cssi/SciSciGPT-SciSciNet huggingface dataset:
Using body temperature and variables commonly available in the EHR to predict acute infection: a proof-of-concept study showing improved pretest probability estimates for acute COVID-19 infection among discharged emergency department patients.

Objectives Obtaining body temperature is a quick and easy method to screen for acute infection such as COVID-19. Currently, the predictive value of body temperature for acute infection is inhibited by failure to account for other readily available variables that affect temperature values. In this proof-of-concept study, we sought to improve COVID-19 pretest probability estimation by incorporating covariates known to be associated with body temperature, including patient age, sex, comorbidities, month, and time of day. Methods For patients discharged from an academic hospital emergency department after testing for COVID-19 in March and April of 2020, we abstracted clinical data. We reviewed physician documentation to retrospectively generate estimates of pretest probability for COVID-19. Using patients' COVID-19 PCR test results as a gold standard, we compared AUCs of logistic regression models predicting COVID-19 positivity that used: (1) body temperature alone; (2) body temperature and pretest probability; (3) body temperature, pretest probability, and body temperature-relevant covariates. Calibration plots and bootstrap validation were used to assess predictive performance for model #3. Results Data from 117 patients were included. The models' AUCs were: (1) 0.69 (2) 0.72, and (3) 0.76, respectively. The absolute difference in AUC was 0.029 (95% CI -0.057 to 0.114, p=0.25) between model 2 and 1 and 0.038 (95% CI -0.021 to 0.097, p=0.10) between model 3 and 2. Conclusions By incorporating covariates known to affect body temperature, we demonstrated improved pretest probability estimates of acute COVID-19 infection. Future work should be undertaken to further develop and validate our model in a larger, multi-institutional sample.
Wall-crossing in genus-zero hybrid theory

The hybrid model is the Landau-Ginszburg-type theory that is expected, via the Landau-Ginzburg/Calabi-Yau correspondence, to match the Gromov-Witten theory of a complete intersection in weighted projective space. We prove a wall-crossing formula exhibiting the dependence of the genus-zero hybrid model on its stability parameter, generalizing the work of the second author and Ruan for quantum singularity theory and paralleling the work of Ciocan-Fontanine--Kim for quasimaps. This completes the proof of the genus-zero Landau-Ginzburg/Calabi-Yau correspondence for complete intersections of hypersurfaces of the same degree, as well as the proof of the all-genus hybrid wall-crossing theorem, which is work of the first author, Janda, and Ruan.
RMA: Rapid Motor Adaptation for Legged Robots

Successful real-world deployment of legged robots would require them to adapt in real-time to unseen scenarios like changing terrains, changing payloads, wear and tear. This paper presents Rapid Motor Adaptation (RMA) algorithm to solve this problem of real-time online adaptation in quadruped robots. RMA consists of two components: a base policy and an adaptation module. The combination of these components enables the robot to adapt to novel situations in fractions of a second. RMA is trained completely in simulation without using any domain knowledge like reference trajectories or predefined foot trajectory generators and is deployed on the A1 robot without any fine-tuning. We train RMA on a varied terrain generator using bioenergetics-inspired rewards and deploy it on a variety of difficult terrains including rocky, slippery, deformable surfaces in environments with grass, long vegetation, concrete, pebbles, stairs, sand, etc. RMA shows state-of-the-art performance across diverse real-world as well as simulation experiments. Project Webpage and Videos: https://ashish-kmr.github.io/rma-legged-robots/
Rhizosphere Microbes Influence Host Circadian Clock Function

The circadian clock is an important determinant of fitness that is entrained by local conditions. Aside from abiotic factors, individual pathogenic soil bacteria affect circadian clock function in ...
Flat band carrier confinement in magic-angle twisted bilayer graphene

Magic-angle twisted bilayer graphene has emerged as a powerful platform for studying strongly correlated electron physics, owing to its almost dispersionless low-energy bands and the ability to tune the band filling by electrostatic gating. Techniques to control the twist angle between graphene layers have led to rapid experimental progress but improving sample quality is essential for separating the delicate correlated electron physics from disorder effects. Owing to the 2D nature of the system and the relatively low carrier density, the samples are highly susceptible to small doping inhomogeneity which can drastically modify the local potential landscape. This potential disorder is distinct from the twist angle variation which has been studied elsewhere. Here, by using low temperature scanning tunneling spectroscopy and planar tunneling junction measurements, we demonstrate that flat bands in twisted bilayer graphene can amplify small doping inhomogeneity that surprisingly leads to carrier confinement, which in graphene could previously only be realized in the presence of a strong magnetic field.
Polarimetric Spatio-Temporal Light Transport Probing

Light can undergo complex interactions with multiple scene surfaces of different material types before being reflected towards a detector. During this transport, every surface reflection and propagation is encoded in the properties of the photons that ultimately reach the detector, including travel time, direction, intensity, wavelength and polarization. Conventional imaging systems capture intensity by integrating over all other dimensions of the light into a single quantity, hiding this rich scene information in the accumulated measurements. Existing methods can untangle these into their spatial and temporal dimensions, fueling geometric scene understanding. However, examining polarimetric material properties jointly with geometric properties is an open challenge that could enable unprecedented capabilities beyond geometric understanding, allowing to incorporate material-dependent semantics. In this work, we propose a computational light-transport imaging method that captures the spatially- and temporally-resolved complete polarimetric response of a scene. Our method hinges on a novel 7D tensor theory of light transport. We discover low-rank structures in the polarimetric tensor dimension and propose a data-driven rotating ellipsometry method that learns to exploit redundancy of the polarimetric structures. We instantiate our theory in two imaging prototypes: spatio-polarimetric imaging and coaxial temporal-polarimetric imaging. This allows us to decompose scene light transport into temporal, spatial, and complete polarimetric dimensions that unveil scene properties hidden to conventional methods. We validate the applicability of our method on diverse tasks, including shape reconstruction with subsurface scattering, seeing through scattering medium, untangling multi-bounce light transport, breaking metamerism with polarization, and spatio-polarimetric decomposition of crystals.
A Facile Process to Fabricate Phosphorus/Carbon Xerogel Composite as Anode for Sodium Ion Batteries

Sodium ion batteries become popular due to their low cost. Among possible anode materials of sodium ion batteries, phosphorus has great potential owing to its high theoretical capacity. Previous research that yielded high capacity and long duration of phosphorus anode used expensive materials such as black phosphorus (BP) and phosphorene. To take advantage of the low cost of sodium ion batteries, we report a simple and low-cost method to fabricate anode: condensing red phosphorus on carbon xerogel. Even with large particle size (~ 50 $\mu$m) and high mass loading (2 mg cm$^{-2}$), the composite cycled at 100 mA g$^{-1}$ yielded a capacity of 357 mA g$^{-1}$ or 2498 mAh g$^{-1}_P$ based on phosphorus after subtracting the contribution of carbon. The average coulombic efficiency is as high as 99.4%. When cycled at 200 mA g$^{-1}$, it yielded a capacity of 242 mAh g$^{-1}$ or 1723 mAh g$^{-1}_P$, with average degradation rate only 0.06% in 80 cycles. Our research provided an innovative approach to synthesize anodes for sodium ion batteries at extremely low cost, with performance exceeding or comparable to state-of-the-art materials, which will promote their commercialization.
Collision Recovery Control of a Foldable Quadrotor

Autonomous missions of small unmanned aerial vehicles (UAVs) are prone to collisions owing to environmental disturbances and localization errors. Consequently, a UAV that can endure collisions and perform recovery control in critical aerial missions is desirable to prevent loss of the vehicle and/or payload. We address this problem by proposing a novel foldable quadrotor system which can sustain collisions and recover safely. The quadrotor is designed with integrated mechanical compliance using a torsional spring such that the impact time is increased and the net impact force on the main body is decreased. The post-collision dynamics is analysed and a recovery controller is proposed which stabilizes the system to a hovering location without additional collisions. Flight test results on the proposed and a conventional quadrotor demonstrate that for the former, integrated spring-damper characteristics reduce the rebound velocity and lead to simple recovery control algorithms in the event of unintended collisions as compared to a rigid quadrotor of the same dimension.
Materials challenges for quantum technologies based on color centers in diamond

Emerging quantum technologies require precise control over quantum systems of increasing complexity. Defects in diamond, particularly the negatively charged nitrogen-vacancy center, are a promising platform with the potential to enable technologies ranging from ultra-sensitive nanoscale quantum sensors, to quantum repeaters for long distance quantum networks, to simulators of complex dynamical processes in many-body quantum systems, to scalable quantum computers. While these advances are due in large part to the distinct material properties of diamond, the uniqueness of this material also presents difficulties, and there is a growing need for novel materials science techniques for characterization, growth, defect control, and fabrication dedicated to realizing quantum applications with diamond. In this article we identify and discuss the major materials science challenges and opportunities associated with diamond quantum technologies.
Proteomic analysis of microtubule inner proteins (MIPs) in Rib72 null Tetrahymena cells reveals functional MIPs

The core structure of motile cilia and flagella, the axoneme, is built from a stable population of doublet microtubules. This unique stability is brought about, at least in part, by a network of Mi...
Finely Tuned Models Sacrifice Explanatory Depth

It is commonly argued that an undesirable feature of a theoretical or phenomenological model is that salient observables are sensitive to values of parameters in the model. But in what sense is it undesirable to have such ‘fine-tuning’ of observables (and hence of the underlying model)? In this paper, we argue that the fine-tuning can be interpreted as a shortcoming of the explanatory capacity of the model: in particular it signals a lack of a particular type of explanatory depth. The aspect of depth that we probe relates most closely to a lack of sensitivity to changes in parameters associated with such models. In support of this argument, we develop a schema—for (a certain class of) models that arise broadly in physical settings—that quantitatively relates fine-tuning of observables to a lack of depth of explanations based on these models. We apply our schema in two different settings in which, within each setting, we compare the depth of two competing explanations. The first setting involves explanations for the Euclidean nature of spatial slices of the universe today: in particular, we compare an explanation provided by the big-bang model of the early 1970s (where no inflationary period is included) with an explanation provided by a general model of cosmic inflation. The second setting has a more phenomenological character, where the goal is to infer from a limited sequence of data points, using maximum entropy techniques, the underlying probability distribution from which these data are drawn. In both of these settings we find that our analysis favors the model that intuitively provides the deeper explanation of the observable(s) of interest. We thus provide an account that relates two ‘theoretical virtues’ of models used broadly in physical settings—namely, a lack of fine-tuning and explanatory depth—and argue that finely tuned models sacrifice explanatory depth.
End-to-end neural systems for automatic children speech recognition: An empirical study

Abstract A key desiderata for inclusive and accessible speech recognition technology is ensuring its robust performance to children’s speech. Notably, this includes the rapidly advancing neural network based end-to-end speech recognition systems. Children speech recognition is more challenging due to the larger intra-inter speaker variability in terms of acoustic and linguistic characteristics compared to adult speech. Furthermore, the lack of adequate and appropriate children speech resources adds to the challenge of designing robust end-to-end neural architectures. This study provides a critical assessment of automatic children speech recognition through an empirical study of contemporary state-of-the-art end-to-end speech recognition systems. Insights are provided on the aspects of training data requirements, adaptation on children data, and the effect of children age, utterance lengths, different architectures and loss functions for end-to-end systems and role of language models on the speech recognition performance.
Enabling GPU accelerated computing in the SUNDIALS time integration library

Abstract As part of the Exascale Computing Project (ECP), a recent focus of development efforts for the SUite of Nonlinear and DIfferential/ALgebraic equation Solvers (SUNDIALS) has been to enable GPU-accelerated time integration in scientific applications at extreme scales. This effort has resulted in several new GPU-enabled implementations of core SUNDIALS data structures, support for programming paradigms which are aware of the heterogeneous architectures, and the introduction of utilities to provide new points of flexibility. In this paper, we discuss our considerations, both internal and external, when designing these new features and present the features themselves. We also present performance results for several of the features on the Summit supercomputer and early access hardware for the Frontier supercomputer, which demonstrate negligible performance overhead resulting from the additional infrastructure and significant speedups when using both NVIDIA and AMD GPUs.
TRIMER: Transcription Regulation Integrated with Metabolic Regulation.

ABSTRACTAdvances in bioengineering have enabled numerous bio-based commodities. Yet most traditional approaches do not extend beyond a single metabolic pathway and do not attempt to modify gene regulatory networks in order to buffer metabolic perturbations. This is despite access to near universal technologies allowing genome-scale engineering. To help overcome this limitation, we have developed a pipeline enabling analysis of Transcription Regulation Integrated with MEtabolic Regulation (TRIMER). TRIMER utilizes a Bayesian network (BN) inferred from transcriptomic data to model the transcription factor regulatory network. TRIMER then infers the probabilities of gene states that are of relevance to the metabolism of interest, and predicts metabolic fluxes resulting from deletion of transcription factors at the genome scale. Additionally, we have developed a simulation framework to mimic the TF-regulated metabolic network, capable of generating both gene expression states and metabolic fluxes, thereby providing a fair evaluation platform for benchmarking models and predictions. Here, we present this computational pipeline. We demonstrate TRIMER’s applicability to both simulated and experimental data and show that it outperforms current approaches on both data types.
On the thermal entrance length of moderately dense gas-particle flows

Abstract The dissipative nature of heat transfer relaxes thermal flows to an equilibrium state that is devoid of temperature gradients. The distance to reach an equilibrium temperature – the thermal entrance length – is a consequence of diffusion and mixing by convection. The presence of particles can modify the thermal entrance length due to interphase heat transfer and turbulence modulation by momentum coupling. In this work, Eulerian–Lagrangian simulations are utilized to probe the effect of solids heterogeneity (e.g., clustering) on the thermal entrance length. For the moderately dense systems considered here, clustering leads to a factor of 2–3 increase in the thermal entrance length, as compared to an uncorrelated (perfectly mixed) distribution of particles. The observed increase is found to be primarily due to the covariance between volume fraction and temperature fluctuations, referred to as the fluid drift temperature. Using scaling arguments and Gene Expression Programming, closure is obtained for this term in a one-dimensional averaged two-fluid equation and is shown to be accurate under a wide range of flow conditions.
Multi-Step Recurrent Q-Learning for Robotic Velcro Peeling

Learning object manipulation is a critical skill for robots to interact with their environment. Even though there has been significant progress in robotic manipulation of rigid objects, interacting with non-rigid objects remains challenging for robots. In this work, we introduce velcro peeling as a new application for robotic manipulation of non-rigid objects in complex environments. We present a method of learning force-based manipulation from noisy and incomplete sensor inputs in partially observable environments by modeling long term dependencies between measurements with a multi-step deep recurrent network. We present experiments on a real robot to show the necessity of modeling these long term dependencies and validate our approach in simulation and robot experiments. Our results show that using tactile input enables the robot to overcome geometric uncertainties present in the environment with high fidelity in ∼ 90% of all cases, outperforming the baselines by a large margin.
Designing Multi-Stage Coupled Convex Programming with Data-Driven McCormick Envelope Relaxations for Motion Planning

For multi-limbed robots, motion planning with posture and force constraints tends to be a difficult optimization problem due to nonlinearities, which also present extended solve times. We propose a multi-stage optimization framework with data-driven inter-stage coupling constraints to address the nonlinearity. Both clustering and evolutionary approaches to find the McCormick envelope relaxations are used to find the problem-specific parameters. The learned constraints are then used in the prior stages, which provides advanced knowledge of the following stages. This leads to improved solve times and interpretability of the results. The planner is validated through multiple walking and climbing tasks on a 10 kg hexapod robot.
Single-shell NODDI using dictionary-learner-estimated isotropic volume fraction

Neurite orientation dispersion and density imaging (NODDI) enables the assessment of intracellular, extracellular, and free water signals from multi-shell diffusion MRI data. It is an insightful approach to characterize brain tissue microstructure. Single-shell reconstruction for NODDI parameters has been discouraged in previous studies caused by failure when fitting, especially for the neurite density index (NDI). Here, we investigated the possibility of creating robust NODDI parameter maps with single-shell data, using the isotropic volume fraction (fISO ) as a prior. Prior estimation was made independent of the NODDI model constraint using a dictionary learning approach. First, we used a stochastic sparse dictionary-based network (DictNet), which is trained with data obtained from in vivo and simulated diffusion MRI data, to predict fISO . In single-shell cases, the mean diffusivity and raw T2 signal with no diffusion weighting (S0 ) was incorporated in the dictionary for the fISO estimation. Then, the NODDI framework was used with the known fISO to estimate the NDI and orientation dispersion index (ODI). The fISO estimated using our model was compared with other fISO estimators in the simulation. Further, using both synthetic data simulation and human data collected on a 3 T scanner (both high-quality HCP and clinical dataset), we compared the performance of our dictionary-based learning prior NODDI (DLpN) with the original NODDI for both single-shell and multi-shell data. Our results suggest that DLpN-derived NDI and ODI parameters for single-shell protocols are comparable with original multi-shell NODDI, and the protocol with b = 2000 s/mm2 performs the best (error ~ 5% in white and gray matter). This may allow NODDI evaluation of studies on single-shell data by multi-shell scanning of two subjects for DictNet fISO training.
Practical Applications of The Role of Contracts in Venture Capital Returns

In The Role of Contracts in Venture Capital Returns, published in the Fall 2021 issue of The Journal of Alternative Investments, Sarina Liu of Wells Fargo creates a novel dataset to empirically test the hypothesis that traditional methods overestimate the post-money value of startup firms. Data about contract terms display four general convertible preferred stock types that are often provided to venture capitalists (VCs). After combining the data on contract terms with two additional datasets, the author analyzes cash flows to the VCs, revealing several interesting statistics regarding early- and late-stage investors in small and large acquisitions for each of the four types of convertible preferred stocks. While possible outcomes include failures, acquisitions, or initial public offerings (IPOs), most involve small acquisitions. Importantly, the traditional method for estimating the post-money value is inaccurate because the assumption of using common stock does not capture the significant added value of preferred stock.
Unsupervised Acute Intracranial Hemorrhage Segmentation With Mixture Models

Intracranial hemorrhage occurs when blood vessels rupture or leak within the brain tissue or elsewhere inside the skull. It can be caused by physical trauma or by various medical conditions and in many cases leads to death. The treatment must be started as soon as possible, and therefore the hemorrhage should be diagnosed accurately and quickly. The diagnosis is usually performed by a radiologist who analyses a Computed Tomography (CT) scan containing a large number of cross-sectional images throughout the brain. Analysing each image manually can be very time-consuming, but automated techniques can help speed up the process. While much of the recent research has focused on solving this problem by using supervised machine learning algorithms, publicly-available training data remains scarce due to privacy concerns. This problem can be alleviated by unsupervised algorithms. In this paper, we propose a fully-unsupervised algorithm which is based on the mixture models. Our algorithm utilizes the fact that the properties of hemorrhage and healthy tissues follow different distributions, and therefore an appropriate formulation of these distributions allows us to separate them through an Expectation-Maximization process. In addition, our algorithm is able to adaptively determine the number of clusters such that all the hemorrhage regions can be found without including noisy voxels. We demonstrate the results of our algorithm on publicly-available datasets that contain all different hemorrhage types in various sizes and intensities, and our results are compared to earlier unsupervised and supervised algorithms. The results show that our algorithm can outperform the other algorithms with most hemorrhage types.
A toolkit for quantification of biological age from blood chemistry and organ function test data: BioAge

Methods to quantify biological aging are emerging as new measurement tools for epidemiology and population science and have been proposed as surrogate measures for healthy lifespan extension in geroscience clinical trials. Publicly available software packages to compute biological aging measurements from DNA methylation data have accelerated dissemination of these measures and generated rapid gains in knowledge about how different measures perform in a range of datasets. Biological age measures derived from blood chemistry data were introduced at the same time as the DNA methylation measures and, in multiple studies, demonstrate superior performance to these measures in prediction of healthy lifespan. However, their dissemination has been slow by comparison, resulting in a significant gap in knowledge. We developed a software package to help address this knowledge gap. The BioAge R package, available for download at GitHub ( http://github.com/dayoonkwon/BioAge ), implements three published methods to quantify biological aging based on analysis of chronological age and mortality risk: Klemera-Doubal biological age, PhenoAge, and homeostatic dysregulation. The package allows users to parametrize measurement algorithms using custom sets of biomarkers, to compare the resulting measurements to published versions of the Klemera-Doubal method and PhenoAge algorithms, and to score the measurements in new datasets. We applied BioAge to safety lab data from the CALERIE™ randomized controlled trial, the first-ever human trial of long-term calorie restriction in healthy, non-obese adults, to test effects of intervention on biological aging. Results contribute evidence that CALERIE intervention slowed biological aging. BioAge is a toolkit to facilitate measurement of biological age for geroscience.
Desensitization of the β-Adrenergic Coupled Adenylate Cyclase. The β-Adrenergic Receptor Kinase Phosphorylates Agonist-Occupied Cyclase-Coupled Receptors

Many membrane bound hormone receptors are coupled to the adenylate cyclase system, and function as mediators of the hormonal stimulation at the cell surface (27). The activity of the effector enzyme, the adenylate cyclase is dually regulated via stimulatory and inhibitory receptors (27,15). After activation by a hormone stimulatory receptors like the β-adrenergic receptors and the prostaglandin E1 receptors couple to the stimulatory guanine nucleotide regulatory protein (Ns-protein) to stimulate the adenylate cyclase, while inhibitory receptors like the α2-adrenergic receptors couple to the inhibitory guanine nucleotide regulatory protein or N1-protein to inhibit the adenylate cyclase (15). Yet prolonged exposure to a hormone — stimulatory or inhibitory — leads to a decreased response to further activation of that receptor, a general phenomenon called tachyphylaxis or desensitization. One of the best studied models for desensitization is the β-adrenergic coupled adenylate cyclase (18, 24, 26).
Productivity, imperfect competition and trade reform: Theory and evidence

Abstract Research on productivity often examines the relationship between productivity increases and structural changes in an economy, such as trade policy reform. If, however, policy reforms affect the nature of competition, then the productivity changes associated with trade reform may be mismeasured. Using a panel of manufacturing firms in the Cote d'Ivoire, this paper measures changing profit margins and productivity following a 1985 trade reform. The paper also exploits cross-section differences in protection, using data on tariffs and import penetration. Ignoring the impact of liberalization on competition leads to biased estimates of the relationship between trade reform and productivity growth.
Amperometric H/sub 2/S sensor with Pd/Au electrode by electrochemical deposition

Amperometric gas sensors fabricated with various working electrode compositions were evaluated for sensitivity to H/sub 2/S in the presence of up to 50% hydrogen. The WEs consisted of a gold thin-film deposited on to a porous membrane substrate and the gold film modified with two different loadings of palladium (0.3 and 8.1 mg/cm/sup 2/). At a +800mV bias (vs. a Pb-mix reference electrode) the H/sub 2/S sensitivities in N/sub 2/ were 65.1, 16.3, 4947 nA/ppm, for pure gold, 0.03, and 8.1 mg/cm/sup 2/ palladium, respectively. In 1% hydrogen, the H/sub 2/S sensitivities were 38.2, 25.0, 120.9 nA/ppm for pure gold, 0.03, and 8.1 mg/cm/sup 2/ palladium, respectively. In 50% hydrogen the H/sub 2/S sensitivities were 32.5 and 14.4 nA/ppm for pure gold and 0.03, but at 8.1 mg/cm/sup 2/ palladium loading the H/sub 2/S response was not observed. Using a Pd/gold electrode increased sensitivity in air but in very high concentration of hydrogen, it was lower than the pure gold electrode.
The effectiveness of argumentation in tutorial dialogues with an Intelligent Tutoring System for genetic risk of breast cancer.

BRCA Gist is an Intelligent Tutoring System that helps women understand issues related to genetic testing and breast cancer risk. In two laboratory experiments and a field experiment with community and web-based samples, an avatar asked 120 participants to produce arguments for and against genetic testing for breast cancer risk. Two raters assessed the number of argumentation elements (claim, reason, backing, etc.) found in response to prompts soliciting arguments for and against genetic testing for breast cancer risk (IRR=.85). When asked to argue for genetic testing, 53.3 % failed to meet the minimum operational definition of making an argument, a claim supported by one or more reasons. When asked to argue against genetic testing, 59.3 % failed to do so. Of those who failed to generate arguments most simply listed disconnected reasons. However, participants who provided arguments against testing (40.7 %) performed significantly higher on a posttest of declarative knowledge. In each study we found positive correlations between the quality of arguments against genetic testing (i.e., number of argumentation elements) and genetic risk categorization scores. Although most interactions did not contain two or more argument elements, when more elements of arguments were included in the argument against genetic testing interaction, participants had greater learning outcomes. Apparently, many participants lack skills in making coherent arguments. These results suggest an association between argumentation ability (knowing how to make complex arguments) and subsequent learning. Better education in developing arguments may be necessary for people to learn from generating arguments within Intelligent Tutoring Systems and other settings.
Functional CAR Models for Large Spatially Correlated Functional Datasets

ABSTRACTWe develop a functional conditional autoregressive (CAR) model for spatially correlated data for which functions are collected on areal units of a lattice. Our model performs functional response regression while accounting for spatial correlations with potentially nonseparable and nonstationary covariance structure, in both the space and functional domains. We show theoretically that our construction leads to a CAR model at each functional location, with spatial covariance parameters varying and borrowing strength across the functional domain. Using basis transformation strategies, the nonseparable spatial-functional model is computationally scalable to enormous functional datasets, generalizable to different basis functions, and can be used on functions defined on higher dimensional domains such as images. Through simulation studies, we demonstrate that accounting for the spatial correlation in our modeling leads to improved functional regression performance. Applied to a high-throughput spatiall...
The rating form of IBD patient concerns: a new measure of health status.

Health status assessment for persons with chronic illness includes not only symptoms, but also an appraisal of the psychosocial concomitants of illness. In this national study of persons with inflammatory bowel disease (IBD), we standardized a disease-specific 25-item measure of perceived health status: the Rating Form of IBD Patient Concerns (RFIPC). Factor analysis yielded four indices: a) impact of disease (e.g., being a burden, loss of energy, loss of bowel control); b) sexual intimacy; c) complications of disease (e.g., developing cancer, having surgery, dying early); and d) body stigma (e.g., feeling dirty or smelly). A higher level of IBD concerns was associated with greater disease severity, female gender, and lower educational status. When controlling for these factors, as well as disease type and age, we found that concerns about: a) impact of disease was positively associated with poorer perception of health and well-being, greater psychological distress (SCL-90), and poorer daily function (Sickness Impact Profile) (p less than 0.0001); b) sexual intimacy was related to poorer psychologic function (p less than 0.01); and c) complications of disease was related to several measures of poorer daily function (p less than 0.0001 to 0.01). This standardized measure of the worries and concerns of persons with IBD may be used in clinical care and research to evaluate the effects of interventions on IBD patient outcomes.
Adjustment of endogenous concentrations in pharmacokinetic modeling.

Purpose Estimating pharmacokinetic parameters in the presence of an endogenous concentration is not straightforward as cross-reactivity in the analytical methodology prevents differentiation between endogenous and dose-related exogenous concentrations. This article proposes a novel intuitive modeling approach which adequately adjusts for the endogenous concentration.
Wavelet and model-based spectral analysis of color doppler optical coherence tomography

Abstract Color doppler optical coherence tomography (CD-OCT) uses time–frequency analysis (TFA) to extract motion-induced Doppler shifted in the interferometric OCT signal. In this paper, the performance of three TFAs are compared in a scattering flow phantom and in in vivo human retina: the short-time Fourier transform, the Morlet-wavelet transform, and the short-time MUSIC transform (STMT). The STMT is a new TFA that incorporates the MUSIC eigenfrequency estimator in a generalized short-time framework. The Morlet transform excels at identifying blood vessels, while the STMT is the most accurate predictor of Doppler shift frequency.
An integral equation and a representation for a Green's function

The final step in the mathematical solution of many problems in mathematical physics and engineering is the solution of a linear, two-point boundary-value problem such as $$\begin{gathered} \ddot u - q(t)u = - g(t), 0< t< x \hfill \\ (0) = 0, \dot u(x) = 0 \hfill \\ \end{gathered} $$ Such problems frequently arise in a variational context. In terms of the Green's functionG, the solution is $$u(t) = \int_0^x {G(t, y, x)g(y) dy} $$ It is shown that the Green's function may be represented in the form $$G(t,y,x) = m(t,y) - \int_y^x {q(s)m(t, s) m(y, s)} ds, 0< t< y< x$$ wherem satisfies the Fredholm integral equation $$m(t,x) = k(t,x) - \int_0^x k (t,y) q(y) m(y, x) dy, 0< t< x$$ and the kernelk is $$k(t, y) = min(t, y)$$
Necessary and Sufficient Conditions for Convergence to Nash Equilibrium: The Almost Absolute Continuity Hypothesis

970572have shown that if play induced by optimal strategies is absolutely continuous with respect to players' beliefs about the future evolution of play, then Bayesian updating eventually leads to Nash equilibrium. However, absolute continuity is not a necessary condition for weak convergence to Nash equilibrium. In this paper, I present a set of necessary and sufficient conditions that ensure that Bayesian updating eventually leads to Nash equilibrium. More important, I show that absolute continuity does not rule out any play that is asymptotically consistent with Nash equilibrium play.Journal of Economic LiteratureClassification Number: D83.
Effects of Various Secretagogues on Gonadotropin Release by Rat Anterior Pituitary Cell Cultures

AbstractRat anterior pituitary cell cultures were utilized to compare LH and FSH release induced by LHRH, cAMP derivatives, cGMP derivatives, the calcium ionophore A23187, and elevated K+. Of the cyclic nucleotide derivatives tested, only 8-Br-cAMP released significant quantities of both LH and FSH. A23187 and elevated K+ released significant quantities of LHand FSH but supramaximal concentrations of these stimuli liberated only approximately 50% of the gonadotropins released by LHRH. Thus, none of the secretagogues tested was as efficacious as LHRH in releasing either LH or FSH.
Electron Microscopy: Sodium Localization in Normal and Ouabain-Treated Transporting Cells

When rabbit corneas are fixed in an osmic acid fixativeto which potassium pyroantimonate has been added, an electron-opaque precipitate of sodiium pyroantimonate is localized along the cellular side of the lateral plasmla membrane of the endothelial cells. This heavy precipitate represents areas of high Na+ concentration and may represent the site of a saturated, high-affinity Na+ carrier. Significantly heavier precipitate occurs at the same site when ouabain is injected into the aqueous humor prior to fixation of the cornea.
Formation of eicosanoids, E2/D2 isoprostanes, and docosanoids following decapitation-induced ischemia, measured in high-energy-microwaved rat brain.

Inflammatory lipid mediators derived from arachidonic acid (AA) and docosahexaenoic acid (DHA) modify the pathophysiology of brain ischemia. The goal of this work was to investigate the formation of eicosanoids and docosanoids generated from AA and DHA, respectively, during no-flow cerebral ischemia. Rats were subjected to head-focused microwave irradiation 5 min following decapitation (complete ischemia) or prior to decapitation (controls). Brain lipids were extracted and analyzed by reverse-phase liquid chromatography-tandem mass spectrometry. After complete ischemia, brain AA, DHA, and docosapentaenoic acid concentrations increased 18-, 5- and 4-fold compared with controls, respectively. Prostaglandin E2 (PGE2) and PGD2 could not be detected in control microwaved rat brain, suggesting little endogenous PGE2/D2 production in the brain in the absence of experimental manipulation. Concentrations of thromboxane B2, E2/D2-isoprostanes, 5-hydroxyeicosatetraenoic acid (5-HETE), 5-oxo-eicosatetraenoic acid, and 12-HETE were significantly elevated in ischemic brains. In addition, DHA products such as mono-, di- and trihydroxy-DHA were detected in control and ischemic brains. Monohydroxy-DHA, identified as 17-hydroxy-DHA and thought to be the immediate precursor of neuroprotectin D1, was 6.5-fold higher in ischemic than in control brain. The present study demonstrated increased formation of eicosanoids, E2/D2-IsoPs, and docosanoids following cerebral ischemia. A balance of these lipid mediators may mediate immediate events of ischemic injury and recovery.
Evidence that a 25 kilodalton protein associated with sugar beet root plasmalemmas may be involved in the enhanced uptake of iron by iron stressed plants

Abstract Electrophoresis of a root plasmalemma fraction from three sugar beet cultivars with different iron uptake capacities revealed a 25 kilodalton (kd) protein which is produced in enhanced amounts in response to Fe deficiency in a manner corresponding to the Fe uptake capacities of the three cultivars. An assay was developed to measure the plasmalemma‐bound ferric iron reductase. Using this assay, we were able to detect differences in specific activity of the reductase among the three cultivars but not increases in activity in response to iron stress. The results suggest that a 25 kD plasma lemma‐bound protein could be involved in the dramatically enhanced uptake of Fe which occurs when Fe is resupplied to Fe deficient sugar beets.
Unveiling the formation pathway of single crystalline porous silicon nanowires.

Porous silicon nanowire is emerging as an interesting material system due to its unique combination of structural, chemical, electronic, and optical properties. To fully understand their formation mechanism is of great importance for controlling the fundamental physical properties and enabling potential applications. Here we present a systematic study to elucidate the mechanism responsible for the formation of porous silicon nanowires in a two-step silver-assisted electroless chemical etching method. It is shown that silicon nanowire arrays with various porosities can be prepared by varying multiple experimental parameters such as the resistivity of the starting silicon wafer, the concentration of oxidant (H2O2) and the amount of silver catalyst. Our study shows a consistent trend that the porosity increases with the increasing wafer conductivity (dopant concentration) and oxidant (H2O2) concentration. We further demonstrate that silver ions, formed by the oxidation of silver, can diffuse upwards and renu...
An acyclicity theorem for cell complexes in d dimensions

Let C be a cell complex in d-dimensional Euclidean space whose faces are obtained by orthogonal projection of the faces of a convex polytope in d + 1 dimensions. For example, the Delaunay triangulation of a finite point set is such a cell complex. This paper shows that the in_front/behind relation defined for the faces of C with respect to any fixed viewpoint x is acyclic. This result has applications to hidden line/surface removal and other problems in computational geometry.
An Examination of E-Reader Devices and Their Implications within an Academic Health Science Library

This article details an academic medical library's examination of the practicality of e-readers as a means of leveraging electronic resources within a classroom. In a comparison of the more popular available e-readers, the authors identify some of the technical and legal considerations affecting the prospect of making a required textbook available to students via an e-reader device.
Spectrum of scattered light in a critical binary mixture

Abstract We have measured the temperature dependence of the diffusion coefficient ( D ) and that of the correlation length (ξ) along the critical solution concentration of a two component system, perfluoro-methy-cyclohexane-carbon tetrachloride by means of signal correlation.
Clinical Faceoff: Trapeziectomy Versus Trapezium Preservation in the Management of Basilar Thumb Arthritis

Basilar thumb, or trapeziometacarpal (TM) arthritis, is a common and often debilitating condition affecting 8% to 12% of the general population, including more than half the people older than 70 [14, 16]. Symptoms range from minor discomfort to severe pain and disability, which may initially be treated nonoperatively with antiinflammatory medication, splints, and corticosteroid injections. Once these have been exhausted, many surgical options exist [13]. The mainstay of these approaches involves trapeziectomy with or without ligament reconstruction or interposition. However, there are concerns regarding proximal and lateral subsidence of the thumb [7]. This collapse can lead to weakness and recurrent symptoms at the base of the thumb, which is of particular concern in younger patients who may someday come to revision surgery [5, 15].
Bradykinin-mediated effects of ACE inhibition.

A 42-year-old black man with a history of essential hypertension was admitted to the Clinical Research Center at the Boston University School of Medicine for participation in a research protocol designed to assess the blood-pressure-lowering effect of the renin inhibitor A-64662 (Abbott Laboratories). He had not taken his previous antihypertensive medication for 3 weeks, and he was in good general health. On admission, the blood pressure was 174/108 mm Hg; the physical examination was otherwise unremarkable. Routine laboratory studies, including renal function tests and urinalysis, were normal. On the second day of hospitalization, and following an overnight fast, the patient received as per protocol an intravenous injection of 0.7 mg/kg of the renin inhibitor while blood pressure was monitored continuously. Baseline blood pressure was 166/104 mm Hg and remained virtually unchanged throughout the 3-hour duration of the study, except for a slight decrease between 95 and 135 minutes. The observed maximal decrease in mean blood pressure was 4 mm Hg and occurred at 105 minutes. At the end of the 3-hour period, he received a 50 mg tablet of the angiotensin-converting-enzyme (ACE) inhibitor captopril. After 30 minutes, his blood pressure was 146/96 mm Hg; it reached its nadir of 138/94 mm Hg at 60 minutes, gradually increasing towards baseline thereafter. The maximal drop in the mean blood pressure after captopril admininistration was 17 mm Hg (Table 1). Blood pressure was 164/104 mm Hg at 24 hours. Plasma renin activity (PRA) was 0.7 ng/ml/hr at baseline and fell to undetectable levels (less than 0.05 ng/ml/hr) 30 minutes after injection of the renin inhibitor; PRA returned to 0.15 ng/ml/hr at 24 hours. Baseline plasma angiotensin II was 3.6 pg/mI, declining to 0.9 pg/mI 30 minutes after injection of the renin inhibitor and returning to 2.4 pglml at 24
Effect of leaf development and temperature on a foliar storage glycoprotein from Poncirus trifoliata (L.) Raf

Abstract A major soluble glycoprotein, gp24, from foliar protein extracts of trifoliate orange ( Poncirus trifoliata (L.) Raf.) (TRI) was partially purified by ammonium sulfate precipitation and preparative sodium dodecyl sulfate (SDS) polyacrylamide gel electrophoresis (PAGE). Glucose was the only sugar component which was detected in HCl-hydrolysates of purified gp24. Immunoblot analyses with polyclonal antisera indicated low levels of related protein bands in foliar leaf extracts from several citrus genotypes. The gp24 bands and related proteins were less in leaves of TRI during progressively cooler temperatures above 0°C. The amount of gp24 in Poncirus also varied during leaf maturation and senescence with highest levels occurring in young, fully expanded leaves. A function of gp24, as a storage glycoprotein for amino acids as well as carbohydrates, is suggested.
Developing a theory to guide the process of designing information retrieval systems

The dominant approaches to information retrieval system design are based on rational theory and cognitive engineering. However, these theories as well as approaches in other disciplines, reviewed in this paper, do not account for communication, or interaction, among design participants which is critical to design outcomes. This research attempts to develop a descriptive design model that accounts for communication among users, designers, and developers throughout the design process. A pilot study has been completed and a preliminary model that represents a first step in understanding participants' evolving perceptions and expectations of the design process and its outcomes is described in this paper.