cssi/SciSciGPT-SciSciNet huggingface dataset:
Deep Reinforcement Learning with an Action Space Defined by Natural Language

In this paper, we propose the deep reinforcement relevance network (DRRN), a novel deep architecture, to design a model for handling an action space characterized using natural language with applications to text-based games. For a particular class of games, a user must choose among a number of actions described by text, with the goal of maximizing long-term reward. In these games, the best action is typically what fits the current situation best (modeled as a state in the DRRN), also described by text. Because of the exponential complexity of natural language with respect to sentence length, there is typically an unbounded set of unique actions. Even with a constrained vocabulary, the action space is very large and sparse, posing challenges for learning. To address this challenge, the DRRN extracts separate high-level embedding vectors from the texts that describe states and actions, respectively, using a general interaction function, such as inner product, bilinear, and DNN interaction, between these embedding vectors to approximate the Q-function. We evaluate the DRRN on two popular text games, showing superior performance over other deep Q-learning architectures.
Game-Theoretic Models of Moral and Other-Regarding Agents (extended abstract).

We investigate Kantian equilibria in finite normal form games, a class of non-Nashian, morally motivated courses of action that was recently proposed in the economics literature. We highlight a number of problems with such equilibria, including computational intractability, a high price of miscoordination, and problematic extension to general normal form games. We give such a generalization based on concept of program equilibria, and point out that that a practically relevant generalization may not exist. To remedy this we propose some general, intuitive, computationally tractable, other-regarding equilibria that are special cases Kantian equilibria, as well as a class of courses of action that interpolates between purely self-regarding and Kantian behavior.
Status of the JWST Science Instrument Payload

The James Webb Space Telescope (JWST) Integrated Science Instrument Module (ISIM) system consists of five sensors (4 science): Mid-Infrared Instrument (MIRI), Near Infrared Imager and Slitless Spectrograph (NIRISS), Fine Guidance Sensor (FGS), Near InfraRed Camera (NIRCam), Near InfraRed Spectrograph (NIRSpec); and nine instrument support systems: Optical metering structure system, Electrical Harness System; Harness Radiator System, ISIM Electronics Compartment, ISIM Remote Services Unit, Cryogenic Thermal Control System, Command and Data Handling System, Flight Software System, Operations Scripts System.
SHAPing the gas: understanding gas shapes in dark matter haloes with interpretable machine learning

The non-spherical shapes of dark matter and gas distributions introduce systematic uncertainties that affect observable-mass relations and selection functions of galaxy groups and clusters. However, the triaxial gas distributions depend on the non-linear physical processes of halo formation histories and baryonic physics, which are challenging to model accurately. In this study we explore a machine learning approach for modelling the dependence of gas shapes on dark matter and baryonic properties. With data from the Illustris-TNG hydrodynamical cosmological simulations, we develop a machine learning pipeline that applies XGBoost, an implementation of gradient boosted decision trees, to predict radial profiles of gas shapes from halo properties. We show that XGBoost models can accurately predict gas shape profiles in dark matter haloes. We also explore model interpretability with SHAP, a method that identifies the most predictive properties at different halo radii. We find that baryonic properties best predict gas shapes in halo cores, whereas dark matter shapes are the main predictors in the halo outskirts. This work demonstrates the power of interpretable machine learning in modelling observable properties of dark matter haloes in the era of multi-wavelength cosmological surveys.
A nonparametric sequential learning procedure for estimating the pure premium

With the advent of the “big” data era, large-sample properties of a statistical learning method are becoming more and more important in an actuary’s daily work. For a fixed sample size, regardless of how large it is, the variance of an estimator can be larger than a pre-assigned level to an arbitrary extent. In this paper, we propose a nonparametric sequential learning procedure for estimating the pure premium. Our method not only provides an accurate estimate of the pure premium but also guarantees that the mean of our random sample sizes is close to the unobservable optimal fixed sample size and the variance of our estimator is close to all small pre-determined levels. In addition, our method is nonparametric and applicable to any claims distribution; hence it avoids potential issues associated with a parametric model such as model misspecification risk and the effect of selection.
Cortical magnification in human visual cortex parallels task performance around the visual field

Human vision has striking radial asymmetries, with performance on many tasks varying sharply with stimulus polar angle. Performance is generally better on the horizontal than vertical meridian, and on the lower than upper vertical meridian, and these asymmetries decrease gradually with deviation from the vertical meridian. Here, we report cortical magnification at a fine angular resolution around the visual field. This precision enables comparisons between cortical magnification and behavior, between cortical magnification and retinal cell densities, and between cortical magnification in twin pairs. We show that cortical magnification in the human primary visual cortex, measured in 163 subjects, varies substantially around the visual field, with a pattern similar to behavior. These radial asymmetries in the cortex are larger than those found in the retina, and they are correlated between monozygotic twin pairs. These findings indicate a tight link between cortical topography and behavior, and suggest that visual field asymmetries are partly heritable.
Chemoprophylaxis vaccination with a Plasmodium liver stage autophagy mutant affords enhanced and long-lasting protection

Genetically attenuated sporozoite vaccines can elicit long-lasting protection against malaria but pose risks of breakthrough infection. Chemoprophylaxis vaccination (CVac) has proven to be the most effective vaccine strategy against malaria. Here, we demonstrate that a liver stage-specific autophagy mutant of Plasmodium berghei (ATG8 overexpressor), when used as a live vaccine under a CVac regimen, provides superior long-lasting protection, in both inbred and outbred mice, as compared to WT-CVac. Uniquely, the protection elicited by this mutant is predominantly dependent on a CD8+ T-cell response through an IFN-γ-independent mechanism and is associated with a stable population of antigen-experienced CD8+ T cells. Jointly, our findings support the exploitation of liver-stage mutants as vaccines under a CVac protocol. This vaccination strategy is also a powerful model to study the mechanisms of protective immunity and discover new protective antigens.
Round-Optimal Black-Box Two-Party Computation.

In [Eurocrypt 2004] Katz and Ostrovsky establish the exact round complexity of secure two-party computation with respect to black-box proofs of security. They prove that 5 rounds are necessary for secure two-party protocols (4-round are sucient if only one party receives the output) and provide a protocol that matches such lower bound. The main challenge when designing such protocol is to parallelize the proofs of consistency provided by both parties ‐ necessary when security against malicious adversaries is considered‐ in 4 rounds. Toward this goal they employ specific proofs in which the statement can be unspecified till the last round but that require non-black-box access to the underlying primitives. A rich line of work [IKLP06, Hai08, CDSMW09, IKOS07, PW09] has shown that the nonblack-box use of the cryptographic primitive in secure two-party computation is not necessary by providing black-box constructions matching basically all the feasibility results that were previously demonstrated only via non-black-box protocols. All such constructions however are far from being round optimal. The reason is that they are based on cut-and-choose mechanisms where one party can safely take an action only after the other party has successfully completed the cut-and-choose phase, therefore requiring additional rounds. A natural question is whether round-optimal constructions do inherently require non-blackbox access to the primitives, and whether the lower bound shown by Katz and Ostrovsky can only be matched by a non-black-box protocol. In this work we show that round-optimality is achievable even with only black-box access to the primitives. We provide the first 4-round black-box oblivious transfer based on any enhanced trapdoor permutation. Plugging a parallel version of our oblivious transfer into the blackbox non-interactive secure computation protocol of [IKO + 11] we obtain the first round-optimal black-box two-party protocol in the plain model for any functionality.
On the sensitivity of the optimal partition for parametric second-order conic optimization

In this paper, using an optimal partition approach, we study the parametric analysis of a second-order conic optimization problem, where the objective function is perturbed along a fixed direction. We characterize the notions of so-called invariancy set and nonlinearity interval, which serve as stability regions of the optimal partition. We then propose, under the strict complementarity condition, an iterative procedure to compute a nonlinearity interval of the optimal partition. Furthermore, under primal and dual nondegeneracy conditions, we show that a boundary point of a nonlinearity interval can be numerically identified from a nonlinear reformulation of the parametric second-order conic optimization problem. Our theoretical results are supported by numerical experiments.
Design principles of apodized grating couplers

We extend an analytical model for designing apodized grating couplers, considering the constraints on the upper and lower bounds of the scattering strength.
Substructure Methods in Vibration

When the response of a structural system to dynamic excitation must be analyzed, a substructure coupling method (or component-mode synthesis method) is frequently employed to reduce the order of the finite element model of the structure. This paper reviews procedures used to formulate component modes for substructures and to assemble substructure models to form reduced-order models of the original system. A brief literature survey covering several applications of substructure coupling is also presented.
Invertible phases of matter with spatial symmetry

We propose a general formula for the group of invertible topological phases on a space $Y$, possibly equipped with the action of a group $G$. Our formula applies to arbitrary symmetry types. When $Y$ is Euclidean space and $G$ a crystallographic group, the term `topological crystalline phases' is sometimes used for these phases of matter.
Beyond Equi-joins: Ranking, Enumeration and Factorization

We study theta-joins in general and join predicates with conjunctions and disjunctions of inequalities in particular, focusing on ranked enumeration where the answers are returned incrementally in ...
An Efficient $k$-modes Algorithm for Clustering Categorical Datasets

Mining clusters from data is an important endeavor in many applications. The $k$-means method is a popular, efficient, and distribution-free approach for clustering numerical-valued data, but does not apply for categorical-valued observations. The $k$-modes method addresses this lacuna by replacing the Euclidean with the Hamming distance and the means with the modes in the $k$-means objective function. We provide a novel, computationally efficient implementation of $k$-modes, called OTQT. We prove that OTQT finds updates to improve the objective function that are undetectable to existing $k$-modes algorithms. Although slightly slower per iteration due to algorithmic complexity, OTQT is always more accurate per iteration and almost always faster (and only barely slower on some datasets) to the final optimum. Thus, we recommend OTQT as the preferred, default algorithm for $k$-modes optimization.
Noncompliance with SEC regulations: evidence from timely loan disclosures

We use required 8-K filings around major borrowings to shed light on firms’ choices of whether to comply with SEC disclosure rules. Exploiting within-firm variation, we find that firms are more likely to hide loans with high spreads and tight financial covenants. We further find that firms appear to exploit the ambiguity of the definition of materiality, as they are more likely to selectively disclose (hide) “immaterial” loans when interest rates are low (high). Firms are less likely to hide loans when investors anticipate borrowing during asset acquisition, when firms are followed by more equity analysts or receive more investor attention, and when the firms’ stock prices are more volatile. Lastly, we provide evidence that the SEC does not rigorously enforce compliance with 8-K loan disclosures.
SyDeBO: Symbolic-Decision-Embedded Bilevel Optimization for Long-Horizon Manipulation in Dynamic Environments

This study proposes a Task and Motion Planning (TAMP) method with symbolic decisions embedded in a bilevel optimization. This TAMP method exploits the discrete structure of sequential manipulation for long-horizon and versatile tasks in dynamic environments. At the symbolic planning level, we propose a scalable decision-making method for long-horizon manipulation tasks using the Planning Domain Definition Language (PDDL) with causal graph decomposition. At the motion planning level, we devise a trajectory optimization (TO) approach based on the Differential Dynamic Programming (DDP) and Alternating Direction Method of Multipliers (ADMM), suitable for solving constrained, large-scale nonlinear optimization in a distributed manner. Distinct from conventional geometric motion planners, our approach generates highly dynamic manipulation motions by incorporating the full robot and object dynamics. Furthermore, in lieu of a hierarchical planning approach, we solve a holistically integrated bilevel optimization problem involving costs from both the low-level TO and the high-level search. Simulation and experimental results demonstrate dynamic manipulation for long-horizon object sorting tasks in clutter and on a moving conveyor belt.
It's Not Always Sunny in Relationally Rich Jobs: The Influence of Negative Beneficiary Contact

Contact with beneficiaries has been described as an important job characteristic for shaping perceptions about the meaning of work; however, little is known about the role of negative beneficiary c...
Choosing priors in Bayesian ecological models by simulating from the prior predictive distribution

Bayesian data analysis is increasingly used in ecology, but prior specification remains focused on choosing non-informative priors (e.g., flat or vague priors). One barrier to choosing more informative priors is that priors must be specified on model parameters (e.g., intercepts, slopes, sigmas), but prior knowledge often exists on the level of the response variable. This is particularly true for common models in ecology, like generalized linear mixed models, which may have a link function and dozens of parameters, each of which needs a prior distribution. We suggest that this difficulty can be overcome by simulating from the prior predictive distribution and visualizing the results on the scale of the response variable. In doing so, some common choices for non-informative priors on parameters can easily be seen to produce biologically impossible values of response variables. Such implications of prior choices are difficult to foresee without visualization. We demonstrate a workflow for prior selection using simulation and visualization with two ecological examples (predator-prey body sizes and spider responses to food competition). This approach is not new, but its adoption by ecologists will help to better incorporate prior information in ecological models, thereby maximizing one of the benefits of Bayesian data analysis.
Monitoring S. pombe genome stress by visualizing end-binding protein Ku

Studies of genome stability have exploited visualization of fluorescently tagged proteins in live cells to characterize DNA damage, checkpoint, and repair responses. In this report, we describe a new tool for fission yeast, a tagged version of the end-binding protein Pku70 which is part of the KU protein complex. We compare Pku70 localization to other markers upon treatment to various genotoxins, and identify a unique pattern of distribution. Pku70 provides a new tool to define and characterize DNA lesions and the repair response.
STRESS: Super-Resolution for Dynamic Fetal MRI Using Self-supervised Learning

Fetal motion is unpredictable and rapid on the scale of conventional MR scan times. Therefore, dynamic fetal MRI, which aims at capturing fetal motion and dynamics of fetal function, is limited to fast imaging techniques with compromises in image quality and resolution. Super-resolution for dynamic fetal MRI is still a challenge, especially when multi-oriented stacks of image slices for oversampling are not available and high temporal resolution for recording the dynamics of the fetus or placenta is desired. Further, fetal motion makes it difficult to acquire high-resolution images for supervised learning methods. To address this problem, in this work, we propose STRESS (Spatio-Temporal Resolution Enhancement with Simulated Scans), a self-supervised super-resolution framework for dynamic fetal MRI with interleaved slice acquisitions. Our proposed method simulates an interleaved slice acquisition along the high-resolution axis on the originally acquired data to generate pairs of low- and high-resolution images. Then, it trains a super-resolution network by exploiting both spatial and temporal correlations in the MR time series, which is used to enhance the resolution of the original data. Evaluations on both simulated and in utero data show that our proposed method outperforms other self-supervised super-resolution methods and improves image quality, which is beneficial to other downstream tasks and evaluations.
Racial and ethnic representation among a sample of nutrition- and obesity-focused professional organizations in the United States.

OBJECTIVE Obesity is a chronic disease that disproportionately affects individuals from nonmajority racial/ethnic groups in the United States. Research shows that individuals from minority racial/ethnic backgrounds consider it important to have access to providers from diverse backgrounds. Health care providers and scientists from minority racial/ethnic groups are more likely than their non-Hispanic White counterparts to treat or conduct research on patients from underrepresented groups. The objective of this study was to characterize the racial/ethnic diversity of nutrition- and obesity-focused professional organizations in the United States. METHODS This study assessed race/ethnicity data from several obesity-focused national organizations including The Obesity Society, the Academy of Nutrition and Dietetics (AND), the American Society for Nutrition, and the American Board of Obesity Medicine (ABOM). Each organization was queried via emailed survey to provide data on racial/ethnic representation among their membership in the past 5 years and among elected presidents from 2010 to 2020. RESULTS Two of the three professional societies queried did not systematically track race/ethnicity data at the time of query. Limited tracking data available from AND show underrepresentation of Black (2.6%), Asian (3.9%), Latinx (3.1%), Native Hawaiian or Pacific Islander (1.3%), or indigenous (American Indian or Alaskan Native: 0.3%) individuals compared with the US population. Underrepresentation of racial/ethnic minorities was also reported for ABOM diplomates (Black: 6.0%, Latinx: 5.0%, Native American: 0.2%). Only AND reported having racial/ethnic diversity (20%) among the organization's presidents within the previous decade (2010-2020). CONCLUSIONS Findings suggest that (1) standardized tracking of race and ethnicity data is needed to fully assess diversity, equity, and inclusion, and (2) work is needed to increase the diversity of membership and leadership at the presidential level within obesity- and nutrition-focused professional organizations. A diverse cadre of obesity- and nutrition-focused health care professionals is needed to further improve nutrition-related health outcomes, including obesity, cardiovascular disease, diabetes, and undernutrition, in this country.
Exploring the Effects of Methylation on the CID of Protonated Lysine: A Combined Experimental and Computational Approach.

We report the results of experiments, simulations, and DFT calculations that focus on describing the reaction dynamics observed within the collision-induced dissociation of l-lysine-H+ and its side-chain methylated analogues, Ne-methyl-l-lysine-H+ (Me1-lysine-H+), Ne,Ne-dimethyl-l-lysine-H+ (Me2-lysine-H+), and Ne,Ne,Ne-trimethyl-l-lysine-H+ (Me3-lysine-H+). The major pathways observed in the experimental measurements were m/z 130 and 84, with the former dominant at low collision energies and the latter at intermediate to high collision energies. The m/z 130 peak corresponds to loss of N(CH3)nH3-n, while m/z 84 has the additional loss of H2CO2 likely in the form of H2O + CO. Within the time frame of the direct dynamics simulations, m/z 130 and 101 were the most populous peaks, with the latter identified as an intermediate to m/z 84. The simulations allowed for the determination of several reaction pathways that result in these products. A graph theory analysis enabled the elucidation of the significant structures that compose each peak. Methylation results in the preferential loss of the side-chain amide group and a reduction of cyclic structures within the m/z 84 peak population in simulations.
Snoopy: Surpassing the Scalability Bottleneck of Oblivious Storage

Existing oblivious storage systems provide strong security by hiding access patterns, but do not scale to sustain high throughput as they rely on a central point of coordination. To overcome this scalability bottleneck, we present Snoopy, an object store that is both oblivious and scalable such that adding more machines increases system throughput. Snoopy contributes techniques tailored to the high-throughput regime to securely distribute and efficiently parallelize every system component without prohibitive coordination costs. These techniques enable Snoopy to scale similarly to a plaintext storage system. Snoopy achieves 13.7x higher throughput than Obladi, a state-of-the-art oblivious storage system. Specifically, Obladi reaches a throughput of 6.7K requests/s for two million 160-byte objects and cannot scale beyond a proxy and server machine. For the same data size, Snoopy uses 18 machines to scale to 92K requests/s with average latency under 500ms.
Benchmarking Multimodal AutoML for Tabular Data with Text Fields

We consider the use of automated supervised learning systems for data tables that not only contain numeric/categorical columns, but one or more text fields as well. Here we assemble 18 multimodal data tables that each contain some text fields and stem from a real business application. Our publicly-available benchmark enables researchers to comprehensively evaluate their own methods for supervised learning with numeric, categorical, and text features. To ensure that any single modeling strategy which performs well over all 18 datasets will serve as a practical foundation for multimodal text/tabular AutoML, the diverse datasets in our benchmark vary greatly in: sample size, problem types (a mix of classification and regression tasks), number of features (with the number of text columns ranging from 1 to 28 between datasets), as well as how the predictive signal is decomposed between text vs. numeric/categorical features (and predictive interactions thereof). Over this benchmark, we evaluate various straightforward pipelines to model such data, including standard two-stage approaches where NLP is used to featurize the text such that AutoML for tabular data can then be applied. Compared with human data science teams, the fully automated methodology that performed best on our benchmark (stack ensembling a multimodal Transformer with various tree models) also manages to rank 1st place when fit to the raw text/tabular data in two MachineHack prediction competitions and 2nd place (out of 2380 teams) in Kaggle's Mercari Price Suggestion Challenge.
Safe Pontryagin Differentiable Programming

We propose a Safe Pontryagin Differentiable Programming (Safe PDP) methodology, which establishes a theoretical and algorithmic safe differentiable framework to solve a broad class of safety-critical learning and control tasks -- problems that require the guarantee of both immediate and long-term constraint satisfaction at any stage of the learning and control progress. In the spirit of interior-point methods, Safe PDP handles different types of state and input constraints by incorporating them into the cost and loss through barrier functions. We prove the following fundamental features of Safe PDP: first, both the constrained solution and its gradient in backward pass can be approximated by solving a more efficient unconstrained counterpart; second, the approximation for both the solution and its gradient can be controlled for arbitrary accuracy using a barrier parameter; and third, importantly, any intermediate results throughout the approximation and optimization are strictly respecting all constraints, thus guaranteeing safety throughout the entire learning and control process. We demonstrate the capabilities of Safe PDP in solving various safe learning and control tasks, including safe policy optimization, safe motion planning, and learning MPCs from demonstrations, on different challenging control systems such as 6-DoF maneuvering quadrotor and 6-DoF rocket powered landing.
Machine learning structure preserving brackets for forecasting irreversible processes

Forecasting of time-series data requires imposition of inductive biases to obtain predictive extrapolation, and recent works have imposed Hamiltonian/Lagrangian form to preserve structure for systems with reversible dynamics. In this work we present a novel parameterization of dissipative brackets from metriplectic dynamical systems appropriate for learning irreversible dynamics with unknown a priori model form. The process learns generalized Casimirs for energy and entropy guaranteed to be conserved and nondecreasing, respectively. Furthermore, for the case of added thermal noise, we guarantee exact preservation of a fluctuation-dissipation theorem, ensuring thermodynamic consistency. We provide benchmarks for dissipative systems demonstrating learned dynamics are more robust and generalize better than either "black-box" or penalty-based approaches.
On the number of equilibria balancing Newtonian point masses with a central force

We consider the critical points (equilibria) of a planar potential generated by n Newtonian point masses augmented with a quadratic term (such as arises from a centrifugal effect). Particular cases of this problem have been considered previously in studies of the circular-restricted n-body problem. We show that the number of equilibria is finite for a generic set of parameters, and we establish estimates for the number of equilibria. We prove that the number of equilibria is bounded below by n + 1, and we provide examples to show that this lower bound is sharp. We prove an upper bound on the number of equilibria that grows exponentially in n. In order to establish a lower bound on the maximum number of equilibria, we analyze a class of examples, referred to as “ring configurations,” consisting of n − 1 equal masses positioned at vertices of a regular polygon with an additional mass located at the center. Previous numerical observations indicate that these configurations can produce as many as 5n − 5 equilibria. We verify analytically that the ring configuration has at least 5n − 5 equilibria when the central mass is sufficiently small. We conjecture that the maximum number of equilibria grows linearly with the number of point masses. We also discuss some mathematical similarities to other equilibrium problems in mathematical physics, namely, Maxwell’s problem from electrostatics and the image counting problem from gravitational lensing.
Chapter 23 The Role of Electrogenic Proton Translocation in Mitochondrial Oxidative Phosphorylation

Publisher Summary The substantial evidence that electrogenic proton translocation is responsible for oxidative phosphorylation in mitochondria makes the elucidation of the molecular mechanisms involved more important than ever. If direct chemiosmosis must be abandoned because of well-substantiated measurements of proton movements in excess of the number of electrons transferred, then the mechanism of proton pumping by the respiratory chain is completely unknown. The chapter discusses that the proton-pumping complexes bear no striking resemblance to simple ion-pumping ATPases, to the mitochondrial F 1 –F 0 ATPase complex, or to each other. Knowledge of the precise pathway of electron transfer within each complex is essential. Experimental verification must be sought for each of the conformational changes so casually invoked.
Spatial organization of redox active centers in the bovine heart ubiquinol-cytochrome c oxidoreductase.

Abstract We have examined the spatial organization of the redox active centers in the Site II segment of the bovine heart respiratory chain by using reconstituted proteoliposomes of ubiquinol-cytochrome c oxidoreductase (Complex III or cytochrome bc1 complex) and EPR techniques. 1) Mutual spin-spin interactions between intrinsic redox active centers were detected. The spin relaxation of the Rieske iron-sulfur cluster was enhanced by the paramagnetic cytochrome c1 and b566 hemes but not by cytochrome b562. 2) Relative distances of the individual redox active centers to the P-side and N-side surfaces of the reconstituted Complex III proteoliposome were measured by our paramagnetic probe method (Blum, H., Bowyer, J. R., Cusanovich, M. A., Waring, A. J., and Ohnishi, T. (1983) Biochim. Biophys. Acta 748, 418-428). The cytochrome b562 heme was shown to be close to the middle of the phospholipid bilayer, while the Rieske iron-sulfur cluster and cytochrome b566 heme were assigned to be near the P-side surface level of the membrane. This probe method is a low resolution technique from the structural viewpoint; however, it can provide direct and reliable assignment of the topographical locations of redox active centers within the membrane. This is the first direct demonstration of the transmembranous location of the two cytochrome b hemes, although electron transfer between these two hemes crosses only half of the membrane thickness. Our data support the assignment of transmembranous distribution of the redox active centers based on electrochromic measurements (Robertson, D.E., and Dutton, P.L. (1988) Biochim, Biophys. Acta 935, 273-291). The implication of these results on the mechanism of Site II energy coupling is discussed.
Using Markov Chains to Analyze GAFOs

Abstract Our theoretical understanding of the properties of genetic algorithms (GAs) being used for function optimization (GAFOs) is not as strong as we would like. Traditional schema analysis provides some first order insights, but doesn't capture the non-linear dynamics of the GA search process very well. Markov chain theory has been used primarily for steady state analysis of GAs. In this paper we explore the use of transient Markov chain analysis to model and understand the behavior of finite population GAFOs observed while in transition to steady states. This approach appears to provide new insights into the circumstances under which GAFOs will (will not) perform well. Some preliminary results are presented and an initial evaluation of the merits of this approach is provided.
Monte Carlo Simulation For Advanced Option Pricing: A Simplifying Tool

Finance students at the undergraduate and MBA levels are increasingly in possession of significant mathematical skills, corresponding with the rise in cross-listings of courses between mathematics and finance departments. This increase in mathematical skill has opened the door for the Black Scholes model to be presented to advanced undergraduate and MBA students. However, although these students can grasp the weaknesses of the Black Scholes model, they are often not mathematically advanced enough to handle more realistic option pricing models. We demonstrate how Monte Carlo simulation may be employed to open the field of advanced option pricing to students without requiring any more mathematical knowledge than basic calculus and intermediate statistics. As an example, we demonstrate how to simulate option values when the underlying process follows Heston's stochastic volatility process, and motivate the example by demonstrating the significant improvement of a properly specified stochastic volatility model over the Black Scholes model.
Mid-infrared hyperbolic metamaterial based on graphene-dielectric multilayers

We have designed and fabricated a mid-infrared hyperbolic metamaterial composed of alternating A1 2 O 3 and chemical-vapor-deposited graphene. Infrared ellipsometry shows a topological transition from elliptical to hyperbolic dispersion at the wavelength of 7.4 μτη.
Systematic review: self-management support interventions for irritable bowel syndrome.

Aliment Pharmacol Ther 2010; 32: 513–521 Summary Background Irritable bowel syndrome is an extremely common and costly condition. Because there is no cure, patients must be supported to manage their own condition. Aim To assess systematically the interventions used to support irritable bowel syndrome patient self-management. Methods A search of PubMed, EMBASE, CINAHL and PsycINFO was performed to identify all studies that involved self-management support interventions for irritable bowel syndrome. Studies that compared the self-management-related intervention to a control group were included. Results Eleven studies that involved a total of 1657 patients were included. For nearly all studies, the intervention was associated with statistically significant benefits. However, across studies there was significant heterogeneity in terms of sample size, diagnostic criteria, study setting, study design, primary outcome, statistical analyses and study quality. Therefore, individual study results could not be statistically combined. Conclusions Many self-management support interventions appear benefit patients with irritable bowel syndrome. However, studies were limited by methodological flaws. Furthermore, feasibility in ‘real world’ clinical practice is uncertain. Thus, practical self-management interventions that can be applied across various clinical settings should be developed, and then tested in well-designed clinical trials.
Chapter IV Quantitative Relationships Between Growth Media Constituents and Cellular Yields and Composition

Publisher Summary This chapter discusses the quantitative relationships between growth media constituents and cellular yields and composition. It provides a guide to calculating the yield of cells expected from media of known composition. From knowledge of the approximate elemental compositions of microbial cells, a medium can be quickly assessed for possible nutrient deficiencies or for which element is likely to be the growth-limiting nutrient. The data provided can only be related to media of known composition, and do not take into account the influence of physical conditions of culture, e.g., temperature, pH or the requirements for oxygen and carbon dioxide. The exceptions to the latter cases seem to be the utilization of carbon and energy sources after growth has ceased; thus a quantitative relationship does not exist between cell yield and carbon source, energy source, and oxygen supplied for aerobes when these nutrients do not limit growth, e.g., they are consumed in the absence of growth.
Translating Climate Change Effects into Everyday Language: An Example of More Driving and Less Angling

Climate change is expected to result in widespread changes in species distributions (e.g., shifting, shrinking, expanding species ranges; e.g., Parmesan and Yohe 2003), especially for freshwater fi...
Analysis of Compounds of Biological Interest by Electron Spectroscopy

Electron spectroscopy involves measuring the energies of electrons ejected when matter is irradiated with photons. One of the more powerful applications of electron spectroscopy is the determination of the binding energies of the inner shells of atoms with soft X-rays. The binding energies of the inner shells are characteristic of the element; thus, a means of qualitative analysis is provided. Quantitative analysis can be done also. In addition, use of high-resolution spectroscopy enables one to determine the oxidation state of each atom, because the binding energies will shift with chemical environment. For example, the K shell binding energy in nitrogen is about 10 V lower for an amine group than for a nitro group. This analysis can be used for all elements with atomic numbers higher than lithium. The advantages of electron spectroscopy for biological research, both for fundamental and in chemical applications, are discussed in general. Recent studies on t -RNA and its bases are given as specific examples of the applicability of the method.
Machine language millisecond timers for the Z-80 microprocessor

Two Z-80 machine language millisecond timers for the Model III TRS-80 are presented and described: MSEC DELAY TIMER and MSEC LATENCY TIMER. The latency timer may be used with keyboard input (key release or key press) and may also be designed to respond to a signal at one of the computer’s I/O ports. The assembly language programs may be revised for other Z-80-based computers. Calibration procedures are described, and a sample two-field tachistoscope program is presented.
On the relative ability of centromeric GNA triplets to form hairpins versus self-paired duplexes.

Abstract While tandem repeats of the human centromere DNA pentamer sequence T GRA A form stable “self-complementary” [T GRA AT GRA A] 2 duplexes (R=G or A) containing the GA-bracketed unpaired purine stack motif, their phase-shifted variants NA AT GNA AT G (N=A, G, C, T) were found to exist in solution as an equilibrium mixture of a duplex containing the GA-bracketed unpaired stack motif and a hairpin containing a single-residue loop closed by a sheared G·A pair. The stability of the hairpin form relative to duplex form of GNA triplets was found to be GCA>GAA/GTA>>GGA, with the CA AT GCA AT G sequence mostly in the hairpin form and the GA AT GGA AT G sequence mostly in the [GAAT GGA ATG] 2 duplex form. The chemical shifts of the H1′ and H4′ protons of the central N residue in GNA triplets were found to differ markedly in the duplex and hairpin forms and are diagnostic indicators of which conformation the oligonucleotide adopts. Comparison between the structures of the G·A-closed C loop motif and the G·A-bracketed unpaired G-stack [GGA] 2 motif reveals remarkably similar stacking by the loop C residue and the intercalated G residue on the adjacent sheared G·A pair. The anomalous upfield chemical shifts of the H1′ and H4′ protons in [GGA] 2 motifs and the H4′ proton in GCA loops, and the different sugar conformations in these two motifs, can be explained by interstrand versus intrastrand stacking of the central (G or C) deoxyribose with the adenine base. Based on these studies, a DNA sequence GT GGA AT GGA AT GGA AC was designed and shown to form a duplex containing three [GGA] 2 motifs, while its 9G→9C analog GT GGA AT GCA AT GGA AC was found to adopt a stable hairpin containing a (GGA) 2 motif in the stem and a G·A-closed single C-loop.
Tumor Necrosis Factor-α and Host Resistance to the Pathogenic Fungus, Histoplasma capsulatum

Tumor necrosis factor-alpha (TNF- α ) is a multifunctional cytokine that is critically important in host defenses to a number of pathogenic microbes. Among those, this cytokine is necessary for control of infection with the pathogenic fungus, Histoplasma capsulatum . Antagonism of endogenous cytokine is associated with an enhanced susceptibility to histoplasmosis in both mice and humans. The mechanisms by which TNF- α modulates the protective immune response will be discussed.
Geopotential Heights and Thicknesses as Predictors of Atlantic Tropical Cyclone Motion and Intensity

Abstract In response to the increasing availability of satellite and aircraft temperature observations on the periphery of tropical cyclones, archived thickness and geopotential height data from National Meteorological Center analyses am compared as Atlantic cyclone predictors. For maximum use in motion prediction, thickness must be converted into deep-layer-mean or midtropospheric heights using an accurate reference-level height analysis. The 1000–700 mb thickness in the vicinity of cyclones in the poleward and eastward quadrant is a significant predictor of 24 h intensity change, even when combined with climatology and persistence.
Free-energy calculations for fluid and solid phases by molecular simulation

The ability to compute accurate free energies by molecular simulation is necessary for many applications. Methods for performing these calculations are well established, but are often misused or misinterpreted. We review ways to avoid and uncover systematic errors in free-energy calculations through simple and appropriate analysis of the simulation averages. One particularly difficult area of application of free-energy methods is in the analysis of solid-phase behaviors. Free energies cannot be computed as easily in solids as in liquids, owing to the inability to perform particle insertions into solids without introducing a defect. We describe our attempts to develop new and efficient methods to circumvent this problem, and show how the analysis of free-energy methods can help to understand the behavior of the newly proposed methods. We examine the method and analysis applied to a simple system of hard rods in one dimension, selected because all of the properties being measured by the simulation method can be evaluated analytically, including finite-size effects.
A Lipschitz-Hankel Integral of K0

Abstract Various representations for an incomplete Lipschitz-Hankel integral of K 0 and related integrals are given in terms of elementary, cylindrical, and Kampe de Feriet functions. In addition, some properties of the Kampe de Feriet functions associated with these integrals are derived.
35.4: Novel Transflective LCD with Micro-lens Array to Double the Brightness

A Novel transflective LCD has been developed with introducing a microlens array and a highly collimated backlight system. This new technology provides excellent visibility both outdoors and indoors without increasing power consumption of backlight.
Autism Spectrum Disorders as a Qualitatively Distinct Category From Typical Behavior in a Large, Clinically Ascertained Sample

The present study evaluated the hypothesis that autism spectrum disorders (ASDs) are best represented as a discrete category distinct from typical behavior within autism-affected families. The late...
Effect of atrial natriuretic peptide on diastolic filling in the stage 21 chick embryo.

ABSTRACT: Atrial natriuretic peptide (ANP) exerts hemodynamic effects by direct venodilation in the chick embryo. We hypothesized that ANP-induced venodilation affects ventricular diastolic filling resulting in reduced ventricular preload. Chick ANP (0.1 μg in 10 μL of normal saline) was suffused onto the vitelline vascular bed in stage 21 (3 1/2 d) chick embryos. Equivalent aliquots of normal saline were suffused as sham controls, and normal embryos received no suffusion. We measured simultaneously dorsal aortic blood velocity and atrioventricular blood velocity with a 20-MHz pulsed-Doppler velocity meter. Analog wave forms were digitally sampled at 500 Hz, and the dorsal aortic cross-sectional area was used to calculate dorsal aortic blood flow. Passive ventricular filling volume equaled dorsal aortic stroke volume multiplied by the fraction of passive area; active filling volume equaled dorsal aortic stroke volume multiplied by the fraction of active area. Data were summarized as mean ± SEM (n≥ 7 per group) and analyzed by analysis of variance. Cycle lengths were similar in ANP-suffused, sham control, and normal embryos. Dorsal aortic blood flow decreased from 0.49 ± 0.04 mm3/s at baseline to 0.27 ± 0.05 mm3/s at 4 min post-ANP suffusion (p 0.05). Passive ventricular filling was reduced by ANP suffusion, whereas active filling was unaffected, resulting in a decreased passive/active filling ratio from 0.64 ± 0.07 at baseline to 0.32 ± 0.08 at 4 min in ANP-suffused embryos (p< 0.05). Passive/active ratio was unchanged in sham control and normal embryos. Thus, ANP-mediated vasodilation reduces cardiac output via decreased passive ventricular filling in the embryonic heart.
Preliminary characterization of phosphotyrosine phosphatase activities in human peripheral blood lymphocytes: identification of CD45 as a phosphotyrosine phosphatase.

A preliminary characterization of the protein phosphotyrosine phosphatase (PT-Pase) activity in human peripheral blood lymphocytes (PBL) has been made using two tyrosine-containing peptides and the epidermal growth factor receptor from A-431 cells as substrates. High PTPase activity with a pH optimum near 7.4 was observed in both the membrane and the cytosolic fractions. At least three distinct fractions with PTPase activity were separated on DEAE cellulose columns, indicating that the enzyme is heterogeneous. Vanadate, molybdate, and salts of zinc, copper, and mercury were all effective enzyme inhibitors, although the inhibition was generally incomplete and showed some variation with the enzyme preparation. The difficulty in completely inhibiting PTPase activity in lymphocytes may help explain the variation between laboratories in studies of tyrosine phosphorylation in these cells. Studies with highly purified T lymphocytes obtained by filtration of PBL through nylon wool columns indicated that the activity is present in T cells. Absorption with agarose containing anti-HLe-I, a mouse monoclonal lgGi antibody specific for the leukocyte-specific surface protein T-200 (CD45), removed up to 40% of the PTPase activity. Enzyme activity was recovered on the immunoadsorbent after extensive washing, confirming that the enzyme was being bound to the beads. Immunoabsorbents containing other mouse lgGi antibodies failed to bind PTPase activity, indicating that the binding to beads with anti-HLe-I antibody is specific. Further characterization of the CD45 and PTPase activities in lymphocytes may provide a better under standing of the role of protein tyrosine phosphorylation in the regulation of proliferation and differentiation in these cells.