purpose motor skills, acquired through years of practice and experience. These skills not only enable humans to perform complex tasks, but also provide powerful priors for guiding their behaviors when learning new tasks. This is in stark contrast to what is common practice in physics-based character animation, where control policies are most typically trained from scratch for each task. In this work, we present a large-scale data-driven framework for learning versatile and reusable skill embeddings for physically simulated characters. Our approach combines techniques from adversarial imitation learning and unsupervised reinforcement learning to develop skill embeddings that produce life-like behaviors, while also providing an easy to control representation for use on new downstream tasks. Our models can be trained using large datasets of unstructured motion clips, without requiring any task-specific annotation or segmentation of the motion data. By leveraging a massively parallel GPU-based simulator, we are able to train skill embeddings using over a decade of simulated experiences, enabling our model to learn a rich and versatile repertoire of skills. We show that a single pre-trained model can be effectively applied to perform a diverse set of new tasks. Our system also allows users to specify tasks through simple reward functions, and the skill embedding then enables the character to automatically synthesize complex and naturalistic strategies in order to achieve the task objectives.

Optimal Control Strategies for Systems with Input Delay using the PIE Framework

The Partial Integral Equation (PIE) framework provides a unified algebraic representation for use in analysis, control, and estimation of infinite-dimensional systems. However, the presence of input delays results in a PIE representation with dependence on the derivative of the control input, u˙. This dependence complicates the problem of optimal state-feedback control for systems with input delay – resulting in a bilinear optimization problem. In this paper, we present two strategies for convexification of the H∞-optimal state-feedback control problem for systems with input delay. In the first strategy, we use a generalization of Young's inequality to formulate a convex optimization problem, albeit with some conservatism. In the second strategy, we filter the actuator signal – introducing additional dynamics, but resulting in a convex optimization problem without conservatism. We compare these two optimal control strategies on four example problems, solving the optimization problem using the latest release of the PIETOOLS software package for analysis, control and simulation of PIEs.

Automatic Melody Harmonization via Reinforcement Learning by Exploring Structured Representations for Melody Sequences

We present a novel reinforcement learning architecture that learns a structured representation for use in symbolic melody harmonization. Probabilistic models are predominant in melody harmonization tasks, most of which only treat melody notes as independent observations and do not take note of substructures in the melodic sequence. To fill this gap, we add substructure discovery as a crucial step in automatic chord generation. The proposed method consists of a structured representation module that generates hierarchical structures for the symbolic melodies, a policy module that learns to break a melody into segments (whose boundaries concur with chord changes) and phrases (the subunits in segments) and a harmonization module that generates chord sequences for each segment. We formulate the structure discovery process as a sequential decision problem with a policy gradient RL method selecting the boundary of each segment or phrase to obtain an optimized structure. We conduct experiments on our preprocessed HookTheory Lead Sheet Dataset, which has 17,979 melody/chord pairs. The results demonstrate that our proposed method can learn task-specific representations and, thus, yield competitive results compared with state-of-the-art baselines.

Machine learning transition temperatures from 2D structure

A priori knowledge of physicochemical properties such as melting and boiling could expedite materials discovery. However, theoretical modeling from first principles poses a challenge for efficient virtual screening of potential candidates. As an alternative, the tools of data science are becoming increasingly important for exploring chemical datasets and predicting material properties. Herein, we extend a molecular representation, or set of descriptors, first developed for quantitative structure-property relationship modeling by Yalkowsky and coworkers known as the Unified Physicochemical Property Estimation Relationships (UPPER). This molecular representation has group-constitutive and geometrical descriptors that map to enthalpy and entropy; two thermodynamic quantities that drive thermal phase transitions. We extend the UPPER representation to include additional information about sp2-bonded fragments. Additionally, instead of using the UPPER descriptors in a series of thermodynamically-inspired calculations, as per Yalkowsky, we use the descriptors to construct a vector representation for use with machine learning techniques. The concise and easy-to-compute representation, combined with a gradient-boosting decision tree model, provides an appealing framework for predicting experimental transition temperatures in a diverse chemical space. An application to energetic materials shows that the method is predictive, despite a relatively modest energetics reference dataset. We also report competitive results on diverse public datasets of melting points (i.e., OCHEM, Enamine, Bradley, and Bergström) comprised of over 47k structures. Open source software is available at https://github.com/USArmyResearchLab/ARL-UPPER.

Learning a representation of tongue dynamics from unlabeled ultrasound videos

Ultrasound imaging of the tongue has been used for decades in studies of speech production and speech motor control, for silent speech interfaces, and in numerous other areas. Despite substantial efforts, however, extraction of reliable features from ultrasound tongue data remains a challenge due to speckle noise and acoustic propagation issues. Recently, Representation Learning has emerged in a variety of fields as a powerful means of generating useful representations of underlying structure in raw, high-dimensional data. In its unsupervised form, Representation Learning discovers structures in unlabelled data, thereby eliminating the need for a time-consuming labelling step. The present work is believed to be the first use of unsupervised Representation Learning to reveal structures related to tongue dynamics in unlabelled ultrasound video. A 3-D Convolutional Neural Network examining a series of unlabelled 60 Hz tongue images is found to accurately predict unseen future images even for large interframe tongue displacements. By comparing the 3DCNN prediction error to that of a simple previous-frame predictor, tongue trajectories containing transitions between regions of acoustic stability can be identified and correlated with formant trajectories in a spectrogram. Prospects for leveraging the tongue dynamic representation for use in subsequent speech processing tasks will be discussed.

A Glutamatergic Spine Model to Enable Multi-Scale Modeling of Nonlinear Calcium Dynamics.

In synapses, calcium is required for modulating synaptic transmission, plasticity, synaptogenesis, and synaptic pruning. The regulation of calcium dynamics within neurons involves cellular mechanisms such as synaptically activated channels and pumps, calcium buffers, and calcium sequestrating organelles. Many experimental studies tend to focus on only one or a small number of these mechanisms, as technical limitations make it difficult to observe all features at once. Computational modeling enables incorporation of many of these properties together, allowing for more complete and integrated studies. However, the scale of existing detailed models is often limited to synaptic and dendritic compartments as the computational burden rapidly increases when these models are integrated in cellular or network level simulations. In this article we present a computational model of calcium dynamics at the postsynaptic spine of a CA1 pyramidal neuron, as well as a methodology that enables its implementation in multi-scale, large-scale simulations. We first present a mechanistic model that includes individually validated models of various components involved in the regulation of calcium at the spine. We validated our mechanistic model by comparing simulated calcium levels to experimental data found in the literature. We performed additional simulations with the mechanistic model to determine how the simulated calcium activity varies with respect to presynaptic-postsynaptic stimulation intervals and spine distance from the soma. We then developed an input-output (IO) model that complements the mechanistic calcium model and provide a computationally efficient representation for use in larger scale modeling studies; we show the performance of the IO model compared to the mechanistic model in terms of accuracy and speed. The models presented here help achieve two objectives. First, the mechanistic model provides a comprehensive platform to describe spine calcium dynamics based on individual contributing factors. Second, the IO model is trained on the main dynamical features of the mechanistic model and enables nonlinear spine calcium modeling on the cell and network level simulation scales. Utilizing both model representations provide a multi-level perspective on calcium dynamics, originating from the molecular interactions at spines and propagating the effects to higher levels of activity involved in network behavior.

A Reduced-Order Model of a Lithium-Ion Cell Using the Absolute Nodal Coordinate Formulation Approach

The widespread use of lithium-ion batteries motivates the need for robust battery management systems (BMSs) that can both observe the internal states of a cell and update degraded physical parameters over time. Many models that are capable of accurately replicating the internal dynamics of cells during high discharge and charging cycles contain too many states to perform real-time estimation, and many models that contain a reasonable number of states for estimation do not adequately replicate the performance of cells at higher rates. In this paper, a model is proposed based on the absolute nodal coordinate formulation approach, which includes as its states concentration levels at different spatial locations within the solid electrode and electrolyte. Its behavior is demonstrated through simulation results reproducing high performance solutions and tractable state representation for use with advanced BMSs.

The ground-state potential energy curve of the radium dimer from relativistic coupled cluster calculations.

The potential energy curve for the ground-state of radium dimer (Ra2) is provided by means of atomic and molecular relativistic coupled cluster calculations. The short-range part of this curve is defined by an equilibrium bond length of 5.324 Å, a dissociation energy of 897 cm(-1), and a harmonic vibrational frequency of 20.5 cm(-1). The asymptotic behavior at large interatomic distances is characterized by the van der Waals coefficients C6 = 5.090 × 10(3), C8 = 6.978 × 10(5), and C10 = 8.786 × 10(7) atomic units. The two regions are matched in an analytical potential to provide a convenient representation for use in further calculations, for instance, to model cold collisions between radium atoms. This might become relevant in future experiments on ultracold, optically trapped, radioactive radium atoms that are used to search for a permanent electric dipole moment.

A volume-conserving representation of cell faces in corner point grids

Corner point grids is currently the standard grid representation for use in reservoir simulation. The cell faces in corner point grids are traditionally represented as bilinear surfaces where the edges between the corner points all are straight lines. This representation has the disadvantage that along faults with varying dip the cell faces on either side will not precisely match, giving overlapping cells or gaps between cells. We propose an alternative representation for the cell faces. The four vertical cell faces are still represented as bilinear surfaces, but instead of having linear edges between the cell corners along the top and bottom faces, we propose a representation of the vertical cell faces where any horizontal intersection will give a straight line, giving column faces whose shape is independent of the corner point locations of the individual grid cells. This ensures that the grid columns match up and that there are no gaps or overlapping volumes between grid cells. This representation gives a local parameterization for the whole grid column, and the top and bottom grid cell surfaces are modeled as bilinear using this parameterization. A set of local coordinates for the grid cell permits all the common grid operations like volume calculation, area calculation for cell faces, and blocking of well traces.

Representing time-dependent freezing behaviour in immersion mode ice nucleation

Abstract. In order to understand the impact of ice formation in clouds, a quantitative understanding of ice nucleation is required, along with an accurate and efficient representation for use in cloud resolving models. Ice nucleation by atmospherically relevant particle types is complicated by interparticle variability in nucleating ability, as well as a stochastic, time-dependent, nature inherent to nucleation. Here we present a new and computationally efficient Framework for Reconciling Observable Stochastic Time-dependence (FROST) in immersion mode ice nucleation. This framework is underpinned by the finding that the temperature dependence of the nucleation-rate coefficient controls the residence-time and cooling-rate dependence of freezing. It is shown that this framework can be used to reconcile experimental data obtained on different timescales with different experimental systems, and it also provides a simple way of representing the complexities of ice nucleation in cloud resolving models. The routine testing and reporting of time-dependent behaviour in future experimental studies is recommended, along with the practice of presenting normalised data sets following the methods outlined here.

Implicit treatment of solvent dispersion forces in protein simulations.

A model is proposed for the evaluation of dispersive forces in a continuum solvent representation for use in large-scale computer simulations. The model captures the short- and long-range effects of water-exclusion in conditions of partial and anisotropic hydration. The model introduces three parameters, one of which represents the degree of hydration (water occupancy) at any point in the system, which depends on the solute conformation, and two that represent the strength of water-water and water-solute dispersive interactions. The model is optimized for proteins, using hydration data of a suboptimally hydrated binding site and results from dynamics simulations in explicit water. The model is applied to a series of aliphatic-alcohol/protein complexes and a set of binary and ternary complexes of various sizes. Implications for weak and ultra-weak protein-protein association and for simulation in crowded media are discussed.

Address event representation neuromorphics to epiretinal colour vision

Presented is a methodology of conversion of a conventional colour measure, e.g. 24-bit colour, to a neuromorphic representation for use with epiretinal prostheses. The conversion implemented, for a 4 by 4 image, within a field programmable gate array outputs three groups of timing signals. These three groups correspond to red (R), green (G) and blue (B) colour channels. In future work the channels will input to a retinal implant stimulator, via a receiver chip housed over the earlobe of a prosthesis recipient, to form biphasic pulses to drive electrodes stimulating the axons of colour specific retinal ganglion cells. A test setup was used to encode the hue pulses back to intensity to display test images on a VGA monitor for validation.

The Effect of Gestalt Laws of Perceptual Organization on the Comprehension of Three-Variable Bar and Line Graphs

We report three experiments investigating the ability of undergraduate college students to comprehend 2 x 2 "interaction" graphs from two-way factorial research designs. Factorial research designs are an invaluable research tool widely used in all branches of the natural and social sciences, and the teaching of such designs lies at the core of many college curricula. Such data can be represented in bar or line graph form. Previous studies have shown, however, that people interpret these two graphical forms differently. In Experiment 1, participants were required to interpret interaction data in either bar or line graphs while thinking aloud. Verbal protocol analysis revealed that line graph users were significantly more likely to misinterpret the data or fail to interpret the graph altogether. The patterns of errors line graph users made were interpreted as arising from the operation of Gestalt principles of perceptual organization, and this interpretation was used to develop two modified versions of the line graph, which were then tested in two further experiments. One of the modifications resulted in a significant improvement in performance. Results of the three experiments support the proposed explanation and demonstrate the effects (both positive and negative) of Gestalt principles of perceptual organization on graph comprehension. We propose that our new design provides a more balanced representation of the data than the standard line graph for nonexpert users to comprehend the full range of relationships in two-way factorial research designs and may therefore be considered a more appropriate representation for use in educational and other nonexpert contexts.

Adaptive volume penalization for ocean modeling

The development of various volume penalization techniques for use in modeling topographical features in the ocean is the focus of this paper. Due to the complicated geometry inherent in ocean boundaries, the stair-step representation used in the majority of current global ocean circulation models causes accuracy and numerical stability problems. Brinkman penalization is the basis for the methods developed here and is a numerical technique used to enforce no-slip boundary conditions through the addition of a term to the governing equations. The second aspect to this proposed approach is that all governing equations are solved on a nonuniform, adaptive grid through the use of the adaptive wavelet collocation method. This method solves the governing equations on temporally and spatially varying meshes, which allows higher effective resolution to be obtained with less computational cost. When penalization methods are coupled with the adaptive wavelet collocation method, the flow near the boundary can be well-resolved. It is especially useful for simulations of boundary currents and tsunamis, where flow near the boundary is important. This paper will give a thorough analysis of these methods applied to the shallow water equations, as well as some preliminary work applying these methods to volume penalization for bathymetry representation for use in either the nonhydrostatic or hydrostatic primitive equations.

U and Th zonation in apatite observed by laser ablation ICPMS, and implications for the (U–Th)/He system

A laser-ablation inductively-coupled plasma mass spectrometry technique was developed to measure U, Th, and Ce zonation in polished sections of apatite for assessing the consequences of parent zonation for (U–Th)/He thermochronometry. The technique produces concentration maps with an averaging length-scale of ∼20 μm, comparable to the α-stopping distance, and a precision of ∼5% down to few ppm concentration levels. A model was developed to transform the measured concentration distribution into a simplified representation for use in spherical-geometry He production–diffusion models. To illustrate these methods, 30 sections of apatite from a single granite (GC863) were mapped. Every analyzed apatite from GC863 is zoned, with most grains having variable thickness rims and terminations that are enriched in U and Th by about a factor of three over the grain cores. Parent zonation has three independent effects on (U–Th)/He He ages: it influences the α ejection correction, the 4He concentration profile which governs diffusive loss, and, via radiation damage trap accumulation, spatial variability of diffusivity within the crystal. If the observed zonation is typical of the apatite population in GC863, use of the standard homogenous α ejection correction would cause He ages to be on average 3% too young, and with a large amount of grain-to-grain variability (9% too young in the most rim-enriched case to 6% too old in a core-enriched case). Independent of the ejection correction, the concentration profile modifies the effective closure temperature of the apatites by placing more (or less) 4He near the grain edge. The parent zonation in GC863 apatites causes closure temperatures to range from four degrees lower (rim-enriched case) to two degrees higher (core-enriched case) than applies in the homogenous case. Alpha ejection and concentration profile effects on He age are additive and of the same sense. In the case of typical grains in GC863 cooled between 1 and 10 °C/Ma, the two effects are roughly equal in magnitude. The effects of intracrystalline variations in radiation damage trap accumulation become apparent at slow cooling rates (1 °C/Ma). For example, in rim-enriched GC863 grains cooled at 1 °C/Ma, preferential accumulation of radiation damage traps near the grain rim almost compensates for the higher loss rate expected of 4He also located preferentially near the rim. Under some circumstances strong rim-enrichment may actually increase the effective closure temperature of an apatite. Zonation at the level observed in GC863 modifies the 4He/ 3He spectra substantially from that expected from a uniform distribution. Measured 4He/ 3He spectra are strikingly similar to predictions based on the mapped eU distributions of the very same crystals, supporting the overall validity of the analytical and interpretive approach presented here. The magnitude and style of U, Th zonation documented in GC863 is one possible source of frequently observed over-dispersion of apatite (U–Th)/He ages as well as anomalous 4He/ 3He spectra.