cssi/SciSciGPT-SciSciNet huggingface dataset:
Balanced Reduced-Order Models for Iterative Nonlinear Control of Large-Scale Systems

We propose a new framework to design controllers for high-dimensional nonlinear systems. The control is designed through the iterative linear quadratic regulator (ILQR), an algorithm that computes control by iteratively applying the linear quadratic regulator on the local linearization of the system at each time step. Since ILQR is computationally expensive, we propose to first construct reduced-order models (ROMs) of the high-dimensional nonlinear system. We derive nonlinear ROMs via projection, where the basis is computed via balanced truncation (BT) and LQG balanced truncation (LQG-BT). Numerical experiments are performed on a semi-discretized nonlinear Burgers equation. We find that the ILQR algorithm produces good control on ROMs constructed either by BT or LQG-BT, with BT-ROM based controllers outperforming LQG-BT slightly for very low-dimensional systems.
The Impact of Experienced and Expressed Emotion on Legal Factfinding

Judges and jurors are asked to comb through horrific evidence of accidents and crimes when choosing verdicts and punishment. These factfinders are likely to experience and express intense emotions ...
Optimized Processing of Localized Collisions in Projective Dynamics

We present a method for the efficient processing of contact and collision in volumetric elastic models simulated using the Projective Dynamics paradigm. Our approach enables interactive simulation of tetrahedral meshes with more than half a million elements, provided that the model satisfies two fundamental properties: the region of the model's surface that is susceptible to collision events needs to be known in advance, and the simulation degrees of freedom associated with that surface region should be limited to a small fraction (e.g. 5\%) of the total simulation nodes. Despite this conscious delineation of scope, our hypotheses hold true for common animation subjects, such as simulated models of the human face and parts of the body. In such scenarios, a partial Cholesky factorization can abstract away the behavior of the collision-safe subset of the face into the Schur Complement matrix with respect to the collision-prone region. We demonstrate how fast and accurate updates of penalty-based collision terms can be incorporated into this representation, and solved with high efficiency on the GPU. We also demonstrate the opportunity to iterate a partial update of the element rotations, akin to a selective application of the local step, specifically on the smaller collision-prone region without explicitly paying the cost associated with the rest of the simulation mesh. We demonstrate efficient and robust interactive simulation in detailed models from animation and medical applications.
Structure interpretation of text formats

Data repositories often consist of text files in a wide variety of standard formats, ad-hoc formats, as well as mixtures of formats where data in one format is embedded into a different format. It is therefore a significant challenge to parse these files into a structured tabular form, which is important to enable any downstream data processing. We present Unravel, an extensible framework for structure interpretation of ad-hoc formats. Unravel can automatically, with no user input, extract tabular data from a diverse range of standard, ad-hoc and mixed format files. The framework is also easily extensible to add support for previously unseen formats, and also supports interactivity from the user in terms of examples to guide the system when specialized data extraction is desired. Our key insight is to allow arbitrary combination of extraction and parsing techniques through a concept called partial structures. Partial structures act as a common language through which the file structure can be shared and refined by different techniques. This makes Unravel more powerful than applying the individual techniques in parallel or sequentially. Further, with this rule-based extensible approach, we introduce the novel notion of re-interpretation where the variety of techniques supported by our system can be exploited to improve accuracy while optimizing for particular quality measures or restricted environments. On our benchmark of 617 text files gathered from a variety of sources, Unravel is able to extract the intended table in many more cases compared to state-of-the-art techniques.
Determinants and consequences of noncompliance with the 2013 COSO framework

Abstract The Securities and Exchange Commission (SEC) requires firms to use a “suitable framework” as a basis for evaluating the effectiveness of internal control over financial reporting. The COSO 1992 framework was the most commonly used suitable framework until it was superseded by the COSO 2013 framework. Because strict compliance with the updated framework was not enforced by regulatory authorities, a nontrivial number of firms did not comply in a timely fashion. We investigate determinants and consequences of noncompliance with the COSO 2013 framework following the supersession of the COSO 1992 framework. We find that noncompliance is positively associated with proxies for resource constraints, financial distress, and a weak internal control environment, and negatively associated with auditor industry specialization, board size, and audit committee accounting expertise. Further tests suggest that following supersession of the 1992 framework, investors view quarterly earnings surprises of the noncompliant firms to be less credible and that noncompliance increases regulatory scrutiny. Finally, we find some evidence that accounting conservatism increases after supersession of the 1992 framework for compliant firms relative to noncompliant firms, suggesting that noncompliance can delay the potential benefits of implementing the updated framework.
Understanding the Effectiveness of Reviews in E-commerce Top-N Recommendation

Modern E-commerce websites contain heterogeneous sources of information, such as numerical ratings, textual reviews and images. These information can be utilized to assist recommendation. Through textual reviews, a user explicitly express her affinity towards the item. Previous researchers found that by using the information extracted from these reviews, we can better profile the users' explicit preferences as well as the item features, leading to the improvement of recommendation performance. However, most of the previous algorithms were only utilizing the review information for explicit-feedback problem i.e. rating prediction, and when it comes to implicit-feedback ranking problem such as top-N recommendation, the usage of review information has not been fully explored. Seeing this gap, in this work, we investigate the effectiveness of textual review information for top-N recommendation under E-commerce settings. We adapt several SOTA review-based rating prediction models for top-N recommendation tasks and compare them to existing top-N recommendation models from both performance and efficiency. We find that models utilizing only review information can not achieve better performances than vanilla implicit-feedback matrix factorization method. When utilizing review information as a regularizer or auxiliary information, the performance of implicit-feedback matrix factorization method can be further improved. However, the optimal model structure to utilize textual reviews for E-commerce top-N recommendation is yet to be determined.
The 2020 global stock market crash: Endogenous or exogenous?

Abstract Starting on February 20, 2020, the global stock markets began to suffer the worst decline since the Great Recession in 2008, and the COVID-19 has been widely blamed on the stock market crashes. In this study, we applied the log-periodic power law singularity (LPPLS) methodology based on multilevel time series to unravel the underlying mechanisms of the 2020 global stock market crash by analyzing the trajectories of 10 major world stock market indexes from both developed and emergent stock markets, including the S&P 500, the DJIA, and the NASDAQ from the United State, the FTSE from the United Kingdom, the DAX from Germany, the NIKKEI from Japan, the CSI 300 from China, the HSI from Hong Kong, the BSESN from India, and the BOVESPA from Brazil. In order to effectively distinguish between endogenous crash and exogenous crash in stock market, we proposed using the LPPLS confidence indicator as a classification proxy. The results show that the apparent LPPLS bubble patterns of the super-exponential increase, corrected by the accelerating logarithm-periodic oscillations, have indeed presented in the price trajectories of the seven indexes: S&P 500, DJIA, NASDAQ, DAX, CSI 300, BSESN, and BOVESPA, indicating that the large positive bubbles have formed endogenously prior to the 2020 stock market crash, and the subsequent crashes for the seven indexes are endogenous, stemming from the increasingly systemic instability of the stock markets inherently, while the well-known external shocks, such as the COVID-19 pandemic, the corporate debt bubble, and the 2020 Russia–Saudi Arabia oil price war, only served as sparks during the 2020 global stock market crash. In contrast, the crashes in the three remaining indexes: FTSE, NIKKEI, and HSI, are exogenous and hence are perhaps the only crashes truly due to the COVID-19 pandemic. We also found that in terms of the regime changes of the stock markets, no obvious LPPLS negative bubble pattern has been observed in the price trajectories of the 10 stock market indexes, indicating that the regime changes from a bear market to a bull market in late March 2020 are exogenous, stemming from external factors. The unprecedented market and economy rescue efforts from federal reserves and central banks across the world in unison may have played a critical role in quelling the 2020 global stock market crash in the nick of time. This paper creates a paradigm for future studies in real-time crash detection and underlying mechanism dissection. It serves to warn us of the imminent risks in not only the stock market but also other financial markets and economic indexes.
Domains whose ideals meet a universal restriction

Let $D$ be an integral domain, $S(D)=I(D)$ $(I_{t}(D))$ the set of proper nonzero ideals (proper $t$-ideals) of $D,$ $Max(D)$ $(t$-$Max(D)$ the set of maximal ($t$-) ideals of $D,$ and let $P$ be a predicate on $S(D)$ with nonempty truth set $\Pi _{S(D)}\subseteq S(D)$, where $P$ can be: "---is invertible" or "---is divisorial" etc.$.$ We say $S(D)$ meets $P$ $% (S(D)\vartriangleleft P)$ if $\forall s\in S(D)\exists \pi \in \Pi _{S(D)}(P) $ $(s\subseteq $ $\pi )$. Clearly $S(D)\vartriangleleft P\Leftrightarrow Max(D)$ ($t$-$Max(D))\subseteq $ $\Pi _{S(D)}(P)$. We show that if $S(D)$ $\vartriangleleft P,$ we have no control over $\dim D$. We also show that $I(D)$ $\vartriangleleft P$ does not imply $I(R)$ $% \vartriangleleft P,$ while $I_{t}(D)$ $\vartriangleleft P$ implies $I_{t}(R)$ $\vartriangleleft P,$ for most choices of $P,$ when $R=D[X]$ and have examples to show that generally $S(D)\vartriangleleft P$ does not extend to rings of fractions. We study restrictions that may control the dimension of $% D$ when $S(D)\vartriangleleft P.$ We also say $S(D)\vartriangleleft P$ with a twist $(S(D)\vartriangleleft ^{t}P)$ if $\forall s\in S(D)$ $\exists \pi \in $ $\Pi _{D}(P)(s^{n}$ $\subseteq \pi $ for some $n\in N)$ and study $% S(D)\vartriangleleft ^{t}P,$ along the same lines as $S(D)\vartriangleleft P$ and provide examples.
With a Little Help From My Friends: Nearest-Neighbor Contrastive Learning of Visual Representations

Self-supervised learning algorithms based on instance discrimination train encoders to be invariant to pre-defined transformations of the same instance. While most methods treat different views of the same image as positives for a contrastive loss, we are interested in using positives from other instances in the dataset. Our method, Nearest-Neighbor Contrastive Learning of visual Representations (NNCLR), samples the nearest neighbors from the dataset in the latent space, and treats them as positives. This provides more semantic variations than pre-defined transformations. We find that using the nearest-neighbor as positive in contrastive losses improves performance significantly on ImageNet classification, from 71.7% to 75.6%, outperforming previous state-of-the-art methods. On semi-supervised learning benchmarks we improve performance significantly when only 1% ImageNet labels are available, from 53.8% to 56.5%. On transfer learning benchmarks our method outperforms state-of-the-art methods (including supervised learning with ImageNet) on 8 out of 12 downstream datasets. Furthermore, we demonstrate empirically that our method is less reliant on complex data augmentations. We see a relative reduction of only 2.1% ImageNet Top-1 accuracy when we train using only random crops.
Transfer learning from an artificial radiograph-landmark dataset for registration of the anatomic skull model to dual fluoroscopic X-ray images.

Abstract Registration of 3D anatomic structures to their 2D dual fluoroscopic X-ray images is a widely used motion tracking technique. However, deep learning implementation is often impeded by a paucity of medical images and ground truths. In this study, we proposed a transfer learning strategy for 3D-to-2D registration using deep neural networks trained from an artificial dataset. Digitally reconstructed radiographs (DRRs) and radiographic skull landmarks were automatically created from craniocervical CT data of a female subject. They were used to train a residual network (ResNet) for landmark detection and a cycle generative adversarial network (GAN) to eliminate the style difference between DRRs and actual X-rays. Landmarks on the X-rays experiencing GAN style translation were detected by the ResNet, and were used in triangulation optimization for 3D-to-2D registration of the skull in actual dual-fluoroscope images (with a non-orthogonal setup, point X-ray sources, image distortions, and partially captured skull regions). The registration accuracy was evaluated in multiple scenarios of craniocervical motions. In walking, learning-based registration for the skull had angular/position errors of 3.9 ± 2.1°/4.6 ± 2.2 mm. However, the accuracy was lower during functional neck activity, due to overly small skull regions imaged on the dual fluoroscopic images at end-range positions. The methodology to strategically augment artificial training data can tackle the complicated skull registration scenario, and has potentials to extend to widespread registration scenarios.
Sharp threshold for the Ising perceptron model

Consider the discrete cube {−1,1}N and a random collection of half spaces which includes each half space H(x):={y∈{−1,1}N:x·y≥κ N} for x∈{−1,1}N independently with probability p. Is the intersection of these half spaces empty? This is called the Ising perceptron model under Bernoulli disorder. We prove that this event has a sharp threshold, that is, the probability that the intersection is empty increases quickly from ϵ to 1−ϵ when p increases only by a factor of 1+o(1) as N→∞.
Real-Time Mapping of Tissue Properties for Magnetic Resonance Fingerprinting

Magnetic resonance fingerprinting (MRF) is a relatively new multi-parametric quantitative imaging method that involves a two-step process: (i) reconstructing a series of time frames from highly-undersampled non-Cartesian spiral k-space data and (ii) pattern matching using the time frames to infer tissue properties (e.g., \(T_1\) and \(T_2\) relaxation times). In this paper, we introduce a novel end-to-end deep learning framework to seamlessly map the tissue properties directly from spiral k-space MRF data, thereby avoiding time-consuming processing such as the non-uniform fast Fourier transform (NUFFT) and the dictionary-based fingerprint matching. Our method directly consumes the non-Cartesian k-space data, performs adaptive density compensation, and predicts multiple tissue property maps in one forward pass. Experiments on both 2D and 3D MRF data demonstrate that quantification accuracy comparable to state-of-the-art methods can be accomplished within 0.5 s, which is 1,100 to 7,700 times faster than the original MRF framework. The proposed method is thus promising for facilitating the adoption of MRF in clinical settings.
The Swendsen-Wang Dynamics on Trees

The Swendsen-Wang algorithm is a sophisticated, widely-used Markov chain for sampling from the Gibbs distribution for the ferromagnetic Ising and Potts models. This chain has proved difficult to analyze, due in part to the global nature of its updates. We present optimal bounds on the convergence rate of the Swendsen-Wang algorithm for the complete d-ary tree. Our bounds extend to the non-uniqueness region and apply to all boundary conditions. We show that the spatial mixing conditions known as Variance Mixing and Entropy Mixing, introduced in the study of local Markov chains by Martinelli et al. (2003), imply Ω(1) spectral gap and O(log n) mixing time, respectively, for the Swendsen-Wang dynamics on the d-ary tree. We also show that these bounds are asymptotically optimal. As a consequence, we establish Θ(log n) mixing for the Swendsen-Wang dynamics for all boundary conditions throughout the tree uniqueness region; in fact, our bounds hold beyond the uniqueness threshold for the Ising model, and for the q-state Potts model when q is small with respect to d. Our proofs feature a novel spectral view of the Variance Mixing condition inspired by several recent rapid mixing results on high-dimensional expanders and utilize recent work on block factorization of entropy under spatial mixing conditions.
Thermal Image Processing via Physics-Inspired Deep Networks

We introduce DeepIR, a new thermal image processing framework that combines physically accurate sensor modeling with deep network-based image representation. Our key enabling observations are that the images captured by thermal sensors can be factored into slowly changing, scene-independent sensor non-uniformities (that can be accurately modeled using physics) and a scene-specific radiance flux (that is well-represented using a deep network-based regularizer). DeepIR requires neither training data nor periodic ground-truth calibration with a known black body target--making it well suited for practical computer vision tasks. We demonstrate the power of going DeepIR by developing new denoising and super-resolution algorithms that exploit multiple images of the scene captured with camera jitter. Simulated and real data experiments demonstrate that DeepIR can perform high-quality non-uniformity correction with as few as three images, achieving a 10dB PSNR improvement over competing approaches.
Optimally Resilient Codes for List-Decoding From Insertions and Deletions

We give a complete answer to the following basic question: “What is the maximal fraction of deletions or insertions tolerable by $q$ -ary list-decodable codes with non-vanishing information rate?” This question has been open even for binary codes, including the restriction to the binary insertion-only setting, where the best-known result was that a $\gamma \leq 0.707$ fraction of insertions is tolerable by some binary code family. For any desired $\varepsilon > 0$ , we construct a family of binary codes of positive rate which can be efficiently list-decoded from any combination of $\gamma $ fraction of insertions and $\delta $ fraction of deletions as long as $\gamma + 2\delta \leq 1 - \varepsilon $ . On the other hand, for any $\gamma, \delta $ with $\gamma + 2\delta = 1$ list-decoding is impossible. Our result thus precisely characterizes the feasibility region of binary list-decodable codes for insertions and deletions. We further generalize our result to codes over any finite alphabet of size $q$ . Surprisingly, our work reveals that the feasibility region for $q>2$ is not the natural generalization of the binary bound above. We provide tight upper and lower bounds that precisely pin down the feasibility region, which turns out to have a $(q-1)$ -piece-wise linear boundary whose $q$ corner-points lie on a quadratic curve. The main technical work in our results is proving the existence of code families of sufficiently large size with good list-decoding properties for any combination of $\delta,\gamma $ within the claimed feasibility region. We achieve this via an intricate analysis of codes introduced by [Bukh and Ma, 2014]. Finally, we give a simple yet powerful concatenation scheme for list-decodable insertion-deletion codes which transforms any such (non-efficient) code family (with vanishing information rate) into an efficiently decodable code family with constant rate.
Lung proteomic biomarkers associated with chronic obstructive pulmonary disease

Background: Identifying protein biomarkers for chronic obstructive pulmonary disease (COPD) has been challenging. Most previous studies have utilized individual proteins or pre-selected protein pan...
STGT: Forecasting Pedestrian Motion Using Spatio-Temporal Graph Transformer

Full understanding human motion is essential for autonomous agents such as self-driving vehicles and social robots for navigating in dense crowded environments. In this paper, we present a trajectory prediction framework which models inter-pedestrian behaviour through graph representations and then apply attention through a Transformer network to better forecast human motion. Previous works have incorporated pedestrian interaction using social and graph pooling mechanisms whereas our work utilizes complete graph structure of pedestrians which helps to obtain robust spatiotemporal representations. We also leverage semantic segmentation architecture to encode scene context. Our experiments highlight the potential of handing pedestrian interaction with graph convolutional networks and Transformer and, on top of that, shows marginal improvement with inclusion of semantic scene features.
SAMPRA: Scalable Analysis, Management, Protection of Research Artifacts

This paper describes SAMPRA, a framework for supporting effective research on sensitive data being deployed at the University of New Mexico. SAMPRA and its associated implementation are designed to support the needs of a diverse set of use-cases from researchers across different disciplines at UNM, including from clinical neurosciences, forensic anthropology, and community health. From these use-cases, we identified a set of common unaddressed demands when handling data with privacy/protection requirements, particularly collaborative research projects, interfacing with scientific instruments, and full-lifecycle management of sensitive data. To properly address and accelerate research projects with these needs, SAMPRA a) integrates privacy-preserving storage and data transfer systems with data-centric virtual environments, and b) supports effective researcher use of the system through active collaboration between local IT personnel, campus enterprise IT service providers, and campus data librarians by defining clear roles with associated personnel. By doing so, SAMPRA seeks to meet the needs of research on sensitive data across the entire data lifecycle and avoid the pitfalls of generic “one-size-fits-all” services.
Ultra-Sharp Nanowire Arrays Natively Permeate, Record, and Stimulate Intracellular Activity in Neuronal and Cardiac Networks

Intracellular access with high spatiotemporal resolution can enhance our understanding of how neurons or cardiomyocytes regulate and orchestrate network activity, and how this activity can be affected with pharmacology or other interventional modalities. Nanoscale devices often employ electroporation to transiently permeate the cell membrane and record intracellular potentials, which tend to decrease rapidly to extracellular potential amplitudes with time. Here, we report innovative scalable, vertical, ultra-sharp nanowire arrays that are individually addressable to enable long-term, native recordings of intracellular potentials. We report large action potential amplitudes that are indicative of intracellular access from 3D tissue-like networks of neurons and cardiomyocytes across recording days and that do not decrease to extracellular amplitudes for the duration of the recording of several minutes. Our findings are validated with cross-sectional microscopy, pharmacology, and electrical interventions. Our experiments and simulations demonstrate that individual electrical addressability of nanowires is necessary for high-fidelity intracellular electrophysiological recordings. This study advances our understanding of and control over high-quality multi-channel intracellular recordings, and paves the way toward predictive, high-throughput, and low-cost electrophysiological drug screening platforms.
Refactorization of a variable step, unconditionally stable method of Dahlquist, Liniger and Nevanlinna

Abstract The one-leg, two-step time-stepping scheme proposed by Dahlquist, Liniger and Nevanlinna has clear advantages in complex, stiff numerical simulations: unconditional G -stability for variable time-steps and second-order accuracy. Yet it has been underutilized due, partially, to its complexity of direct implementation. We prove herein that this method is equivalent to the backward Euler method with pre- and post arithmetic steps added. This refactorization eases implementation in complex, possibly legacy codes. The realization we develop reduces complexity, including cognitive complexity and increases accuracy over both first order methods and constant time steps second order methods.
Invariance of immersed Floer cohomology under Maslov flows

We show that immersed Lagrangian Floer cohomology in compact rational symplectic manifolds is invariant under Maslov flows such as coupled mean curvature/Kaehler-Ricci flow in the sense of Smoczyk as a pair of self-intersection points is born or dies at a self-tangency, using results of Ekholm-Etnyre-Sullivan. This proves part of a conjecture of Joyce. We give a lower bound on the time for which the Floer cohomology is invariant under the (forward or backwards) flow, if it exists.
A polyfold proof of the Arnold conjecture

We give a detailed proof of the homological Arnold conjecture for nondegenerate periodic Hamiltonians on general closed symplectic manifolds M via a direct Piunikhin–Salamon–Schwarz morphism. Our constructions are based on a coherent polyfold description for moduli spaces of pseudoholomorphic curves in a family of symplectic manifolds degenerating from $${{\mathbb {C}}{\mathbb {P}}}^1\times M$$ to $${{\mathbb {C}}}^+ \times M$$ and $${{\mathbb {C}}}^-\times M$$ , as developed by Fish–Hofer–Wysocki–Zehnder as part of the Symplectic Field Theory package. To make the paper self-contained we include all polyfold assumptions, describe the coherent perturbation iteration in detail, and prove an abstract regularization theorem for moduli spaces with evaluation maps relative to a countable collection of submanifolds. The 2011 sketch of this proof was joint work with Peter Albers, Joel Fish.
Accessibility of the Boundary of the Thurston Set

Consider two objects associated to the Iterated Function System (IFS) $\{1+\lambda z,-1+\lambda z\}$: the locus $\mathcal{M}$ of parameters $\lambda\in\mathbb{D}\setminus\{0\}$ for which the corresponding attractor is connected; and the locus $\mathcal{M}_0$ of parameters for which the related attractor contains $0$. The set $\mathcal{M}$ can also be characterized as the locus of parameters for which the attractor of the IFS $\{1+\lambda z, \lambda z, -1+\lambda z\}$ contains $\lambda^{-1}$. Exploiting the asymptotic similarity of $\mathcal{M}$ and $\mathcal{M}_0$ with the respective associated attractors, we give sufficient conditions on $\lambda\in\partial\mathcal{M}$ or $\partial\mathcal{M}_0$ to guarantee it is path accessible from the complement $\mathbb{D}\setminus\mathcal{M}$.
The Normative Implications of Political Decision-Making for Benefit-Cost Analysis

The Potential Pareto criterion, or Kaldor-Hicks standard, presumes that costs are not fully compensated. Yet, uncompensated costs can incentivize costly political activity and create uncertainty about political outcomes. These consequences are not reckoned in the standard benefit-cost analysis. This study models political costs and uncertainty as a function of project parameters and political-institutional characteristics. The economic consequences of political behavior are then incorporated into an adjusted project evaluation standard. This standard assures that the project’s conventionally measured net benefits are sufficient to cover political costs and uncertainty about the decision-making outcome.
Realism in Teaching Cybersecurity Research: The Agile Research Process

As global threats to information systems continue to increase, the value of effective cybersecurity research has never been greater. There is a pressing need to educate future researchers about the research process itself, which is increasingly unpredictable, multi-disciplinary, multi-organizational, and team-oriented. In addition, there is a growing demand for cybersecurity research that can produce fast, authoritative, and actionable results. In short, speed matters. Organizations conducting cyber defense can benefit from the knowledge and experience of the best minds in order to make effective decisions in difficult and fast moving situations. The Agile Research process is a new approach to provide such rapid, authoritative, applied research. It is designed to be fast, transparent, and iterative, with each iteration producing results that can be applied quickly. Purdue University is employing Agile Research as a teaching vehicle in an innovative, multi-university graduate program with government sponsor participation, as described in this paper. Because it simulates real-world operations and processes, this program is equipping students to become effective contributors to cybersecurity research.
Phylogeography of Emerita analoga (Crustacea, Decapoda, Hippidae), an eastern Pacific Ocean sand crab with long-lived pelagic larvae

Aim Phylogeographic analyses have confirmed high dispersal in many marine taxa but have also revealed many cryptic lineages and species, raising the question of how population and regional genetic diversity arise and persist in dynamic oceanographic settings. Here we explore the geographic evolution of Emerita analoga, an inter-tidal sandy beach crab with an exceptionally long pelagic larval phase and wide latitudinal, amphitropical, distribution. We test the hypothesis that eastern Pacific E. analoga constitute a single panmictic population and examine the location(s), timing and cause(s) of phylogeographic differentiation. Location Principally the eastern Pacific Ocean. Methods We sequenced cytochrome c oxidase subunit I (COI) from 742 E. analoga specimens collected between 1997 and 2000 and downloaded homologous sequences of congeners from GenBank. We reconstructed a phylogeny for Emerita species using maximum likelihood and Bayesian methods and estimated times to most recent common ancestors (TMRCAs), using a COI divergence rate of 1% Myr−1 and timing of closure of the Central American Seaway. We constructed the COI haplotype network of E. analoga using statistical parsimony, calculated population genetic and spatial structure statistics in Arlequin, and estimated the demographic history of E. analoga using Bayesian skyline analysis. Results Population subdivision and allele frequency differences were insignificant among north-eastern Pacific locations over 2000 km apart (ΦST = 0.00, P = 0.70), yet two distinct phylogroups were recovered from the north-eastern and south-eastern Pacific (ΦCT = 0.87, P < 0.001). Amphitropical differentiation of these temperate clades occurred after TMRCA 1.9 ± 0.02 (mean ± SE) Ma and E. analoga has expanded into its present-day north-eastern Pacific range since c. 250 ka. Main conclusions Emerita analoga is not panmictic but is very widely dispersed and approaching genetic homogeneity, i.e. ‘eurymixis’, in the north-eastern Pacific. North-eastern and south-eastern Pacific populations of E. analoga probably became isolated c. 1.5 Ma as the tropical eastern Pacific Ocean warmed and expanded, intensifying barriers to gene flow. The fragmentation of a widespread ancestral species previously connected by long-distance gene flow (‘soft vicariance’) coincident with changing oceanographic conditions may be a common theme in the evolution of Emerita species and in other highly dispersive taxa. Highly dispersive species may differentiate because of, not despite, the dynamic oceanographic setting.
TRACHEOSTOMY IN EPIGLOTTITIS

Mortality in epiglottitis was compared in two groups of patients. There was no mortality in a small group of patients who were tracheotomized electively, whereas patients who were observed and tracheotomized on clinical indication had a 25% mortality or near mortality. It is suggested that elective tracheostomy, performed in patients with the confirmed diagnosis of epiglottitis, may protect them from the unpredictability of this disease.
In Vitro Selection for Mutants of Higher Plants

Tobacco plants resistant to wildfire disease have been regenerated from methionine sulfoximine-resistant calluses selected in vitro. The intracellular concentration of free methionine is elevated in two of the three mutant plants. Three rice cell lines which are resistant to the lysine analogue, S-(s-minoethyl)-cysteine have also been isolated in vitro. Analyses of free and protein-incorporated amino acid composition reveal increased levels of several amino acids in the variant cell lines.
TISSUE CULTURE STUDIES I. STANDARDIZATION OF TECHNIQUES FOR QUANTITATIVE CULTURING

Reproducible conditions have been described for quantitative tissue culturing in which all constituents including the tissue itself are added in known quantities to the culture. The conditions for transfer, cutting, and manipulation of tissue are also described. The data show that such culturing can be performed without loss or significant alteration of the nitrogen and phosphorus content of the tissue.
Macromolecular structure of coals, 7. Mass spectrometry of pyridine extracts and depolymerized coal

Electron impact mass spectrometry and mass ion kinetic energy spectrometry were used to analyze: (i) the structure of uncrosslinked, predominantly aliphatic chains obtained by pyridine extraction of bituminous coals; and (ii) the identity of compounds found in depolymerized coal samples produced by the technique of Hooker et al. (1986). Die aus den Pyridinextrakten von Steinkohle erhaltenen unvernetzten, hauptsachlich aliphatischen Polymerketten wurden analysiert. Die Technik von Hooker et al. wurde zur Depolymerisation der Kohleproben benutzt. Die hierbei erhaltenen Komponenten wurden ebenfalls untersucht. Als Analysenmethode diente die Massenspektroskopie: (i) „electron impact mass spectrometry” (Ionisierung durch Elektronenstos), (ii) „mass ion kinetic energy spectrometry” (Untersuchung der Ionen, die im feldfreien Raum zerfallen).
The Trevo XP 3×20 mm retriever (‘Baby Trevo’) for the treatment of distal intracranial occlusions

Objective To report our single-center initial experience using the Trevo XP ProVue Retriever 3×20 mm (‘Baby Trevo’) for distal intracranial occlusions. Methods We performed a retrospective review of our interventional database for consecutive patients who underwent treatment for acute ischemic stroke with the Baby Trevo device between February and December 2014. Results Of 134 patients treated during the study period, 8 underwent treatment with the Baby Trevo for distal occlusions. Their mean age was 51±20 years, 5 (62.5%) were male, mean baseline National Institutes of Health Stroke Scale was 19±5. The mean interval between the time the patient was last-known normal to groin puncture was 527±285 min, and the overall procedural length was 110±26 min. Intra-arterial tissue plasminogen activator was used in 5 (62.5%) cases. The device was used for a total of 10 branches: five middle cerebral artery (four superior M3 and one inferior M3), three anterior cerebral arteries (two pericallosal and one callosomarginal), and two posterior cerebral arteries (one P2 and one P3) occlusions. All patients achieved complete recanalization of the artery targeted by the Baby Trevo (arterial occlusive lesion 3). Good capillary reperfusion (TICI 2b–3) was noted in 6 (75%) cases. One pass was performed in 7 vessels and 2 passes in three branches. Vasospasm was noted in 5 (62.5%) of the vessels and fully responded to intra-arterial vasodilator infusion. Follow-up MRI revealed no infarct within the territory vascularized by the artery targeted by the Baby Trevo in 4 cases, partial infarct in 5, and complete infarct in 1. Two patients had parenchymal hematomas (one PH1 and one PH2). No vessel perforations, dissections, or subarachnoid hemorrhage were noted. Conclusions Our initial data suggest that treatment of distal cerebrovascular occlusions with the Trevo XP 3×20 mm Retriever is feasible. Although this device emerges as a promising technology for small and tortuous distal intracranial vessels, larger studies are still necessary to establish its safety and clinical benefit.
A protein tyrosine kinase inhibitor, imatinib mesylate (Gleevec), improves erectile and vascular function secondary to a reduction of hyperglycemia in diabetic rats.

ABSTRACT Introduction Erectile dysfunction (ED) afflicts 50% of diabetic men, many of whom experience poor results with phosphodiesterase type 5 inhibitors. The protein tyrosine kinase (PTK) inhibitor imatinib (Gleevec, Novartis Pharmaceuticals, Basel, Switzerland) has therapeutic potential in diabetic men by maintaining β-cell function. Aim To determine if imatinib has a beneficial effect on erectile and vascular function in diabetic rats. Methods Male Sprague-Dawley rats were divided into six groups: (i) control; (ii) imatinib (50mg/kg, daily gavage)-treated control; (iii) diabetic; (iv) preventive imatinib (8 weeks); (v) reversal imatinib (4 weeks untreated diabetes and 4 weeks of treatment); and (vi) insulin (8 weeks)-treated diabetic rats. Main Outcome Measures After 8 weeks, all groups underwent cavernosal nerve stimulation and measurements of intracavernosal pressure (ICP) and mean arterial pressure (MAP). Contractile and relaxation responses were evaluated using isolated strips of corpus cavernosum smooth muscle (CCSM) and aorta. Results Diabetic rats exhibited a 32% decrease in weight and fivefold increase in blood glucose levels. Imatinib-treated diabetic rats gained weight and partially improved blood glucose levels. Diabetic rats displayed a decrease in ICP/MAP. While maximum electrical field stimulation- and acetylcholine (ACh)-induced relaxations in CCSM strips from the diabetics were reduced, preventive imatinib or insulin treatment normalized ICP/MAP ratios and improved relaxation responses. ACh responses in diabetic aortas were diminished by 50.1% and restored by imatinib. While contractile responses to phenylephrine in diabetic CCSM were not altered, there was a significant enhancement (59.4 %) in the aortic contractile response in diabetic rats, which was restored by imatinib and insulin treatment. Conclusions In diabetic rats, prolonged therapy with imatinib improves diabetes-related ED and vascular function, which may involve normalization of high glucose levels and restoration of PTK activation. Future studies are needed to elaborate on the actions of imatinib on diabetic vascular complications. Gur S, Kadowitz PJ, and Hellstrom WJG. A protein tyrosine kinase inhibitor, imatinib mesylate (Gleevec), improves erectile and vascular function secondary to a reduction of hyperglycemia in diabetic rats.
Two Theobroma cacao genotypes with contrasting pathogen tolerance show aberrant transcriptional and ROS responses after salicylic acid treatment

Understanding the genetic basis of pathogen susceptibility in various crop plants is crucial to increasing the stability of food, feed, and fuel production. Varietal differences in defence responses provide insights into the mechanisms of resistance and are a key resource for plant breeders. To explore the role of salicylic acid in the regulation of defence in cacao, we demonstrated that SA treatment decreased susceptibility to a pod rot pathogen, Phytophthora tropicalis in two genotypes, Scavina 6 and Imperial College Selection 1, which differ in their resistance to several agriculturally important pathogens. Transient overexpression of TcNPR1, a major transcriptional regulator of the SA-dependent plant immune system, also increased pathogen tolerance in cacao leaves. To explore further the genetic basis of resistance in cacao, we used microarrays to measure gene expression profiles after salicylic acid (SA) treatment in these two cacao genotypes. The two genotypes displayed distinct transcriptional responses to SA. Unexpectedly, the expression profile of the susceptible genotype ICS1 included a larger number of pathogenesis-related genes that were induced by SA at 24h after treatment, whereas genes encoding many chloroplast and mitochondrial proteins implicated in reactive oxygen species production were up-regulated in the resistant genotype, Sca6. Sca6 accumulated significantly more superoxide at 24h after treatment of leaves with SA. These experiments revealed critical insights regarding the molecular differences between cacao varieties, which will allow a better understanding of defence mechanisms to help guide breeding programmes.
Microfabricated glucose biosensor with glucose oxidase entrapped in sol-gel matrix.

A microfabricated glucose biosensor based on an amperometeric hydrogen peroxide electrode has been developed. A sol–gel layer with 5 A pore size and 2 μm thickness was used as the glucose oxidase entrapping matrix. The sol-gel matrix formed over the silicon-based sensor has good mechanical and chemical stability, and the ability to entrap a large amount of enzyme. The miniaturized electrode sensing system is composed of platinum as both working and counter electrodes and silver as a reference electrode. Nafion® coating was applied as the interference limiting layer. A series of technologies, such as standard photolithography, electron beam evaporation and image reverse lift-off were utilized for mass production allowing 143 electrodes to be produced at the same time. The effect of oxidable interferences was <10% of the background value of the sensor response. Calibration tests of a series of individual sensors manufactured from the same silicon wafer and dip coated in the same conditions, showed a highly reproducible response characteristics (linear range up to 500 mg dl−1 and mean sensitivity of 0.54±0.14 nA mg−1 dl−1 (n=10)).
Material planning for production kits under uncertainty

A kit is a specific collection of components and/or tools needed for completing a procedure or producing a product. A multiperiod material planning system for production kits under the demand and procurement lead-time uncertainty is considered when component sharing among kits is plausible. Simulation experiments show that component sharing improves the system's service measure of average kit availability and average backorder quantity per period. Also, carrying component safety stock only enhances the benefits of component sharing by reducing the average backorder quantity at the expense of increased inventories, but does not improve the average kit availability.
Rapid field assessment of antifouling and foul‐release coatings

The development of non‐toxic or non‐polluting coatings that provide effective control of biofouling is a high‐priority goal. The technology for such coatings involves antifouling methods other than traditional ones that rely on the sustained release of broad‐spectrum biocides. Rapid field‐testing procedures have been developed to facilitate and accelerate the evaluation of novel antifouling coatings. These procedures provide temporal discrimination of effectiveness which is important in improving potentially useful antifouling approaches that fail due to problems ranging from breakdown of active agents to physical failure of the coating. Two test procedures are described. The first provides information on intrinsic antifouling properties of coatings, and the second allows quantitative assessment of foul‐release properties. Rapid field assessment fills the need for a bridge between laboratory findings and the more laborious, costly, and time‐consuming tests based on long‐term exposure of coated panels or p...
Renal osteodystrophy: neurosurgical considerations and challenges.

Background Dialysis-associated destructive spondyloarthropathy (DSA) is the major bony complication of end-stage renal disease, most commonly found in the lower cervical region. The risk factors for developing dialysis-associated DSA include duration of hemodialysis and patient age. Patients with DSA have a higher incidence of osteoporosis and poor bone mineral density, which may place them at greater risk of atraumatic fractures, instrumentation failure, and neurologic compromise. Methods We describe a case of cervical radiculopathy due to dialysis-associated DSA atraumatic vertebral body fractures with a postoperative course that was complicated by instrumentation failure. We reviewed the literature regarding all 138 published cases, presenting the complications, surgical treatment options, and outcomes. Results A 44-year-old dialysis-dependent man presented with acute neck pain, radiculopathy, and weakness due to atraumatic fracture of C5 and C6 vertebral bodies. He underwent anterior C5 and C6 corpectomies, reconstruction with mesh cage and plate, and supplemental posterior instrumentation (C4-T1). Six weeks later, a computed tomography scan revealed anterior translation across the instrumented area with failure of the posterior instrumentation. He subsequently underwent traction, revision reinstrumentation from C2 to T5, and placement of external halo ring/jacket for 6 months. At 18 months later, he remains ambulatory without evidence of construct failure. Conclusions Patients with renal osteodystrophy present a challenge for the spine surgeon due to compromised bone density. Hardware failure at the bone-construct interface is common in these patients, with revision surgery needed in 22% of published cases. Longer constructs with circumferential instrumentation and halo immobilization may minimize the risk of pseudoarthrosis and construct pull-out.
Nestedeness, separability, information systems, and the role of the manager

Abstract This paper models the information processing problems of a firm by using the computation and optimization of mathematical functions as an analogy to the firm's information processing and decision making activities. This representation allows us to model the division of labor through the concept of functional nestedness, while the decentralization of information and decision making can be modeled through functional separability. The approach is illustrated through a series of simple applications, which examine, for example, how decision problem structures affect the demand for information technology, and how developments in information technology influence the demand for managerial skill.