cssi/SciSciGPT-SciSciNet huggingface dataset:
An efficient optimization based microstructure reconstruction approach with multiple loss functions

Abstract Stochastic microstructure reconstruction involves digital generation of microstructures that match key statistics and characteristics of a (set of) target microstructure(s). This process enables computational analyses on ensembles of microstructures without having to perform exhaustive and costly experimental characterizations. Statistical functions-based and deep learning-based methods are among the stochastic microstructure reconstruction approaches applicable to a wide range of material systems. In this paper, we integrate statistical descriptors as well as feature maps from a pre-trained deep neural network into an overall loss function for an optimization based reconstruction procedure. This helps us to achieve significant computational efficiency in reconstructing microstructures that retain the critically important physical properties of the target microstructure. A numerical example for the microstructure reconstruction of bi-phase random porous ceramic material demonstrates the efficiency of the proposed methodology. We further perform a detailed finite element analysis (FEA) of the reconstructed microstructures to calculate effective elastic modulus, effective thermal conductivity and effective hydraulic conductivity, in order to analyse the algorithm’s capacity to capture the variability of these material properties with respect to those of the target microstructure. This method provides an economic, efficient and easy-to-use approach for reconstructing random multiphase materials in 2D which has potential to be extended to 3D structures.
Put More Women in Charge and Other Leadership Lessons from COVID-19

COVID-19 teaches us lessons about leadership, the most important of which is to put more women in charge. This Article provides an interdisciplinary analysis of these lessons, which come at the very high price of many forever disrupted and lost human lives. COVID-19 is a global tragedy. COVID-19 can also be a cruel, relentless and unforgiving teacher of valuable lessons about leadership. During COVID-19, leaders had to quickly mobilize many resources and convince many people to change their established behaviors and familiar routines. Leaders had to rely on effective and persuasive communication to achieve buy-in and voluntary compliance by a diverse public. This is because enforcement of non-compliance is effectively infeasible. This Article introduces the phrase, thoughtful leadership, to describe such leadership and leadership communications. In response to COVID-19, some leaders have been compassionate, courageous, data-based, decisive, and kind. These leaders’ communications with the media and the public were calm, caring, clear, empathetic, honest, science-driven, and transparent. This Article applies to leadership mathematician Eugenia Cheng’s category-theory based notion of congressive qualities, such as being collaborative, communitarian, cooperative, curious, empathetic, inclusive, open-minded, modest, other-regarding, and prudent. This Article analyzes what leaders who were more successful during COVID-19 did and said. During COVID-19, women were many of the most admired and more successful leaders. These female leaders of cities, states and nations were exemplary. There were also some exemplary male leaders. This Article considers explanations and theories about why so many women leaders were among the most successful during COVID-19. This Article profiles in some detail three exemplars of leadership: New Zealand prime minister Jacinda Ardern, Secretary to the Governor of New York Melissa DeRosa, and New York state governor Andrew Cuomo. Finally, congressive and thoughtful leadership are also applicable to parenting and teaching.
CHARACTERISTICS OF DAIRY FARMERS OF NORTH GUJARAT

A study was conducted on the characteristics of dairy farmers of North Gujarat (Banaskantha, Sabarkantha and Mehsana districts) and the data of 240 respondent farmers’ was collected by personal interview. The findings of the present study revealed that more than 3/4th of the dairy farmers were in middle age group, about 87 per cent were literate, more numbers were in low level of social participation, medium dairy farming experience, in OBC category, with medium extension contact and extension participation, small to medium farmer groups and in medium herd size (4 to 6 animals) category. The average milk production at their home was 29.76 liters per day and majority (45.48 per cent) dairy farmer were in big producers’ category. More than 48 per cent were in low-income group (Rs.<50,000 per annum) but average income was Rs.72,566.00 per annum. The findings regarding psychological attributes revealed that majority (70 per cent) respondents were in medium economic motivation group and more than 62 per cent were in medium scientific orientation category.
Interplay between COVID-19 vaccines and social measures for ending the SARS-CoV-2 pandemic

Background : The development and authorization of COVID-19 vaccines has provided the clearest path forward to eliminate community spread and thus end the ongoing SARS-CoV-2 pandemic. However, the limited pace at which the vaccine can be administered motivates the question, to what extent must we continue to adhere to social intervention measures such as mask wearing and social distancing? Methods : We develop a mathematical model of COVID-19 spread incorporating both vaccine dynamics and socio-epidemiological parameters. We use this model to study two important measures of disease control and eradication, the effective reproductive number R t and the peak intensive care unit (ICU) caseload, over three key parameters: social measure adherence, vaccination rate, and vaccination coverage. Results : Our results suggest that, due to the slow pace of vaccine administration, social measures must be maintained by a large proportion of the population until a sufficient proportion of the population becomes vaccinated for the pandemic to be eradicated. By contrast, with reduced adherence to social measures, hospital ICU cases will greatly exceed capacity, resulting in increased avoidable loss of life. We then investigate the threat of localized outbreaks in low-vaccinated populations that have removed all social intervention mandates, and show that such populations could remain highly susceptible to major outbreaks particularly in the face of more easily transmissible variants. Conclusions : These findings highlight the complex interplay involved between vaccination and social protective measures, and indicate the practical importance of continuing with extant social measures while vaccines are scaled up to allow the development of the herd immunity needed to end or control SARS-CoV-2 sustainably.
Alternate history: A synthetic ensemble of ocean chlorophyll concentrations

Internal climate variability plays an important role in the abundance and distribution of phytoplankton in the global ocean. Previous studies using large ensembles of Earth system models (ESMs) hav...
Information-theoretic lower bounds for zero-order stochastic gradient estimation

In this paper we analyze the necessary number of samples to estimate the gradient of any multidimensional smooth (possibly non-convex) function in a zero-order stochastic oracle model. In this model, an estimator has access to noisy values of the function, in order to produce the estimate of the gradient. We also provide an analysis on the sufficient number of samples for the finite difference method, a classical technique in numerical linear algebra. For $T$ samples and d dimensions, our information-theoretic lower bound is Ω(√d/T). We show that the finite difference method for a bounded-variance oracle has rate O(d4/3 / √T) for functions with zero third and higher order derivatives. These rates are tight for Gaussian oracles. Thus, the finite difference method is not minimax optimal, and therefore there is space for the development of better gradient estimation methods. A full version of this paper is accessible at: https://arxiv.org/pdf/2003.13881.pdf
Mulan: a part-per-million measurement of the muon lifetime and determination of the Fermi constant

The part-per-million measurement of the positive muon lifetime and determination of the Fermi constant by the MuLan experiment at the Paul Scherrer Institute is reviewed. The experiment used an innovative, time-structured, surface muon beam and a near-4pi, finely-segmented, plastic scintillator positron detector. Two in-vacuum muon stopping targets were used: a ferromagnetic foil with a large internal magnetic field, and a quartz crystal in a moderate external magnetic field. The experiment obtained a muon lifetime 2 196 980.3(2.2) ps (1.0 ppm) and a Fermi constant 1.166 378 7(6) 10^-5 GeV^-2 (0.5 ppm). The thirty-fold improvement in the muon lifetime has proven valuable for precision measurements in nuclear muon capture and the commensurate improvement in the Fermi constant has proven valuable for precision tests of the standard model.
STEM the bullying: An empirical investigation of abusive supervision in academic science.

Abstract Background Academic bullying is a topic of significant interest of late, with high profile cases featured in scientific journals. Our aim is to document the nature and extent of academic bullying behaviors, examining who are the primary targets and perpetrators as well as the responses to and outcomes of bullying. Methods We developed a cross-sectional global survey aimed primarily at those in academic science institutions. The survey was administered via Qualtrics and data were collected (between November 2019 and July 2021) from 2006 individuals whose participation was solicited through various means including advertisements in Science and Nature magazines and the American Chemical Society. Findings Among the 2006 survey participants, the majority of targets were graduate students or postdocs. An overwhelming proportion of participants reported either experiencing (84%) or witnessing (59%) abusive supervision, or both (49%). While a majority of perpetrators were male, they were proportionately no more likely to abuse than females. Perpetrators were more likely from the highest-ranked institutions and they were most likely PIs. Females were more likely to report being bullied but their scores on the Tepper abusive supervision scale and the contextual behavior checklist we developed were not greater than male targets. Male targets actually reported higher levels of certain bullying behaviors. While international scholars were no more likely to report being bullied, the severity of the behaviors they reported was significantly greater. Targets (64%) were most likely to use avoidant tactics (not reporting and relying on family/friends for support) in response to bullying due to fear of retaliation (61%). The small percentage that did report the abuse (29%) overwhelmingly reported unfair and biased (58%) outcomes. Additional qualitative analysis of open-ended comments revealed similar patterns. We also noticed that the COVID-19 pandemic has exacerbated academic bullying and changed the patterns of behaviors possibly due to the remote nature of interactions. Open-ended responses from targets are analyzed with examples provided. Interpretation Our results elucidated the various forms of abuse, the most likely perpetrators and targets, as well as the typical reactions of targets and witnesses. We investigated the results of targets’ actions following chronic bullying. Our findings highlight the domain, extent, and dynamics of academic bullying to hopefully motivate the scientific community to take action. Funding None.
Uncertainty quantification of machine learning models: on conformal prediction

Prediction intervals which describe the reliability of the predictive performance of machine learning models are important to guide decision making and to improve trust in deep learning and other forms of machine learning and artificial intelligence. Conformal prediction is a relatively recent, increasingly popular, rigorously proven and simple methodology to address this need for both classification and regression problems, and it does not use distributional assumptions like Gaussianity or the Bayesian framework; one new variant combines it with another technique to generate prediction intervals known as quantile regression. We will illustrate the strengths and limitations of different conformal prediction procedures for a regression problem involving line edge roughness (LER) estimation; LER affects semiconductor device performance and the yield of the manufacturing process. Low-dose images from the scanning electron microscope (SEM) are often used for roughness measurements because of relatively small acquisition times and resist shrinkage, but such images are corrupted by noise, blur, edge effects and other instrument errors. We consider prediction intervals for the deep convolutional neural network EDGENet, which was trained on a large dataset of simulated SEM images and directly estimates the edge positions from a SEM rough line image containing an unknown level of Poisson noise.
Chaos as an interpretable benchmark for forecasting and data-driven modelling.

The striking fractal geometry of strange attractors underscores the generative nature of chaos: like probability distributions, chaotic systems can be repeatedly measured to produce arbitrarily-detailed information about the underlying attractor. Chaotic systems thus pose a unique challenge to modern statistical learning techniques, while retaining quantifiable mathematical properties that make them controllable and interpretable as benchmarks. Here, we present a growing database currently comprising 131 known chaotic dynamical systems spanning fields such as astrophysics, climatology, and biochemistry. Each system is paired with precomputed multivariate and univariate time series. Our dataset has comparable scale to existing static time series databases; however, our systems can be re-integrated to produce additional datasets of arbitrary length and granularity. Our dataset is annotated with known mathematical properties of each system, and we perform feature analysis to broadly categorize the diverse dynamics present across the collection. Chaotic systems inherently challenge forecasting models, and across extensive benchmarks we correlate forecasting performance with the degree of chaos present. We also exploit the unique generative properties of our dataset in several proof-of-concept experiments: surrogate transfer learning to improve time series classification, importance sampling to accelerate model training, and benchmarking symbolic regression algorithms.
Progress On A Perimeter Surveillance Problem

We consider a perimeter surveillance problem introduced by Kingston, Beard, and Holt in 2008 and studied by Davis, Humphrey, and Kingston in 2019. In this problem, n drones surveil a finite interval, moving at uniform speed and exchanging information only when they meet another drone. Kingston et al. described a particular online algorithm for coordinating their behavior and asked for an upper bound on how long it can take before the drones are fully synchronized. They divided the algorithm’s behavior into two phases and presented upper bounds on the length of each phase based on conjectured worst-case configurations. Davis et al. presented counterexamples to the conjecture for phase 1. We present sharp upper bounds on phase 2 which show that in this case the conjectured worst case is correct, and we report new lower bounds on phase 1.
Partnerships in Urban Mobility: Incentive Mechanisms for Improving Public Transit Adoption

Problem definition: Because of a prolonged decline in public transit ridership over the last decade, transit agencies across the United States are in financial crisis. To entice commuters to travel...
Symmetry enriched phases of quantum circuits

Abstract Quantum circuits consisting of random unitary gates and subject to local measurements have been shown to undergo a phase transition, tuned by the rate of measurement, from a state with volume-law entanglement to an area-law state. From a broader perspective, these circuits generate a novel ensemble of quantum many-body states at their output. In this paper, we characterize this ensemble and classify the phases that can be established as steady states. Symmetry plays a nonstandard role in that the physical symmetry imposed on the circuit elements does not on its own dictate the possible phases. Instead, it is extended by dynamical symmetries associated with this ensemble to form an enlarged symmetry. Thus, we predict phases that have no equilibrium counterpart and could not have been supported by the physical circuit symmetry alone. We give the following examples. First, we classify the phases of a circuit operating on qubit chains with Z 2 symmetry. One striking prediction, corroborated with numerical simulation, is the existence of distinct volume-law phases in one dimension, which nonetheless support true long-range order. We furthermore argue that owing to the enlarged symmetry, this system can in principle support a topological area-law phase, protected by the combination of the circuit symmetry and a dynamical permutation symmetry. Second, we consider a Gaussian fermionic circuit that only conserves fermion parity. Here the enlarged symmetry gives rise to a U ( 1 ) critical phase at moderate measurement rates and a Kosterlitz–Thouless transition to area-law phases. We comment on the interpretation of the different phases in terms of the capacity to encode quantum information. We discuss close analogies to the theory of spin glasses pioneered by Edwards and Anderson as well as crucial differences that stem from the quantum nature of the circuit ensemble.
Predictive 3D Sonar Mapping of Underwater Environments via Object-specific Bayesian Inference

Recent work has achieved dense 3D reconstruction with wide-aperture imaging sonar using a stereo pair of orthogonally oriented sonars. This allows each sonar to observe a spatial dimension that the other is missing, without requiring any prior assumptions about scene geometry. However, this is achieved only in a small region with overlapping fields-of-view, leaving large regions of sonar image observations with an unknown elevation angle. Our work aims to achieve large-scale 3D reconstruction more efficiently using this sensor arrangement. We propose dividing the world into semantic classes to exploit the presence of repeating structures in the subsea environment. We use a Bayesian inference framework to build an understanding of each object class’s geometry when 3D information is available from the orthogonal sonar fusion system, and when the elevation angle of our returns is unknown, our framework is used to infer unknown 3D structure. We quantitatively validate our method in a simulation and use data collected from a real outdoor littoral environment to demonstrate the efficacy of our framework in the field.
Agglomeration-based geometric multigrid solvers for compact discontinuous Galerkin discretizations on unstructured meshes

Abstract We present a geometric multigrid solver for the Compact Discontinuous Galerkin method through building a hierarchy of coarser meshes using a simple agglomeration method which handles arbitrary element shapes and dimensions. The method is easily extendable to other discontinuous Galerkin discretizations, including the Local DG method and the Interior Penalty method. We demonstrate excellent solver performance for Poisson's equation, provided a flux formulation is used for the operator coarsening and a suitable switch function chosen for the numerical fluxes.
Evaluating Pointing Strategies for Future Solar Flare Missions

Solar flares are events of intense scientific interest. Although certain solar conditions are known to be associated with flare activity, the exact location and timing of an individual flare on the Sun cannot as yet be predicted with certainty. Missions whose science objectives depend on observing solar flares must often make difficult decisions on where to target their observations if they do not observe the full solar disk. Yet, there is little analysis in the literature that might guide these mission operations to maximize their opportunities to observe flares. In this study, we analyze and simulate the performance of different observation strategies using historical flare and active region data from 2011 to 2014. We test a number of different target selection strategies based on active region complexity and recent flare activity, each of which is examined under a range of operational assumptions. In each case, we investigate various metrics such as the number of flares observed, the size of flares observed, and operational considerations such as the number of instrument repoints required. Overall, target selection methods based on recent flare activity showed the best overall performance but required more repointings than other methods. The mission responsiveness to new information is identified as another strong factor determining flare observation performance. It is also shown that target selection methods based on active region complexities show a significant pointing bias toward the western solar hemisphere. As expected, the number of flares observed grows quickly with field-of-view size until the approximate size of an active region is reached, but further improvements beyond the active region size are much more incremental. These results provide valuable performance estimates for a future mission focused on solar flares and inform the requirements that would ensure mission success.
Distinct roles for the Charcot-Marie-tooth disease-causing endosomal regulators Mtmr5 and Mtmr13 in axon radial sorting and Schwann cell myelination

The form of Charcot-Marie-Tooth type 4B (CMT4B) disease caused by mutations in myotubularin-related 5 (MTMR5; also called SET Binding Factor 1; SBF1) shows a spectrum of axonal and demyelinating nerve phenotypes. This contrasts with the CMT4B subtypes caused by MTMR2 or MTMR13 (SBF2) mutations, which are characterized by myelin outfoldings and classic demyelination. Thus, it is unclear whether MTMR5 plays an analogous or distinct role from that of its homolog, MTMR13, in the peripheral nervous system (PNS). MTMR5 and MTMR13 are pseudophosphatases predicted to regulate endosomal trafficking by activating Rab GTPases and binding to the phosphoinositide 3-phosphatase MTMR2. In the mouse PNS, Mtmr2 was required to maintain wild type levels of Mtmr5 and Mtmr13, suggesting that these factors function in discrete protein complexes. Genetic elimination of both Mtmr5 and Mtmr13 in mice led to perinatal lethality, indicating that the two proteins have partially redundant functions during embryogenesis. Loss of Mtmr5 in mice did not cause CMT4B-like myelin outfoldings. However, adult Mtmr5-/- mouse nerves contained fewer myelinated axons than control nerves, likely as a result of axon radial sorting defects. Consistently, Mtmr5 levels were highest during axon radial sorting and fell sharply after postnatal day seven. Our findings suggest that Mtmr5 and Mtmr13 ensure proper axon radial sorting and Schwann cell myelination, respectively, perhaps through their direct interactions with Mtmr2. This study enhances our understanding of the non-redundant roles of the endosomal regulators MTMR5 and MTMR13 during normal peripheral nerve development and disease.
DSKReG: Differentiable Sampling on Knowledge Graph for Recommendation with Relational GNN

In the information explosion era, recommender systems (RSs) are widely studied and applied to discover user-preferred information. A RS performs poorly when suffering from the cold-start issue, which can be alleviated if incorporating Knowledge Graphs (KGs) as side information. However, most existing works neglect the facts that node degrees in KGs are skewed and massive amount of interactions in KGs are recommendation-irrelevant. To address these problems, in this paper, we propose Differentiable Sampling on Knowledge Graph for Recommendation with Relational GNN (DSKReG) that learns the relevance distribution of connected items from KGs and samples suitable items for recommendation following this distribution. We devise a differentiable sampling strategy, which enables the selection of relevant items to be jointly optimized with the model training procedure. The experimental results demonstrate that our model outperforms state-of-the-art KG-based recommender systems. The code is available online at https://github.com/YuWang-1024/DSKReG.
A Closer Look at the Worst-case Behavior of Multi-armed Bandit Algorithms

One of the key drivers of complexity in the classical (stochastic) multi-armed bandit (MAB) problem is the difference between mean rewards in the top two arms, also known as the instance gap. The celebrated Upper Confidence Bound (UCB) policy is among the simplest optimism-based MAB algorithms that naturally adapts to this gap: for a horizon of play n, it achieves optimal O(log n) regret in instances with "large" gaps, and a near-optimal O(\sqrt{n log n}) minimax regret when the gap can be arbitrarily "small." This paper provides new results on the arm-sampling behavior of UCB, leading to several important insights. Among these, it is shown that arm-sampling rates under UCB are asymptotically deterministic, regardless of the problem complexity. This discovery facilitates new sharp asymptotics and a novel alternative proof for the O(\sqrt{n log n}) minimax regret of UCB. Furthermore, the paper also provides the first complete process-level characterization of the MAB problem under UCB in the conventional diffusion scaling. Among other things, the "small" gap worst-case lens adopted in this paper also reveals profound distinctions between the behavior of UCB and Thompson Sampling, such as an "incomplete learning" phenomenon characteristic of the latter.
Customization of a dada2-based pipeline for fungal internal transcribed spacer 1 (ITS 1) amplicon datasets.

Identification and analysis of fungal communities commonly rely on internal transcribed spacer (ITS)-based amplicon sequencing. There is no gold standard to infer and classify fungal constituents since methodologies have been adapted from analyses of bacterial communities. To achieve high resolution inference of fungal constituents, we customized a DADA2-based pipeline using a mix of eleven medically relevant fungi. While DADA2 allowed the discrimination of ITS1 sequences differing by single nucleotides, quality filtering, sequencing bias, and database selection were identified as key variables determining the accuracy of sample inference. Due to species-specific differences in sequencing quality, default filtering settings removed most reads that originated from Aspergillus species, Saccharomyces cerevisiae, and Candida glabrata. By fine-tuning the quality filtering process, we achieved an improved representation of the fungal communities. By adapting a wobble nucleotide in the ITS1 forward primer region, we further increased the yield of S. saccharomyces and C. glabrata sequences. Finally, we showed that a BLAST-based algorithm based on the UNITE+INSD or the NCBI NT database achieved a higher reliability in species-level taxonomic annotation than the naive Bayesian classifier implemented in DADA2. These steps optimized a robust fungal ITS1 sequencing pipeline that, in most instances, enabled species level-assignment of community members.
On length densities

For a commutative cancellative monoid $M$, we introduce the notion of the length density of both a nonunit $x\in M$, denoted $\mathrm{LD}(x)$, and the entire monoid $M$, denoted $\mathrm{LD}(M)$. This invariant is related to three widely studied invariants in the theory of non-unit factorizations, $L(x)$, $\ell(x)$, and $\rho(x)$. We consider some general properties of $\mathrm{LD}(x)$ and $\mathrm{LD}(M)$ and give a wide variety of examples using numerical semigroups, Puiseux monoids, and Krull monoids. While we give an example of a monoid $M$ with irrational length density, we show that if $M$ is finitely generated, then $\mathrm{LD}(M)$ is rational and there is a nonunit element $x\in M$ with $\mathrm{LD}(M)=\mathrm{LD}(x)$ (such a monoid is said to have accepted length density). While it is well-known that the much studied asymptotic versions of $L(x)$, $\ell (x)$ and $\rho (x)$ (denoted $\overline{L}(x)$, $\overline{\ell}(x)$, and $\overline{\rho} (x)$) always exist, we show the somewhat surprising result that $\overline{\mathrm{LD}}(x) = \lim_{n\rightarrow \infty} \mathrm{LD}(x^n)$ may not exist. We also give some finiteness conditions on $M$ that force the existence of $\overline{\mathrm{LD}}(x)$.
In vivo kinetics of SARS-CoV-2 infection and its relationship with a person's infectiousness.

The within-host viral kinetics of SARS-CoV-2 infection and how they relate to a person’s infectiousness are not well understood. This limits our ability to quantify the impact of interventions on viral transmission. Here, we develop viral dynamic models of SARS-CoV-2 infection and fit them to data to estimate key within-host parameters such as the infected cell half-life and the within-host reproductive number. We then develop a model linking viral load (VL) to infectiousness and show a person’s infectiousness increases sublinearly with VL and that the logarithm of the VL in the upper respiratory tract is a better surrogate of infectiousness than the VL itself. Using data on VL and the predicted infectiousness, we further incorporated data on antigen and RT-PCR tests and compared their usefulness in detecting infection and preventing transmission. We found that RT-PCR tests perform better than antigen tests assuming equal testing frequency; however, more frequent antigen testing may perform equally well with RT-PCR tests at a lower cost but with many more false-negative tests. Overall, our models provide a quantitative framework for inferring the impact of therapeutics and vaccines that lower VL on the infectiousness of individuals and for evaluating rapid testing strategies.
New Algorithms for Determining Optimal Control: A Differential Dynamic Programming Approach

Differential Dynamic Programming is a successive approximation technique, based on Dynamic Programming rather than the Calculus of Variations, for determining optimal control of non-linear systems. In each iteration, the system equations are integrated in forward time using the current nominal control, and accessory equations which yield the coefficients of a linear or quadratic expansion of the cost function in the neighbourhood of the nominal trajectory are integrated in reverse time, yielding an improved control law. This control law is applied to the system equations, producing a new, improved trajectory. By continued iteration, the procedure produces control functions which successively approximate to the optimal control function.
On the computation of nonlinear planetary waves

A numerical method is developed to solve a class of nonlinear, nonlocal eigenvalue problems defined in an infinite strip, and is applied to compute solitary planetary waves in a sheared zonal current on the beta-plane. This method, an iterative procedure derived from the natural variational structure of these problems, is implemented in the physical case when the ambient parallel flow has a linear or a quadratic velocity profile. The results of the numerical experiments establish rigorous limits on the range of validity of the formal asymptotic theory of weakly nonlinear long waves, and also reveal some new phenomena involving strongly nonlinear waves. The iterative procedure is analyzed in a general setting, and is shown to be globally convergent without restriction on the wave amplitude.
Diazotrophy and Nitrogenase Activity in the Archaebacterium Methanosarcina barkeri 227.

Abstract Nitrogen fixation (diazotrophy) has recently been demonstrated in several methanogenic archaebacteria. To compare the process in an archaebacterium with that in eubacteria, we examined the properties of diazotrophic growth and nitrogenase activity in Methanosarcina barkeri 227. Growth yields with methanol or acetate as a growth substrate were significantly lower in N2-grown cultures than in NH4+-grown cultures, and the culture doubling times were increased, indicating that diazotrophy was energetically costly, as it is in eubacteria. Growth of nitrogen-fixing cells was inhibited when molybdenum was omitted from the medium; addition of 10 nM molybdate stimulated growth, while 1 μM molybdate restored maximum diazotrophic growth. Omission of molybdenum did not inhibit growth of ammonia-grown cells. Tungstate (100 μM) strongly inhibited growth of molybdenum-deficient diazotrophic cells, while ammonia-grown cells were unaffected. The addition of 100 nM vanadate or chromate did not stimulate diazotrophic growth of molybdenum-starved cells. These results are consistent with the presence of a molybdenum-containing nitrogenase in M. barkeri. Acetylene, the usual substrate for assaying nitrogenase activity, inhibited methanogenesis by M. barkeri and consequently needed to be used at a low partial pressure (0.3% of the headspace) when acetylene reduction by whole cells was assayed. Whole cells reduced 0.3% acetylene to ethylene at a very low rate (1 to 2 nmol h−1 mg of protein−1), and they “switched off” acetylene reduction in response to added ammonia or glutamine. Crude extracts from diazotrophic cells reduced 10% acetylene at a rate of 4 to 5 nmol of C2H4 formed h−1 mg of protein−1 when supplied with ATP and reducing power, while extracts of Klebsiella pneumoniae prepared by the same procedures had rates 100-fold higher. Acetylene reduction by extracts required ATP and was completely inhibited by 1 mM ADP in the presence of 5 mM ATP. The low rates of C2H2 reduction could be due to improper assay conditions, to switched-off enzyme, or to the nitrogenase's having lower activity towards acetylene than towards dinitrogen.
CD8+ T Cells Specific for Immunodominant Trans-Sialidase Epitopes Contribute to Control of Trypanosoma cruzi Infection but Are Not Required for Resistance

CD8 + T cells are essential for controlling Trypanosoma cruzi infection. During Brazil strain infection, C57BL/6 mice expand parasite-specific CD8 + T cells recognizing the dominant TSKB20 (ANYKFTLV) and subdominant TSKB74 (VNYDFTLV) trans -sialidase gene (TS)-encoded epitopes with up to 40% of all CD8 + T cells specific for these epitopes. Although this is one of the largest immunodominant T cell responses described for any infection, most mice fail to clear T. cruzi and subsequently develop chronic disease. To determine if immunodominant TS-specific CD8 + T cells are necessary for resistance to infection, we epitope-tolerized mice by high-dose i.v. injections of TSKB20 or TSKB74 peptides. Tolerance induction led to deletion of TS-specific CD8 + T cells but did not prevent the expansion of other effector CD8 + T cell populations. Mice tolerized against either TSKB20 or TSKB74, or both epitopes simultaneously, exhibited transient increases in parasite loads, although ultimately they controlled the acute infection. Furthermore, BALB/c mice tolerized against the TSKD14 peptide effectively controlled acute T. cruzi infection. These data are consistent with the hypothesis that development of high-frequency CD8 + T cell populations focused on TS-derived epitopes contributes to optimal control of acute infection but is not required for the development of immune resistance.
A comparison of equity carve-outs and seasoned equity offerings

Abstract This paper investigates share price reactions of parent firms to announcements of public offerings of stock of wholly-owned subsidiaries. The average abnormal gains associated with ‘equity carve-out’ announcements contrast with the average abnormal losses documented here and elsewhere upon announcements of public offerings of parent equity. Four features distinguishing equity carve-outs from parent equity offerings are discussed. Evidence is provided on these features as potential explanations for the positive average share price reaction associated with announcements of equity carve-outs.
Intestinal absorption of [3H]folic acid in the chronic alcoholic monkey

Abstract The intestinal absorption of labeled folic acid ([3H]pteroylmonoglutamate) was determined from urinary and fecal recoveries of tritium in pairs of monkeys fed control liquid diets or diets containing 50% of energy as ethanol for a 24-mo period. Weight gain, fecal fat excretion, nitrogen balance, d-xylose absorption, serum folate levels, jejunal histology, and intestinal enzyme activities were similar in each group. Liver biopsies obtained after 12 and 24 mo of feeding demonstrated steatosis and megamitochondria in the ethanol-fed group, with decreased hepatic levels of folate at 24 mo. Intestinal malabsorption of labeled folic acid in the ethanol-fed monkeys was indicated by decreased urinary recovery of tritium but increased fecal recovery of tritium after intragastric administration of [3H]pteroylmonoglutamate. These studies suggest that folic acid malabsorption follows the chronic administration of ethanol together with a nutritious diet.
Adrenergic regulation of distention-induced gastrin release in humans.

To investigate the possible role of adrenergic nerves in neurally mediated gastrin release, we evaluated the effect of selective adrenergic blockade on the serum gastrin response to gastric distention in healthy human subjects. On separate days, 2 mg of propranolol (beta-adrenergic antagonist), 5 mg of phentolamine (alpha-adrenergic antagonist), or saline (control) was injected intravenously just before distending the stomach with 700 ml of isotonic saline. For 30 min following distention, intragastric pH was kept constant at 5.0 by in vivo titration. Propranolol reduced distention-induced gastrin release by approximately 90% (p less than 0.02), whereas phentolamine had no significant effect on the gastrin response to distention. In additional experiments, we evaluated the effect of the same doses of propranolol or phentolamine on the exaggerated gastrin response to gastric distention that occurred during cholinergic blockade with atropine. In the presence of atropine (2.3 microgram/kg i.v.), propranolol significantly (p less than 0.01) reduced distention-induced gastrin release, whereas phentolamine significantly enhanced the gastrin response to distention (p less than 0.01). We conclude that: (1) distention-induced gastrin release was reduced by propranolol, suggesting that gastric distention releases gastrin by a beta-adrenergic mechanism and (2) distention-induced gastrin release was enhanced by phentolamine, but only in the presence of atropine. Thus, adrenergic nerves appear to regulate the gastrin response to gastric distention in humans: beta-adrenergic pathways stimulate gastrin release, and alpha-adrenergic pathways may inhibit gastrin release under certain circumstances.
Getting to the Heart of the Matter: An Overview of Cardiac Toxicity Related to Cancer Therapy

With the improvement in cancer survival, long-term cardiotoxicity has become an area of increased interest. Various cancer therapies, including chemotherapy and radiation therapy can lead to cardiac toxicities with both acute and chronic manifestations. Awareness and early recognition can lead to improvement in cardiac survival and patient outcomes. The focus of this review is to summarize the cancer therapy agents most often associated with cardiovascular side effects, highlighting their mechanism of action and strategies for surveillance and prevention.
Use of flow visualization and tracer gas studies for designing an InP/InGaAsP OMVPE reactor

Studies of gas flow using smoke particle visualization and a tracer gas measurement technique were used to provide design information about growth tubes for organometallic vapor phase epitaxy (OMVPE) of InP and InGaAsP alloys. It is shown that convective cells may form when a gas of a different density than that of the gas already flowing in the tube is rapidly switched into the reactor, even when the vent and run lines of the switching manifold are pressure and flow balanced. This effect, reported here for the first time, could severely degrade the material quality of InP/InGaAsP heterojunctions. This transient convection after gas switching is particularly acute for a vertical growth tube with a large cross-sectional area (≈ 80 cm2), such as the vertical reactor tube studied has a rectangular tube with a high aspect ratio (7.6/1.6) and a susceptor surface parallel to the gas flow. The cross-sectional area (≈ 10 cm2) is greatly reduced and for the same volumetric flow rate the gas velocities are increased, which eliminated convection, even for density changes considerably greater than those encountered in InP/InGaAsP growth. Susceptor rotation in this geometry is expected to average growth nonuniformities which result from gas phase depletion. At high rotation rates (> 200 rpm), the susceptor motion induces vortices which are likely to produce nonuniform growth and graded interfaces. However, at ≈ 100 rpm there is no significant perturbation of the gas flow with mean gas velocities of >15 cm/s. This rotation rate is sufficient to achieve considerable averaging at typical OMVPE growth rates.
GENERATION OF FIRST AND SECOND ORDER TRANSFORMATION ELEMENTS FROM A GIVEN MAGNETIC FIELD.

Abstract Equations of motion and a simple elimination scheme suitable for obtaining first and second order transformation elements by numerically integrating through a magnetic field are developed. The results are applied to ideal magnets with a constant field, a quadrupole field, and a sextupole field. The results obtained by numerical integration are compared with analytically obtained values.
Adoptive transfer of experimental allergic encephalomyelitis: conditions influencing memory and effector cell development.

Abstract The cellular transfer of clinical experimental allergic encephalomyelitis (EAE) with immune spleen cells is only accomplished following lymphoid cell stimulation during an intervening in vitro culture activation period. Recipients of these cells recover from the ensuing adoptively transferred paralytic episode and subsequently respond to active challenge with myelin basic protein (BP)-CFA in an accelerated time frame consistent with the presence of memory cells in the initial cell transfer inoculum. We have found that the addition of anti-CD4 antibody or dexamethasone during the activation period inhibits the development of the transfer active EAE effector cell subpopulation, but does not alter the in vitro development and subsequent expression of the BP-specific memory cell subpopulation. Additional experiments also suggest the development of memory cells in the absence of effector cell activity. PMA + ionomycin when used as a stimulus during the culture activation period leads to effector and memory cell development. The stimulation response is dose dependent, in that a reduced concentration of PMA + ionomycin does not lead to EAE effector cell development; however, at these reduced levels of PMA + ionomycin, memory cell development still occurred. Additional evidence which supports the concept of independent development of memory cells and effector cells was obtained with a BP-specific cell line. Following recovery from cell line-mediated clinical EAE, as well as following adoptive transfer of the cell lines in the precursor stage, cell recipients did not develop an early onset of active EAE when subsequently immunized with BP-CFA. Thus the BP-specific T-cell line appears to contain the precursors of the effector cell subpopulation but does not appear to contain the BP memory cell subpopulation. Collectively these observations suggest the existence of distinct T-cell subsets or pathways of development that are followed during the response to BP as measured by the development of Clinical EAE.
Chemically Induced Destabilization of Water-in-Crude Oil Emulsions

The effect of systematic changes in the structure of chemicals commonly used in demulsification operations (alkylphenol polyalkoxylated resins and polyurethanes) on the stability and properties of brine-in-crude oil emulsions was assessed experimentally. The relative rates of water separation were characterized via bottle tests and rheometry. Equilibrium interfacial tensions were also measured. Transient changes in drop size distributions were quantified using nuclear magnetic resonance. The phenolic resins promoted coalescence of droplets. Optimum performance was obtained with resins exhibiting intermediate hydrophilicity in a manner consistent with the condition of least emulsion stability for conventional oil−water−surfactant systems. In contrast, polyurethanes promoted flocculation but only slow coalescence. The performance of polyurethanes improved with increase of molecular weight. Phenolic resins and polyurethanes acted synergistically when added simultaneously, rendering water separation rates sig...
Synthesis of 3-amino-2,4(1H,3H)-quinazolinediones for testing as anticonvulsants

A number of 3-amino-2,4(1H,3H)-quinazolinediones were synthesized from o-aminobenzoylhydrazines and ethyl chloroformate in pyridine or from 2-methoxycarbonylphenyl isocyanate and 1,1-disubstituted hydrazines. Four of the ten compounds screened exhibited anticonvulsant activity in mice.
Impact of 750kV Transmission Line Series Compensation Capacity on Power Frequency Overvoltage

The overvoltage calculation of 750kV transmission line with series compensation has great significance on the design, insulation coordination and protection of the line. In this paper, a transient model of 750kV power transmission system with series compensation is established. Effects of different capacity on no-load capacitive rise overvoltage, single-phase grounding overvoltage, two-phase grounding overvoltage are calculated. Secondary arc current and recovery voltage of different series compensation capacity in single-phase grounding is also calculated.
A new approach to clinical electrocardiography: The phase plane cardiogram

Abstract A method for increasing the diagnostic capability of clinical electrocardiography is presented. The coordinated display of voltage against the time derivative of voltage, i.e., phase plane cardiogram, was found to be remarkably sensitive to subtle aberrations in QRS contours not easily visualized in the standard electrocardiographic portrayal—voltage against time. In preliminary studies, the method revealed statistically significant differences in selected phase plane parameters of patients with left ventricular hypertrophy as compared to normal subjects. The method allows information gained by means of standard clinical procedures to be viewed in a fashion which reveals details otherwise lost. The extent to which this new knowledge concerning the ECG can be related to or be predictive of disease states can only be assessed by future intensive application of the technique.
Treatment of ANCA-associated vasculitis.

Vasculitis is defined by inflammation of the blood-vessel wall and forms the pathological foundation of a diverse group of individual disease entities. Among the primary systemic vasculitic diseases, Wegener's granulomatosis and microscopic polyangiitis share several common features, including pulmonary capillaritis, pauci-immune focal crescentic necrotizing glomerulonephritis, and circulating antineutrophil cytoplasmic antibodies (ANCA) that are specific for proteinase 3 or myeloperoxidase. Because both diseases are highly associated with ANCA, they are sometimes collectively referred to as ANCA-associated vasculitis. However, the presence of granulomatous inflammation in Wegener's granulomatosis represents a potentially important distinction between these two rare diseases, and it remains unclear whether . . .