cssi/SciSciGPT-SciSciNet huggingface dataset:
Political ideology and CEO performance under crisis

Management quality is known to influence depository institution performance, but less understood are the characteristics of managers that influence performance. We empirically examine how the political ideology of a credit union’s CEO influenced decision making and performance during the financial crisis. Our results indicate that the return on assets of credit unions run by conservative CEOs are 22 basis points lower during the crisis relative to liberal CEOs. Returns are shown to be lower as a direct result of credit unions with conservative CEOs applying more conservative accounting practices for loan losses than their counterparts during the crisis, despite similar loan quality.
Observations and Models of Star Formation in the Tidal Features of Interacting Galaxies

Multi-color surface photometry (BVri) is presented for the tidal features in a sample of interacting galaxies. Large color variations are found between the morphological components and within the individual components. The blue colors in the primary and the tidal features are most dramatic in B-V, and not in V-i, indicating that star formation instead of metallicity or age dominates the colors. Color variations between components is larger in systems shortly after interaction begins and diminishes to a very low level in systems which are merged. Photometric models for interacting systems are presented which suggest that a weak burst of star formation in the tidal features could cause the observed color distributions. Dynamical models indicate that compression occurs during the development of tidal features causing an increase in the local density by a factor of between 1.5 and 5. Assuming this density increase can be related to the star formation rate by a Schmidt law, the density increases observed in the dynamical models may be responsible for the variations in color seen in some of the interacting systems. Limitations of the dynamical models are also discussed.
Does distributing SNAP benefits later in the month smooth expenditures

Abstract In this paper, we explore whether the alignment of the date a household receives Supplemental Nutrition Assistance Program (SNAP) benefits with the start of the calendar month affects the smoothness of household monthly expenditures. Across states, what day in the calendar month households receive SNAP benefits varies substantially. Further, other income sources (including transfer payments) and many regular household expenditures (e.g., rent) tend to arrive near the beginning of the calendar month. If alignment of SNAP benefit receipt with the start of the calendar month reduces expenditure smoothing, there may be important health and behavioral impacts for the SNAP population. Our main result is that increasing the median SNAP distribution date in a state by 10 days reduces the standard deviation of weekly spending by 0.23 dollars or approximately 6.5% for SNAP-eligible households. Results are robust to alternative measures of SNAP date distribution and expenditure smoothing.
LION: Lidar-Inertial Observability-Aware Navigator for Vision-Denied Environments

State estimation for robots navigating in GPS-denied and perceptually-degraded environments, such as underground tunnels, mines and planetary sub-surface voids [1], remains challenging in robotics. Towards this goal, we present LION (Lidar-Inertial Observability-Aware Navigator), which is part of the state estimation framework developed by the team CoSTAR [2] for the DARPA Subterranean Challenge [3], where the team achieved second and first places in the Tunnel and Urban circuits in August 2019 and February 2020, respectively. LION provides high-rate odometry estimates by fusing high-frequency inertial data from an IMU and low-rate relative pose estimates from a lidar via a fixed-lag sliding window smoother. LION does not require knowledge of relative positioning between lidar and IMU, as the extrinsic calibration is estimated online. In addition, LION is able to self-assess its performance using an observability metric that evaluates whether the pose estimate is geometrically ill-constrained. Odometry and confidence estimates are used by HeRO [4], a supervisory algorithm that provides robust estimates by switching between different odometry sources. In this paper we benchmark the performance of LION in perceptually-degraded subterranean environments, demonstrating its high technology readiness level for deployment in the field.
Fitting quantum machine learning potentials to experimental free energy data: predicting tautomer ratios in solution

The computation of tautomer ratios of druglike molecules is enormously important in computer-aided drug discovery, as over a quarter of all approved drugs can populate multiple tautomeric species in solution. Unfortunately, accurate calculations of aqueous tautomer ratios—the degree to which these species must be penalized in order to correctly account for tautomers in modeling binding for computer-aided drug discovery—is surprisingly difficult. While quantum chemical approaches to computing aqueous tautomer ratios using continuum solvent models and rigid-rotor harmonic-oscillator thermochemistry are currently state of the art, these methods are still surprisingly inaccurate despite their enormous computational expense. Here, we show that a major source of this inaccuracy lies in the breakdown of the standard approach to accounting for quantum chemical thermochemistry using rigid rotor harmonic oscillator (RRHO) approximations, which are frustrated by the complex conformational landscape introduced by the migration of double bonds, creation of stereocenters, and introduction of multiple conformations separated by low energetic barriers induced by migration of a single proton. Using quantum machine learning (QML) methods that allow us to compute potential energies with quantum chemical accuracy at a fraction of the cost, we show how rigorous relative alchemical free energy calculations can be used to compute tautomer ratios in vacuum free from the limitations introduced by RRHO approximations. Furthermore, since the parameters of QML methods are tunable, we show how we can train these models to correct limitations in the underlying learned quantum chemical potential energy surface using free energies, enabling these methods to learn to generalize tautomer free energies across a broader range of predictions.
A laser interferometer for measuring skin friction in three-dimensional flows

A new, nonintrusive method is described for measuring skin friction in three-dimensional flows with unknown direction. The method uses a laser interferometer to measure the changing slope of a thin oil film applied to a surface experiencing shear stress. The details of the method are described, and skin friction measurements taken in a swirling three-dimensional boundary-layer flow are presented. Comparisons between analytical results and experimental values from the laser interferometer method and from a bidirectional surface-fence gauge are made.
Approximation Algorithms for Min-Distance Problems in DAGs.

The min-distance between two nodes $u, v$ is defined as the minimum of the distance from $v$ to $u$ or from $u$ to $v$, and is a natural distance metric in DAGs. As with the standard distance problems, the Strong Exponential Time Hypothesis [Impagliazzo-Paturi-Zane 2001, Calabro-Impagliazzo-Paturi 2009] leaves little hope for computing min-distance problems faster than computing All Pairs Shortest Paths, which can be solved in $\tilde{O}(mn)$ time. So it is natural to resort to approximation algorithms in $\tilde{O}(mn^{1-\epsilon})$ time for some positive $\epsilon$. Abboud, Vassilevska W., and Wang [SODA 2016] first studied min-distance problems achieving constant factor approximation algorithms on DAGs, obtaining a $3$-approximation algorithm for min-radius on DAGs which works in $\tilde{O}(m\sqrt{n})$ time, and showing that any $(2-\delta)$-approximation requires $n^{2-o(1)}$ time for any $\delta>0$, under the Hitting Set Conjecture. We close the gap, obtaining a $2$-approximation algorithm which runs in $\tilde{O}(m\sqrt{n})$ time. As the lower bound of Abboud et al only works for sparse DAGs, we further show that our algorithm is conditionally tight for dense DAGs using a reduction from Boolean matrix multiplication. Moreover, Abboud et al obtained a linear time $2$-approximation algorithm for min-diameter along with a lower bound stating that any $(3/2-\delta)$-approximation algorithm for sparse DAGs requires $n^{2-o(1)}$ time under SETH. We close this gap for dense DAGs by obtaining a $3/2$-approximation algorithm which works in $O(n^{2.350})$ time and showing that the approximation factor is unlikely to be improved within $O(n^{\omega - o(1)})$ time under the high dimensional Orthogonal Vectors Conjecture, where $\omega$ is the matrix multiplication exponent.
Systemic Risk in Financial Networks: A Survey

We provide an overview of the relationship between financial networks and systemic risk. We present a taxonomy of different types of systemic risk, differentiating between direct externalities betw...
Sasakians and the geometry of a mass term

A gauge-invariant mass term for nonabelian gauge fields in two dimensions can be expressed as the Wess–Zumino–Witten (WZW) action. Hard thermal loops in the gauge theory in four dimensions at finit...
Variation in the modality of a yeast signaling pathway is mediated by a single regulator.

Bimodal gene expression by genetically identical cells is a pervasive feature of signaling networks, and has been suggested to allow organisms to hedge their "bets" in uncertain conditions. In the galactose-utilization (GAL) pathway of Saccharomyces cerevisiae, gene induction is unimodal or bimodal depending on natural genetic variation and pre-induction conditions. Here, we find that this variation in modality arises from regulation of two features of the pathway response: the fraction of cells that show induction, and their level of expression. GAL3, the galactose sensor, controls the fraction of induced cells, and titrating its expression is sufficient to control modality; moreover, all the observed differences in modality between different pre-induction conditions and amongst natural isolates can be explained by changes in GAL3's regulation and activity. The ability to switch modality by tuning the activity of a single protein may allow rapid adaptation of bet hedging to maximize fitness in complex environments.
High-repetition-rate hydrogen spark gap

A high-power spark-gap switch capable of 100- mu s recovery times can be constructed without gas flow by using high-pressure hydrogen gas and by triggering the main gap in a highly undervolted state. These techniques have been demonstrated to work at voltages up to 120 kV, peak currents of up to 170 kA, and energy transfers of up to 12.5 kJ. Tests underway to test the concepts at 600 kV and for multiple-pulse bursts are discussed. >
Contractible open manifolds which embed in no compact, locally connected and locally 1–connected metric space

We revisit a famous contractible open 3–manifold W3 proposed by R H Bing in the 1950s. By the finiteness theorem, Haken (1968) proved that W3 does not embed in any compact 3–manifold. However, until now, the question of whether W3 can embed in a more general compact space, such as a compact, locally connected and locally 1–connected metric 3–space, was unknown. Using the techniques developed in Sternfeld’s 1977 PhD thesis, we answer this question in the negative. Furthermore, it is shown that W3 can be utilized to produce counterexamples to the proposition that every contractible open n–manifold (n≥4) embeds in a compact, locally connected and locally 1–connected metric n–space.
On the utility of a well-mixed model for predicting disease transmission on an urban bus.

The transport of virus-laden aerosols from a host to a susceptible person is governed by complex turbulent airflow and physics related to breathing, coughing and sneezing, mechanical and passive ventilation, thermal buoyancy effects, surface deposition, masks, and air filtration. In this paper, we study the infection risk via airborne transmission on an urban bus using unsteady Reynolds-averaged Navier–Stokes equations and a passive-scalar model of the virus-laden aerosol concentration. Results from these simulations are directly compared to the widely used well-mixed model and show significant differences in the concentration field and number of inhaled particles. Specifically, in the limit of low mechanical ventilation rates, the well-mixed model will overpredict the concentration far from the infected passenger and substantially underpredict the concentration near the infected passenger. The results reported herein also apply to other enclosed spaces.
Systems of small-noise stochastic reaction–diffusion equations satisfy a large deviations principle that is uniform over all initial data

Abstract Large deviations principles characterize the exponential decay rates of the probabilities of rare events. Cerrai and Rockner (2004) proved that systems of stochastic reaction–diffusion equations satisfy a large deviations principle that is uniform over bounded sets of initial data. This paper proves uniform large deviations results for a system of stochastic reaction–diffusion equations in a more general setting than Cerrai and Rockner. Furthermore, this paper identifies two common situations where the large deviations principle is uniform over unbounded sets of initial data, enabling the characterization of Freidlin–Wentzell exit time and exit shape asymptotics from unbounded sets.
Interaction rates in cosmology: heavy particle production and scattering

We study transition rates and cross sections from first principles in a spatially flat radiation dominated cosmology. We consider a model of scalar particles to study scattering and heavy particle production from pair annihilation, drawing more general conclusions. The S-matrix formulation is ill suited to study these ubiquitous processes in a rapidly expanding cosmology. We introduce a physically motivated adiabatic expansion that relies on wavelengths much smaller than the particle horizon at a given time. The leading order in this expansion dominates the transition rates and cross sections. Several important and general results are direct consequences of the cosmological redshift and a finite particle horizon: i) a violation of local Lorentz invariance, ii) freeze-out of the production cross section at a finite time, iii) sub-threshold production of heavier particles as a consequence of the uncertainty in the local energy from a finite particle horizon, a manifestation of the \emph{antizeno} effect. If heavy dark matter is produced via annihilation of a lighter species, sub-threshold production yields an enhanced abundance. We discuss several possible consequences of these effects.
smol: Sensing Soil Moisture using LoRa

Technologies for environmental and agricultural monitoring are on the rise, however there is a lack of small, low-power, and low-cost sensing devices in the industry. One of these monitoring tools is a soil moisture sensor. Soil moisture has significant effects on crop health and yield, but commercial monitors are very expensive, require manual use, or constant attention. This calls for a simple and low cost solution based on a novel technology. In this work we introduce smol: Sensing Soil Moisture using LoRa, a low-cost system to measure soil moisture using received signal strength indicator (RSSI) and transmission power. It is compact and can be deployed in the field to collect data automatically with little manual intervention. Our design is enabled by the phenomenon that soil moisture attenuates wireless signals, so the signal strength between a transmitter-receiver pair decreases. We exploit this physical property to determine the variation in soil moisture. We designed and tested our measurement-based prototype in both indoor and outdoor environments. With proper regression calibration, we show soil moisture can be predicted using LoRa parameters.
Generalized reinforcement learning for building control using Behavioral Cloning

Abstract Advanced building control methods such as model predictive control (MPC) offer significant benefits to both consumers and grid operators, but high computational requirements have acted as barriers to more widespread adoption. Local control computation requires installation of expensive computational hardware, while cloud computing introduces data security and privacy concerns. In this paper, we drastically reduce the local computational requirements of advanced building control through a reinforcement learning (RL)-based approach called Behavioral Cloning, which represents the MPC policy as a neural network that can be locally implemented and quickly computed on a low-cost programmable logic controller. While previous RL and approximate MPC methods must be specifically trained for each building, our key improvement is that the proposed controller can generalize to many buildings, electricity rates, and thermostat setpoint schedules without additional, effort-intensive retraining. To provide this versatility, we have adapted the traditional Behavioral Cloning approach through two innovations: (1) a constraint-informed parameter grouping (CIPG) method that provides a more efficient representation of the training data and (2) a new deep learning model-structure called reverse-time recurrent neural networks (RT-RNN) that allows future information to flow backward in time to more effectively interpret the temporal information in disturbance predictions. The result is an easy-to-deploy, generalized behavioral clone of MPC that can be implemented on a programmable logic controller and requires little building-specific controller tuning, reducing the effort and costs associated with implementing smart residential heat pump control.
A high-dimensional M-estimator framework for bi-level variable selection

In high-dimensional data analysis, bi-level sparsity is often assumed when covariates function group-wisely and sparsity can appear either at the group level or within certain groups. In such cases, an ideal model should be able to encourage the bi-level variable selection consistently. Bi-level variable selection has become even more challenging when data have heavy-tailed distribution or outliers exist in random errors and covariates. In this paper, we study a framework of high-dimensional M-estimation for bi-level variable selection. This framework encourages bi-level sparsity through a computationally efficient two-stage procedure. In theory, we provide sufficient conditions under which our two-stage penalized M-estimator possesses simultaneous local estimation consistency and the bi-level variable selection consistency if certain non-convex penalty functions are used at the group level. Both our simulation studies and real data analysis demonstrate satisfactory finite sample performance of the proposed estimators under different irregular settings.
PyLCP: A Python package for computing laser cooling physics

Abstract We present a Python object-oriented computer program for simulating various aspects of laser cooling physics. Our software is designed to be both easy to use and adaptable, allowing the user to specify the level structure, magnetic field profile, or the laser beams' geometry, detuning, and intensity. The program contains three levels of approximation for the motion of the atom, applicable in different regimes offering cross checks for calculations and computational efficiency depending on the physical situation. We test the software by reproducing well-known phenomena, such as damped Rabi flopping, electromagnetically induced transparency, stimulated Raman adiabatic passage, and optical molasses . We also use our software package to quantitatively simulate recoil-limited magneto-optical traps, like those formed on the narrow 1S → 3 0 P1 transition in 88Sr and 87Sr.
Platformizing family production: The contradictions of rural digital labor in China:

How is the rise of platform capitalism reinventing the traditional regime of familial production, while at the same time being energized by it? How do the historically informed, lived experiences o...
Silencing alanine transaminase 2 in diabetic liver attenuates hyperglycemia by reducing gluconeogenesis from amino acids

SUMMARY Hepatic gluconeogenesis from amino acids contributes significantly to diabetic hyperglycemia, but the molecular mechanisms involved are incompletely understood. Alanine transaminases (ALT1 and ALT2) catalyze the interconversion of alanine and pyruvate, which is required for gluconeogenesis from alanine. We found that ALT2 was overexpressed in liver of diet-induced obese and db/db mice and that the expression of the gene encoding ALT2 (GPT2) was downregulated following bariatric surgery in people with obesity. The increased hepatic expression of Gpt2 in db/db liver was mediated by activating transcription factor 4; an endoplasmic reticulum stress-activated transcription factor. Hepatocyte-specific knockout of Gpt2 attenuated incorporation of 13C-alanine into newly synthesized glucose by hepatocytes. In vivo Gpt2 knockdown or knockout in liver had no effect on glucose concentrations in lean mice, but Gpt2 suppression alleviated hyperglycemia in db/db mice. These data suggest that ALT2 plays a significant role in hepatic gluconeogenesis from amino acids in diabetes.
Hearing gravity from the cosmos: GWTC-2 probes general relativity at cosmological scales

Abstract Gravitational-wave (GW) catalogs are rapidly increasing in number, allowing for statistical analyses of the population of compact binaries. Nonetheless, GW inference of cosmology has typically relied on additional electromagnetic counterparts or galaxy catalogs. I present a new probe of cosmological modifications of general relativity with GW data only. I focus on deviations of the GW luminosity distance constrained with the astrophysical population of binary black holes (BBHs). The three key observables are 1) the number of events as a function of luminosity distance, 2) the stochastic GW background of unresolved binaries and 3) the location of any feature in the source mass distribution, such as the pair instability supernova (PISN) gap. Modifications of the GW luminosity distance are a priori degenerate with the unknown evolution of the merger rate and source masses. However, a large damping of the GW amplitude predicts a reduction of the events and lowering of the edges of the PISN gap with redshift that is against standard astrophysical expectations. Applying a hierarchical Bayesian analysis to the current LIGO–Virgo catalog (GWTC-2), the strongest constraints to date are placed on deviations from the GW luminosity distance, finding c M = − 3.2 − 2.0 + 3.4 at 68% C.L., which is ∼10 times better than multi-messenger GW170817 bounds. These modifications also affect the determination of the BBH masses, which is crucial to accommodate the high-mass binary GW190521 away from the PISN gap. In this analysis it is found that the maximum mass of 99% of the population shifts to lower masses with increased uncertainty, m 99 % = 46.2 − 9.1 + 11.4 M ⊙ at 68% C.L. Testing gravity at large scales with the population of BBHs will become increasingly relevant with future catalogs, providing an independent and self-contained test of the standard cosmological model.
A weighted relative isoperimetric inequality in convex cones

A weighted relative isoperimetric inequality in convex cones is obtained via the Monge-Ampere equation. The method improves several inequalities in the literature, e.g. constants in a theorem of Cabre--Ros--Oton--Serra. Applications are given in the context of a generalization of the log-convex density conjecture due to Brakke and resolved by Chambers: in the case of $\alpha-$homogeneous ($\alpha>0$), concave densities, (mod translations) balls centered at the origin and intersected with the cone are proved to uniquely minimize the weighted perimeter with a weighted mass constraint. In particular, if the cone is taken to be $\{x_n>0\}$, reflecting the density, balls intersected with $\{x_n>0\}$ remain (mod translations) unique minimizers in the $\mathbb{R}^n$ analog in the case when the density vanishes on $\{x_n=0\}$.
Effects of early geometric confinement on the transcriptomic profile of human cerebral organoids.

Background Human cerebral organoids (hCO) are attractive systems due to their ability to model important brain regions and transcriptomics of early in vivo brain development. To date, they have been used to understand the effects of genetics and soluble factors on neurodevelopment. Interestingly, one of the main advantages of hCOs are that they provide three dimensionality that better mimics the in vivo environment; yet, despite this central feature it remains unclear how spatial and mechanical properties regulate hCO and neurodevelopment. While biophysical factors such as shape and mechanical forces are known to play crucial roles in stem cell differentiation, embryogenesis and neurodevelopment, much of this work investigated two dimensional systems or relied on correlative observations of native developing tissues in three dimensions. Using hCOs to establish links between spatial factors and neurodevelopment will require the use of new approaches and could reveal fundamental principles of brain organogenesis as well as improve hCOs as an experimental model. Results Here, we investigated the effects of early geometric confinements on transcriptomic changes during hCO differentiation. Using a custom and tunable agarose microwell platform we generated embryoid bodies (EB) of diverse shapes mimicking several structures from embryogenesis and neurodevelopment and then further differentiated those EBs to whole brain hCOs. Our results showed that the microwells did not have negative gross impacts on the ability of the hCOs to differentiate towards neural fates, and there were clear shape dependent effects on neural lineage specification. In particular we observed that non-spherical shapes showed signs of altered neurodevelopmental kinetics and favored the development of medial ganglionic eminence-associated brain regions and cell types over cortical regions. Transcriptomic analysis suggests these mechanotransducive effects may be mediated by integrin and Wnt signaling. Conclusions The findings presented here suggest a role for spatial factors in brain region specification during hCO development. Understanding these spatial patterning factors will not only improve understanding of in vivo development and differentiation, but also provide important handles with which to advance and improve control over human model systems for in vitro applications.
Collision-Aware Target-Driven Object Grasping in Constrained Environments

Grasping a novel target object in constrained environments (e.g., walls, bins, and shelves) requires intensive reasoning about grasp pose reachability to avoid collisions with the surrounding structures. Typical 6-DoF robotic grasping systems rely on the prior knowledge about the environment and intensive planning computation, which is ungeneralizable and inefficient. In contrast, we propose a novel Collision-Aware Reachability Predictor (CARP) for 6-DoF grasping systems. The CARP learns to estimate the collision-free probabilities for grasp poses and significantly improves grasping in challenging environments. The deep neural networks in our approach are trained fully by self-supervision in simulation. The experiments in both simulation and the real world show that our approach achieves more than 75% grasping rate on novel objects in various surrounding structures. The ablation study demonstrates the effectiveness of the CARP, which improves the 6-DoF grasping rate by 95.7%.
Boosted dark matter from diffuse supernova neutrinos

The XENON collaboration recently reported an excess of electron recoil events in the low energy region with a significance of around $3.3\ensuremath{\sigma}$. An explanation of this excess in terms of thermal dark matter seems challenging. We propose a scenario where dark matter in the Milky Way halo gets boosted as a result of scattering with the diffuse supernova neutrino background. This interaction can accelerate the dark-matter to semi-relativistic velocities, and this flux, in turn, can scatter with the electrons in the detector, thereby providing a much better fit to the data. We identify regions in the parameter space of dark-matter mass and interaction cross section which satisfy the excess. Furthermore, considering the data only hypothesis, we also impose bounds on the dark-matter scattering cross section, which are competitive with bounds from other experiments.
Molecular mechanisms underlying enhanced hemichannel function of a cataract-associated Cx50 mutant

Abstract Connexin-50 (Cx50) is among the most frequently mutated genes associated with congenital cataracts. Although most of these disease-linked variants cause loss of function because of misfolding or aberrant trafficking, others directly alter channel properties. The mechanistic bases for such functional defects are mostly unknown. We investigated the functional and structural properties of a cataract-linked mutant, Cx50T39R (T39R), in the Xenopus oocyte system. T39R exhibited greatly enhanced hemichannel currents with altered voltage-gating properties compared to Cx50 and induced cell death. Coexpression of mutant T39R with wild-type Cx50 (to mimic the heterozygous state) resulted in hemichannel currents whose properties were indistinguishable from those induced by T39R alone, suggesting that the mutant had a dominant effect. Furthermore, when T39R was coexpressed with Cx46, it produced hemichannels with increased activity, particularly at negative potentials, which could potentially contribute to its pathogenicity in the lens. In contrast, coexpression of wild-type Cx50 with Cx46 was associated with a marked reduction in hemichannel activity, indicating that it may have a protective effect. All-atom molecular dynamics simulations indicate that the R39 substitution can form multiple electrostatic salt-bridge interactions between neighboring subunits that could stabilize the open-state conformation of the N-terminal (NT) domain while also neutralizing the voltage-sensing residue D3 as well as residue E42, which participates in loop gating. Together, these results suggest T39R acts as a dominant gain-of-function mutation that produces leaky hemichannels that may cause cytotoxicity in the lens and lead to development of cataracts.
On the computational tractability of statistical estimation on amenable graphs

We consider the problem of estimating a vector of discrete variables $$\theta = (\theta _1,\cdots ,\theta _n)$$ , based on noisy observations $$Y_{uv}$$ of the pairs $$(\theta _u,\theta _v)$$ on the edges of a graph $$G=([n],E)$$ . This setting comprises a broad family of statistical estimation problems, including group synchronization on graphs, community detection, and low-rank matrix estimation. A large body of theoretical work has established sharp thresholds for weak and exact recovery, and sharp characterizations of the optimal reconstruction accuracy in such models, focusing however on the special case of Erdos–Renyi-type random graphs. An important finding of this line of work is the ubiquity of an information-computation gap. Namely, for many models of interest, a large gap is found between the optimal accuracy achievable by any statistical method, and the optimal accuracy achieved by known polynomial-time algorithms. Moreover, it is expected in many situations that this gap is robust to small amounts of additional side information revealed about the $$\theta _i$$ ’s. How does the structure of the graph G affect this picture? Is the information-computation gap a general phenomenon or does it only apply to specific families of graphs? We prove that the picture is dramatically different for graph sequences converging to amenable graphs (including, for instance, d-dimensional grids). We consider a model in which an arbitrarily small fraction of the vertex labels is revealed, and show that a linear-time local algorithm can achieve reconstruction accuracy that is arbitrarily close to the information-theoretic optimum. We contrast this to the case of random graphs. Indeed, focusing on group synchronization on random regular graphs, we prove that local algorithms are unable to have non-trivial performance below the so-called Kesten–Stigum threshold, even when a small amount of side information is revealed.
A regression framework for brain network distance metrics

Analyzing brain networks has long been a prominent research topic in neuroimaging. However, statistical methods to detect differences between these networks and relate them to phenotypic traits are still sorely needed. Our previous work developed a novel permutation testing framework to detect differences between two groups. Here we advance that work to allow both assessing differences by continuous phenotypes and controlling for confounding variables. To achieve this, we propose an innovative regression framework to relate distances between brain network features to functions of absolute differences in continuous covariates and indicators of difference for categorical variables. We explore several similarity metrics for comparing distances between connection matrices, and adapt several standard methods for estimation and inference within our framework: Standard F-test, F-test with individual level effects (ILE), Generalized Least Squares (GLS), Mixed Model, and Permutation. We also propose a novel modification of the standard GLS approach for estimation and inference. We assess all approaches for estimation and inference via simulation studies, and illustrate the utility of our framework by analyzing the relationship between fluid intelligence and brain network distances in Human Connectome Project (HCP) data.
A synthetic distributed genetic multi-bit counter

Summary A design for genetically-encoded counters is proposed via repressor-based circuits. An N-bit counter reads sequences of input pulses and displays the total number of pulses, modulo 2N. The design is based on distributed computation, with specialized cell types allocated to specific tasks. This allows scalability and bypasses constraints on the maximal number of circuit genes per cell due to toxicity or failures due to resource limitations. The design starts with a single-bit counter. The N-bit counter is then obtained by interconnecting (using diffusible chemicals) a set of N single-bit counters and connector modules. An optimization framework is used to determine appropriate gate parameters and to compute bounds on admissible pulse widths and relaxation (inter-pulse) times, as well as to guide the construction of novel gates. This work can be viewed as a step toward obtaining circuits that are capable of finite-automaton computation, in analogy to digital central processing units.
Arsenic and lead uptake by Brassicas grown on an old orchard site

Arugula (Eruca sativa) and collards (Brassica oleracea var. acephala), were grown at a former orchard where soils had been variably contaminated by lead arsenate pesticides. To test for the effect of compost on As and Pb transfer into plants, compost was added (0, 5, and 10% DW) to five plots representing a wide range of soil Pb and As. Arugula accumulated about 5 times higher As concentrations in above-ground tissues than collards, with high variability in individual plant concentrations. Soil to arugula transfer (uptake) coefficients were higher for As than for Pb, and increased with soil As. Crop concentrations of Pb varied widely within replicate samples of both arugula and collards. Arugula contamination by Pb was significantly correlated to soil total Pb, but collard contamination was not. Evidence was found using Al as an indicator of soil particle contamination of plant tissues that Pb in arugula was primarily due to soil particle deposition on foliar surfaces. Compost amendments reduced 0.01 M CaCl2 -extractable Pb but increased extractable As in the orchard soils. However, compost had the beneficial effect of reducing both As and Pb concentrations in harvested arugula grown on most of the plots.
Role of Membrane Ion Transport in Cisplatin Accumulation

Cisplatin (DDP) accumulation into cells cannot be explained by passive diffusion alone. Accumulation can be modulated by energy poisons, ion concentrations, cAMP levels, osmotic strength, protein kinase C agonists, ras expression, and calmodulin antagonists1–9. Although this evidence argues for the existence of a DDP transporter, the findings that DDP accumulation cannot be saturated nor competitively inhibited with structural analogues imply that a direct carrier is not involved in DDP transport4,10–12. The observation that decreased DDP accumulation is a frequent change in cells selected for DDP-resistance in vitro suggests that the DDP accumulation mechanism might be an important determinant of the cellular response to DDP1. The biochemical basis for this decrease is not known. Our studies have been focused on the elucidation of the process by which DDP enters cells and how this has changed in resistant cells. We have found that DDP-resistant human ovarian carcinoma cells with accumulation defects have alterations in their ion transport properties. We present evidence that suggests that these changes may be linked to the DDP accumulation defect in these cells.
Knowledge supply chain matrix approach for balanced knowledge management: an airline industry firm case.

SUMMARY Knowledge management projects suffer from concepts which overemphasize a single factor, e.g., information technology. This single factor frequently dominates other factors as processes, structures or strategies. The research question of the paper is how to develop and apply a knowledge management tool that balances different factors in knowledge management projects. The introduced tool, called knowledge supply chain matrix, is derived from a generic business model and four knowledge management processes. The paper describes how the tool is applied within a knowledge management project in the airline industry.
An Opening in the Congregational Closet? Boundary-Bridging Culture and Membership Privileges for Gays and Lesbians in Christian Religious Congregations

Openness to homosexuality at the congregational level of American religious life has only recently received scholarly attention. This research reports patterns of membership openness to gays and lesbians among American Christian congregations and synthesizes emerging hypotheses to explain such openness. Using data from the second wave of the National Congregations Study (Chaves 2007), multinomial logistic regression models demonstrate evidence for the importance of clergy characteristics, membership demographics, local context, local theological culture, and religious tradition. A boundary-bridging cultural model also conceptualizes how the bridging practices of congregations influence membership openness. Interfaith volunteering and interracial worship express an organizational approach to social boundaries that prioritizes diversity and openness. With a controversial social issue (homosexuality), and a relative lack of local organizational processes to deal with such an issue, boundary-bridging customs may shape the sexuality boundaries of congregations. This research develops knowledge of cultural processes and homosexuality within American religious congregations.
A Discrete Wavenumber Boundary Element Method for study of the 3‐D response 2‐D scatterers

A mathematical formulation of the 2·5D elastodynamic scattering problem is presented and validated. The formulation is a straightforward extension of the Discrete Wave number Boundary Integral Equation Method (DWBIEM) originally proposed by Kawase1 for 2D scattering problems and subsequently extended to the 3D problem by Kim and Papageorgiou.2 It is demonstrated that the Green's function which is appropriate for a boundary formulation of the 2·5D elastodynamic scattering problem is the one corresponding to a unit force moving on a straight line with constant velocity. Such a Green's function is derived in the present study. The formulation may be used to study the wavefields in models of sedimentary deposits (e.g. valleys) or topography (e.g. canyons or ridges) with a 2D variation in structure but obliquely incident plane waves. The advantage of a 2·5D formulation is that it provides the means for calculations of 3D wavefields in scattering problems by requiring a storage comparable to that of the corresponding 2D calculations. © 1998 John Wiley & Sons, Ltd.
Nitric oxide inhibited peroxyl and alkoxyl radical formation with concomitant protection against oxidant injury in intestinal epithelial cells.

Abstract A model compound of lipid peroxidation, tert -butyl hydroperoxide (tBOOH), was used in vitro to investigate (i). the generation of tBOOH-derived peroxyl and alkoxyl radicals by rat intestinal epithelial cells or enterocytes and (ii) the role of nitric oxide (NO) on cell-generated free radical formation and cellular cytotoxicity. Peroxyl, alkoxyl, and methyl radicals were detected and characterized by direct and spin-trapping electron paramagnetic resonance spectroscopy in incubations containing tBOOH and hematin, enterocytes, or intestinal epithelial cell line-6 cells. The direct interactions of tBOOH-derived radicals and NO from nitrosoglutathione (GSNO), nitrosoacetyl penicillamine (SNAP), or 1-{b3-aminopropy-4-(3-aminopropylammonio)} butylamino-diazeniumdiolate (SpNONOate) were demonstrated as their levels were depleted in these incubations. SNAP, not GSNO or SpNONOate, was capable of trapping methyl radical produced during hematin-catalyzed decomposition of tBOOH. Cellular cytotoxicity expressed by percentage of dead cells and lactate dehydrogenase was increased with tBOOH treatment. Addition of GSNO, SNAP, or SpNONOate suppressed tBOOH-induced elevation of cell cytotoxicity. The NO donor precursor glutathione, acetylpenicillamine, or spermine did not have any effects on tBOOH-derived radical generation or cell cytotoxicity. These findings demonstrated free radical–free radical reactions between NO- and tBOOH-derived alkoxyl and peroxyl radicals generated by enterocytes. These reactions, at least in part, describe the protective role of NO from hydroperoxide-induced injury in intestinal epithelial cells.
Abstract PR15: Primary resistance to MAPK inhibition in BRAF-mutant cancer is determined by kinetics of feedback release

Activating mutations in the serine/threonine kinase BRAF are among the most common genetic lesions across all cancers. Despite the clinical success of drugs that either inhibit the oncogene or its downstream effects on the MAPK pathway, only subgroups of BRAF-mutant patients enriched in different tumor lineages will respond to these therapeutics. Here, we show that MAPK-inhibition results in feedback-mediated reactivation of RAS signaling and a resulting paradoxical activation of BRAF/CRAF dimerization. We find, that the formation of paradoxical BRAF/CRAF dimers is dependent on the levels of GTP-RAS whereas transcriptional regulation of ERK signaling modulators as well as activation of receptor tyrosine kinases does not primarily contribute to this process. Using inhibitor-coupled beads, we provide evidence that MAPK inhibition leads to hyperactivation of BRAF and CRAF that results in reactivation of downstream effectors and therefore leads to primary resistance to MAPK inhibition. This feedback-dependent resistance can be suppressed by either genetic depletion of BRAF or the combination of MEK and RAF inhibitors. Overall, our data provide novel insights into the kinetics of primary resistance to MAPK inhibitors in BRAF-mutant tumors and might help to improve the efficacy of targeted therapies in these patients. This abstract is also presented as Poster B03. Citation Format: Martin L. Sos, John D. Gordan, Kevan M. Shokat. Primary resistance to MAPK inhibition in BRAF-mutant cancer is determined by kinetics of feedback release. [abstract]. In: Proceedings of the AACR Precision Medicine Series: Synthetic Lethal Approaches to Cancer Vulnerabilities; May 17-20, 2013; Bellevue, WA. Philadelphia (PA): AACR; Mol Cancer Ther 2013;12(5 Suppl):Abstract nr PR15.
On consulting to new, changing, or innovative organizations.

This paper reports the experiences of a community mental health team engaged in consulting to a Job Corps camp. The characteristics of the camp are described and salient aspects of its evolution delineated: in particular, the issues of growth, change, and phase. The problems of consulting with a radical changing organization are noted and some general and particular facets touched upon. Finally, the need for critical self-examination in consultants is emphasized.
Aven-Dependent Activation of ATM Following DNA Damage

Summary Background In response to DNA damage, cells undergo either cell-cycle arrest or apoptosis, depending on the extent of damage and the cell's capacity for DNA repair. Cell-cycle arrest induced by double-stranded DNA breaks depends on activation of the ataxia-telangiectasia (ATM) protein kinase, which phosphorylates cell-cycle effectors such as Chk2 and p53 to inhibit cell-cycle progression. ATM is recruited to double-stranded DNA breaks by a complex of sensor proteins, including Mre11/Rad50/Nbs1, resulting in autophosphorylation, monomerization, and activation of ATM kinase. Results In characterizing Aven protein, a previously reported apoptotic inhibitor, we have found that Aven can function as an ATM activator to inhibit G2/M progression. Aven bound to ATM and Aven overexpressed in cycling Xenopus egg extracts prevented mitotic entry and induced phosphorylation of ATM and its substrates. Immunodepletion of endogenous Aven allowed mitotic entry even in the presence of damaged DNA, and RNAi-mediated knockdown of Aven in human cells prevented autophosphorylation of ATM at an activating site (S1981) in response to DNA damage. Interestingly, Aven is also a substrate of the ATM kinase. Mutation of ATM-mediated phosphorylation sites on Aven reduced its ability to activate ATM, suggesting that Aven activation of ATM after DNA damage is enhanced by ATM-mediated Aven phosphorylation. Conclusions These results identify Aven as a new ATM activator and describe a positive feedback loop operating between Aven and ATM. In aggregate, these findings place Aven, a known apoptotic inhibitor, as a critical transducer of the DNA-damage signal.
STEPPARENT: A PEJORATIVE TERM?

The number of stepparents and stepchildren continues to increase. Among the challenges facing stepfamily members is the potentially negative connotation of the prefix “step.” This study examined co...
Photosystem I electron transport and phosphorylation supported by electron donation to the plastoquinone region

Abstract In chloroplasts, tetramethyl-p-hydroquinone supports high rates of phosphorylation-coupled, noncyclic electron flow through Photosystem I to methylviologen. The reaction is totally sensitive to dibromothymoquinone, indicating an electron donation to the plastoquinone region of the photosynthetic chain. The uncoupled electron flow rate exceeds 1000 μequivalents per hour per mg chlorophyll. The phosphorylation efficiency ( P e 2 ) at the optimal pH of 8 is 0.6–0.65. Presumably this ratio represents the efficiency of energy coupling in the electron transfer step plastoquinone → cytochrome f.