cssi/SciSciGPT-SciSciNet huggingface dataset:
Moderate-intensity exercise versus high-intensity interval training to recover walking post-stroke: protocol for a randomized controlled trial.

Stroke results in neurologic impairments and aerobic deconditioning that contribute to limited walking capacity which is a major barrier post-stroke. Current exercise recommendations and stroke rehabilitation guidelines recommend moderate-intensity aerobic training post-stroke. Locomotor high-intensity interval training is a promising new strategy that has shown significantly greater improvements in aerobic fitness and motor performance than moderate-intensity aerobic training in other populations. However, the relative benefits and risks of high-intensity interval training and moderate-intensity aerobic training remain poorly understood following stroke. In this study, we hypothesize that locomotor high-intensity interval training will result in greater improvements in walking capacity than moderate-intensity aerobic training. Using a single-blind, 3-site randomized controlled trial, 50 chronic (> 6 months) stroke survivors are randomly assigned to complete 36 locomotor training sessions of either high-intensity interval training or moderate-intensity aerobic training. Main eligibility criteria are age 40–80 years, single stroke for which the participant received treatment (experienced 6 months to 5 years prior to consent), walking speed ≤ 1.0 m/s, able to walk at least 3 min on the treadmill at ≥ 0.13 m/s (0.3 mph), stable cardiovascular condition (American Heart Association class B), and the ability to walk 10 m overground without continuous physical assistance. The primary outcome (walking capacity) and secondary outcomes (self-selected and fast gait speed, aerobic fitness, and fatigue) are assessed prior to initiating training and after 4 weeks, 8 weeks, and 12 weeks of training. This study will provide fundamental new knowledge to inform the selection of intensity and duration dosing parameters for gait recovery and optimization of aerobic training interventions in chronic stroke. Data needed to justify and design a subsequent definitive trial will also be obtained. Thus, the results of this study will inform future stroke rehabilitation guidelines on how to optimally improve walking capacity following stroke. ClinicalTrials.gov NCT03760016 . Registered on November 30, 2018.
DIAmeter: matching peptides to data-independent acquisition mass spectrometry data.

Motivation Tandem mass spectrometry data acquired using data independent acquisition (DIA) is challenging to interpret because the data exhibits complex structure along both the mass-to-charge (m/z) and time axes. The most common approach to analyzing this type of data makes use of a library of previously observed DIA data patterns (a 'spectral library'), but this approach is expensive because the libraries do not typically generalize well across laboratories. Results Here, we propose DIAmeter, a search engine that detects peptides in DIA data using only a peptide sequence database. Although some existing library-free DIA analysis methods (i) support data generated using both wide and narrow isolation windows, (ii) detect peptides containing post-translational modifications, (iii) analyze data from a variety of instrument platforms and (iv) are capable of detecting peptides even in the absence of detectable signal in the survey (MS1) scan, DIAmeter is the only method that offers all four capabilities in a single tool. Availability and implementation The open source, Apache licensed source code is available as part of the Crux mass spectrometry analysis toolkit (http://crux.ms). Supplementary information Supplementary data are available at Bioinformatics online.
The Geometrization of Maxwell's Homogenous Equation and the Emergence of Gravity.

A recently proposed classical field theory in which the Maxwell tensor is coupled to the Riemann-Christoffel curvature tensor in a fundamentally new way is reviewed and extended. This proposed geometrization of the Maxwell tensor leaves the classical equations of electromagnetism unchanged, but also leads to the emergence of gravity as all solutions of the proposed field equations are shown to be solutions of Einstein's equation of General Relativity augmented by a term that can mimic the properties of dark matter and/or dark energy. Using specific solutions to the proposed theory, the unification brought to electromagnetic and gravitational phenomena as well as the consistency of those solutions with those of the classical Maxwell and Einstein field equations are emphasized throughout. Unique to the four fundamental field equations that comprise the proposed theory, and based on specific solutions to them are: the emergence of antimatter and its behavior in gravitational fields, the emergence of dark matter and dark energy mimicking terms in the context of General Relativity, an underlying relationship between electromagnetic and gravitational radiation, the impossibility of negative mass solutions that would generate repulsive gravitational fields or antigravity, and a method for quantizing the charge and mass of particle-like solutions.
Utilizing ensemble learning for performance and power modeling and improvement of parallel cancer deep learning CANDLE benchmarks

Machine learning (ML) continues to grow in importance across nearly all domains and is a natural tool in modeling to learn from data. Often a tradeoff exists between a model's ability to minimize bias and variance. In this paper, we utilize ensemble learning to combine linear, nonlinear, and tree-/rule-based ML methods to cope with the bias-variance tradeoff and result in more accurate models. Hardware performance counter values are correlated with properties of applications that impact performance and power on the underlying system. We use the datasets collected for two parallel cancer deep learning CANDLE benchmarks, NT3 (weak scaling) and P1B2 (strong scaling), to build performance and power models based on hardware performance counters using single-object and multiple-objects ensemble learning to identify the most important counters for improvement. Based on the insights from these models, we improve the performance and energy of P1B2 and NT3 by optimizing the deep learning environments TensorFlow, Keras, Horovod, and Python under the huge page size of 8 MB on the Cray XC40 Theta at Argonne National Laboratory. Experimental results show that ensemble learning not only produces more accurate models but also provides more robust performance counter ranking. We achieve up to 61.15% performance improvement and up to 62.58% energy saving for P1B2 and up to 55.81% performance improvement and up to 52.60% energy saving for NT3 on up to 24,576 cores.
Proactive Provenance Policies for Automatic Cryptographic Data Centric Security

Data provenance analysis has been used as an assistive measure for ensuring system integrity. However, such techniques are typically reactive approaches to identify the root cause of an attack in its aftermath. This is in part due to the fact that the collection of provenance metadata often results in a deluge of information that cannot easily be queried and analyzed in real time. This paper presents an approach for proactively reasoning about provenance metadata within the Automatic Cryptographic Data Centric (ACDC) security architecture, a new security infrastructure in which all data interactions are considered at a coarse granularity, similar to the Function as a Service model. At this scale, we have found that data interactions are manageable for the proactive specification and evaluation of provenance policies—constraints placed on provenance metadata to prevent the consumption of untrusted data. This paper provides a model for proactively evaluating provenance metadata in the ACDC paradigm as well as a case study of an electronic voting scheme to demonstrate the applicability of ACDC and the provenance policies needed to ensure data integrity.
Wavesplit: End-to-End Speech Separation by Speaker Clustering

We introduce Wavesplit, an end-to-end source separation system. From a single mixture, the model infers a representation for each source and then estimates each source signal given the inferred representations. The model is trained to jointly perform both tasks from the raw waveform. Wavesplit infers a set of source representations via clustering, which addresses the fundamental permutation problem of separation. For speech separation, our sequence-wide speaker representations provide a more robust separation of long, challenging recordings compared to prior work. Wavesplit redefines the state-of-the-art on clean mixtures of 2 or 3 speakers (WSJ0-2/3mix), as well as in noisy and reverberated settings (WHAM/WHAMR). We also set a new benchmark on the recent LibriMix dataset. Finally, we show that Wavesplit is also applicable to other domains, by separating fetal and maternal heart rates from a single abdominal electrocardiogram.
Non-asymptotic Identification of Linear Dynamical Systems Using Multiple Trajectories

This paper considers the problem of linear time-invariant (LTI) system identification using input/output data. Recent work has provided non-asymptotic results on partially observed LTI system identification using a single trajectory but is only suitable for stable systems. We provide finite-time analysis for learning Markov parameters based on the ordinary least-squares (OLS) estimator using multiple trajectories, which covers both stable and unstable systems. For unstable systems, our results suggest that the Markov parameters are harder to estimate in the presence of process noise. Without process noise, our upper bound on the estimation error is independent of the spectral radius of system dynamics with high probability. These two features are different from fully observed LTI systems for which recent work has shown that unstable systems with a bigger spectral radius are easier to estimate. Extensive numerical experiments demonstrate the performance of our OLS estimator.
A convergent finite difference method for optimal transport on the sphere

Abstract We introduce a convergent finite difference method for solving the optimal transportation problem on the sphere. The method applies to both the traditional squared geodesic cost (arising in mesh generation) and a logarithmic cost (arising in the reflector antenna design problem). At each point on the sphere, we replace the surface PDE with a Generated Jacobian equation posed on the local tangent plane using geodesic normal coordinates. The discretization is inspired by recent monotone methods for the Monge-Ampere equation, but requires significant adaptations in order to correctly handle the mix of gradient and Hessian terms appearing inside the nonlinear determinant operator, as well as the singular logarithmic cost function. Numerical results demonstrate the success of this method on a wide range of challenging problems involving both the squared geodesic and the logarithmic cost functions.
LDPC Codes with Soft Interference Cancellation for Uncoordinated Unsourced Multiple Access

This article presents a novel enhancement to the random spreading based coding scheme developed by Pradhan et al. for the unsourced multiple access channel. The original coding scheme features a polar outer code in conjunction with a successive cancellation list decoder (SCLD) and a hard-input soft-output MMSE estimator. In contrast, the proposed scheme employs a soft-input soft-output MMSE estimator for multi-user detection. This is accomplished by replacing the SCLD based polar code with an LDPC code amenable to belief propagation decoding. This novel framework is leveraged to successfully pass pertinent soft information between the MMSE estimator and the outer code. LDPC codes are carefully designed using density evolution techniques to match the iterative process. This enhanced architecture exhibits significant performance improvements and represents the state-of-the-art over a wide range of system parameters.
High Force Density Multi-Stage Electrohydrodynamic Jets using Folded Laser Microfabricated Electrodes

The electrohydrodynamic (EHD) force produced by ions ejected from a corona plasma is a solid state, silent mechanism for accelerating air, useful for applications ranging from electronics cooling to flying microrobots. This paper presents the theoretical motivation and the first implementation of a multi-stage, highly miniaturized EHD device, which can provide both improved absolute power output and power density as compared to single-stage devices. A laser microfabricated, folded electrode design reduces component count and assembly time. Data from one, two, and three-stage devices demonstrates a near linear scaling of output force with stage count, indicating inter-stage ducting successfully reduces losses. Device lifetime is assessed to validate the use of stainless-steel emission electrodes. Areal thrust, force density, and volumetric power density for the three-stage device are among the highest ever measured from an EHD actuator.
Solving the Non-Crossing MAPF with CP

We introduce a new Multi-Agent Path Finding (MAPF) problem which is motivated by an industrial application. Given a fleet of robots that move on a workspace that may contain static obstacles, we must find paths from their current positions to a set of destinations, and the goal is to minimise the length of the longest path. The originality of our problem comes from the fact that each robot is attached with a cable to an anchor point, and that robots are not able to cross these cables. We formally define the Non-Crossing MAPF (NC-MAPF) problem and show how to compute lower and upper bounds by solving well known assignment problems. We introduce a Variable Neighbourhood Search (VNS) approach for improving the upper bound, and a Constraint Programming (CP) model for solving the problem to optimality. We experimentally evaluate these approaches on randomly generated instances.
Epigenetic instability may alter cell state transitions and anticancer drug resistance

Drug resistance is a significant obstacle to successful and durable anti-cancer therapy. Targeted therapy is often effective during early phases of treatment; however, eventually cancer cells adapt and transition to drug-resistant cells states rendering the treatment ineffective. It is proposed that cell state can be a determinant of drug efficacy and manipulated to affect the development of anticancer drug resistance. In this work, we developed two stochastic cell state models and an integrated stochastic-deterministic model referenced to brain tumors. The stochastic cell state models included transcriptionally-permissive and -restrictive states based on the underlying hypothesis that epigenetic instability mitigates lock-in of drug-resistant states. When moderate epigenetic instability was implemented the drug-resistant cell populations were reduced, on average, by 60%, whereas a high level of epigenetic disruption reduced them by about 90%. The stochastic-deterministic model utilized the stochastic cell state model to drive the dynamics of the DNA repair enzyme, methylguanine-methyltransferase (MGMT), that repairs temozolomide (TMZ)-induced O6-methylguanine (O6mG) adducts. In the presence of epigenetic instability, the production of MGMT decreased that coincided with an increase of O6mG adducts following a multiple-dose regimen of TMZ. Generation of epigenetic instability via epigenetic modifier therapy could be a viable strategy to mitigate anticancer drug resistance.
An improved method for evaluating inverted encoding models

Abstract Inverted encoding models have recently become popular as a method for decoding stimuli and investigating neural representations. Here we present a novel modification to inverted encoding models that improves the flexibility and interpretability of stimulus reconstructions, addresses some key issues inherent in the standard inverted encoding model procedure, and provides trial-by-trial stimulus predictions and goodness-of-fit estimates. The standard inverted encoding model approach estimates channel responses (or “reconstructions”), which are averaged and aligned across trials and then typically evaluated using a metric such as slope, amplitude, etc. We discuss how this standard procedure can produce spurious results and other interpretation issues. Our modifications are not susceptible to these methodological issues and are further advantageous due to our decoding metric taking into account the choice of population-level tuning functions and employing a prediction error-based metric directly comparable across experiments. Our modifications also allow researchers to obtain trial-by-trial confidence estimates independent of prediction error which can be used to threshold reconstructions and increase statistical power. We validate and demonstrate the improved utility of our modified inverted encoding model procedure across three real fMRI datasets, and additionally offer a Python package for easy implementation of our approach.
Policies for elementary links in a quantum network

Distributing entanglement over long distances is one of the central tasks in quantum networks. An important problem, especially for near-term quantum networks, is to develop optimal entanglement distribution protocols that take into account the limitations of current and near-term hardware, such as quantum memories with limited coherence time. We address this problem by initiating the study of quantum network protocols for entanglement distribution using the theory of decision processes, such that optimal protocols (referred to as policies in the context of decision processes) can be found using dynamic programming or reinforcement learning algorithms. As a first step, in this work we focus exclusively on the elementary link level. We start by defining a quantum decision process for elementary links, along with figures of merit for evaluating policies. We then provide two algorithms for determining policies, one of which we prove to be optimal (with respect to fidelity and success probability) among all policies. Then we show that the previously-studied memory-cutoff protocol can be phrased as a policy within our decision process framework, allowing us to obtain several new fundamental results about it. The conceptual developments and results of this work pave the way for the systematic study of the fundamental limitations of near-term quantum networks, and the requirements for physically realizing them.
Divisorial instability and Vojta’s main conjecture for $\mathbb{Q}$-Fano varieties

We study Diophantine arithmetic properties of birational divisors in conjunction with concepts that surround $\mathrm{K}$-stability for Fano varieties. There is also an interpretation in terms of the barycentres of Newton-Okounkov bodies. Our main results show how the notion of divisorial instability, in the sense of K. Fujita, implies instances of Vojta's Main Conjecture for Fano varieties. A main tool in the proof of these results is an arithmetic form of Cartan's Second Main Theorem that has been obtained by M. Ru and P. Vojta.
How to Reduce Dimension With PCA and Random Projections

In our “big data” age, the size and complexity of data is steadily increasing. Methods for dimension reduction are ever more popular and useful. Two distinct types of dimension reduction are “data-oblivious” methods such as random projections and sketching, and “data-aware” methods such as principal component analysis (PCA). Both have their strengths, such as speed for random projections, and data-adaptivity for PCA. In this work, we study how to combine them to get the best of both. We study “sketch and solve” methods that take a random projection (or sketch) first, and compute PCA after. We compute the performance of several popular sketching methods (random iid projections, random sampling, subsampled Hadamard transform, CountSketch, etc) in a general “signal-plus-noise” (or spiked) data model. Compared to well-known works, our results are: 1) give asymptotically exact results; 2) apply when the signal components are only slightly above the noise, but the projection dimension is non-negligible. We also study stronger signals allowing more general covariance structures. We find that: 1) signal strength decreases under projection in a delicate way depending on the structure of the data and the sketching method; 2) orthogonal projections are slightly more accurate; 3) randomization does not hurt too much, due to concentration of measure; 4) the CountSketch can be somewhat improved by a normalization method. Our results have implications for statistical learning and data analysis. We also illustrate that the results are highly accurate in simulations and in analyzing empirical data.
Random walks and quasi-convexity in acylindrically hyperbolic groups

It is known that every infinite index quasi-convex subgroup $H$ of a non-elementary hyperbolic group $G$ is a free factor in a larger quasi-convex subgroup of $G$. We give a probabilistic generalization of this result. That is, we show that when $R$ is a subgroup generated by independent random walks in $G$, then $\langle H, R\rangle\cong H\ast R$ with probability going to one as the lengths of the random walks go to infinity and this subgroup is quasi-convex in $G$. Moreover, our results hold for a large class of groups acting on hyperbolic metric spaces and subgroups with quasi-convex orbits. In particular, when $G$ is the mapping class group of a surface and $H$ is a convex cocompact subgroup we show that $\langle H, R\rangle$ is convex cocompact and isomorphic to $ H\ast R$.
Deep Neural Dynamic Bayesian Networks Applied to EEG Sleep Spindles Modeling

We propose a generative model for single–channel EEG that incorporates the constraints experts actively enforce during visual scoring. The framework takes the form of a dynamic Bayesian network with depth in both the latent variables and the observation likelihoods—while the hidden variables control the durations, state transitions, and robustness, the observation architectures parameterize Normal–Gamma distributions. The resulting model allows for time series segmentation into local, reoccurring dynamical regimes by exploiting probabilistic models and deep learning. Unlike typical detectors, our model takes the raw data (up to resampling) without pre–processing (e.g., filtering, windowing, thresholding) or post–processing (e.g., event merging). This not only makes the model appealing to real–time applications, but it also yields interpretable hyperparameters that are analogous to known clinical criteria. We derive algorithms for exact, tractable inference as a special case of Generalized Expectation Maximization via dynamic programming and backpropagation. We validate the model on three public datasets and provide support that more complex models are able to surpass state–of–the–art detectors while being transparent, auditable, and generalizable.
Genetically Encoded Green Fluorescent Biosensors for Monitoring UDP-GlcNAc in Live Cells

Uridine diphosphate N-acetylglucosamine (UDP-GlcNAc) is a nucleotide sugar used by glycosyltransferases to synthesize glycoproteins, glycosaminoglycans, glycolipids, and glycoRNA. UDP-GlcNAc also serves as the donor substrate for forming O-GlcNAc, a dynamic intracellular protein modification involved in diverse signaling and disease processes. UDP-GlcNAc is thus a central metabolite connecting nutrition, metabolism, signaling, and disease. There is a great interest in monitoring UDP-GlcNAc in biological systems. Here, we present the first genetically encoded, green fluorescent UDP-GlcNAc sensor (UGAcS), an optimized insertion of a circularly permuted green fluorescent protein (cpGFP) into an inactive mutant of an Escherichia coli UDP-GlcNAc transferase, for ratiometric monitoring of UDP-GlcNAc dynamics in live mammalian cells. Although UGAcS responds to UDP-GlcNAc quite selectively among various nucleotide sugars, UDP and uridine triphosphate (UTP) interfere with the response. We thus developed another biosensor named UXPS, which is responsive to UDP and UTP but not UDP-GlcNAc. We demonstrated the use of the biosensors to follow UDP-GlcNAc levels in cultured mammalian cells perturbed with nutritional changes, pharmacological inhibition, and knockdown or overexpression of key enzymes in the UDP-GlcNAc synthesis pathway. We further utilized the biosensors to monitor UDP-GlcNAc concentrations in pancreatic MIN6 β-cells under various culture conditions.
Tverberg’s Theorem, Disks, and Hamiltonian Cycles

For a finite set of S points in the plane and a graph with vertices on S, consider the disks with diameters induced by the edges. We show that for any odd set S, there exists a Hamiltonian cycle for which these disks share a point, and for an even set S, there exists a Hamiltonian path with the same property. We discuss high-dimensional versions of these theorems and their relation to other results in discrete geometry.
An Impact of Capital Adequacy Ratio on the Profitability of Private Sector Banks in India – A Study

Profitability being one of the cardinal principles of bank lending acts as a game changer for the survival and success of private sector banks in India. In order to stay profitable, banks have to capitalise on every penny advanced to yield the expected returns. However, considering the constraints laid down by the Reserve Bank of India, banks have to maintain a minimum capital adequacy ratio, as per the current BASEL III regulations active in India. With the mergers of public sector banks, the challenge has got just tougher for the private sector banks in India. Expansion and Diversification are the key strategies adopted by the key players from the private banking sector, however, with the minimum capital adequacy ratio observed by them, it is necessary to understand its actual impact on the bank’s profitability. This research paper aims to throw light upon the linkage that capital adequacy has with the bank’s profitability. It attempts to establish a relation between the Capital Adequacy Ratio with the Net profits of the bank. For the purpose of this study, data from the past 5 years of the leading private sector banks has been collected, namely, HDFC Bank, ICICI Bank, Kotak Mahindra Bank, AXIS Bank and YES Bank. The collected data has been analysed using Pearson’s Correlation to establish a relation between the CAR Ratio & the bank’s profitability. Hypothesis testing has been further done to study the quantum of proportionate change in the profitability with a change in the CAR Ratio for private sector banks using applicable research tools. The said research tools are applied to achieve the desired results while maintaining the required quantum of accuracy. It also aims to understand the proportionate impact of changes in CAR to the bank’s profitability, which can act as a suggested measure for banks to develop a reliable framework for efficient capital management and increase overall efficiency. The results derived from the data collected and analyzed aim to provide scope for further study on the subject matter.
Locomotion and Control of a Friction-Driven Tripedal Robot

This paper presents a novel omnidirectional gait design and feedback control of a radially symmetric tripedal friction-driven robot. The robot features 3 servo motors mounted on a 3-D printed chassis 7 cm from the center of mass and separated 120 degrees. These motors drive limbs, which impart frictional reactive forces on the body. We first introduce a mathematical model for the robot motion, then show experimental observations performed on a uniform friction surface, which validated the accuracy of the model. This model was then used to create an omnidirectional gait that allows the robot to translate in any direction. Based on this gait, we also introduce a Proportional-Integral (PI) feedback control framework that enables the robot to closely follow a desired path. Contrasting with feedforward motion generation, the proposed feedback controller reduced the tracking error by approximately 46%. We have successfully demonstrated the approach in our robot hardware for the problem of line following using live feedback from an overhead tracking camera. Our controller is also able to correct for aerodynamic disturbances generated by a high-volume industrial fan with a mean flow speed of 5.5ms−1, reducing path error by 65% relative to the basic position update procedure.
Normalized coefficient of variation (nCV): a method to evaluate circadian clock robustness in population scale data.

Summary Robust oscillation of clock genes is a core feature of the circadian system. Relative amplitude (rAMP) measures the robustness of clock gene oscillations but only works for longitudinal samples. We lack a method for estimating robust oscillations from human samples without labeled time. We show that the normalized coefficient of variation (nCV) of 10 clock genes is linearly correlated with their normalized rAMP, independent of time labels. We found that the mean nCV of clock genes are consistently decreased in tumors compared to non-tumors, suggesting a new therapeutic target in cancer treatment by enhancing clock robustness. nCV can provide a simple measure of the clock robustness in population-level datasets. Availability and implementation The nCV package (https://github.com/gangwug/nCV) and web application (https://github.com/gangwug/nCVapp) are available on the GitHub repository. Supplementary information Supplementary data are available at Bioinformatics online.
Fast Core Pricing for Rich Advertising Auctions

Standard ad auction formats do not immediately extend to settings where multiple size configurations and layouts are available to advertisers. In these settings, the sale of web advertising space i...
The incidence and importance of the pseudogestational sac revisited.

Background In a pregnancy of unknown location, an intrauterine fluid collection may represent either the early gestational sac of an intrauterine pregnancy, or as reported in previous literature, the pseudogestational sac of an ectopic pregnancy. Various sonographic features have been used to distinguish these 2 entities, but the clinical relevance of the pseudogestational sac remains unclear. Objective To establish the incidence and relative rate of intrauterine fluid collection among ectopic and intrauterine pregnancies and to determine if the size of the collection differs between ectopic and intrauterine pregnancies Study Design We performed a retrospective cohort study of women with pregnancies of unknown location and pelvic or abdominal pain or bleeding. We calculated the incidences of intrauterine fluid collections among ectopic and intrauterine pregnancies, including both ongoing pregnancies and spontaneous abortions, given that that our focus was location and not viability. We calculated the relative risk of ectopic pregnancy if an intrauterine fluid collection was present, adjusting for age and vaginal bleeding. We compared the incidences of ectopic and intrauterine pregnancies among those with and without intrauterine fluid collections. Among those with collections, we compared the mean sac diameter between ectopic and intrauterine pregnancies in continuous and categorical fashions. Results We evaluated 1236 women presenting with a pregnancy of unknown location. The rates of ectopic and intrauterine pregnancies (including spontaneous abortions) were 13.1% and 63.9%, respectively, with the remainder lost to follow-up. On ultrasound, 452 women (36.6%) had an intrauterine fluid collection. Eight of 162 ectopic pregnancies (4.9%) had a collection, compared with 363 of 789 intrauterine pregnancies (46.0%) (P=.01). Of the ectopics with a fluid collection, 5 had an adnexal mass. The presence of intrauterine fluid collection decreased the risk of ectopic pregnancy (adjusted relative risk, 0.09; 95% confidence interval, 0.05–0.19) after adjusting for age and the presence of bleeding. Among those with an intrauterine fluid collection, the rate of ectopic pregnancy was 2.2%, and the rate of intrauterine pregnancy was 97.8%; among those without a collection, the rate of ectopic pregnancy was 26.7%, and the rate of intrauterine pregnancy was 73.3%. The mean sac diameter did not differ between ectopic and intrauterine pregnancies, whether analyzed continuously or categorically. Conclusion In the presence of an intrauterine fluid collection, the rate of ectopic pregnancy is very low. The size of the intrauterine fluid collection in a woman with a pregnancy of unknown location cannot be used to distinguish between a gestational sac and a pseudogestational sac. Pseudogestational sacs are uncommon and of little clinical consequence. In assessing pregnancies of unknown location, clinicians should incorporate the entire clinical picture, including other sonographic findings, to avoid incorrect or delayed diagnoses.
WfChef: Automated Generation of Accurate Scientific Workflow Generators

Scientific workflow applications have become mainstream and their automated and efficient execution on large-scale compute platforms is the object of extensive research and development. For these efforts to be successful, a solid experimental methodology is needed to evaluate workflow algorithms and systems. A foundation for this methodology is the availability of realistic workflow instances. Dozens of workflow instances for a few scientific applications are available in public repositories. While these are invaluable, they are limited: workflow instances are not available for all application scales of interest. To address this limitation, previous work has developed generators of synthetic, but representative, workflow instances of arbitrary scales. These generators are popular, but implementing them is a manual, labor-intensive process that requires expert application knowledge. As a result, these generators only target a handful of applications, even though hundreds of applications use workflows in production.In this work, we present WfChef, a framework that fully automates the process of constructing a synthetic workflow generator for any scientific application. Based on an input set of workflow instances, WfChef automatically produces a synthetic workflow generator. We define and evaluate several metrics for quantifying the realism of the generated workflows. Using these metrics, we compare the realism of the workflows generated by WfChef generators to that of the workflows generated by the previously available, hand-crafted generators. We find that the WfChef generators not only require zero development effort (because it is automatically produced), but also generate workflows that are more realistic than those generated by hand-crafted generators.
College Readiness, Early Post-secondary Academic Performance and the GED Degree: Evidence from Kentucky

The General Education Development (GED) certificate is recognized as a credible alternative to a high school diploma in the US. In this paper, we use detailed administrative data from the state of Kentucky to measure the efficacy of a GED degree. We examine the association between the GED college readiness and early post-secondary academic performance. Following previous work in this area, we address selection issues by focusing on a sample of students who are identified as at-risk prior to high school entry. Our results suggest the GED credential is not a credible signal of PSE readiness, particularly in the area of mathematics. GED graduates attain a lower first semester GPA and are also less likely to re-enroll in second semester courses. Differences in the highest level of high school math coursework between GED holders and traditional graduates account for a majority of the observed GED related math-readiness disparities. We also test whether the substantial changes made to the GED exam in 2014 improved student readiness in PSE institutions, however we find no meaningful improvements in PSE math readiness.
The relation between earnings and price momentum: Does it vary across regimes?

This paper investigates the time-varying relationship between earnings momentum and price momentum. Using a Markov-switching framework, allowing for variation between high volatility and low volatility states, we find that price momentum is significantly more influenced by earnings momentum in the high volatility state. Further for price momentum we find that loser firms display a higher degree of differential response to earnings momentum across the low and high volatility states than winner firms. Limited financing and investor sensitivity to future investment opportunities might explain these two results. Additional analysis indicates that loser firms tend to be more financially constrained. The results are robust using alternative instrument variables.
The essential synchronization backbone problem

Network optimization strategies for the process of synchronization have generally focused on the re-wiring or re-weighting of links in order to (1) expand the range of coupling strengths that achieve synchronization, (2) expand the basin of attraction for the synchronization manifold, or (3) lower the average time to synchronization. A new optimization goal is proposed in seeking the minimum subset of the edge set of the original network that enables the same essential ability to synchronize in that the synchronization manifolds have conjugate stability. We call this type of minimal spanning subgraph an essential synchronization backbone of the original system, and we present two algorithms: one is a strategy for an exhaustive search for a true solution, while the other is a method of approximation for this combinatorial problem. The solution spaces that result from different choices of dynamical systems and coupling schemes vary with the level of a hierarchical structure present and also the number of interwoven central cycles. Applications can include the important problem in civil engineering of power grid hardening, where new link creation may be costly, and the defense of certain key links to the functional process may be prioritized.
Lipid Composition of Purified Vesicular Stomatitis Viruses

Abstract Methods are described for the production of vesicular stomatitis (VS) virus of sufficient purity for reliable chemical analysis. VS virions released from infected cells were concentrated and purified at least 150-fold by sequential steps of precipitation with polyethylene glycol, column chromatography, rate zonal centrifugation, and equilibrium centrifugation. The Indiana serotype (VSInd virus) propagated in L-cells was found to contain 3% ribonucleic acid, 64% protein, 13% carbohydrate, and 20% lipid; the molar ratio of cholesterol to phospholipid was 0.6 or greater. Thin-layer chromatography revealed no unusual neutral lipids or phospholipids and gas-liquid chromatography revealed no unusual fatty acids incorporated into VS virions. The antigenically distinct New Jersey serotype (VSNJ virus) grown in L-cells showed a similar lipid profile except that the proportion of neutral lipids was larger than in VSInd virus also grown in L-cells. This differences was less pronounced when the lipid composition of VSInd and VSNJ viruses grown in chick embryo cells was compared, but VSNJ virus grown in either cell type always contained larger amounts of neutral lipids other than cholesterol than did VSInd virus. The lipid composition of both VSInd and VSNJ viruses grown in L-cells or chick embryo cells more closely resembled that of plasma membrane than of whole cells. A consistent finding was the relatively large amounts of phosphatidylethanolamine and sphingomyelin and the relatively small amounts of phosphatidylcholine in both VS viruses compared with uninfected whole L-cells and chick embryo cells or their plasma membranes. The methods available for isolation of plasma membranes were inadequate for conclusive comparison of the lipids of VS virions with the lipids of the plasma membranes of their host cells. Nevertheless, the data obtained are consistent with two hypotheses: (i) the lipid composition of VS viruses primarily reflects their membrane site of maturation, and (ii) the newly synthesized viral proteins inserted into cell membranes influence the proportions of phospholipids and neutral lipids selected for incorporation into the viral membrane.
Observations of large-scale structure in the inner heliosphere with Doppler scintillation measurements

A study of large-scale structure in the inner heliosphere based on unique and extensive Pioneer Venus Doppler scintillation data has been initiated. Preliminary results include the first observation of corotating stream structure inside 35 solar radii. Results on variations of the Doppler scintillations with solar cycle and heliocentric distance, and their implications in terms of solar wind structure and mass flux density, are discussed.
Encountering Food Systems

AbstractThis essay centers on an encounter with an idea, one that has become central to teaching—and activism—around food and agriculture—explored through an ongoing conversation. Neither of us presumed to have a full understanding of the history and uses of the term “food systems.” However, to understand its potential in teaching about food and agriculture, we considered how and when each of us—sociologists by training, but interdisciplinary by necessity—encountered and began using the term. According to Hewitt and Stokes (1975; see also Stokes and Hewitt 1976), a disclaimer is a way of protecting oneself in advance from negative comments or potentially stigmatizing identities. While neither of us disclaims our identity as sociologists whose projects span disciplinary boundaries, neither do we represent ourselves as having definitive knowledge about food systems. We therefore look forward to having others contest and embellish the definitions, theories and categorizations we discussed at conferences, ove...
Studies on the soluble and lysosomal neuraminidases of rat liver

Abstract The presence of two neuraminidases in rat liver, one strongly bound to the lysosomes and another that occurs in soluble form in the cytosol has been established. The properties of these two enzymes differ markedly. The pH optimum, with neuramin lactose as the substrate, is 5.8 for the soluble neuraminidase (5.2 after partial purification) and 4.4 for the lysosomal enzyme. Soluble neuraminidase is inhibited by Cu2+, Hg2+, and Zn2+ which have no effect on the lysosomal enzyme. Conversely, Na+, K+, and Li+ inhibit the lysosomal but not the soluble neuraminidase. The soluble enzyme hydrolyzes low molecular weight substrates such as neuramin lactose, neuramin lactose sulfate, and a sialoglycopeptide fraction isolated from a Pronase digest of ovine submaxillary glycoprotein but no activity can be detected when brain gangliosides and intact ovine submaxillary glycoprotein are used as substrates. The lysosomal neuraminidase, on the other hand, hydrolyzes all these substrates very efficiently.
Interactions of cobalt carbonyls with oxide surfaces: I. (CO)9Co3CCH3 on silicas, aluminas, and zeolites

Abstract The adsorption and subsequent reactions of the cobalt cluster compound μ 3 -ethylidine-tris-(tricarbonyl cobalt), I , on silica, alumina, and zeolite supports were investigated. The supports were pretreated, in vacuo , at temperatures between 295 and 700K. On silica, I appears to be physisorbed and reacts only slowly with O 2 to produce decarbonylated products. Adsorption on alumina produces physisorbed I and at least three cobalt carbonyl products of the reactions of I with the support. Aluminas pretreated at lower temperatures are the most reactive toward I , as evidenced by the greater tendency of I to react and form novel cobalt carbonyls on these aluminas. These reaction products are immediatley oxidized by O 2 to produce carbonate, bicarbonate, and oxidized cobalt. Compound I adsorbs at room temperature inside the supercages of a Na Y zeolite; it is subsequently converted, at elevated temperatures (350K), to the carbonyl species which were identified on alumina. Infrared and manometric data show that thermolysis and oxidation of I on zeolite ultimately lead to its total decarbonylation to produce carbonates, bicarbonates, and organic carbonyl fragments.
Small-Scale Yielding at the Tip of a Through-Crack in a Shell.

Abstract : Deformation theory is used to model plastic deformation at the tip of a through-crack in a thin shell. In the vicinity of the crack the shell is subjected to both stretching and bending, but stretching is assumed to dominate. Thus the stresses are tensile, but with a non-uniform distribution through the thickness, which depends on the material properties as well as on the geometry. The non-linear near-tip fields (which are singular) have been analyzed asymptotically. Cracks in shallow shells and spherical shells have been investigated in some detail. It is shown that the angular variations are the same as for generalized plane-stress plate problems. Assuming small-scale yielding a path-independent integral, which is valid in a region close to the crack edge, is used to connect the nonlinear near-tip fields with the corresponding singular parts of the linear fields. It is shown that the nonlinear behavior significantly affects the through-the-thickness variations of the near-tip fields. The singular parts of the membrane stresses tend to become more uniform through the thickness of the shell with stronger strain hardening. (Author)
A review of edible mushroom radioactivity

Abstract The review deals mainly with the situation in Europe where wild-growing mushrooms are widely consumed as a delicacy and some species have been found to be extensively contaminated by radioactive fallout from the Chernobyl disaster in 1986. The natural isotope 40K usually causes activities of 0.8–1.5 kBq kg−1 dry matter. Activities of 137Cs, from nuclear weapons testing, below 1 kBq kg−1 dry matter, were commonly reported until 1985. The situation changed dramatically after the Chernobyl accident and activities up to tens of kBq kg−1 dry matter of 137Cs and to a lesser extent of 134Cs were observed in the following years in some edible species. Among the heavily accumulating species belong Xerocomus (Boletus) badius, Xerocomus chrysenteron, Suillus variegatus, Rozites caperata and Hydnum repandum. Activity concentrations have been affected by several environmental factors, such as rate of soil contamination with fallout, the horizon from which mycelium takes nutrients, soil moisture and time from the disaster. Wild mushroom consumption contributed up to 0.2 mSv to the effective dose in individuals consuming about 10 kg (fresh weight) of heavily contaminated species per year. The radioactivity of cultivated mushrooms is negligible. Contamination can be considerably decreased by soaking or cooking of dried or frozen mushroom slices. Animals, such as deer, eating mushrooms, have elevated levels of radionuclides in their tissues.
Search for Cosmic Ray Related Magnetic Monopoles at Ground Level

Magnetic monopoles have been sought at ground level using the large magnetic moment of the Fermilab 15-foot bubble-chamber magnet for collection and acceleration and Lexan polycarbonate for particle detection. The 100 days of operation with proper polarity provides an (area) x (time) factor of 8.4 x 10/sup 14/ cm/sup 2/ sec for slowed north-seeking monopoles. The 3.5 years of exposure also provides 10/sup 13/ cm/sup 2/ sec for high-energy directly penetrating monopoles of both polarities. The lack of observable poles sets new limits on monopole mass and charge for experiments that make no assumptions about long-term trapping of magnetic charges.