cssi/SciSciGPT-SciSciNet huggingface dataset:
Dissecting Adaptive Traits with Nested Association Mapping: Genetic Architecture of Inflorescence Morphology in Sorghum.

In the cereal crop sorghum (Sorghum bicolor) inflorescence morphology variation underlies yield variation and confers adaptation across precipitation gradients, but its genetic basis is poorly understood. We characterized the genetic architecture of sorghum inflorescence morphology using a global nested association mapping (NAM) population (2200 recombinant inbred lines) and 198,000 phenotypic observations from multi-environment trials for four inflorescence morphology traits (upper branch length, lower branch length, rachis length, and rachis diameter). Trait correlations suggest that lower and upper branch length are under somewhat independent control, while lower branch length and rachis diameter are highly pleiotropic. Joint linkage and genome-wide association mapping revealed an oligogenic architecture with 1-22 QTL per trait, each explaining 0.1-5.0% of variation across the entire NAM population. There is a significant enrichment (2.twofold) of QTL colocalizing with grass inflorescence gene homologs, notably with orthologs of maize Ramosa2 and rice Aberrant Panicle Organization1 and TAWAWA1. Still, many QTL do not colocalize with inflorescence gene homologs. In global georeferenced germplasm, allelic variation at the major inflorescence QTL is geographically patterned but only weakly associated with the gradient of annual precipitation. Comparison of NAM with diversity panel association suggests that naive association models may capture some true associations not identified by mixed linear models. Overall, the findings suggest that global inflorescence diversity in sorghum is largely controlled by oligogenic, epistatic, and pleiotropic variation in ancestral regulatory networks. The findings also provide a basis for genomics-enabled breeding of locally-adapted inflorescence morphology.
Time response of water-based liquid scintillator from X-ray excitation

Water-based liquid scintillators (WbLS) present an attractive target medium for large-scale detectors with the ability to enhance the separation of Cherenkov and scintillation signals from a single target. This work characterizes the scintillation properties of WbLS samples based on LAB/PPO liquid scintillator (LS). X-ray luminescence spectra, decay profiles, and relative light yields are measured for WbLS of varying LS concentration as well as for pure LS with a range of PPO concentrations up to 90 g L−1. The scintillation properties of the WbLS are related to the precursor LAB/PPO: starting from 90 g L−1 PPO in LAB before synthesis, the resulting WbLS have spectroscopic properties that instead match 10 g L−1 PPO in LAB. This could indicate that the concentration of active PPO in the WbLS samples depends on their processing.
In vitro evolution of herpes simplex virus 1 (HSV-1) reveals selection for syncytia and other minor variants in cell culture

The large dsDNA virus herpes simplex virus 1 (HSV-1) is considered to be genetically stable, yet it can rapidly evolve in response to strong selective pressures such as antiviral treatment. Deep sequencing has revealed that clinical and laboratory isolates of this virus exist as populations that contain a mixture of minor alleles or variants, similar to many RNA viruses. The classic virology approach of plaque purifying virus creates a genetically homogenous population, but it is not clear how closely this represents the mixed virus populations found in nature. We sought to study the evolution of mixed versus highly purified HSV-1 populations in controlled cell culture conditions, to examine the impact of this genetic diversity on evolution. We found that a mixed population of HSV-1 acquired more genetic diversity and underwent a more dramatic phenotypic shift than a plaque-purified population, producing a viral population that was almost entirely syncytial after just ten passages. At the genomic level, adaptation and genetic diversification occurred at the level of minor alleles or variants in the viral population. Certain genetic variants in the mixed viral population appeared to be positively selected in cell culture, and this shift was also observed in clinical samples during their first passages in vitro. In contrast, the plaque-purified viral population did not appear to change substantially in phenotype or overall quantity of minor allele diversity. These data indicate that HSV-1 is capable of evolving rapidly in a given environment, and that this evolution is facilitated by diversity in the viral population.
Constrained Spectral Clustering for Dynamic Community Detection

Networks are useful representations of many systems with interacting entities, such as social, biological and physical systems. Characterizing the meso-scale organization, i.e. the community structure, is an important problem in network science. Community detection aims to partition the network into sets of nodes that are densely connected internally but sparsely connected to other dense sets of nodes. Current work on community detection mostly focuses on static networks. However, many real world networks are dynamic, i.e. their structure and properties change with time, requiring methods for dynamic community detection. In this paper, we propose a new stochastic block model (SBM) for modeling the evolution of community membership. Unlike existing SBMs, the proposed model allows each community to evolve at a different rate. This new model is used to derive a maximum a posteriori estimator for community detection, which can be written as a constrained spectral clustering problem. In particular, the transition probabilities for each community modify the graph adjacency matrix at each time point. This formulation provides a relationship between statistical network inference and spectral clustering for dynamic networks. The proposed method is evaluated on both simulated and real dynamic networks.
Solving Non-Convex Non-Differentiable Min-Max Games Using Proximal Gradient Method

Min-max saddle point games appear in a wide range of applications in machine leaning and signal processing. Despite their wide applicability, theoretical studies are mostly limited to the special convex-concave structure. While some recent works generalized these results to special smooth non-convex cases, our understanding of nonsmooth scenarios is still limited. In this work, we study special form of non-smooth min-max games when the objective function is (strongly) convex with respect to one of the player’s decision variable. We show that a simple multi-step proximal gradient descent-ascent algorithm converges to -first-order Nash equilibrium of the min-max game with the number of gradient evaluations being polynomial in 1/. Finally, we evaluate the performance of the proposed algorithm through adversarial attack on a LASSO estimator.
Housing Supply and Housing Bubbles

Like many other assets, housing prices are quite volatile relative to observable changes in fundamentals. If we are going to understand boom-bust housing cycles, we must incorporate housing supply. In this paper, we present a simple model of housing bubbles which predicts that places with more elastic housing supply have fewer and shorter bubbles, with smaller price increases. However, the welfare consequences of bubbles may actually be higher in more elastic places because those places will overbuild more in response to a bubble. The data show that the price run-ups of the 1980s were almost exclusively experienced in cities where housing supply is more inelastic. More elastic places had slightly larger increases in building during that period. Over the past five years, a modest number of more elastic places also experienced large price booms, but as the model suggests, these booms seem to have been quite short. Prices are already moving back towards construction costs in those areas.
Off Axis Growth of Strontium Titanate Films with High Dielectric Constant Tuning and Low Loss

We have measured the nonlinear dielectric properties of strontium titanate (STO) thin films grown on neodymium gallate (NGO) and lanthanum aluminate (LAO) substrates. The films prepared by off-axis pulsed laser deposition were characterized by their dielectric constant and loss tangent at 1 MHz and 2 GHz, and from room temperature down to 4 K. The resulting films show significant variations of dielectric properties with position of the substrates with respect to the plume axis. STO films on LAO substrates show low loss and high dielectric constant in regions near the plume axis. On the other hand, STO on NGO shows this effect only on the films grown far from the plume axis. We also obtained a figure of merit from the relative variation of the dielectric constant divided by the loss tangent in the presence of a DC electric field up to +/- 4 V/μm. Careful mapping of the plume crossection allowed us to improve the quality and reproducibility of the dielectric films, obtaining a best figure of merit at 2 GHz and 4 K close to 100 for NGO substrate but only well off axis.
Optical Radiation Sources and Standards

Artificial visible and near-visible electromagnetic radiation is produced and characterized in several different ways. From incandescent light bulbs and gas-discharge lamps to LEDs and lasers, light sources have been evolving for years. This chapter describes the various ways that light is produced as well as standardized. The first section discusses some of the different types of sources of radiation. Illustrated along with the different sources are examples of their uses. With all these sources, standardization and calibration are very important. The second section will discuss how, along with improved technology, the methods and procedures used to realize standards have evolved. Discussed is the evolution of radiation standards leading to the current method of realizing them. In addition, we discuss the methods used to calibrate optical devices using these standards. Keywords: luminous flux; incandescence; fluorescence; candela; luminous intensity; integrating sphere; lamp; detector-based; blackbody; discharge tube; arc tube; optical sources; optical standards
Rotating wind solution and the acceleration of the cosmic radio jets

A wind-type solution is obtained in an axisymmetric spacetime in the case when matter has angular momentum. An application of the solution in understanding the acceleration of matter in radio jets as it flows along the funnel of a thick accretion disk is suggested. It is observed that the location of the critical point depends strongly upon the angular momentum of the wind. Apart from the usual acceleration of matter due to the expansion of the flow tube, the energy due to the rotational motion is found to contribute to the poloidal motion, hence enhancing the flow speed.
Twin-Screw Extrusion Processing of Distillers Dried Grains with Solubles (DDGS)-Based Yellow Perch ( Perca flavescens ) Feeds

Increases in global aquaculture production, compounded with limited availabilities of fish meal for fish feed, has created the need for alternative protein sources. Twin-screw extrusion studies were performed to investigate the production of nutritionally balanced feeds for juvenile yellow perch (Perca flavescens). Five isocaloric (~3.06 kcal/g) ingredient blends, adjusted to a target protein content of 36.7% db, were formulated with 0%, 10%, 20%, 30%, and 40% distillers dried grains with solubles (DDGS) at an initial moisture content of 5–7% db, with appropriate amounts of fish meal, fish oil, whole wheat flour, corn gluten meal, and vitamin and mineral premixes. During processing, varying amounts of steam (6.9–9.7 kg/h) were injected into the conditioner and water (6.7–13.1 kg/h) into the extruder to modulate the cohesiveness of the final extrudates. Extrusion cooking was performed at 226–298 rpm using a 1.9 mm die. Mass flow rate and processing temperatures generally decreased with progressively higher DDGS content. Moisture content, water activity, unit density, bulk density, expansion ratio, compressive strength and modulus, pellet durability index, water stability, angle of repose, and color were extensively analyzed to quantify the effects of varying DDGS content on the physical properties of the final extrudates. Significant differences (P < 0.05) among the blends were observed for color and bulk density for both the raw and extruded material, respectively, and for the unit density of the extruded product. There were also significant changes in brightness (L), redness (a), and yellowness (b) among the final products when increasing the DDGS content of the blends. Expansion ratio and compressive strength of the extrudates were low. On the other hand, all extruded diets resulted in very good water stability properties and nearly all blends achieved high pellet durability indices. In summary, each of the ingredient blends resulted in viable extrudates.
FIRING COSTS AND BUSINESS CYCLE FLUCTUATIONS

This article considers a real business cycle model with establishment level dynamics and uses it to analyze the effects of firing taxes. It finds that firing taxes can have significant consequences on business cycle fluctuations, that the largest effects are on aggregate employment, and that even relatively small firing taxes have substantial effects. A significant contribution of the article is computational: It describes how to use standard linear-quadratic methods to solve for a stochastic equilibrium of an (S,s) economy with tax distortions.
Convergence of the phase-field equations to the mullins-sekerka problem with kinetic undercooling

I prove that the solutions of the phase-field equations, on a subsequence, converge to a weak solution of the Mullins-Sekerka problem with kinetic undercooling. The method is based on energy estimates, a monotonicity formula, and the equipartition of the energy at each time. I also show that for almost all t, the limiting interface is (d − 1)-rectifiable with a square-integrable mean-curvature vector.
Barriers to single-dose intravesical chemotherapy in non-muscle invasive bladder cancer: what's the problem?

AbstractIntroduction:The intravesical instillation of mitomycin C immediately following surgery for nonmuscle invasive bladder cancer has been shown to be efficacious in reducing cancer recurrence....
CrkL is required for donor T cell migration to GvHD target organs.

// Nathan H. Roy 1 , Mahinbanu Mammadli 2 , Janis K. Burkhardt 1 and Mobin Karimi 2 1 Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia Research Institute and Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA 2 Department of Microbiology and Immunology, SUNY Upstate Medical University, Syracuse, NY 13210, USA Correspondence to: Mobin Karimi, email: karimim@upstate.edu Keywords: graft-versus-host disease; CrkL; T cell; migration; inflammation Received: January 03, 2020 Accepted: February 17, 2020 Published: April 28, 2020 ABSTRACT The success of cancer therapies based on allogeneic hematopoietic stem cell transplant relies on the ability to separate graft-versus-host disease (GvHD) from graft-versus-tumor (GVT) responses. Controlling donor T cell migration into peripheral tissues is a viable option to limit unwanted tissue damage, but a lack of specific targets limits progress on this front. Here, we show that the adaptor protein CrkL, but not the closely related family members CrkI or CrkII, is a crucial regulator of T cell migration. In vitro , CrkL-deficient T cells fail to polymerize actin in response to the integrin ligand ICAM-1, resulting in defective migration. Using a mouse model of GvHD/GVT, we found that while CrkL-deficient T cells can efficiently eliminate hematopoietic tumors they are unable to migrate into inflamed organs, such as the liver and small intestine, and thus do not cause GvHD. These results suggest a specific role for CrkL in trafficking to peripheral organs but not the lymphatic system. In line with this, we found that although CrkL-deficient T cells could clear hematopoietic tumors, they failed to clear the same tumor growing subcutaneously, highlighting the role of CrkL in controlling T cell migration into peripheral tissues. Our results define a unique role for CrkL in controlling T cell migration, and suggest that CrkL function could be therapeutically targeted to enhance the efficacy of immunotherapies involving allogeneic donor cells.
Fiscal consequences of armed conflict and terrorism in low- and middle-income countries

Abstract The paper examines the fiscal effects of armed conflict and terrorism on low- and middle-income countries using two approaches. First, an analysis of 22 conflict episodes shows that armed conflict is associated with lower growth and higher inflation, and has adverse effects on tax revenues and investment. It also leads to higher government spending on defense, but this tends to be at the expense of macroeconomic stability rather than at the cost of lower spending on education and health. Second, econometric estimates provide some support for the hypothesis that conflict and terrorism have a significant negative impact on growth through changes in the composition of government spending. There is also some evidence that armed conflict has a direct, significant impact on growth, independent of its impact on government spending. Thus, there is potential for a sizable “peace dividend” for countries that are able to resolve conflict and terrorism.
Uniform Random Numbers

Pseudorandom numbers, uniformly distributed on the interval (0, 1), are widely used in the development of statistical methods when analytic methods become intractable. The following methods of generation are described: linear congruential, shift register, multiple recursive, matrix congruential, and combination generator. The merits of each are discussed. Keywords: linear congruential; shift register; multiple recursive; matrix congruential; combination generator; Monte Carlo
Rank and duality in representation theory

There is both theoretical and numerical evidence that the set of irreducible representations of a reductive group over local or finite fields is naturally partitioned into families according to analytic properties of representations. Examples of such properties are the rate of decay at infinity of “matrix coefficients” in the local field setting, and the order of magnitude of “character ratios” in the finite field situation. In these notes we describe known results, new results, and conjectures in the theory of “size” of representations of classical groups over finite fields (when correctly stated, most of them hold also in the local field setting), whose ultimate goal is to classify the above mentioned families of representations and accordingly to estimate the relevant analytic properties of each family. Specifically, we treat two main issues: the first is the introduction of a rigorous definition of a notion of size for representations of classical groups, and the second issue is a method to construct and obtain information on each family of representation of a given size. In particular, we propose several compatible notions of size that we call U-rank, tensor rank and asymptotic rank, and we develop a method called eta correspondence to construct the families of representation of each given rank. Rank suggests a new way to organize the representations of classical groups over finite and local fields—a way in which the building blocks are the “smallest” representations. This is in contrast to Harish-Chandra’s philosophy of cusp forms that is the main organizational principle since the 60s, and in it the building blocks are the cuspidal representations which are, in some sense, the “largest”. The philosophy of cusp forms is well adapted to establishing the Plancherel formula for reductive groups over local fields, and led to Lusztig’s classification of the irreducible representations of such groups over finite fields. However, the understanding of certain analytic properties, such as those mentioned above, seems to require a different approach.
A Graph-Based Approach for Active Learning in Regression.

Active learning aims to reduce labeling efforts by selectively asking humans to annotate the most important data points from an unlabeled pool and is an example of human-machine interaction. Though active learning has been extensively researched for classification and ranking problems, it is relatively understudied for regression problems. Most existing active learning for regression methods use the regression function learned at each active learning iteration to select the next informative point to query. This introduces several challenges such as handling noisy labels, parameter uncertainty and overcoming initially biased training data. Instead, we propose a feature-focused approach that formulates both sequential and batch-mode active regression as a novel bipartite graph optimization problem. We conduct experiments on both noise-free and noisy settings. Our experimental results on benchmark data sets demonstrate the effectiveness of our proposed approach.
Shake It or Shrink It: Mass Transport and Kinetics in Surface Bioassays Using Agitation and Microfluidics.

Surface assays, such as ELISA and immunofluorescence, are nothing short of ubiquitous in biotechnology and medical diagnostics today. The development and optimization of these assays generally focu...
Inner functions in weighted Hardy spaces

Inner functions play a central role in function theory and operator theory on the Hardy space over the unit disk. Motivated by recent works of C. Beneteau et al. and of D. Seco, we discuss inner functions on more general weighted Hardy spaces and investigate a method to construct analogues of finite Blaschke products.
Investigating shear flow through continuum gyrokinetic simulations of limiter biasing in the Texas Helimak

Previous limiter-biasing experiments on the Texas Helimak, a simple magnetized torus, have been inconclusive on the effect of flow shear on turbulence levels. To investigate this, the first gyrokinetic simulations of limiter biasing in the Helimak using the plasma physics code Gkeyll have been carried out, and the results are presented here. For the scenarios considered, turbulence is mostly driven by the interchange instability, which depends on gradients of steady-state density profiles. An analysis of both experimental and simulation data demonstrates that shear rates are mostly less than local linear growth rates, and not all requirements for shear stabilization are met. Rather, the mostly vertical shear flow has an important effect on bulk transport and experimental steady-state density profiles, and changes in the gradients correspond to changes in turbulence levels.
Deep-Urban Unaided Precise Global Navigation Satellite System Vehicle Positioning

This article presents the most thorough study to date of vehicular carrier-phase differential Global Navigation Satellite System (CDGNSS) positioning performance in a deep urban setting unaided by complementary sensors. Using data captured during approximately 2 h of driving in and around the dense urban center of Austin, Texas, a CDGNSS system is demonstrated to achieve 17-cm-accurate 3D urban positioning (95% probability) with a solution availability greater than 87%. The results are achieved without the aid of inertial, electro-optical, or odometry sensors. The development and evaluation of the unaided, GNSS-based precise positioning system is a key milestone toward the overall goal of combining precise GNSS, vision, radar, and inertial sensing for all-weather, high-integrity, high-absolute-accuracy positioning for automated and connected vehicles. The system described and evaluated herein is composed of a densely spaced reference network, a software-defined GNSS receiver, and a real-time kinematic (RTK) positioning engine. A performance- sensitivity analysis reveals that navigation data wipeoff for fully modulated GNSS signals and a dense reference network are key to high-performance urban RTK positioning. A comparison with existing unaided systems for urban GNSS processing indicates that the proposed system has significantly greater availability or accuracy.
Accelerating quantum approximate optimization algorithm using machine learning

We propose a machine learning based approach to accelerate quantum approximate optimization algorithm (QAOA) implementation which is a promising quantum-classical hybrid algorithm to prove the so-called quantum supremacy. In QAOA, a parametric quantum circuit and a classical optimizer iterates in a closed loop to solve hard combinatorial optimization problems. The performance of QAOA improves with increasing number of stages (depth) in the quantum circuit. However, two new parameters are introduced with each added stage for the classical optimizer increasing the number of optimization loop iterations. We note a correlation among parameters of the lower-depth and the higher-depth QAOA implementations and, exploit it by developing a machine learning model to predict the gate parameters close to the optimal values. As a result, the optimization loop converges in a fewer number of iterations. We choose graph MaxCut problem as a prototype to solve using QAOA. We perform a feature extraction routine using 100 different QAOA instances and develop a training data-set with 13, 860 optimal parameters. We present our analysis for 4 flavors of regression models and 4 flavors of classical optimizers. Finally, we show that the proposed approach can curtail the number of optimization iterations by on average 44.9% (up to 65.7%) from an analysis performed with 264 flavors of graphs.
Leverage Score Sampling for Faster Accelerated Regression and ERM

Given a matrix $\mathbf{A}\in\mathbb{R}^{n\times d}$ and a vector $b \in\mathbb{R}^{d}$, we show how to compute an $\epsilon$-approximate solution to the regression problem $ \min_{x\in\mathbb{R}^{d}}\frac{1}{2} \|\mathbf{A} x - b\|_{2}^{2} $ in time $ \tilde{O} ((n+\sqrt{d\cdot\kappa_{\text{sum}}})\cdot s\cdot\log\epsilon^{-1}) $ where $\kappa_{\text{sum}}=\mathrm{tr}\left(\mathbf{A}^{\top}\mathbf{A}\right)/\lambda_{\min}(\mathbf{A}^{T}\mathbf{A})$ and $s$ is the maximum number of non-zero entries in a row of $\mathbf{A}$. Our algorithm improves upon the previous best running time of $ \tilde{O} ((n+\sqrt{n \cdot\kappa_{\text{sum}}})\cdot s\cdot\log\epsilon^{-1})$. We achieve our result through a careful combination of leverage score sampling techniques, proximal point methods, and accelerated coordinate descent. Our method not only matches the performance of previous methods, but further improves whenever leverage scores of rows are small (up to polylogarithmic factors). We also provide a non-linear generalization of these results that improves the running time for solving a broader class of ERM problems.
Improving Complex Audit Judgments: A Framework and Evidence

: Regulators and researchers provide evidence that auditors’ judgment quality is problematic in complex audit tasks. We introduce a framework for improving auditor judgment in these tasks. The framework builds on dual-process theory to recognize that high-quality judgment in complex tasks requires that auditors (i) possess the knowledge needed for the task, (ii) recognize the need for analytical (versus heuristic) processing, and (iii) have sufficient cognitive capacity to complete the analytical processing. Based on the framework, we predict that auditors’ need for cognition (NFC), a characteristic theoretically linked to recognizing the need for analytical processing, is associated with higher quality complex judgments. Analysis of 11 studies supports this assertion. We demonstrate the usefulness of the framework by predicting and finding that priming auditors with an accuracy goal improves judgments, particularly for lower NFC auditors who are less likely to spontaneously engage in analytical processing. The framework facilitates systematic development of interventions to improve auditor judgment by highlighting that solutions should address the specific conditions causing judgment problems.
Boxer: Interactive Comparison of Classifier Results

Machine learning practitioners often compare the results of different classifiers to help select, diagnose and tune models. We present Boxer, a system to enable such comparison. Our system facilitates interactive exploration of the experimental results obtained by applying multiple classifiers to a common set of model inputs. The approach focuses on allowing the user to identify interesting subsets of training and testing instances and comparing performance of the classifiers on these subsets. The system couples standard visual designs with set algebra interactions and comparative elements. This allows the user to compose and coordinate views to specify subsets and assess classifier performance on them. The flexibility of these compositions allow the user to address a wide range of scenarios in developing and assessing classifiers. We demonstrate Boxer in use cases including model selection, tuning, fairness assessment, and data quality diagnosis.
Broadcasting Real-Time Flows in Integrated Backhaul and Access 5G Networks

We study the problem of broadcasting realtime flows in multi-hop wireless networks. We assume that each packet has a stringent deadline, and each node in the network obtains some utility based on the number of packets delivered to it on time for each flow. We propose a distributed protocol called the delegated-set routing (DSR) that incurs virtually no overhead of coordination among nodes. We also develop distributed algorithms that aim to maximize the total timely utility under DSR. The utility of the DSR protocol and distributed algorithms are demonstrated by both theoretical analysis and simulation results. We show that our algorithms achieve higher timely throughput even when compared against centralized throughput optimal policies that do not consider deadline constraints.
Foveated Haptic Gaze

As digital worlds become ubiquitous via video games, simulations, virtual and augmented reality, people with disabilities who cannot access those worlds are becoming increasingly disenfranchised. More often than not the design of these environments focuses on vision, making them inaccessible in whole or in part to people with visual impairments. Accessible games and visual aids have been developed but their lack of prevalence or unintuitive interfaces make them impractical for daily use. To address this gap, we present Foveated Haptic Gaze, a method for conveying visual information via haptics that is intuitive and designed for interacting with real-time 3-dimensional environments. To validate our approach we developed a prototype of the system along with a simplified first-person shooter game. Lastly we present encouraging user study results of both sighted and blind participants using our system to play the game with no visual feedback.
Mutator Foci Are Regulated by Developmental Stage, RNA, and the Germline Cell Cycle in Caenorhabditis elegans

RNA interference is a crucial gene regulatory mechanism in Caenorhabditis elegans Phase-separated perinuclear germline compartments called Mutator foci are a key element of RNAi, ensuring robust gene silencing and transgenerational epigenetic inheritance. Despite their importance, Mutator foci regulation is not well understood, and observations of Mutator foci have been largely limited to adult hermaphrodite germlines. Here we reveal that punctate Mutator foci arise in the progenitor germ cells of early embryos and persist throughout all larval stages. They are additionally present throughout the male germline and in the cytoplasm of post-meiotic spermatids, suggestive of a role in paternal epigenetic inheritance. In the adult germline, transcriptional inhibition results in a pachytene-specific loss of Mutator foci, indicating that Mutator foci are partially reliant on RNA for their stability. Finally, we demonstrate that Mutator foci intensity is modulated by the stage of the germline cell cycle and specifically, that Mutator foci are brightest and most robust in the mitotic cells, transition zone, and late pachytene of adult germlines. Thus, our data defines several new factors that modulate Mutator foci morphology which may ultimately have implications for efficacy of RNAi in certain cell stages or environments.
Ask the Experts: What Should Be on an IoT Privacy and Security Label?

Information about the privacy and security of Internet of Things (IoT) devices is not readily available to consumers who want to consider it before making purchase decisions. While legislators have proposed adding succinct, consumer accessible, labels, they do not provide guidance on the content of these labels. In this paper, we report on the results of a series of interviews and surveys with privacy and security experts, as well as consumers, where we explore and test the design space of the content to include on an IoT privacy and security label. We conduct an expert elicitation study by following a three-round Delphi process with 22 privacy and security experts to identify the factors that experts believed are important for consumers when comparing the privacy and security of IoT devices to inform their purchase decisions. Based on how critical experts believed each factor is in conveying risk to consumers, we distributed these factors across two layers—a primary layer to display on the product package itself or prominently on a website, and a secondary layer available online through a web link or a QR code. We report on the experts’ rationale and arguments used to support their choice of factors. Moreover, to study how consumers would perceive the privacy and security information specified by experts, we conducted a series of semi-structured interviews with 15 participants, who had purchased at least one IoT device (smart home device or wearable). Based on the results of our expert elicitation and consumer studies, we propose a prototype privacy and security label to help consumers make more informed IoT-related purchase decisions.
Optogenetic activation of muscle contraction in vivo.

Purpose: Optogenetics is an emerging alternative to traditional electrical stimulation to initiate action potentials in activatable cells both ex vivo and in vivo. Optogenetics has been commonly us...
Political Scandal: A Theory

We study a model that characterizes the conditions under which past misbehavior becomes the subject of present scandal, with consequences for both the implicated politician and the parties that work with him. In the model, both authentic and fake scandals arise endogenously within a political framework involving two parties that trade off benefits of continued collaboration with a suspect politician against the possibility of reputational fallout. Rising polarization between the two parties, we show, increases the likelihood of scandal while decreasing its informational value. Scandals that are triggered by only the opposing party, we also find, are reputationally damaging to both parties and, in some instances, reputationally enhancing to the politician. The model also reveals that jurisdictions with lots of scandals are not necessarily beset by more misbehavior. Under well‐defined conditions, in fact, scandals can be a sign of political piety.
Diffractive Chips for Magneto-Optical Trapping of Two Atomic Species

We investigate diffractive grating chips that can be used as part of a magneto-optical trap (MOT) to trap both Rb and Cs atoms with a single input beam for each atom species.
On anti-powers in aperiodic recurrent words

Abstract Fici, Restivo, Silva, and Zamboni define a k-anti-power to be a concatenation of k consecutive words that are pairwise distinct and have the same length. They ask for the maximum k such that every aperiodic recurrent word must contain a k-anti-power, and they prove that this maximum must be 3, 4, or 5. We resolve this question by demonstrating that the maximum is 5. We also conjecture that if W is a reasonably nice aperiodic morphic word, then there is some constant C = C ( W ) such that for all i , k ≥ 1 , W contains a k-anti-power with blocks of length at most Ck beginning at its ith position. We settle this conjecture for binary words that are generated by a uniform morphism, characterizing the small exceptional set of words for which such a constant cannot be found. This generalizes recent results of the second author, Gaetz, and Narayanan that have been proven for the Thue-Morse word, which also show that such a linear bound is the best one can hope for in general.
Geometric Fusion via Joint Delay Embeddings

We introduce geometric and topological methods to develop a new framework for fusing multi-sensor time series. This framework consists of two steps: (1) a joint delay embedding, which reconstructs a high-dimensional state space in which our sensors correspond to observation functions, and (2) a simple orthogonalization scheme, which accounts for tangencies between such observation functions, and produces a more diversified geometry on the embedding space. We conclude with some synthetic and real-world experiments demonstrating that our framework outperforms traditional metric fusion methods.
MultiRegeneration for polynomial system solving

We demonstrate our implementation of a continuation method as described in [7] for solving polynomials systems. Given a sequence of (multi)homogeneous polynomials, the software multiregeneration outputs the respective (multi)degree in a wide range of cases and partial multidegree in all others. We use Python for the file processing, while Bertini [2] is needed for the continuation. Moreover, parallelization options and several strategies for solving structured polynomial systems are available.
Soft, Round, High Resolution Tactile Fingertip Sensors for Dexterous Robotic Manipulation

High resolution tactile sensors are often bulky and have shape profiles that make them awkward for use in manipulation. This becomes important when using such sensors as fingertips for dexterous multi-fingered hands, where boxy or planar fingertips limit the available set of smooth manipulation strategies. High resolution optical based sensors such as GelSight have until now been constrained to relatively flat geometries due to constraints on illumination geometry. Here, we show how to construct a rounded fingertip that utilizes a form of light piping for directional illumination. Our sensors can replace the standard rounded fingertips of the Allegro hand. They can capture high resolution maps of the contact surfaces, and can be used to support various dexterous manipulation tasks.
Osteocyte Vegf-a contributes to myeloma-associated angiogenesis and is regulated by Fgf23

Multiple Myeloma (MM) induces bone destruction, decreases bone formation, and increases marrow angiogenesis in patients. We reported that osteocytes (Ocys) directly interact with MM cells to increase tumor growth and expression of Ocy-derived factors that promote bone resorption and suppress bone formation. However, the contribution of Ocys to enhanced marrow vascularization in MM is unclear. Since the MM microenvironment is hypoxic, we assessed if hypoxia and/or interactions with MM cells increases pro-angiogenic signaling in Ocys. Hypoxia and/or co-culture with MM cells significantly increased Vegf-a expression in MLOA5-Ocys, and conditioned media (CM) from MLOA5s or MM-MLOA5 co-cultured in hypoxia, significantly increased endothelial tube length compared to normoxic CM. Further, Vegf-a knockdown in MLOA5s or primary Ocys co-cultured with MM cells or neutralizing Vegf-a in MM-Ocy co-culture CM completely blocked the increased endothelial activity. Importantly, Vegf-a-expressing Ocy numbers were significantly increased in MM-injected mouse bones, positively correlating with tumor vessel area. Finally, we demonstrate that direct contact with MM cells increases Ocy Fgf23, which enhanced Vegf-a expression in Ocys. Fgf23 deletion in Ocys blocked these changes. These results suggest hypoxia and MM cells induce a pro-angiogenic phenotype in Ocys via Fgf23 and Vegf-a signaling, which can promote MM-induced marrow vascularization.
FLECSPH: The next generation FleCSIble parallel computational infrastructure for smoothed particle hydrodynamics

Abstract FleCSPH is a smoothed particle hydrodynamics simulation tool, based on the compile-time configurable framework FleCSI . The asynchronous distributed tree topology combined with a fast multipole method allows FleCSPH to efficiently compute hydrodynamics and long range particle–particle interactions. FleCSPH provides initial data generators, particle relaxation techniques, and standard evolution drivers, which can be easily modified and extended to user-specific setups. Data input/output uses the H5part format, compatible with modern visualization software.
Age of Information in Multiple Sensing

Having timely and fresh knowledge about the current state of information sources is critical in a variety of applications. In particular, a status update may arrive at the destination much later than its generation time due to processing and communication delays. The freshness of the status update at the destination is captured by the notion of age of information. In this study, we first analyze a network with a single source, n servers, and the monitor (destination). The servers independently sense the source of information and send the status update to the monitor. We then extend our result to multiple independent sources of information in the presence of n servers. We assume that updates arrive at the servers according to Poisson random processes. Each server sends its update to the monitor through a direct link, which is modeled as a queue. The service time to transmit an update is considered to be an exponential random variable. We examine both homogeneous and heterogeneous service and arrival rates for the single-source case, and only homogeneous arrival and service rates for the multiple sources case. We derive a closed-form expression for the average age of information under a last-come-first-serve (LCFS) queue for a single source and arbitrary n homogeneous servers. For n = 2, 3, we derive the explicit average age of information for arbitrary sources and homogeneous servers, and for a single source and heterogeneous servers. For n = 2 we find the optimal arrival rates given fixed sum arrival rate and service rates.
Streamlined CFD simulation framework to generate wind-pressure coefficients on building facades for airflow network simulations

Building energy modeling software generally comes with capable airflow network solvers for natural ventilation evaluation in multi-zone building energy models. These approaches rely on arrays of pressure coefficients representing different wind directions derived from simple box-shaped buildings without contextual obstructions. For urban or obstructed sites, or more complex building shapes, however, further evaluation is needed to avoid geometric oversimplification. In this study, we present an automated and easy-to-use simulation workflow for OpenFOAM-based exterior airflow simulations to generate arrays of pressure coefficients for arbitrary building shapes and contextual situations. The workflow is compared to other methods commonly used to obtain pressure coefficients for natural ventilation analysis. Finally, we assess for which climate zones and building types modelers should rely on more accurate CFD-based pressure coefficients and where it may be justifiable to rely on easier and readily available analytical approaches to determine pressure coefficients. Results suggest that existing workflows lead to significant error in predicted comfort hours for climates in the Global South and modelers should consider CFD-based facade pressure coeficients.
Particle motion near high-spin black holes

General relativity predicts that the Kerr black hole develops qualitatively new and surprising features in the limit of maximal spin. Most strikingly, the region of spacetime near the event horizon stretches into an infinitely long throat and displays an emergent conformal symmetry. Understanding dynamics in this NHEK (Near-Horizon Extreme Kerr) geometry is necessary for connecting theory to upcoming astronomical observations of high-spin black holes. We review essential properties of NHEK and its relationship to the rapidly rotating Kerr black hole. We then completely solve the geodesic equation in the NHEK region and describe how the resulting trajectories transform under the action of its enhanced symmetries. In the process, we derive explicit expressions for the angular integrals appearing in the Kerr geodesic equation and obtain a useful formula, valid at arbitrary spin, for a particle's polar angle in terms of its radial motion. These results will aid in the analytic computation of astrophysical observables relevant to ongoing and future experiments.
On the Structure, Covering, and Learning of Poisson Multinomial Distributions

An (n, k)-Poisson Multinomial Distribution (PMD) is the distribution of the sum of n independent random vectors supported on the set Bk={e1,, ek} of standard basis vectors in Rk. We prove a structural characterization of these distributions, showing that, for all a#x03B5; > 0, any (n, k)-Poisson multinomial random vector is a#x03B5;-close, in total variation distance, to the sum of a discretized multidimensional Gaussian and an independent (poly(k/a#x03B5;), k)-Poisson multinomial random vector. Our structural characterization extends the multi-dimensional CLT of Valiant and Valiant, by simultaneously applying to all approximation requirements a#x03B5;. In particular, it overcomes factors depending on log n and, importantly, the minimum Eigen value of the PMD's covariance matrix. We use our structural characterization to obtain an a#x03B5;-cover, in total variation distance, of the set of all (n, k)-PMDs, significantly improving the cover size of Daskalakis and Papadimitriou, and obtaining the same qualitative dependence of the cover size on n and a#x03B5; as the k=2 cover of Daskalakis and Papadimitriou. We further exploit this structure to show that (n, k)-PMDs can be learned to within a#x03B5; in total variation distance from Ok(1/a#x03B5;2) samples, which is near-optimal in terms of dependence on a#x03B5; and independent of n. In particular, our result generalizes the single-dimensional result of Daskalakis, Diakonikolas and Servedio for Poisson binomials to arbitrary dimension. Finally, as a corollary of our results on PMDs, we give a Ok(1/a#x03B5;2) sample algorithm for learning (n, k)-sums of independent integer random variables (SIIRVs), which is near-optimal for constant k.
Crossed-beam slowing to enhance narrow-line ytterbium magneto-optic traps

We demonstrate a method to enhance the atom loading rate of a ytterbium (Yb) magneto-optic trap (MOT) operating on the 556 nm 1S0 → 3P1 intercombination transition (narrow linewidth Γg = 2π × 182 kHz). Following traditional Zeeman slowing of an atomic beam near the 399 nm 1S0 → 1P1 transition (broad linewidth Γp = 2π × 29 MHz), two laser beams in a crossed-beam geometry, frequency tuned near the same transition, provide additional slowing immediately prior to the MOT. Using this technique, we observe an improvement by a factor of 6 in the atom loading rate of a narrow-line Yb MOT. The relative simplicity and generality of this approach make it readily adoptable to other experiments involving narrow-line MOTs. We also present a numerical simulation of this two-stage slowing process, which shows good agreement with the observed dependence on experimental parameters, and use it to assess potential improvements to the method.