cssi/SciSciGPT-SciSciNet huggingface dataset:
Impact and Estimation of Balance Coordinate System Rotations and Translations in Wind-Tunnel Testing

Discrepancies between the model and balance coordinate systems lead to biases in the aerodynamic measurements during wind-tunnel testing. The reference coordinate system relative to the calibration coordinate system at which the forces and moments are resolved is crucial to the overall accuracy of force measurements. This paper discusses sources of discrepancies and estimates of coordinate system rotation and translation due to machining and assembly differences. A methodology for numerically estimating the coordinate system biases will be discussed and developed. Two case studies are presented using this methodology to estimate the model alignment. Examples span from angle measurement system shifts on the calibration system to discrepancies in actual wind-tunnel data. The results from these case-studies will help aerodynamic researchers and force balance engineers to better the understand and identify potential differences in calibration systems due to coordinate system rotation and translation.
Tracing Player Knowledge in a Parallel Programming Educational Game.

This paper focuses on "tracing player knowledge" in educational games. Specifically, given a set of concepts or skills required to master a game, the goal is to estimate the likelihood with which the current player has mastery of each of those concepts or skills. The main contribution of the paper is an approach that integrates machine learning and domain knowledge rules to find when the player applied a certain skill and either succeeded or failed. This is then given as input to a standard knowledge tracing module (such as those from Intelligent Tutoring Systems) to perform knowledge tracing. We evaluate our approach in the context of an educational game called "Parallel" to teach parallel and concurrent programming with data collected from real users, showing our approach can predict students skills with a low mean-squared error.
On the Relation between Innovation and Housing Prices – A Metro Level Analysis of the US Market

We examine the extent to which the quality of innovation created in different locations is related to subsequent changes in house prices in these metropolitan areas. Cities that foster a healthy quality of innovation are likely the home of many successful entrepreneurs and firms that provide high paying jobs. We hypothesize that the relation between innovation and changes in house prices is positive because, all else equal, locations with higher quality of innovation should not only be more desirable places to live, but also support higher rate of wealth and income growth, which allow for higher house price appreciation. We find results consistent with our hypothesis: there is a statistically and economically significant positive relation between innovation quality and subsequent house price appreciation. We find that this association runs from innovation quality to house price appreciation but not the reverse, and that the effect is stronger in areas with inelastic land supply.
Found a Reason for me? Weakly-supervised Grounded Visual Question Answering using Capsules

The problem of grounding VQA tasks has seen an increased attention in the research community recently, with most attempts usually focusing on solving this task by using pretrained object detectors. However, pre-trained object detectors require bounding box annotations for detecting relevant objects in the vocabulary, which may not always be feasible for real-life large-scale applications. In this paper, we focus on a more relaxed setting: the grounding of relevant visual entities in a weakly supervised manner by training on the VQA task alone. To address this problem, we propose a visual capsule module with a query-based selection mechanism of capsule features, that allows the model to focus on relevant regions based on the textual cues about visual information in the question. We show that integrating the proposed capsule module in existing VQA systems significantly improves their performance on the weakly supervised grounding task. Overall, we demonstrate the effectiveness of our approach on two state-of-the-art VQA systems, stacked NMN and MAC, on the CLEVR-Answers benchmark, our new evaluation set based on CLEVR scenes with groundtruth bounding boxes for objects that are relevant for the correct answer, as well as on GQA, a real world VQA dataset with compositional questions. We show that the systems with the proposed capsule module consistently outperform the respective baseline systems in terms of answer grounding, while achieving comparable performance on VQA task.1
Prediction of Payload Vibration Environments by Mechanical Admittance Test Techniques

A series of experiments was conducted with simple beam and mass launch vehicle and payload models in order to determine the validity of mechanical admittance/impedance techniques applied to development of improved payload vibration tests. Admittances and impedances were measured from tests of the individual components to form matrices which were combined analytically to allow prediction of responses for the complete system. Results were computed for a transmission matrix approach and an admittance matrix approach. Both a rigid body and a flexible payload model were considered. The results clearly demonstrate that the transmission matrix method is too sensitive to measurement error to be practical for this application, while the pure admittance matrix method produces quite satisfactory results. The effects of various errors on the final results are demonstrated.
Interlayer electronic coupling on demand in a 2D magnetic semiconductor.

When monolayers of two-dimensional (2D) materials are stacked into van der Waals structures, interlayer electronic coupling can introduce entirely new properties, as exemplified by recent discoveries of moire bands that host highly correlated electronic states and quantum dot-like interlayer exciton lattices. Here we show the magnetic control of interlayer electronic coupling, as manifested in tunable excitonic transitions, in an A-type antiferromagnetic 2D semiconductor CrSBr. Excitonic transitions in bilayers and above can be drastically changed when the magnetic order is switched from the layered antiferromagnetic ground state to a field-induced ferromagnetic state, an effect attributed to the spin-allowed interlayer hybridization of electron and hole orbitals in the latter, as revealed by Green’s function–Bethe–Salpeter equation (GW-BSE) calculations. Our work uncovers a magnetic approach to engineer electronic and excitonic effects in layered magnetic semiconductors. Interlayer hybridization in 2D van der Waals materials can change their properties. Here, it is shown that the coupling in CrSBr can be changed from switching the magnetic order from antiferromagnetic to ferromagnetic states.
A convex optimization approach to high-dimensional sparse quadratic discriminant analysis

In this paper, we study high-dimensional sparse Quadratic Discriminant Analysis (QDA) and aim to establish the optimal convergence rates for the classification error. Minimax lower bounds are established to demonstrate the necessity of structural assumptions such as sparsity conditions on the discriminating direction and differential graph for the possible construction of consistent high-dimensional QDA rules. We then propose a classification algorithm called SDAR using constrained convex optimization under the sparsity assumptions. Both minimax upper and lower bounds are obtained and this classification rule is shown to be simultaneously rate optimal over a collection of parameter spaces, up to a logarithmic factor. Simulation studies demonstrate that SDAR performs well numerically. The algorithm is also illustrated through an analysis of prostate cancer data and colon tissue data. The methodology and theory developed for high-dimensional QDA for two groups in the Gaussian setting are also extended to multigroup classification and to classification under the Gaussian copula model.
Integrative multi-omics profiling reveals cAMP-independent mechanisms regulating hyphal morphogenesis in Candida albicans

Microbial pathogens grow in a wide range of different morphologies that provide distinct advantages for virulence. In the fungal pathogen Candida albicans, adenylyl cyclase (Cyr1) is thought to be a master regulator of the switch to invasive hyphal morphogenesis and biofilm formation. However, faster growing cyr1Δ/Δ pseudorevertant (PR) mutants were identified that form hyphae in the absence of cAMP. Isolation of additional PR mutants revealed that their improved growth was due to loss of one copy of BCY1, the negative regulatory subunit of protein kinase A (PKA) from the left arm of chromosome 2. Furthermore, hyphal morphogenesis was improved in some of PR mutants by multigenic haploinsufficiency resulting from loss of large regions of the left arm of chromosome 2, including global transcriptional regulators. Interestingly, hyphal-associated genes were also induced in a manner that was independent of cAMP. This indicates that basal protein kinase A activity is an important prerequisite to induce hyphae, but activation of adenylyl cyclase is not needed. Instead, phosphoproteomic analysis indicated that the Cdc28 cyclin-dependent kinase and the casein kinase 1 family member Yck2 play key roles in promoting polarized growth. In addition, integrating transcriptomic and proteomic data reveals hyphal stimuli induce increased production of key transcription factors that contribute to polarized morphogenesis.
Fundamental gaps of spherical triangles

We show that the spherical equilateral triangle of diameter $$\frac{\pi }{2}$$ is a strict local minimizer of the fundamental gap on the space of the spherical triangles with diameter $$\frac{\pi }{2}$$ , which partially extends Lu-Rowlett’s result–(Commun Math Phys 319(1): 111–145, 2013) from the plane to the sphere.
The local employment effect of house prices: Evidence from U.S. States

Abstract We examine the local demand channel hypothesis of the housing market that predicts a larger response of employment in the non-tradable sector to house price shocks than employment in the tradable sector using state-level monthly data from 1990:M1-2019:M12. Our results from a panel VAR model and out-of-sample forecasting analysis confirm a larger response of non-tradable employment to house price changes. In addition, we also find that unlike short-lived responses to shocks to income and building permits, house price shocks have a very persistent effect on employment. Our findings suggest that states with inelastic housing supply and more volatile house prices tend to have a bigger improvement in forecasting performance for non-tradable employment than for tradable employment.
Initializing LSTM internal states via manifold learning.

We present an approach, based on learning an intrinsic data manifold, for the initialization of the internal state values of long short-term memory (LSTM) recurrent neural networks, ensuring consistency with the initial observed input data. Exploiting the generalized synchronization concept, we argue that the converged, “mature” internal states constitute a function on this learned manifold. The dimension of this manifold then dictates the length of observed input time series data required for consistent initialization. We illustrate our approach through a partially observed chemical model system, where initializing the internal LSTM states in this fashion yields visibly improved performance. Finally, we show that learning this data manifold enables the transformation of partially observed dynamics into fully observed ones, facilitating alternative identification paths for nonlinear dynamical systems.
Levenshtein distance as a measure of accuracy and precision in forensic PCR-MPS methods.

Abstract Accuracy and precision determinations are standard components of method validations where they help to describe the performance of methods. Despite their importance, a standard approach to calculating these parameters is not available for the DNA sequence property of samples in forensic PCR-MPS methods. DNA sequence is a nominal property lacking magnitude and therefore standard approaches to expressing accuracy and precision do not apply. However, sequence determinations by PCR-MPS methods can be expressed in terms of Levenshtein distance, which is a numeric metric to which standard statistical calculations can be applied. We illustrate how Levenshtein distance expresses method accuracy in terms of the closeness of sequencing reads to template DNA sequences, and method precision in terms of the agreement among sequencing reads. By expressing method performance in terms of a numeric metric, this method places PCR-MPS on equal footing with numeric measures of accuracy and precision in PCR-CE methods. Summary statistics based on Levenshtein distance can be used to compare performance of different forensic kits, markers, or methods. Levenshtein distance for expression of accuracy and precision of the DNA sequence is contrasted with the alternative measures phred score, Bhattacharyya distance, Shannon’s entropy, and Gibbs’ index of qualitativevariation (M1).
Data-Driven Mapping Between Functional Connectomes Using Optimal Transport

Functional connectomes derived from functional magnetic resonance imaging have long been used to understand the functional organization of the brain. Nevertheless, a connectome is intrinsically linked to the atlas used to create it. In other words, a connectome generated from one atlas is different in scale and resolution compared to a connectome generated from another atlas. Being able to map connectomes and derived results between different atlases without additional pre-processing is a crucial step in improving interpretation and generalization between studies that use different atlases. Here, we use optimal transport, a powerful mathematical technique, to find an optimum mapping between two atlases. This mapping is then used to transform time series from one atlas to another in order to reconstruct a connectome. We validate our approach by comparing transformed connectomes against their “gold-standard” counterparts (i.e., connectomes generated directly from an atlas) and demonstrate the utility of transformed connectomes by applying these connectomes to predictive models based on a different atlas. We show that these transformed connectomes are significantly similar to their “gold-standard” counterparts and maintain individual differences in brain-behavior associations, demonstrating both the validity of our approach and its utility in downstream analyses. Overall, our approach is a promising avenue to increase the generalization of connectome-based results across different atlases.
An Osmotic Laxative Renders Mice Susceptible to Prolonged Clostridioides difficile Colonization and Hinders Clearance

Antibiotics are a major risk factor for Clostridioides difficile infections (CDIs) because of their impact on the microbiota. However, nonantibiotic medications such as the ubiquitous osmotic laxative polyethylene glycol 3350 (PEG 3350) also alter the microbiota. Clinicians also hypothesize that PEG helps clear C. difficile. But whether PEG impacts CDI susceptibility and clearance is unclear. To examine how PEG impacts susceptibility, we treated C57BL/6 mice with 5-day and 1-day doses of 15% PEG in the drinking water and then challenged the mice with C. difficile 630. We used clindamycin-treated mice as a control because they consistently clear C. difficile within 10 days postchallenge. PEG treatment alone was sufficient to render mice susceptible, and 5-day PEG-treated mice remained colonized for up to 30 days postchallenge. In contrast, 1-day PEG-treated mice were transiently colonized, clearing C. difficile within 7 days postchallenge. To examine how PEG treatment impacts clearance, we administered a 1-day PEG treatment to clindamycin-treated, C. difficile-challenged mice. Administering PEG to mice after C. difficile challenge prolonged colonization up to 30 days postchallenge. When we trained a random forest model with community data from 5 days postchallenge, we were able to predict which mice would exhibit prolonged colonization (area under the receiver operating characteristic curve [AUROC] = 0.90). Examining the dynamics of these bacterial populations during the postchallenge period revealed patterns in the relative abundances of Bacteroides, Enterobacteriaceae, Porphyromonadaceae, Lachnospiraceae, and Akkermansia that were associated with prolonged C. difficile colonization in PEG-treated mice. Thus, the osmotic laxative PEG rendered mice susceptible to C. difficile colonization and hindered clearance. IMPORTANCE Diarrheal samples from patients taking laxatives are typically rejected for Clostridioides difficile testing. However, there are similarities between the bacterial communities from people with diarrhea and those with C. difficile infections (CDIs), including lower diversity than the communities from healthy patients. This observation led us to hypothesize that diarrhea may be an indicator of C. difficile susceptibility. We explored how osmotic laxatives disrupt the microbiota's colonization resistance to C. difficile by administering a laxative to mice either before or after C. difficile challenge. Our findings suggest that osmotic laxatives disrupt colonization resistance to C. difficile and prevent clearance among mice already colonized with C. difficile. Considering that most hospitals recommend not performing C. difficile testing on patients taking laxatives, and laxatives are prescribed prior to administering fecal microbiota transplants via colonoscopy to patients with recurrent CDIs, further studies are needed to evaluate if laxatives impact microbiota colonization resistance in humans.
Accelerating bandwidth-bound deep learning inference with main-memory accelerators

Matrix-matrix multiplication operations (GEMMs) are important in many HPC and machine-learning applications. They are often mapped to discrete accelerators (e.g., GPUs) to improve performance. However, we find that large tall/skinny and fat/short matrices benefit little from discrete acceleration and also do not perform well on a CPU. Such matrices are prevalent in important workloads, such as deep-learning inference within large-scale datacenters. We demonstrate the large potential of accelerating these GEMMs with processing in the main CPU memory, where processing in memory units (PIMs) take advantage of otherwise untapped bandwidth without requiring data copies. We develop a novel GEMM execution flow and corresponding memory-side address-generation logic that exploits GEMM locality and enables long-running PIM kernels despite the complex address-mapping functions employed by the CPU. Our evaluation of StepStone variants at the channel, device, and within-device PIM levels demonstrate 12X better minimum latency than a CPU and 2.8X greater throughput for strict query latency constraints. End-to-end performance analysis of recent recommendation and language models shows that StepStone outperforms a fast CPU by up to 16X and also the best prior main-memory acceleration approaches by up to 2.4X.
Almost complex manifolds with small Nijenhuis tensor

We give several explicit examples of compact manifolds with a $1$-parameter family of almost complex structures having arbitrarily small Nijenhuis tensor in the $C^0$-norm. The $4$ dimensional examples possess no complex structure, whereas the dimension $6$ examples do not possess left invariant complex structures, and whether they possess complex structures appears to be unknown.
Uniform Forward-modeling Analysis of Ultracool Dwarfs. II. Atmospheric Properties of 55 Late-T Dwarfs

We present a large forward-modeling analysis for 55 late-T (T7-T9) dwarfs, using low-resolution ($R\approx150$) near-infrared spectra and cloudless Sonora-Bobcat model atmospheres. We derive the objects' effective temperatures, surface gravities, metallicities, radii, masses, and luminosities using our newly developed Bayesian framework, and use the resulting population properties to test the model atmospheres. We find (1) our objects' fitted metallicities are 0.3-0.4 dex lower than those of nearby stars; (2) their ages derived from spectroscopic parameters are implausibly young; (3) their fitted temperatures show a similar spread as empirical temperature scales at a given spectral type but are $\sim100$ K hotter for $\geqslant$T8 dwarfs; and (4) their spectroscopically inferred masses are unphysically small. These results suggest the Sonora-Bobcat assumptions of cloudless and chemical-equilibrium atmospheres do not adequately reproduce late-T dwarf spectra. We also find a gravity- and a metallicity-dependence of temperatures. Combining the resulting parameter posteriors of our sample, we quantify the degeneracy between surface gravity and metallicity such that an increase in $Z$ combined with a $3.4\times$ increase in $\log{g}$ results in a spectrum that has similar fitted parameters. We note the systematic difference between our 1.0-2.5 $\mu$m spectra and the Sonora-Bobcat models is $\approx$2-4% of the objects' peak $J$-band fluxes, implying modeling systematics will exceed measurement uncertainties when analyzing data with $J$-band S/N $\gtrsim50$. Using our large sample, we examine the fitting residuals as a function of wavelength and atmospheric properties to discern how to improve the models. Our work constitutes the largest analysis of brown dwarf spectra using multi-metallicity models and the most systematic examination of ultracool model atmospheres to date.
Improved semiclassical model for real-time evaporation of matrix black holes

In this paper, we study real-time classical matrix mechanics of a simplified 2 × 2 matrix model inspired by the black hole evaporation problem. This is a step towards making a quantitative model of...
Optical manipulations reveal strong reciprocal inhibition but limited recurrent excitation within olfactory bulb glomeruli.

The local circuitry within olfactory bulb glomeruli filters, transforms, and facilitates information transfer from olfactory sensory neurons to bulb output neurons. Two key elements of this circuit are glutamatergic tufted cells (TCs) and GABAergic periglomerular (PG) cells, both of which actively shape mitral cell activity and bulb output. A subtype of TCs, the external tufted cells (eTCs), can synaptically excite PG cells, but there are unresolved questions about other aspects of the glomerular connections, including the extent of connectivity between eTCs and the precise nature of reciprocal interactions between TCs and PG cells. We combined patch-clamp recordings in OB slices and optophysiological tools to investigate local functional connections within glomeruli of mice and rats. When TCs that express cholecystokinin (CCK) were optically suppressed, excitatory post-synaptic currents (EPSCs) in “uniglomerular” PG cells that extend dendrites to one glomerulus were decreased, consistent with TC activation being required for most excitation of these PG cells. However, TC suppression had no effect on EPSCs in eTCs, arguing that TCs make few, if any, direct glutamatergic synaptic connections onto eTCs. The absence of synaptic connections between eTCs was also supported by recordings in eTC pairs. Lastly, we show using similar optical suppression methods that GAD65-expressing PG cells, mainly uniglomerular cells, provide strong inhibition onto eTCs. Our results imply that the local network of CCK-expressing TCs form potent reciprocal chemical synaptic connections with GAD65-expressing uniglomerular PG cells but not eTCs. This configuration favors local inhibition over recurrent excitation within a glomerulus, limiting its output. Significance Statement The brain circuits involved with the initial processing of sensory information are comprised of networks of excitatory and inhibitory neurons. The strength of local connections can impact the weighting of excitation versus inhibition and determine whether sensory signals are passed on to higher-order brain structures. We investigated the connectivity between excitatory and inhibitory neurons in discrete glomerular structures of the rodent olfactory bulb. We found that excitatory neurons make potent chemical synaptic connections with a class of inhibitory cells with dendrites confined to one glomerulus. However, excitatory cells do not form chemical synaptic connections with each other. This circuit configuration suggests that sensory input is generally biased toward driving more local inhibition than recurrent excitation, thus suppressing bulbar output.
Contraction theory for nonlinear stability analysis and learning-based control: A tutorial overview

Abstract Contraction theory is an analytical tool to study differential dynamics of a non-autonomous (i.e., time-varying) nonlinear system under a contraction metric defined with a uniformly positive definite matrix, the existence of which results in a necessary and sufficient characterization of incremental exponential stability of multiple solution trajectories with respect to each other. By using a squared differential length as a Lyapunov-like function, its nonlinear stability analysis boils down to finding a suitable contraction metric that satisfies a stability condition expressed as a linear matrix inequality, indicating that many parallels can be drawn between well-known linear systems theory and contraction theory for nonlinear systems. Furthermore, contraction theory takes advantage of a superior robustness property of exponential stability used in conjunction with the comparison lemma. This yields much-needed safety and stability guarantees for neural network-based control and estimation schemes, without resorting to a more involved method of using uniform asymptotic stability for input-to-state stability. Such distinctive features permit systematic construction of a contraction metric via convex optimization, thereby obtaining an explicit exponential bound on the distance between a time-varying target trajectory and solution trajectories perturbed externally due to disturbances and learning errors. The objective of this paper is therefore to present a tutorial overview of contraction theory and its advantages in nonlinear stability analysis of deterministic and stochastic systems, with an emphasis on deriving formal robustness and stability guarantees for various learning-based and data-driven automatic control methods. In particular, we provide a detailed review of techniques for finding contraction metrics and associated control and estimation laws using deep neural networks.
Information Directed Sampling for Sparse Linear Bandits

Stochastic sparse linear bandits offer a practical model for high-dimensional online decision-making problems and have a rich information-regret structure. In this work we explore the use of information-directed sampling (IDS), which naturally balances the information-regret trade-off. We develop a class of information-theoretic Bayesian regret bounds that nearly match existing lower bounds on a variety of problem instances, demonstrating the adaptivity of IDS. To efficiently implement sparse IDS, we propose an empirical Bayesian approach for sparse posterior sampling using a spike-and-slab Gaussian-Laplace prior. Numerical results demonstrate significant regret reductions by sparse IDS relative to several baselines.
On the Stability Region of Intermittent Interference Networks

Recent information-theoretic studies have resulted in several interference management (IM) techniques that promise significant capacity improvements over interference avoidance techniques. However, in practice, the stable throughput region is a more relevant metric compared to the capacity region. In this work, we focus on the stable throughput region of a two-pair intermittent interference network with distributed transmitters and propose a queue-based transmission protocol in different regimes to handle the data between queues. In this context, we translate physical-layer IM protocols to accommodate stochastic message arrivals. To evaluate our proposed techniques, we compare the stable throughput region to the capacity region and show, through simulations, that the stable throughput region matches the capacity region when the latter is known. We show that in order to achieve the optimal stable throughput region, new ingredients are needed when compared to prior results. We quantify the trade-off between encoding/decoding complexity of the proposed scheme (in terms of number of required algebraic operations), and the achievable rates. Finally, we study the lifetime of messages (i.e. the duration from arrival to successful delivery) versus the total communication time, and we observe that the average lifetime scales as the square root of the total communication time.
The generation and use of categorical model base structures to support adaptive planning and positioning

The concept of generic model base structures is presented as being crucial towards the implementation aspects of model base technology. This dissertation research presents how categorical model base technology can be employed as a basis for an adaptive (vs. traditional preadaptive) approach to readiness with respect to organizational planning and positioning. Categorical model base structures deal with the planning related activities of an organization and differ from the latter in their level of abstraction from Discrete-Bipartite model bases. The research proposes a generic, high-level representative structure for the generation, use, and manipulation of model bases and their elements. It is organized into three major sections: (1) categorical model base structures, (2) the generation of model base structures, and (3) the manipulation, or application of model base structures. Of keen interest and introduction are concepts involving the transition of management support systems from being data based and instrument based to model base driven, the reach and limits of traditional discrete/bipartite contingency planning technology and, the employment of categorical model base technology as a complement to, or substitute for, traditional contingency planning technology. Among the secondary technical areas of study, this research embraces the functional areas of knowledge engineering, decision support systems, model management systems, neural networks theory, parallel processing, distributed system architecture, and axiomatic systems theory. Surrogate imputational cases and data have been employed for purposes of discussion, for evaluating the performance of the proposed model base structures, and providing a platform for technological implementation.
Developing a Digital National Library for Science, Mathematics, Engineering and Technology Education: Building upon NEEDS—The National Engineering Education Delivery System

A Digital National Library for Science, Mathematics, Engineering and Technology Education is envisioned as a one-stop shop for digital learning resources to support a wide range learners from K-12 through higher education and beyond. With funding from the National Science Foundation, we are developing an Information Portal into Science, Mathematics, Engineering and Technology Education (SMETE) based upon our past five years of experience developing NEEDS, a digital library for the engineering education community. This digital learning space supports cataloging, searching, displaying and reviewing digital learning materials from target SMETE disciplines. We will demonstrate the Information Portal, see www.smete.org, and present our findings about the SMETE community’s views regarding the potential value of a digital library for SMET education; level of probable use, as well as how community members envision using it; and type(s) of editorial oversight needed.
Abstract 101: WHSC1L1 as a therapeutic target in squamous cell carcinoma of the head and neck

Proceedings: AACR 106th Annual Meeting 2015; April 18-22, 2015; Philadelphia, PA Background: Squamous cell carcinoma of the head and neck (SCCHN) is a common malignancy with high recurrence rates and poor survival, thus new therapies are eagerly awaited. Protein lysine methyltransferases (PKMTs) have recently become one of the most important targets for drug discovery efforts, but only a few studies have explored their role in the pathogenesis of SCCHN. WHSC1L1, a nuclear SET-Suppressor of variegation 3-9, Enhancer of zeste and Trithorax-domain (NSD) PKMT and chromatin modifier, was reported to be amplified (8p11.23) in 6% of SCCHN tumors. This study aims to elucidate the pathophysiological role of WHSC1L1 as an oncogenic force in SCCHN and provide rationale for the development of inhibitors targeting WHSC1L1. Methods: Cytotoxicity assays (MTTs) were conducted in one HPV-positive and one HPV-negative SCCHN cell line with WHSC1L1-specific siRNAs. Quantitative polymerase chain reaction (qPCR) and Western blotting were performed to examine WHSC1L1 expression levels in 14 SCCHN cell lines in comparison to a normal keratinocyte cell line (KGM). Immunohistochemistry (IHC) for WHSC1L1 in 115 patients with SCCHN and correlation of its expression with H3K36me2 was also performed. To identify candidate substrates that are directly methylated by WHSC1L1, in vitro methyltransferase assays using a recombinant protein library were conducted. Results: siRNA-mediated knockdown of WHSC1L1 caused significant cell death in 2 SCCHN cell lines. Approximately 30% of SCCHN cell lines overexpressed WHSC1L1 compared to normal keratinocytes. WHSC1L1 expression as evaluated by IHC was significantly stronger in SCCHN compared to normal epithelium (Kruskal-Wallis test, p = 0.002) and 24% of SCCHN sections expressed high levels of WHSC1L1, compared to 6% of dysplastic lesions and 0% of normal epithelium sections. Interestingly, no association between WHSC1L1 expression levels and H3K36me2 was observed. Conclusions: WHSC1L1 is genetically altered in multiple cancer types. In SCCHN, it is significantly overexpressed in 24% of the tumors and its knockdown caused significant growth suppression of both HPV-positive and HPV-negative SCCHN cell lines. Taken together, WHSC1L1 merits further investigation as a potential therapeutic target in SCCHN. Citation Format: Vassiliki Saloura, Theodore Vougiouklakis, Mark Lingen, Tanguy Seiwert, Everett Vokes, Yusuke Nakamura, Ryuji Hamamoto. WHSC1L1 as a therapeutic target in squamous cell carcinoma of the head and neck. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 101. doi:10.1158/1538-7445.AM2015-101
Anesthetic considerations for the pediatric oncology patient – Part 3: pain, cognitive dysfunction, and preoperative evaluation

In part three of this three-part review, we continue with discussion of the effects of tumor and its therapy as they impact neurocognitive functioning, psychosocial issues of the patient and family, and the mechanisms and experience of pain in the child with cancer. A discussion of preanesthetic testing and evaluation in this patient population is next presented for the reader, focusing on the factors which pose the commonest and greatest risks to the child undergoing surgery. Lastly, an algorithmic approach to evaluating and managing key components of the medical history of pediatric patients is presented.
Task-level object grasping for simulated agents

Simulating a human figure performing a manual task requires that the agent interact with objects in the environment in a realistic manner. Graphic or programming interfaces to control human figure animation, however, do not allow the animator to instruct the system with concise “high-level” commands. Instructions coming from a high-level planner cannot be directly given to a synthetic agent because they do not specify such details as which end-effector to use or where on the object to grasp. Because current animation systems require joint angle displacement descriptions of motion-even for motions that incorporate upwards of 15 joints-an efficient connection between high-level specifications and low-level hand joint motion is required. In this paper we describe a system that directs task-level, general-purpose, object grasping for a simulated human agent. The Object-Specific Reasoner OSR is a reasoning module that uses knowledge of the object of the underspecified action to generate values for missing parameters. The Grasp Behavior manages simultaneous motions of the joints in the hand, wrist, and arm, and provides a programmer with a high-level description of the desired action. When composed hierarchically, the OSR and the Grasp behavior interpret task-level commands and direct specific motions to the animation system. These modules are implemented as part of the Jock system at the University of Pennsylvania.
INSURGENCY IN A TIME OF TERRORISM

?Se puede escindir el terrorismo de la insurgencia armada? ?Toda expresion armada es terrorismo? ?El territorio es un metodo de accion o una logica de accion? Este articulo presenta un analisis de la insurgencia a la luz de la era antiterrorista del nuevo siglo. Con la utilizacion del ejemplo de Vietnam el autor senala como la insurgencia armada ha sido senalada de “terrorista”; sin embargo, debe revisarse detenidamente de manera estrategica este concepto para entender mejor la naturaleza de la violencia de dos fenomenos diferentes: la insurgencia transnacional y el terrorismo transnacional.
Aquarius instrument design for sea surface salinity measurements

Sea surface salinity is a key parameter for the study of ocean circulation, global water cycle and hence climate changes. In response to these measurement needs, Aquarius was selected recently for the third NASA Earth System Science Pathfinder (ESSP) Announcement of Opportunity for a planned launch date in September 2008. The characteristics of the Aquarius instrument are provided in this paper.
O déficit da enfermagem: um problema mundial

O Grupo Consultivo Global, um grupo multidisciplinar da Organizacao Mundial de Saude, tem admitido a existencia de um deficit de enfermeiros em nivel mundial. Tal deficit e o resultado de uma maior demanda por enfermeiros, ja que um numero pequeno de pessoas optam pela profissao e os enfermeiros atualmente ativos em todo o mundo ja estao envelhecendo. O deficit e encontrado tanto entre os enfermeiros clinicos como entre os docentes de enfermagem. O recrutamento entre-paises e a migracao de enfermeiros de paises em desenvolvimento para paises desenvolvidos faz com que o problema se torne mais complexo. Embora pesquisas de opiniao publica identifiquem o enfermeiro como a pessoa que faz com que o sistema de saude funcione, as condicoes do ambiente de trabalho no qual o enfermeiro atua sao insatisfatorias e precisam mudar. Varios estudos demonstram os efeitos positivos sobre o enfermeiro de um ambiente de trabalho saudavel, assim como as relacoes positivas entre o cuidado de enfermagem e os resultados apresentados pelos pacientes. E importante que oficiais do governo, empresas de planos de saude, administradores e lideres dos sistemas de cuidado a saude reconhecam e operacionalizem a importância do enfermeiro para o sistema de saude a fim de estabelecer e manter a sua integridade e viabilidade.
What We Talk About When We Talk About Taxes

Taxes are a preeminent issue in domestic politics, but the prevalence, content, and shape of public discussion on taxation is often perplexing to social researchers. We argue that part of the confusion arises from the lack of qualitative data on the meanings Americans associate with taxation. In order to remedy this lack we conducted semi-structured interviews with white, Southern, small business owners. In answering our questions, our respondents constructed narratives that connected taxation with exploitation and a loss of personal freedom. We propose that everyday fiscal discourse is morally charged and interconnected with boundary work and a sense of group position. Further, qualitative research into tax talk can help make sense of the current political and social landscape (e.g., the continued cultural saliency of the Tea Party).
Computational wear damage analysis on retrieved tibial components

In recent years, the incidence of knee joint degeneration has increased considerably in young and elderly population. Due to the complicated geometry and movements of the knee, the development and improvement of knee joint prostheses have been a slow process that includes not only in vivo and in vitro studies, but also, computational analysis. Different studies have demonstrated that far too often ultra-high-molecular weight polyethylene (UHMWPE) components sustain premature failure and require replacement. In spite of its widespread use, the bearing properties of this polymer continue to limit the wear resistance and the clinical life span of implanted knee prosthetics. UHMWPE is subjected to complex-multi-axial stress states in vivo causing multiaxial shearing at the articulating surfaces, which in turn, has lead to wear debris formation. Characterization of UHMWPE retrieved tibial inserts with implantation times up to 20 years was performed to identify damage areas and the relative deterioration severity. Additionally, several computational models were used to determine contact areas between the femoral implant and the polyethylene component and to analyze deformation of the latter. The modeling of all knee joint components was also completed to simulate, as close as possible, a realistic interaction between the assembly components. The components were tested with compressive and bearing loads with magnitudes up to 11kN, representing four different activities (walking, squatting, rising from a chair, and stair climbing). The damage regions predicted by the simulations were in excellent agreement with those observed on the central portion of each implant contact surface. Nonetheless, small posterior damage regions observed analytically were not present on the retrieved components, possibly due to problems with components positioning in vivo or the input kinematics in the model.
Overlooking roots: a framework for making nondeferred reference-counting garbage collection fast

Numerous optimizations exist for improving the performance of nondeferred reference-counting (RC) garbage collection. Their designs are ad hoc, intended to exploit different count removal opportunities. This paper shows that many of these optimizations can be unified using a notion called overlooking roots. The paper also shows how the notionenables more powerful versions of past optimizations and makes new optimizations possible. While recent static analyses have dramatically improved nondeferred RC performance, margins relative to the deferred variant were still significant in the worst case. With the optimizations made possible by overlooking roots, we show that these margins can be reduced to within 4% on nearly all programs in a test suite, at even large heap sizes, and to within 23% in the worst case.
Identification of heavy hypernuclei from K- capture by primary star analysis

Several hypernuclei ofA ≫ 7 have been uniquely identified from an analysis of the parent K- capture reactions. This method has proved of great value in the choice of the correct identity when the decay process offered various alternative interpretations. An example of the decay13Cλ → π- +13N, Bλ = (10.8 + 0.5) MeV has been thus identified for the first time. A second example of the decay12Bλ→ π- + 34He, Bλ = (99 + 0.6) MeV is reported here, confirming the previous observation of this hypernuclide (1). Some progress is made in resolving the composition of a group of heavy two-body decays. None of the events studied here is inconsistent with K- capture on light nuclei (C, N, O); two events require a two-nucleon capture process.
Social change and the adoption and adaptation of knowledge claims: Whose truth do you trust in regard to sustainable agriculture?

This paper examines sustainable agriculture’s steady rise as a legitimate farm management system. In doing this, it offers an account of social change that centers on trust and its intersection with networks of knowledge. The argument to follow is informed by the works of Foucault and Latour but moves beyond this literature in important ways. Guided by and building upon earlier conceptual framework first forwarded by Carolan and Bell (2003, Environmental Values 12: 225–245), sustainable agriculture is examined through the lens of a “phenomenological challenge.” In doing this, analytic emphasis centers on the interpretative resources of everyday life and the artful act of practice – in other words, on “the local.” Research data involving Iowa farmers and agriculture professionals are examined to understand how social relations of trust and knowledge are contested and shaped within and between agricultural social networks and organizational configurations. All of this is meant to further our understanding of what “sustainable agriculture” is and is not, who it is, and how these boundaries change over time.
Diel vertical migration of sediment-associated meiofauna in subtropical sand and seagrass habitats

Abstract The active vertical migration of meiofaunal organisms was examined throughout the night in a subtropical sand and seagrass habitat. Samples of migrating meiofauna were collected every 2 h, beginning before sunset and ending after sunrise, on two study dates (March, August) using specially designed emergence traps. Harpacticoid copepods, the predominant migrating taxon, exhibited diel, along with date and habitat, differences in the numbers and species which entered the water column. The diel timing of maximum harpacticoid migration coincided with sunset: more species migrated in greater numbers during the sunset sampling period. On a given date, many of the same species migrated in both habitats, but numbers migrating were not independent of date. Numbers migrating also were dependent on habitat with twice the harpacticoids migrating in August in seagrass compared with sand samples. Differences in the active vertical migration of meiobenthic organisms were attributed to differences in species composition and sediment-associated densities, the presence of structure and changes in light levels.
On the Roles of Anionic Lipids in Protein Localization and Permeability of Membranes

Anionic lipids, such as phosphatidylglycerol and phosphatidylserine, play important structural and functional roles in cell membranes. In particular, they appear to provide strong interactions with positively charged protein side chains to promote membrane localization of lipid binding domains and antimicrobial peptides, as well as to modulate the function of many membrane proteins. All-atom molecular dynamics simulations were used to explore the strength of these interactions and the impact they have on the ability of charged protein residues to penetrate into membranes. Using an analog of arginine and bilayers of pure phosphatidylcholine or mixtures with phosphatidylglycerol, we have computed the thermodynamics of charged side chain translocation, as well as the binding affinity of each lipid within the membrane. We found that arginine deforms the bilayer in a similar fashion, regardless of composition, and that the free energy profile for translocation is relatively unaffected by anionic lipids: the “neutralization” of the protein side chain does not reduce the large ∼20 kcal/mol barrier significantly. We decomposed these free energies to explain why anionic lipids do not play a significant role, with implications for the actions of many charged peptides and ion permeability. We also find that arginine binding to phosphatidylglycerol is more favorable by only ∼1 kcal/mol, suggesting that lipid binding domains and antimicrobial peptides likely require many charged side chains acting together to promote membrane localization.