cssi/SciSciGPT-SciSciNet huggingface dataset:
HALO: Learning to Prune Neural Networks with Shrinkage.

Deep neural networks achieve state-of-the-art performance in a variety of tasks by extracting a rich set of features from unstructured data, however this performance is closely tied to model size. Modern techniques for inducing sparsity and reducing model size are (1) network pruning, (2) training with a sparsity inducing penalty, and (3) training a binary mask jointly with the weights of the network. We study different sparsity inducing penalties from the perspective of Bayesian hierarchical models and present a novel penalty called Hierarchical Adaptive Lasso (HALO) which learns to adaptively sparsify weights of a given network via trainable parameters. When used to train over-parametrized networks, our penalty yields small subnetworks with high accuracy without fine-tuning. Empirically, on image recognition tasks, we find that HALO is able to learn highly sparse network (only 5% of the parameters) with significant gains in performance over state-of-the-art magnitude pruning methods at the same level of sparsity. Code is available at this https URL.
The small acid-soluble proteins of Clostridioides difficile are important for UV resistance and serve as a check point for sporulation

Clostridioides difficile is a nosocomial pathogen which causes severe diarrhea and colonic inflammation. C. difficile causes disease in susceptible patients when endospores germinate into the toxin-producing vegetative form. The action of these toxins results in diarrhea and the spread of spores into the hospital and healthcare environments. Thus, the destruction of spores is imperative to prevent disease transmission between patients. However, spores are resilient and survive extreme temperatures, chemical exposure, and UV treatment. This makes their elimination from the environment difficult and perpetuates their spread between patients. In the model spore-forming organism, Bacillus subtilis, the small acid-soluble proteins (SASPs) contribute to these resistances. The SASPs are a family of small proteins found in all endospore-forming organisms, C. difficile included. Although these proteins have high sequence similarity between organisms, the role(s) of the proteins differ. Here, we investigated the role of the main α/β SASPs, SspA and SspB, and two annotated putative SASPs, CDR20291_1130 and CDR20291_3080, in protecting C. difficile spores from environmental insults. We found that SspA is necessary for conferring spore UV resistance, SspB minorly contributes, and the annotated putative SASPs do not contribute to UV resistance. In addition, the SASPs minorly contribute to the resistance of nitrous acid. Surprisingly, the combined deletion of sspA and sspB prevented spore formation. Overall, our data indicate that UV resistance of C. difficile spores is dependent on SspA and that SspA and SspB regulate/serve as a checkpoint for spore formation, a previously unreported function of SASPs.
Towards understanding sensor and control nodes selection in nonlinear dynamic systems: Lyapunov theory meets branch-and-bound

Abstract Sensor and actuator selection problems (SASPs) are some of the core problems in dynamic systems design and control. These problems correspond to determining the optimal selection of sensors (measurements) or actuators (control nodes) such that certain estimation/control objectives can be achieved. While the literature on SASPs are indeed inveterate, the vast majority of the work focuses on linear(ized) representation of the network dynamics, resulting in the placements of sensors or actuators (SAs) that are valid for confined operating regions. As an alternative, herein we propose a new general framework for addressing SASPs in nonlinear dynamic systems (NDSs), assuming that the inputs and outputs are linearly coupled with the nonlinear dynamics. This is investigated through (i) classifying and parameterizing the NDSs into various nonlinear function sets, (ii) utilizing rich Lyapunov theoretic formulations, and (iii) designing a new customized branch-and-bound (BnB) algorithm that exploits problem structure of the SASPs. The newly designed BnB routines are computationally more attractive than the standard one and also directly applicable to solve SASPs for linear systems. In contrast with contemporary approaches from the literature, our approach is suitable for finding the optimal SAs combination for stable/unstable NDSs that ensures stabilization of estimation error and closed-loop dynamics through a simple linear feedback control policy.
Optimal Causal Rate-Constrained Sampling for a Class of Continuous Markov Processes

Consider the following communication scenario. An encoder observes a stochastic process and causally decides when and what to transmit about it, under a constraint on the expected number of bits transmitted per second. A decoder uses the received codewords to causally estimate the process in real time. The encoder and the decoder are synchronized in time. For a class of continuous Markov processes satisfying regularity conditions, we find the optimal encoding and decoding policies that minimize the end-to-end estimation mean-square error under the rate constraint. We show that the optimal encoding policy transmits a 1-bit codeword once the process innovation passes one of two thresholds. The optimal decoder noiselessly recovers the last sample from the 1-bit codewords and codeword-generating time stamps, and uses it to decide the running estimate of the current process, until the next codeword arrives. In particular, we show the optimal causal code for the Ornstein-Uhlenbeck process and calculate its distortion-rate function. Furthermore, we show that the optimal causal code also minimizes the mean-square cost of a continuous-time control system driven by a continuous Markov process and controlled by an additive control signal.
Sciviewer enables interactive visual interrogation of single-cell RNA-Seq data from the python programming environment.

Motivation Visualizing two-dimensional (2D) embeddings (such as UMAP or tSNE) is a useful step in interrogating single-cell RNA sequencing (scRNA-Seq) data. Subsequently, users typically iterate between programmatic analyses (including clustering and differential expression) and visual exploration (e.g., coloring cells by interesting features) to uncover biological signals in the data. Interactive tools exist to facilitate visual exploration of embeddings such as performing differential expression on user-selected cells. However, the practical utility of these tools is limited because they don't support rapid movement of data and results to and from the programming environments where most of the data analysis takes place, interrupting the iterative process. Results Here, we present the Single-cell Interactive Viewer (Sciviewer), a tool that overcomes this limitation by allowing interactive visual interrogation of embeddings from within Python. Beyond differential expression analysis of user-selected cells, Sciviewer implements a novel method to identify genes varying locally along any user-specified direction on the embedding. Sciviewer enables rapid and flexible iteration between interactive and programmatic modes of scRNA-Seq exploration, illustrating a useful approach for analyzing high-dimensional data. Availability Code and examples are provided at https://github.com/colabobio/sciviewer.
Stochastic Optimization for Numerical Evaluation of Imprecise Probabilities.

In applications of imprecise probability, analysts must compute lower (or upper) expectations, defined as the infimum of an expectation over a set of parameter values. Monte Carlo methods consistently approximate expectations at fixed parameter values, but can be costly to implement in grid search to locate minima over large subsets of the parameter space. We investigate the use of stochastic iterative root-finding methods for efficiently computing lower expectations. In two examples we illustrate the use of various stochastic approximation methods, and demonstrate their superior performance in comparison to grid search.
Changing the blame game: Does the presence of a pay ratio disclosure impact nonprofessional investors’ reactions to CEOs’ internal attributions for poor firm performance?

We conduct two experiments to investigate how the presence of the CEO pay ratio, a recently mandated disclosure, influences nonprofessional investors’ reactions to a CEO’s internal attributions for poor firm performance. Results of our first experiment suggest that relative to blaming oneself, blaming other firm employees for poor firm performance more effectively absolves a CEO from responsibility for poor firm performance and damages perceptions of the CEO’s trustworthiness less when a pay ratio disclosure is present versus absent. These perceptions, in turn, affect investors’ support for the CEO’s compensation and the company’s attractiveness as an investment. Our second experiment provides evidence of the underlying process, showing the pay ratio disclosure and the CEO’s attribution to other employees affects the perceived status of a CEO. Together, our findings inform managers about the impact of their attributions for poor firm performance and regulators about potential unintended consequences of pay ratio disclosures.
Comprehensive evaluation of COVID-19 patient short- and long-term outcomes: Disparities in healthcare utilization and post-hospitalization outcomes.

Background Understanding risk factors for short- and long-term COVID-19 outcomes have implications for current guidelines and practice. We study whether early identified risk factors for COVID-19 persist one year later and through varying disease progression trajectories. Methods This was a retrospective study of 6,731 COVID-19 patients presenting to Michigan Medicine between March 10, 2020 and March 10, 2021. We describe disease progression trajectories from diagnosis to potential hospital admission, discharge, readmission, or death. Outcomes pertained to all patients: rate of medical encounters, hospitalization-free survival, and overall survival, and hospitalized patients: discharge versus in-hospital death and readmission. Risk factors included patient age, sex, race, body mass index, and 29 comorbidity conditions. Results Younger, non-Black patients utilized healthcare resources at higher rates, while older, male, and Black patients had higher rates of hospitalization and mortality. Diabetes with complications, coagulopathy, fluid and electrolyte disorders, and blood loss anemia were risk factors for these outcomes. Diabetes with complications, coagulopathy, fluid and electrolyte disorders, and blood loss were associated with lower discharge and higher inpatient mortality rates. Conclusions This study found differences in healthcare utilization and adverse COVID-19 outcomes, as well as differing risk factors for short- and long-term outcomes throughout disease progression. These findings may inform providers in emergency departments or critical care settings of treatment priorities, empower healthcare stakeholders with effective disease management strategies, and aid health policy makers in optimizing allocations of medical resources.
Design of Composites by Infiltration Process: A Case Study of Liquid Ir-Si Alloy/SiC Systems.

The design of processing routes involving the presence of the liquid phase is mainly associated with the knowledge of its surface and transport properties. Despite this need, due to experimental difficulties related to high temperature measurements of metallic melts, for many alloy systems neither thermodynamic nor thermophysical properties data are available. A good example of a system lacking these datasets is the Ir-Si system, although over the last fifty years, the structures and properties of its solid phases have been widely investigated. To compensate the missing data, the Gibbs free energy of mixing of the Ir-Si liquid phase was calculated combining the model predicted values for the enthalpy and entropy of mixing using Miedema’s model and the free volume theory, respectively. Subsequently, in the framework of statistical mechanics and thermodynamics, the surface properties were calculated using the quasi-chemical approximation (QCA) for the regular solution, while to obtain the viscosity, the Moelwyn-Hughes (MH) and Terzieff models were applied. Subsequently, the predicted values of the abovementioned thermophysical properties were used to model the non-reactive infiltration isotherm of Ir-Si (eutectic)/SiC system.
Emulation of Astrocyte Induced Neural Phase Synchrony in Spin-Orbit Torque Oscillator Neurons

Astrocytes play a central role in inducing concerted phase synchronized neural-wave patterns inside the brain. In this article, we demonstrate that injected radio-frequency signal in underlying heavy metal layer of spin-orbit torque oscillator neurons mimic the neuron phase synchronization effect realized by glial cells. Potential application of such phase coupling effects is illustrated in the context of a temporal "binding problem." We also present the design of a coupled neuron-synapse-astrocyte network enabled by compact neuromimetic devices by combining the concepts of local spike-timing dependent plasticity and astrocyte induced neural phase synchrony.
ZePHyR: Zero-shot Pose Hypothesis Rating

Pose estimation is a basic module in many robot manipulation pipelines. Estimating the pose of objects in the environment can be useful for grasping, motion planning, or manipulation. However, current state-of-the-art methods for pose estimation either rely on large annotated training sets or simulated data. Further, the long training times for these methods prohibit quick interaction with novel objects. To address these issues, we introduce a novel method for zero-shot object pose estimation in clutter. Our approach uses a hypothesis generation and scoring framework, with a focus on learning a scoring function that generalizes to objects not used for training. We achieve zero-shot generalization by rating hypotheses as a function of unordered point differences. We evaluate our method on challenging datasets with both textured and untextured objects in cluttered scenes and demonstrate that our method significantly outperforms previous methods on this task. We also demonstrate how our system can be used by quickly scanning and building a model of a novel object, which can immediately be used by our method for pose estimation. Our work allows users to estimate the pose of novel objects without requiring any retraining. Additional information can be found on our website https://bokorn.github.io/zephyr/
How smooth is quantum complexity

The "quantum complexity" of a unitary operator measures the difficulty of its construction from a set of elementary quantum gates. While the notion of quantum complexity was first introduced as a quantum generalization of the classical computational complexity, it has since been argued to hold a fundamental significance in its own right, as a physical quantity analogous to the thermodynamic entropy. In this paper, we present a unified perspective on various notions of quantum complexity, viewed as functions on the space of unitary operators. One striking feature of these functions is that they can exhibit non-smooth and even fractal behaviour. We use ideas from Diophantine approximation theory and sub-Riemannian geometry to rigorously quantify this lack of smoothness. Implications for the physical meaning of quantum complexity are discussed.
An Efficient Quantitative Approach for Optimizing Convolutional Neural Networks

With the increasing popularity of deep learning, Convolutional Neural Networks (CNNs) have been widely applied in various domains, such as image classification and object detection, and achieve stunning success in terms of their high accuracy over the traditional statistical methods. To exploit the potentials of CNN models, a huge amount of research and industry efforts have been devoted to optimizing CNNs. Among these endeavors, CNN architecture design has attracted tremendous attention because of its great potential of improving model accuracy or reducing model complexity. However, existing work either introduces repeated training overhead in the search process or lacks an interpretable metric to guide the design. To clear these hurdles, we propose 3D-Receptive Field (3DRF), an explainable and easy-to-compute metric, to estimate the quality of a CNN architecture and guide the search process of designs. To validate the effectiveness of 3DRF, we build a static optimizer to improve the CNN architectures at both the stage level and the kernel level. Our optimizer not only provides a clear and reproducible procedure but also mitigates unnecessary training efforts in the architecture search process. Extensive experiments and studies show that the models generated by our optimizer can achieve up to 5.47% accuracy improvement and up to 65.38% parameters deduction, compared with state-of-the-art CNN structures like MobileNet and ResNet.
Search for fast radio transients using Arecibo drift-scan observations at 1.4 GHz

We conducted a drift-scan observation campaign using the 305-m Arecibo telescope in January and March 2020 when the observatory was temporarily closed during the intense earthquakes and the initial outbreak of the COVID-19 pandemic, respectively. The primary objective of the survey was to search for fast radio transients, including Fast Radio Bursts (FRBs) and Rotating Radio Transients (RRATs). We used the 7-beam ALFA receiver to observe different sections of the sky within the declination region $\sim$(10$-$20) deg on 23 nights and collected 160 hours of data in total. We searched our data for single-pulse transients, covering up to a maximum dispersion measure of 11 000 pc cm$
Smooth loops and loop bundles

Abstract A loop is a rather general algebraic structure that has an identity element and division, but is not necessarily associative. Smooth loops are a direct generalization of Lie groups. A key example of a non-Lie smooth loop is the loop of unit octonions. In this paper, we study properties of smooth loops and their associated tangent algebras, including a loop analog of the Maurer-Cartan equation. Then, given a manifold, we introduce a loop bundle as an associated bundle to a particular principal bundle. Given a connection on the principal bundle, we define the torsion of a loop bundle structure and show how it relates to the curvature, and also develop aspects of a non-associative gauge theory. Throughout, we see how some of the known properties of G 2 -structures can be seen from this more general setting.
Online Selective Classification with Limited Feedback

Motivated by applications to resource-limited and safety-critical domains, we study selective classification in the online learning model, wherein a predictor may abstain from classifying an instance. For example, this may model an adaptive decision to invoke more resources on this instance. Two salient aspects of the setting we consider are that the data may be non-realisable, due to which abstention may be a valid long-term action, and that feedback is only received when the learner abstains, which models the fact that reliable labels are only available when the resource intensive processing is invoked. Within this framework, we explore strategies that make few mistakes, while not abstaining too many times more than the best-in-hindsight error-free classifier from a given class. That is, the one that makes no mistakes, while abstaining the fewest number of times. We construct simple versioning-based schemes for any $\mu \in (0,1],$ that make most $T^\mu$ mistakes while incurring \smash{$\tilde{O}(T^{1-\mu})$} excess abstention against adaptive adversaries. We further show that this dependence on $T$ is tight, and provide illustrative experiments on realistic datasets.
Tail bounds for gaps between eigenvalues of sparse random matrices

We prove the first eigenvalue repulsion bound for sparse random matrices. As a consequence, we show that these matrices have simple spectrum, improving the range of sparsity and error probability from work of the second author and Vu. We also show that for sparse Erdős–Renyi graphs, weak and strong nodal domains are the same, answering a question of Dekel, Lee, and Linial.
TU-AB-207-03: Tomosynthesis: Clinical Applications

Digital Tomosynthesis (DT) is becoming increasingly common in breast imaging and many other applications. DT is a form of computed tomography in which a limited set of projection images are acquired over a small angular range and reconstructed into a tomographic data set. The angular range and number of projections is determined both by the imaging task and equipment manufacturer. For example, in breast imaging between 9 and 25 projections are acquired over a range of 15° to 60°. It is equally valid to treat DT as the digital analog of classical tomography - for example, linear tomography. In fact, the name “tomosynthesis” is an acronym for “synthetic tomography”. DT shares many common features with classical tomography, including the radiographic appearance, dose, and image quality considerations. As such, both the science and practical physics of DT systems is a hybrid between CT and classical tomographic methods. This lecture will consist of three presentations that will provide a complete overview of DT, including a review of the fundamentals of DT, a discussion of testing methods for DT systems, and a description of the clinical applications of DT. While digital breast tomosynthesis will be emphasized, analogies will be drawn to body imaging to illustrate and compare tomosynthesis methods. Learning Objectives: 1. To understand the fundamental principles behind tomosynthesis, including the determinants of image quality and dose. 2. To learn how to test the performance of tomosynthesis imaging systems. 3. To appreciate the uses of tomosynthesis in the clinic and the future applications of tomosynthesis.
Underlying Spatial Skills to Support Navigation Through Large, Unconstrained Environments

Summary Studies of spatial navigation in real-world settings have been limited to neighborhoods, campuses, and buildings. These locations have structural components, such as roads or hallways, which may direct navigation. The current study assessed navigational skills within a large-scale forested environment that contained few pre-established paths. Participants were asked to find flags using only a map and compass; dependent variables included target-finding accuracy and efficiency. In addition, measurements of sense of direction, strategy, and working memory were taken to identify how these cognitive abilities influence performance. The results demonstrate the expected correlations between sense of direction and navigational success. An unexpected correlation between spatial working memory and navigational success was also found, which was the only significant predictor of performance when all measures were regressed together. These results suggest that studies should not forget basic cognitive abilities, which may predict success more than measures of sense of direction. Copyright © 2015 John Wiley & Sons, Ltd.
Flow birefringence of suspensions of rigid particles

Abstract Equations for the extinction angle and the two partial birefringences of suspensions undergoing flow have been derived. It is shown that in general the extinction angle is a function of the optical properties of the particles. However, for particles with cylindrical symmetry the extinction angle is independent of the optical properties of the particle. The presence of cylindrical symmetry simplifies the expressions for the partial birefringences also, but these simplifications do not seem to have a simple interpretation.
Executive Pay Restrictions for TARP Recipients: An Assessment

This is the testimony of Kevin J. Murphy before the U.S. Congressional Oversight Panel, Hearing on TARP and Executive Compensation Restrictions, October 21, 2010.
An r-curve approach for fracture of quasi-brittle materials

An R-curve approach for fracture of quasi-brittle materials is proposed in this paper. An R-curve is defined as an envelope of fracture energy release rate of specimens with different sizes but the same initial notch length. By assuming that an effective traction-free critical crack is the function of an initial crack length contained in a material, an expression of the R-curve with two parameters can be derived by solving a differential equation. The parameters of the R-curve can be uniquely determined according to KICS and CTODc for positive geometry specimens, and according to KICS and dK1/da = 0 for negative geometry specimens. Load-CMOD and loaddisplacement response can be predicted based on the proposed R-curve by requiring that the crack driving force and crack growth resistance are equal at every equilibrium crack length. The predicted curves show a good agreement with the wide range of experimental results. Pre-critical stable crack propagation of quasi-brittle materials can be well described by the present approach.
Speech watermarking with objective fidelity and robustness criteria

Speech watermarking strategies inevitably alter original signal content. Fidelity is adversely affected by increased perturbation while watermark robustness to attack is generally improved. Parameter-embedded watermarking is effected through slight perturbations of parametric models of some deeply integrated dynamics of the speech. Within this framework, a specific algorithm is presented in which the fidelity-robustness tradeoff can be objectively assessed and quantifiably adjusted according to specific measures. An overview of the general parameter-embedding strategy is followed by presentation of the featured algorithm, analysis of its properties, and experiments with speech data to assess fidelity, robustness, and other performance properties.
Edge inference for image interpolation

Image interpolation algorithms try to fit a function to a matrix of samples in a "natural-looking" way. This paper presents edge inference, an algorithm that does this by mixing neural network regression with standard image interpolation techniques. Results on gray level images are presented, and it is demonstrated that edge inference is capable of producing sharp, natural-looking results. A technique for reintroducing noise is given, and it is shown that, with noise added using a bicubic interpolant, edge inference can be regarded as a generalization of bicubic interpolation. Extension into RGB color space and additional applications of the algorithm are discussed, and some tips for optimization are given.
Capture into resonance: A novel method of efficient control

In the present paper we propose a method to use capture into resonance to control the behavior of a certain class of dynamical systems. In many dynamical systems the coupling between the unperturbed system and weak periodic perturbations (wave) can be reduced to a purely resonant interaction occurring in the vicinity of a certain surface in the phase space. While resonance interaction can change invariants of the unperturbed system (e.g. energy), it is random in nature, and, consequently, is rather inefficient as a mechanism of regular transport. We propose a method to structure the resonance interaction with little additional cost. When the nominal dynamics brings the system close to a resonance surface we apply a short control pulse to force the capture of a phase point into the resonance with the wave. A captured point is transported by the wave across the energy levels. We apply the second pulse to release a phase point from the resonance when the desired energy level is achieved. As a model problem we consider dynamics of a charged particle in an electromagnetic field. We compare the cost of the proposed control with other methods and discuss possible applications of this technique.
Very Low Frequency Radio Astronomy from Lunar Orbit

This paper discusses the use of very low frequency aperture synthesis as a probe of astrophysical phenomena. Specifically, the science achievable with the Lunar Observer Radio Astronomy Experiment (LORAE) is discussed. The density distribution and the degree of turbulence of the galactic plasma can be determined from its effect on background radio sources. These same absorption phenomena can be used to determine magnetic field strengths within astrophysical plasmas. Detection of unabsorbed synchrotron emission can provide information on the relativistic plasma component. The detection of new pulsars may prove feasible because of their steep spectra. Observations of planets in the solar system will be used to study their interaction with the solar wind. VLF studies of the Sun will provide new information on the outer layers of its atmosphere and magnetic field as well as provide insight on the propagation of MHD disturbances through the corona. The plasma in the earth's magnetic field can be observed in detail and as a function of time. This will prove invaluable in understanding auroral phenomena and reconnection events.
Heterogeneous clasts as windows into magma mingling at Soufrière Hills volcano

[1] A clast erupted from Soufriere Hills volcano during the syn-collapse Vulcanian explosions of July 2003 reveals several incorporated silicic zones within a more mafic host. These incorporated silicics display variable microlite contents that reflect different stages of magma crystallization. One (zone A) shows evidence of heating in several mineralogical features, including completely pseudomorphed hornblende crystals that broke down over a minimum time period of 37–78 days. The lower limit of this time period roughly correlates with the onset of hybrid seismic swarms prior to the 2003 dome collapse. Element X-ray maps reveal percolation of Ca-rich melt into the incorporated silicic margins, suggesting sustained contact that allowed isolation of portions of the resident magma. These characteristics suggest that magma mixing at Soufriere Hills may not only result from disaggregation of mafic inclusions, but also from disaggregation and incorporation of the resident magma into the mafic intrusion.
Anemia and iron deficiency: effects on pregnancy outcome

This article reviews current knowledge of the effects of maternal anemia and iron deficiency on pregnancy outcome. A considerable amount of information remains to be learned about the benefits of maternal iron supplementation on the health and iron status of the mother and her child during pregnancy and postpartum. Current knowledge indicates that iron deficiency anemia in pregnancy is a risk factor for preterm delivery and subsequent low birth weight, and possibly for inferior neonatal health. Data are inadequate to determine the extent to which maternal anemia might contribute to maternal mortality. Even for women who enter pregnancy with reasonable iron stores, iron supplements improve iron status during pregnancy and for a considerable length of time postpartum, thus providing some protection against iron deficiency in the subsequent pregnancy. Mounting evidence indicates that maternal iron deficiency in pregnancy reduces fetal iron stores, perhaps well into the first year of life. This deserves further exploration because of the tendency of infants to develop iron deficiency anemia and because of the documented adverse consequences of this condition on infant development. The weight of evidence supports the advisability of routine iron supplementation during pregnancy.
Signaling between periglomerular cells reveals a bimodal role for GABA in modulating glomerular microcircuitry in the olfactory bulb

In the mouse olfactory bulb glomerulus, the GABAergic periglomerular (PG) cells provide a major inhibitory drive within the microcircuit. Here we examine GABAergic synapses between these interneurons. At these synapses, GABA is depolarizing and exerts a bimodal control on excitability. In quiescent cells, activation of GABAA receptors can induce the cells to fire, thereby providing a means for amplification of GABA release in the glomerular microcircuit via GABA-induced GABA release. In contrast, GABA is inhibitory in neurons that are induced to fire tonically. PG–PG interactions are modulated by nicotinic acetylcholine receptors (nAChRs), and our data suggest that changes in intracellular calcium concentrations triggered by nAChR activation can be amplified by GABA release. Our results suggest that bidirectional control of inhibition in PG neurons can allow for modulatory inputs, like the cholinergic inputs from the basal forebrain, to determine threshold set points for filtering out weak olfactory inputs in the glomerular layer of the olfactory bulb via the activation of nAChRs.
Findings from a novel approach to publication guideline revision: user road testing of a draft version of SQUIRE 2.0

Background The Standards for Quality Improvement Reporting Excellence (SQUIRE) Guideline was published in 2008 (SQUIRE 1.0) and was the first publication guideline specifically designed to advance the science of healthcare improvement. Advances in the discipline of improvement prompted us to revise it. We adopted a novel approach to the revision by asking end-users to ‘road test’ a draft version of SQUIRE 2.0. The aim was to determine whether they understood and implemented the guidelines as intended by the developers. Methods Forty-four participants were assigned a manuscript section (ie, introduction, methods, results, discussion) and asked to use the draft Guidelines to guide their writing process. They indicated the text that corresponded to each SQUIRE item used and submitted it along with a confidential survey. The survey examined usability of the Guidelines using Likert-scaled questions and participants’ interpretation of key concepts in SQUIRE using open-ended questions. On the submitted text, we evaluated concordance between participants’ item usage/interpretation and the developers’ intended application. For the survey, the Likert-scaled responses were summarised using descriptive statistics and the open-ended questions were analysed by content analysis. Results Consistent with the SQUIRE Guidelines’ recommendation that not every item be included, less than one-third (n=14) of participants applied every item in their section in full. Of the 85 instances when an item was partially used or was omitted, only 7 (8.2%) of these instances were due to participants not understanding the item. Usage of Guideline items was highest for items most similar to standard scientific reporting (ie, ‘Specific aim of the improvement’ (introduction), ‘Description of the improvement’ (methods) and ‘Implications for further studies’ (discussion)) and lowest (<20% of the time) for those unique to healthcare improvement (ie, ‘Assessment methods for context factors that contributed to success or failure’ and ‘Costs and strategic trade-offs’). Items unique to healthcare improvement, specifically ‘Evolution of the improvement’, ‘Context elements that influenced the improvement’, ‘The logic on which the improvement was based’, ‘Process and outcome measures’, demonstrated poor concordance between participants’ interpretation and developers’ intended application. Conclusions User testing of a draft version of SQUIRE 2.0 revealed which items have poor concordance between developer intent and author usage, which will inform final editing of the Guideline and development of supporting [supplementary materials][1]. It also identified the items that require special attention when teaching about scholarly writing in healthcare improvement. [1]: http://qualitysafety.bmj.com/lookup/suppl/doi:10.1136/bmjqs-2015-004117/-/DC1
Education in Pediatric Basic and Advanced Life Support

Curricula in pediatric resuscitation must be based on adult learning principles. The Pediatric Basic and Advanced Life Support Courses (PBLS, PALS) should use educational strategies fostering positive interactions between the instructor and learners and should take into consideration the learner's motivation for taking the course. Materials should be developed for a specific target audience; course design should be flexible to meet individual needs. The PBLS and PALS courses have different audiences. PBLS is targeted toward caretakers of children. This course will continue to emphasize primary injury prevention as the major strategy but also will give learners the knowledge and skills to perform CPR. To optimize retention of knowledge and skills, the course content must be simplified, and the sequencing of steps in basic life support must be modified. Health care providers caring for pediatric patients in acute care settings are the primary audience for the PALS course. This course already incorporates adult learning principles; only minor revisions are anticipated. Discussion of the trauma patient is limited in the PALS course; additional trauma education could be facilitated by the addition of a trauma module or by other educational courses. All aspects of the PBLS and PALS educational programs must be evaluated continually to determine whether learning objectives have been met and whether the teaching format is appropriate. The answers to evaluation questions will help determine the degree to which the American Heart Association is meeting its educational objectives and how to allocate resources for development and training.
Integration of large-scale metabolic, signaling, and gene regulatory networks with application to infection responses

Mathematical models of biochemical networks, such as metabolic, signaling, and gene networks, have been studied extensively and have been shown to provide accurate descriptions of various cell processes. Nevertheless, their usage is restricted by the fact that they are usually studied in isolation, without feedback from the environment in which they evolve. Integrating these models in a global framework is a promising direction in order to increase both their accuracy and predictive capacity. In this paper, we describe the integration of large-scale metabolic and signaling networks with a regulatory gene network. We focus on the response to infection in mouse macrophage cells. Our computational framework allows to virtually simulate any type of infection and to follow its effect on the cell. The model comprises 3,507 chemical species involved in 4,630 reactions evolving at the fast time scale of metabolic and signaling processes. These interact with 20 genes evolving at the slow time scale of gene expression and regulation. We develop a simulator for this model and use it to study infections with Porphyromonas gingivalis.
Designing a Curriculum for Training Community-Responsive Physicians

There is a need for physicians who will promote health for all, especially for the out-of-hospital patient and the medically underserved. Community-based medical education is central to the training of these physicians. Ad hoc programs have improved student awareness of community-oriented primary care (COPC) during the past two decades. These programs have included student-initiated clinics in medically underserved communities. Future goals to advance COPC should include recruitment of motivated students, longitudinal community-based training programs, research opportunities in COPC, and the development of a cadre of COPC role models.
SU-FF-T-37: Measurement of Cranio-Caudal Catheter Displacement Between Fractions in CT-Based HDR Brachytherapy of Prostate Cancer

Purpose: To measure the cranio-caudal displacement of catheters occurring between consecutive fractions of transrectal ultrasound (TRUS) guided high dose rate (HDR) prostate brachytherapy. Method and Materials: Ten consecutive patients were treated with 2 fractions of 9.5 Gy TRUS guided HDR brachytherapy using dental putty for the fixation of catheters. For each patient, a CT scan with 3 mm slice thickness was acquired before each of the two fractions. Two different references were employed to measure the catheter displacement between fractions: the ischial bone as a bony marker (BM) and the center of two gold markers (COGM) implanted in the prostate. The catheter displacement was calculated by multiplying the thickness of CT slice with the difference in number of CT slices between the reference slice and the slice containing the tip of a catheter. Results: The average (range) magnitude of caudal catheter displacement was 4.1 mm: 2.7 mm (−6.0 to 13.5 mm) for BM method and 5.4 mm (−3.75 to 18.0 mm) for COGM method, respectively. The measurement results obtained from BM and COGM methods verified that both prostate movement and catheter displacement independently occurred between fractions. The most anterior and medial two catheters (catheter position 8 and 12) had the greatest tendency to be displaced in the caudal direction because they were located at the most distant position from the fulcrum, susceptible to the rotation of the dental putty in lateral plane due to patient movement between fractions. Conclusion: The catheter displacement using dental putty for the fixation of catheters is smaller than the conventional technique using pre-fabricated template. The use of both BM and COGM methods for catheter displacement measurement can demonstrate the prostate movement relative to the catheter displacement between fractions.
Sorting algorithms for the implementation of a generalized vector product.

This article presents two sorting procedures with computational complexity O(nk2 log(k)) and O(nk2) to perform the generalized vector product A1⊗ A2⊗ ⊗ An, where each vector Ai has k distinct elements arranged in strictly increasing order. The vector product identifies the k minimal sums, each sum being defined as having one addend from each vector.
Evolution and functional impact of rare coding variation from deep sequencing of human exomes

As a first step toward understanding how rare variants contribute to risk for complex diseases, we sequenced 15,585 human protein-coding genes to an average median depth of 111× in 2440 individuals of European (n = 1351) and African (n = 1088) ancestry. We identified over 500,000 single-nucleotide variants (SNVs), the majority of which were rare (86% with a minor allele frequency less than 0.5%), previously unknown (82%), and population-specific (82%). On average, 2.3% of the 13,595 SNVs each person carried were predicted to affect protein function of ~313 genes per genome, and ~95.7% of SNVs predicted to be functionally important were rare. This excess of rare functional variants is due to the combined effects of explosive, recent accelerated population growth and weak purifying selection. Furthermore, we show that large sample sizes will be required to associate rare variants with complex traits.
Technological innovation and entrepreneurship from the human side

Abstract This paper discusses and briefly reviews the importance of technological innovation in the United States and the role of the human factor. Technological innovation and entrepreneur are defined. The reasons for negativism toward innovation are reviewed. Characteristics of the technological entrepreneurs are identified. The nature of technological innovation is discussed. Finally, ways to encourage corporate entrepreneurs are suggested.
Why study interstellar matter very close to the Sun

Abstract Interstellar matter (ISM) sets the boundary conditions of the heliosphere and dominates the interplanetary medium. The heliosphere configuration has varied over recent history, as the Sun emerged from the Local Bubble and entered a turbulent outflow of cloudlets originating from the Scorpius-Centaurus Association direction. Several indicators suggest that the local interstellar magnetic field is weak and parallel to the galactic plane. Observations of the interaction products of the ISM with the heliosphere, such as pickup ions and anomalous cosmic rays, when combined with data on the ISM towards nearby stars, provide unique constraints on the composition and physical properties of nearby gas. These data suggest abundances in nearby ISM are subsolar, and that gas and dust are not well mixed at the solar location.
Adsorption of selenium on hydrous alumina

Adsorption of selenite (SeO[sub 3][sup 2[minus]]) and selenate (SeO[sub 4][sup 2[minus]]) on hydrous alumina ([gamma]-Al[sub 2]O[sub 3]) was studied using batch equilibrium experiments. pH, the single most significant parameter, affected both speciations of selenium (Se) and of surface hydroxo groups on alumina that, in turn, determined the extent of adsorption of the oxyanions. Adsorption of both anions could be described satisfactorily by the Triple Layer Model (TLM). In the presence of SO[sub 4][sup 2[minus]], Se(VI) adsorption decreased significantly. Fixed bed adsorbers of alumina with an empty bed contact time (EBCT) of 2 minutes or longer provided excellent removal of Se(IV) from dilute aqueous solutions in the pH range of 5 to 8 for a long period. Spent alumina beds were successfully regenerated with 0.25 percent NaOH solution followed by rinsing with deionized water and 0.05 M HCl. While two percent of alumina was lost per cycle on an average, the loss in adsorption capacity of regenerated alumina was insignificant. 43 refs., 10 figs., 7 tabs.
Biological Studies on the Nitidulid Beetles Found in Pineapple Fields (Nitidulidae, Coleoptera)

The Nitidulid beetles of pineapple fields are primarily scavengers of decaying plant wastes and have become very abundant coincident with the expansion of the pineapple industry. In harvesting pineapples, only the fruit is removed while the leaves and stumps which have an extremely high content of sugars and starches remain in the fields to decompose. This decomposing material represents, in most cases, considerably more material than is harvested in the form of fruit. The stumps particularly, during decomposition, form an ideal environment wherein the beetles may breed.
Characterization of lignin using multi-angle laser light scattering and atomic force microscopy

Small differences in the isolation techniques of lignin can result in significant changes in its molecular structure and configuration. Light scattering (evaluated at 18 different angles in a plane), Atomic Force Microscopy (AFM) and Near Infrared Spectroscopy (NIR) proved very effective for evaluating the characteristics of lignin. Zimm plots were generated using Zimm, Debye and Berry formalisms to evaluate the weight average molecular weight (MW), radius of gyration (rg), hydrodynamic radius (rh) and second virial coefficient (A2). Two types of lignin and nine different solvents were used for the study, to analyze the conformation of lignin molecules in different solvents expected to be used in lignin degradation and subsequent analysis. Absolute MW and rg decreased and the dn/dc increased when the solvent used for lignin was changed from water to sodium hydroxide. The two types of lignin also exhibited different values for all the above estimated parameters. This study also highlighted the differences between the unlyophilized and lyophilized lignin in terms of aggregation, pH dependence and stability over time. This aggregation has never been seen on a ultraviolet (UV) or refractive index (RI) detector that has been used so far for liquid chromatography (LC) reducing the reliability of lignin depolymerization data obtained without light scattering.
Adaptive Identification of Fluid-Dynamic Systems

Abstract : Fluid-dynamic systems are inherently nonlinear and are subject to a combination of coherent and random unsteady disturbances. As a result, accurate low-order dynamic models are difficult to obtain for real-time control of such systems. Therefore, controllers implementing adaptive on-line system identification are ideally suited to flow control problems. Adaptive linear and nonlinear filters for real-time system identification are presented in this paper. The linear models studied are traditional FIR and IIR filters, and the nonlinear models include a 2nd-order Volterra filter and the Bilinear filter. The coefficients of the adaptive filter models are calculated and updated using two of the most popular recursive methods, the normalized LMS and RLS algorithms. The adaptive filters are tested offline in software and then implemented on real-time DSP hardware. The focus of this study is on model accuracy and viability in real-time applications. The real-time performance is measured in terms of achievable sampling frequency. Specific applications to relevant nonlinear systems, a spring-mass damper model and a drag-law problem, are also considered in detail.